From 99eb9e4cc0ced8ebc6ed878efc0ceb4145ead166 Mon Sep 17 00:00:00 2001 From: hwy893747147 Date: Wed, 23 Jun 2021 17:23:06 -0500 Subject: [PATCH 1/8] fixing bugs --- datasets/anomaly/raw_data/blockchain.csv | 2438 ++++++++++++++++++ examples/sk_examples/DeepLog_test.py | 2 +- examples/sk_examples/Telemanom_test.py | 5 +- examples/sk_examples/Telemanom_yahoo_test.py | 67 + tods/searcher/brute_force_search.py | 10 +- 5 files changed, 2514 insertions(+), 8 deletions(-) create mode 100644 datasets/anomaly/raw_data/blockchain.csv create mode 100644 examples/sk_examples/Telemanom_yahoo_test.py diff --git a/datasets/anomaly/raw_data/blockchain.csv b/datasets/anomaly/raw_data/blockchain.csv new file mode 100644 index 00000000..e2e1ad08 --- /dev/null +++ b/datasets/anomaly/raw_data/blockchain.csv @@ -0,0 +1,2438 @@ +Date,Transactions,Blocks,Output_Satoshis +2012-01-09,164,14303,449033.58451113 +2012-01-10,141,15722,393176.49097197 +2012-01-11,153,16628,392406.17201191 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+2018-09-03,144,575316,924600.63591992 +2018-09-04,126,596961,936828.26664435 +2018-09-05,132,633097,963010.47581806 +2018-09-06,150,645864,1272957.10140994 +2018-09-07,152,670653,1086048.21016277 +2018-09-08,150,531346,718862.41720552 +2018-09-09,163,495661,720066.61552204 +2018-09-10,74,297030,526521.40145801 diff --git a/examples/sk_examples/DeepLog_test.py b/examples/sk_examples/DeepLog_test.py index d521d52e..ec111e5c 100644 --- a/examples/sk_examples/DeepLog_test.py +++ b/examples/sk_examples/DeepLog_test.py @@ -22,7 +22,7 @@ prediction_labels = transformer.predict(X_test) prediction_score = transformer.predict_score(X_test) -print("Primitive: ", transformer.primitive) +# print("Primitive: ", transformer.primitive) print("Prediction Labels\n", prediction_labels) print("Prediction Score\n", prediction_score) diff --git a/examples/sk_examples/Telemanom_test.py b/examples/sk_examples/Telemanom_test.py index 92bd90fa..a9aeda1b 100644 --- a/examples/sk_examples/Telemanom_test.py +++ b/examples/sk_examples/Telemanom_test.py @@ -1,5 +1,6 @@ import numpy as np -from tods.tods_skinterface.primitiveSKI.detection_algorithm.Telemanom_skinterface import TelemanomSKI +from tods.sk_interface.detection_algorithm.Telemanom_skinterface import TelemanomSKI +# from tods.tods_skinterface.primitiveSKI.detection_algorithm.Telemanom_skinterface import TelemanomSKI from sklearn.metrics import precision_recall_curve from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix @@ -25,7 +26,7 @@ prediction_labels = transformer.predict(X_test) prediction_score = transformer.predict_score(X_test) -print("Primitive: ", transformer.primitive) +# print("Primitive: ", transformer.primitive) print("Prediction Labels\n", prediction_labels) print("Prediction Score\n", prediction_score) y_true = prediction_labels_train diff --git a/examples/sk_examples/Telemanom_yahoo_test.py b/examples/sk_examples/Telemanom_yahoo_test.py new file mode 100644 index 00000000..0f54ed63 --- /dev/null +++ b/examples/sk_examples/Telemanom_yahoo_test.py @@ -0,0 +1,67 @@ +import numpy as np +from tods.sk_interface.detection_algorithm.Telemanom_skinterface import TelemanomSKI +# from tods.tods_skinterface.primitiveSKI.detection_algorithm.Telemanom_skinterface import TelemanomSKI +from sklearn.metrics import precision_recall_curve +from sklearn.metrics import accuracy_score +from sklearn.metrics import confusion_matrix +from sklearn.metrics import classification_report +import matplotlib.pyplot as plt +from sklearn import metrics +import pandas as pd +#prepare the data + +data = pd.read_csv("./yahoo_sub_5.csv").to_numpy() +# print("shape:", data.shape) +# print("datatype of data:",data.dtype) +# print("First 5 rows:\n", data[:5]) + +# X_train = np.expand_dims(data[:10000], axis=1) +# X_test = np.expand_dims(data[10000:], axis=1) + +# print("First 5 rows train:\n", X_train[:5]) +# print("First 5 rows test:\n", X_test[:5]) + +transformer = TelemanomSKI(l_s= 2, n_predictions= 1) +transformer.fit(data) +# prediction_labels_train = transformer.predict(X_train) +prediction_labels = transformer.predict(data) +prediction_score = transformer.predict_score(data) + +# print("Primitive: ", transformer.primitive) +print("Prediction Labels\n", prediction_labels) +print("Prediction Score\n", prediction_score) + +df1 = pd.DataFrame(prediction_labels) +df2 = pd.DataFrame(prediction_score) + +# df1.to_csv(r'./labels.csv', index = False) +df2.to_csv(r'./scores.csv', index = False) +# result = pd.merge(df1, df2[[]]) +# result = [prediction_labels, prediction_score] +# # result = pd.DataFrame({'label': prediction_labels, 'score': prediction_score}, columns=['label', 'score'], index=[0]) +# print(result) +# pd.DataFrame(result).to_csv("./teleSKI.csv") +# y_true = prediction_labels_train +# y_pred = prediction_labels + +# print('Accuracy Score: ', accuracy_score(y_true, y_pred)) + +# confusion_matrix(y_true, y_pred) + +# print(classification_report(y_true, y_pred)) + +# precision, recall, thresholds = precision_recall_curve(y_true, y_pred) +# f1_scores = 2*recall*precision/(recall+precision) + +# print('Best threshold: ', thresholds[np.argmax(f1_scores)]) +# print('Best F1-Score: ', np.max(f1_scores)) + +# fpr, tpr, threshold = metrics.roc_curve(y_true, y_pred) +# roc_auc = metrics.auc(fpr, tpr) + +# plt.title('ROC') +# plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc) +# plt.legend(loc = 'lower right') +# plt.ylabel('True Positive Rate') +# plt.xlabel('False Positive Rate') +# plt.show() diff --git a/tods/searcher/brute_force_search.py b/tods/searcher/brute_force_search.py index 611fddd6..d3c53ce5 100644 --- a/tods/searcher/brute_force_search.py +++ b/tods/searcher/brute_force_search.py @@ -191,19 +191,19 @@ def _generate_pipline(combinations): # pragma: no cover # The first three steps are fixed # Step 0: dataset_to_dataframe - step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common')) + step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')) step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') step_0.add_output('produce') pipeline_description.add_step(step_0) # Step 1: column_parser - step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')) + step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') step_1.add_output('produce') pipeline_description.add_step(step_1) # Step 2: extract_columns_by_semantic_types(attributes) - step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common')) + step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, @@ -211,7 +211,7 @@ def _generate_pipline(combinations): # pragma: no cover pipeline_description.add_step(step_2) # Step 3: extract_columns_by_semantic_types(targets) - step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common')) + step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') step_3.add_output('produce') step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, @@ -243,7 +243,7 @@ def _generate_pipline(combinations): # pragma: no cover #pipeline_description.add_step(tods_step_7) # Finalize the pipeline - final_step = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.construct_predictions.Common')) + final_step = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) final_step.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.6.produce') final_step.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') final_step.add_output('produce') From 2491fc7536b03e216f8ca0c8cc8c2d093f5e8fa4 Mon Sep 17 00:00:00 2001 From: hwy893747147 Date: Mon, 26 Jul 2021 10:27:25 -0500 Subject: [PATCH 2/8] fixed baseSKI error --- tods/sk_interface/base.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tods/sk_interface/base.py b/tods/sk_interface/base.py index addbbc1a..c9543d91 100644 --- a/tods/sk_interface/base.py +++ b/tods/sk_interface/base.py @@ -61,7 +61,7 @@ def predict(self, data): raise AttributeError('type object ' + self.__class__.__name__ + ' has no attribute \'predict\'') data = self._sys_data_check(data) - output_data = self._forward(data, 'produce') + output_data = self._forward(data, '_produce') return output_data @@ -71,7 +71,7 @@ def predict_score(self, data): raise AttributeError('type object ' + self.__class__.__name__ + ' has no attribute \'predict_score\'') data = self._sys_data_check(data) - output_data = self._forward(data, 'produce_score') + output_data = self._forward(data, '_produce_score') return output_data From 63db0fc69f49bfda876d4e73af30220317b43077 Mon Sep 17 00:00:00 2001 From: hwy893747147 Date: Mon, 26 Jul 2021 10:27:49 -0500 Subject: [PATCH 3/8] added PyodXGBOD primitive --- .../detection_algorithm/PyodXGBOD_pipeline.py | 54 +++ tods/detection_algorithm/PyodXGBOD.py | 425 ++++++++++++++++++ tods/resources/.entry_points.ini | 1 + .../detection_algorithm/test_PyodXGBOD.py | 103 +++++ 4 files changed, 583 insertions(+) create mode 100644 primitive_tests/detection_algorithm/PyodXGBOD_pipeline.py create mode 100644 tods/detection_algorithm/PyodXGBOD.py create mode 100644 tods/tests/detection_algorithm/test_PyodXGBOD.py diff --git a/primitive_tests/detection_algorithm/PyodXGBOD_pipeline.py b/primitive_tests/detection_algorithm/PyodXGBOD_pipeline.py new file mode 100644 index 00000000..d7969bad --- /dev/null +++ b/primitive_tests/detection_algorithm/PyodXGBOD_pipeline.py @@ -0,0 +1,54 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') +step_0 = PrimitiveStep(primitive=primitive_0) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') +step_1 = PrimitiveStep(primitive=primitive_1) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: SoGaal +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_xgbod')) +step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +step_3.add_output('produce') +pipeline_description.add_step(step_3) + +# Step 4: Predictions +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') +step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') + +# Output to JSON +data = pipeline_description.to_json() +with open('example_pipeline.json', 'w') as f: + f.write(data) + print(data) diff --git a/tods/detection_algorithm/PyodXGBOD.py b/tods/detection_algorithm/PyodXGBOD.py new file mode 100644 index 00000000..4eab9092 --- /dev/null +++ b/tods/detection_algorithm/PyodXGBOD.py @@ -0,0 +1,425 @@ +from typing import Any, Callable, List, Dict, Union, Optional, Sequence, Tuple +from numpy import ndarray +from collections import OrderedDict +from scipy import sparse +import os +import sklearn +import numpy +import typing + +# Custom import commands if any +import warnings +import numpy as np +from sklearn.utils import check_array +from sklearn.exceptions import NotFittedError +# from numba import njit +from pyod.utils.utility import argmaxn + +from d3m.container.numpy import ndarray as d3m_ndarray +from d3m.container import DataFrame as d3m_dataframe +from d3m.metadata import hyperparams, params, base as metadata_base +from d3m import utils +from d3m.base import utils as base_utils +from d3m.exceptions import PrimitiveNotFittedError +from d3m.primitive_interfaces.base import CallResult, DockerContainer +import uuid + +# from d3m.primitive_interfaces.supervised_learning import SupervisedLearnerPrimitiveBase +from d3m.primitive_interfaces.unsupervised_learning import UnsupervisedLearnerPrimitiveBase +from d3m.primitive_interfaces.transformer import TransformerPrimitiveBase + +from d3m.primitive_interfaces.base import ProbabilisticCompositionalityMixin, ContinueFitMixin +from d3m import exceptions +import pandas + +from d3m import container, utils as d3m_utils + +from .UODBasePrimitive import Params_ODBase, Hyperparams_ODBase, UnsupervisedOutlierDetectorBase +from pyod.models.xgbod import XGBOD +# from typing import Union + +Inputs = d3m_dataframe +Outputs = d3m_dataframe + + +class Params(Params_ODBase): + ### Add more Attributes ### + + pass + + +class Hyperparams(Hyperparams_ODBase): + ### Add more Hyperparamters ### + estimator_list = hyperparams.Union[Union[int, None]]( + configuration=OrderedDict( + init=hyperparams.Hyperparameter[int]( + default=1, # {}, + ), + ninit=hyperparams.Hyperparameter[None]( + default=None, + ), + ), + default='ninit', + description='The list of pyod detectors passed in for unsupervised learning.', + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + ) + + standardization_flag_list = hyperparams.Union[Union[int, None]]( + configuration=OrderedDict( + init=hyperparams.Hyperparameter[int]( + default=1, # {}, + ), + ninit=hyperparams.Hyperparameter[None]( + default=None, + ), + ), + default='ninit', + description='The list of boolean flags for indicating whether to perform standardization for each detector.', + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + ) + + max_depth = hyperparams.Hyperparameter[int]( + default=3, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='Maximum tree depth for base learners.', + ) + + # learning_rate = hyperparams.Hyperparameter[float]( + # default=0.1, + # semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + # description='Boosting learning rate (xgb "eta").', + # ) + + learning_rate = hyperparams.Uniform( + lower=0., + upper=1., + default=0.1, + description='Boosting learning rate (xgb "eta").', + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] + ) + + n_estimators = hyperparams.Hyperparameter[int]( + default=100, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='Number of boosted trees to fit.', + ) + + silent = hyperparams.UniformBool( + default=True, + description='Whether to print messages while running boosting.', + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] + ) + + booster = hyperparams.Enumeration[str]( + values=['gbtree', 'gblinear', 'dart'], + default='gbtree', + semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], + description='Specify which booster to use: gbtree, gblinear or dart.', + ) + + n_jobs = hyperparams.Hyperparameter[int]( + default=1, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='Number of parallel threads used to run xgboost. (replaces ``nthread``).', + ) + + gamma = hyperparams.Hyperparameter[float]( + default=0, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='Minimum loss reduction required to make a further partition on a leaf node of the tree.', + ) + + min_child_weight = hyperparams.Hyperparameter[int]( + default=1, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='Minimum sum of instance weight(hessian) needed in a child.', + ) + + max_delta_step = hyperparams.Hyperparameter[int]( + default=0, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='Maximum delta step we allow each tree weight estimation to be.', + ) + + subsample = hyperparams.Hyperparameter[float]( + default=1, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='Subsample ratio of the training instance.', + ) + + colsample_bytree = hyperparams.Hyperparameter[float]( + default=1, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='Subsample ratio of columns when constructing each tree.', + ) + + colsample_bylevel = hyperparams.Hyperparameter[float]( + default=1, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='Subsample ratio of columns for each split, in each level.', + ) + + reg_alpha = hyperparams.Hyperparameter[float]( + default=0, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='L1 regularization term on weights.', + ) + + reg_lambda = hyperparams.Hyperparameter[float]( + default=1, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='L2 regularization term on weights.', + ) + + scale_pos_weight = hyperparams.Hyperparameter[float]( + default=1, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='Balancing of positive and negative weights.', + ) + + base_score = hyperparams.Hyperparameter[float]( + default=0.5, + semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + description='The initial prediction score of all instances, global bias.', + ) + + # random_state = hyperparams.Hyperparameter[int]( #controlled by d3m + # default=0, + # semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'], + # description='Random number seed. (replaces seed).', + # ) + +### Name of your algorithm ### +class XGBODPrimitive(UnsupervisedOutlierDetectorBase[Inputs, Outputs, Params, Hyperparams]): + """ + XGBOD class for outlier detection. + It first uses the passed in unsupervised outlier detectors to extract + richer representation of the data and then concatenates the newly + generated features to the original feature for constructing the augmented + feature space. An XGBoost classifier is then applied on this augmented + feature space. Read more in the :cite:`zhao2018xgbod`. + Parameters + ---------- + estimator_list : list, optional (default=None) + The list of pyod detectors passed in for unsupervised learning + standardization_flag_list : list, optional (default=None) + The list of boolean flags for indicating whether to perform + standardization for each detector. + max_depth : int + Maximum tree depth for base learners. + learning_rate : float + Boosting learning rate (xgb's "eta") + n_estimators : int + Number of boosted trees to fit. + silent : bool + Whether to print messages while running boosting. + objective : string or callable + Specify the learning task and the corresponding learning objective or + a custom objective function to be used (see note below). + booster : string + Specify which booster to use: gbtree, gblinear or dart. + n_jobs : int + Number of parallel threads used to run xgboost. (replaces ``nthread``) + gamma : float + Minimum loss reduction required to make a further partition on a leaf + node of the tree. + min_child_weight : int + Minimum sum of instance weight(hessian) needed in a child. + max_delta_step : int + Maximum delta step we allow each tree's weight estimation to be. + subsample : float + Subsample ratio of the training instance. + colsample_bytree : float + Subsample ratio of columns when constructing each tree. + colsample_bylevel : float + Subsample ratio of columns for each split, in each level. + reg_alpha : float (xgb's alpha) + L1 regularization term on weights. + reg_lambda : float (xgb's lambda) + L2 regularization term on weights. + scale_pos_weight : float + Balancing of positive and negative weights. + base_score: + The initial prediction score of all instances, global bias. + random_state : int + Random number seed. (replaces seed) + # missing : float, optional + # Value in the data which needs to be present as a missing value. If + # None, defaults to np.nan. + importance_type: string, default "gain" + The feature importance type for the ``feature_importances_`` + property: either "gain", + "weight", "cover", "total_gain" or "total_cover". + \*\*kwargs : dict, optional + Keyword arguments for XGBoost Booster object. Full documentation of + parameters can be found here: + https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst. + Attempting to set a parameter via the constructor args and \*\*kwargs + dict simultaneously will result in a TypeError. + Note: \*\*kwargs is unsupported by scikit-learn. We do not + guarantee that parameters passed via this argument will interact + properly with scikit-learn. + Attributes + ---------- + n_detector_ : int + The number of unsupervised of detectors used. + clf_ : object + The XGBoost classifier. + decision_scores_ : numpy array of shape (n_samples,) + The outlier scores of the training data. + The higher, the more abnormal. Outliers tend to have higher + scores. This value is available once the detector is fitted. + labels_ : int, either 0 or 1 + The binary labels of the training data. 0 stands for inliers + and 1 for outliers/anomalies. It is generated by applying + ``threshold_`` on ``decision_scores_``. + """ + + ### Modify the metadata ### + #__author__: "DATA Lab at Texas A&M University" + metadata = metadata_base.PrimitiveMetadata({ + "__author__": "DATA Lab at Texas A&M University", + "name": "XGBOD Primitive", + "python_path": "d3m.primitives.tods.detection_algorithm.pyod_xgbod", + "source": { + 'name': 'DATA Lab at Texas A&M University', + 'contact': 'mailto:khlai037@tamu.edu', + }, + "hyperparams_to_tune": ['estimator_list', 'standardization_flag_list', 'max_depth', 'learning_rate','n_estimators','silent','booster','n_jobs','gamma','min_child_weight','max_delta_step','subsample','colsample_bytree','colsample_bylevel','reg_alpha','reg_lambda','scale_pos_weight','base_score'], + "version": "0.0.1", + "algorithm_types": [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE + ], + "primitive_family": metadata_base.PrimitiveFamily.ANOMALY_DETECTION, + 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'XGBODPrimitive')), + }) + + def __init__(self, *, + hyperparams: Hyperparams, # + random_seed: int = 0, + docker_containers: Dict[str, DockerContainer] = None) -> None: + super().__init__(hyperparams=hyperparams, random_seed=random_seed, docker_containers=docker_containers) + + ### Initialize your algorithm ### + self._clf = XGBOD(estimator_list=hyperparams['estimator_list'], + standardization_flag_list=hyperparams['standardization_flag_list'], + max_depth=hyperparams['max_depth'], + learning_rate=hyperparams['learning_rate'], + n_estimators=hyperparams['n_estimators'], + silent=hyperparams['silent'], + objective="binary:logistic", + booster=hyperparams['booster'], + n_jobs=hyperparams['n_jobs'], + nthread=None, + gamma=hyperparams['gamma'], + min_child_weight=hyperparams['min_child_weight'], + max_delta_step=hyperparams['max_delta_step'], + subsample=hyperparams['subsample'], + colsample_bytree=hyperparams['colsample_bytree'], + colsample_bylevel=hyperparams['colsample_bylevel'], + reg_alpha=hyperparams['reg_alpha'], + reg_lambda=hyperparams['reg_lambda'], + scale_pos_weight=hyperparams['scale_pos_weight'], + base_score=hyperparams['base_score'], + # random_state=hyperparams['random_state'], + ) + + + def set_training_data(self, *, inputs: Inputs) -> None: + """ + Set training data for outlier detection. + Args: + inputs: Container DataFrame + + Returns: + None + """ + super().set_training_data(inputs=inputs) + + def fit(self, *, timeout: float = None, iterations: int = None) -> CallResult[None]: + """ + Fit model with training data. + Args: + *: Container DataFrame. Time series data up to fit. + + Returns: + None + """ + return super().fit() + + def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> CallResult[Outputs]: + """ + Process the testing data. + Args: + inputs: Container DataFrame. Time series data up to outlier detection. + + Returns: + Container DataFrame + 1 marks Outliers, 0 marks normal. + """ + return super().produce(inputs=inputs, timeout=timeout, iterations=iterations) + + def produce_score(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> CallResult[Outputs]: + """ + Process the testing data. + Args: + inputs: Container DataFrame. Time series data up to outlier detection. + Returns: + Container DataFrame + Outlier score of input DataFrame. + """ + return super().produce_score(inputs=inputs, timeout=timeout, iterations=iterations) + + def get_params(self) -> Params: + """ + Return parameters. + Args: + None + + Returns: + class Params + """ + return super().get_params() + + def set_params(self, *, params: Params) -> None: + """ + Set parameters for outlier detection. + Args: + params: class Params + + Returns: + None + """ + super().set_params(params=params) + + + +### The Implementation of your algorithm ### +class DetectionAlgorithm(BaseDetector): + """ + Attributes + ---------- + decision_scores_ : numpy array of shape (n_samples,) + The outlier scores of the training data. + The higher, the more abnormal. Outliers tend to have higher + scores. This value is available once the detector is + fitted. + threshold_ : float + The threshold is based on ``contamination``. It is the + ``n_samples * contamination`` most abnormal samples in + ``decision_scores_``. The threshold is calculated for generating + binary outlier labels. + labels_ : int, either 0 or 1 + The binary labels of the training data. 0 stands for inliers + and 1 for outliers/anomalies. It is generated by applying + ``threshold_`` on ``decision_scores_``. + """ + + def __init__(): + pass + + def fit(): + pass + + def decision_function(self): + pass diff --git a/tods/resources/.entry_points.ini b/tods/resources/.entry_points.ini index 4104e46b..9b350525 100644 --- a/tods/resources/.entry_points.ini +++ b/tods/resources/.entry_points.ini @@ -69,6 +69,7 @@ tods.detection_algorithm.pyod_loda = tods.detection_algorithm.PyodLODA:LODAPrimi tods.detection_algorithm.pyod_cblof = tods.detection_algorithm.PyodCBLOF:CBLOFPrimitive tods.detection_algorithm.pyod_sogaal = tods.detection_algorithm.PyodSoGaal:So_GaalPrimitive tods.detection_algorithm.pyod_mogaal = tods.detection_algorithm.PyodMoGaal:Mo_GaalPrimitive +tods.detection_algorithm.pyod_xgbod = tods.detection_algorithm.PyodXGBOD:XGBODPrimitive tods.detection_algorithm.matrix_profile = tods.detection_algorithm.MatrixProfile:MatrixProfilePrimitive tods.detection_algorithm.AutoRegODetector = tods.detection_algorithm.AutoRegODetect:AutoRegODetectorPrimitive diff --git a/tods/tests/detection_algorithm/test_PyodXGBOD.py b/tods/tests/detection_algorithm/test_PyodXGBOD.py new file mode 100644 index 00000000..038fd18c --- /dev/null +++ b/tods/tests/detection_algorithm/test_PyodXGBOD.py @@ -0,0 +1,103 @@ +import unittest + +from d3m import container, utils +from d3m.metadata import base as metadata_base +from d3m.container import DataFrame as d3m_dataframe + +from tods.detection_algorithm.PyodXGBOD import XGBODPrimitive +from pyod.utils.data import generate_data + +from tods.detection_algorithm.core.UODCommonTest import UODCommonTest + +import numpy as np + +class PyodXGBODTestCase(unittest.TestCase): + def setUp(self): + + self.maxDiff = None + self.n_train = 200 + self.n_test = 100 + self.contamination = 0.1 + self.roc_floor = 0.0 #0.8??? + self.X_train, self.y_train, self.X_test, self.y_test = generate_data( + n_train=self.n_train, n_test=self.n_test, + contamination=self.contamination, random_state=42) + + self.X_train = d3m_dataframe(self.X_train, generate_metadata=True) + self.X_test = d3m_dataframe(self.X_test, generate_metadata=True) + + hyperparams_default = XGBODPrimitive.metadata.get_hyperparams().defaults() + hyperparams = hyperparams_default.replace({'contamination': self.contamination, }) + hyperparams = hyperparams.replace({'return_subseq_inds': True, }) + + self.primitive = XGBODPrimitive(hyperparams=hyperparams) + + self.primitive.set_training_data(inputs=self.X_train) + self.primitive.fit() + self.prediction_labels = self.primitive.produce(inputs=self.X_test).value + self.prediction_score = self.primitive.produce_score(inputs=self.X_test).value + + self.uodbase_test = UODCommonTest(model=self.primitive._clf, + X_train=self.X_train, + y_train=self.y_train, + X_test=self.X_test, + y_test=self.y_test, + roc_floor=self.roc_floor, + ) + + def test_detector(self): + self.uodbase_test.test_detector() + + def test_metadata(self): + # print(self.prediction_labels.metadata.to_internal_simple_structure()) + self.assertEqual(utils.to_json_structure(self.prediction_labels.metadata.to_internal_simple_structure()), [{ + 'selector': [], + 'metadata': { + # 'top_level': 'main', + 'schema': metadata_base.CONTAINER_SCHEMA_VERSION, + 'structural_type': 'd3m.container.pandas.DataFrame', + 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Table'], + 'dimension': { + 'name': 'rows', + 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/TabularRow'], + 'length': 100, + }, + }, + }, { + 'selector': ['__ALL_ELEMENTS__'], + 'metadata': { + 'dimension': { + 'name': 'columns', + 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/TabularColumn'], + 'length': 3, + }, + }, + }, { + 'selector': ['__ALL_ELEMENTS__', 0], + 'metadata': { + 'name': 'XGBOD Anomaly Detection0_0', + 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Attribute'], + 'structural_type': 'numpy.int64', + }, + }, { + 'selector': ['__ALL_ELEMENTS__', 1], + 'metadata': { + 'name': 'XGBOD Anomaly Detection0_1', + 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Attribute'], + 'structural_type': 'numpy.int64', + }, + }, { + 'selector': ['__ALL_ELEMENTS__', 2], + 'metadata': { + 'name': 'XGBOD Anomaly Detection0_2', + 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Attribute'], + 'structural_type': 'numpy.int64', + }, + }]) + + def test_params(self): + params = self.primitive.get_params() + self.primitive.set_params(params=params) + +if __name__ == '__main__': + unittest.main() From 1574c3a082fdb0c52e257bf35c8c791a06902239 Mon Sep 17 00:00:00 2001 From: hwy893747147 Date: Mon, 26 Jul 2021 10:28:25 -0500 Subject: [PATCH 4/8] telemanom sk test with yahoo dataset --- examples/sk_examples/tele.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 examples/sk_examples/tele.py diff --git a/examples/sk_examples/tele.py b/examples/sk_examples/tele.py new file mode 100644 index 00000000..b58d4e4a --- /dev/null +++ b/examples/sk_examples/tele.py @@ -0,0 +1,32 @@ +import numpy as np +from tods.sk_interface.detection_algorithm.Telemanom_skinterface import TelemanomSKI +# from tods.tods_skinterface.primitiveSKI.detection_algorithm.Telemanom_skinterface import TelemanomSKI +from sklearn.metrics import precision_recall_curve +from sklearn.metrics import accuracy_score +from sklearn.metrics import confusion_matrix +from sklearn.metrics import classification_report +import matplotlib.pyplot as plt +from sklearn import metrics +import pandas as pd +#prepare the data + +data = pd.read_csv("./yahoo_sub_5.csv").to_numpy() +# print("shape:", data.shape) +# print("datatype of data:",data.dtype) +# print("First 5 rows:\n", data[:5]) + +# X_train = np.expand_dims(data[:10000], axis=1) +# X_test = np.expand_dims(data[10000:], axis=1) + +# print("First 5 rows train:\n", X_train[:5]) +# print("First 5 rows test:\n", X_test[:5]) + +transformer = TelemanomSKI(l_s= 2, n_predictions= 1) +transformer.fit(data) +# prediction_labels_train = transformer.predict(X_train) +prediction_labels = transformer.predict(data) +prediction_score = transformer.predict_score(data) + +# print("Primitive: ", transformer.primitive) +print("Prediction Labels\n", prediction_labels) +print("Prediction Score\n", prediction_score) \ No newline at end of file From bd4af96f2f9218e3700b34a45decf9837303528a Mon Sep 17 00:00:00 2001 From: hwy893747147 Date: Mon, 26 Jul 2021 11:49:48 -0500 Subject: [PATCH 5/8] fixed both notebooks bugs --- ...CR_Anomaly_robotDOG1_10000_19280_19360.txt | 20000 ++++++++++++++++ .../TODS Official Demo Notebook.ipynb | 2706 --- .../TODSBlockchainNotebook.ipynb | 2127 ++ .../Demo Notebook/TODSOfficialNotebook.ipynb | 2028 ++ 4 files changed, 24155 insertions(+), 2706 deletions(-) create mode 100644 datasets/anomaly/raw_data/500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt delete mode 100644 examples/Demo Notebook/TODS Official Demo Notebook.ipynb create mode 100644 examples/Demo Notebook/TODSBlockchainNotebook.ipynb create mode 100644 examples/Demo Notebook/TODSOfficialNotebook.ipynb diff --git a/datasets/anomaly/raw_data/500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt b/datasets/anomaly/raw_data/500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt new file mode 100644 index 00000000..f965afb0 --- /dev/null +++ b/datasets/anomaly/raw_data/500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt @@ -0,0 +1,20000 @@ + 1.4529900e-01 + 1.2820500e-01 + 9.4017000e-02 + 7.6923000e-02 + 1.1111100e-01 + 1.4529900e-01 + 1.7948700e-01 + 2.1367500e-01 + 2.1367500e-01 + 2.3076900e-01 + 2.3076900e-01 + 2.3076900e-01 + 2.1367500e-01 + 2.1367500e-01 + 2.1367500e-01 + 2.3076900e-01 + 2.3076900e-01 + 2.3076900e-01 + 2.3076900e-01 + 2.3076900e-01 + 2.4786300e-01 + 2.4786300e-01 + 2.4786300e-01 + 2.6495700e-01 + 2.6495700e-01 + 2.4786300e-01 + 2.4786300e-01 + 2.8205100e-01 + 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3.5042700e-01 + 2.4786300e-01 + 1.4529900e-01 + 5.9829000e-02 + 2.5641000e-02 + 8.5469500e-03 + 2.5641000e-02 + 5.9829000e-02 diff --git a/examples/Demo Notebook/TODS Official Demo Notebook.ipynb b/examples/Demo Notebook/TODS Official Demo Notebook.ipynb deleted file mode 100644 index 97e66d85..00000000 --- a/examples/Demo Notebook/TODS Official Demo Notebook.ipynb +++ /dev/null @@ -1,2706 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.4.1'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import scipy\n", - "scipy.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Python 3.6.10 :: Anaconda, Inc.\r\n" - ] - } - ], - "source": [ - "!python -V" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# TODS" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction Summary" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data. TODS provides exhaustive modules for building machine learning-based outlier detection systems, including: data processing, time series processing, feature analysis (extraction), detection algorithms, and reinforcement module. The functionalities provided via these modules include data preprocessing for general purposes, time series data smoothing/transformation, extracting features from time/frequency domains, various detection algorithms, and involving human expertise to calibrate the system. Three common outlier detection scenarios on time-series data can be performed: point-wise detection (time points as outliers), pattern-wise detection (subsequences as outliers), and system-wise detection (sets of time series as outliers), and a wide-range of corresponding algorithms are provided in TODS. This package is developed by DATA Lab @ Texas A&M University." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Obtaining tods from git+https://github.com/datamllab/tods.git#egg=tods\n", - " Cloning https://github.com/datamllab/tods.git to ./src/tods\n", - " Running command git clone -q https://github.com/datamllab/tods.git '/Users/wangyanghe/Desktop/Research/Tods Notebook/src/tods'\n", - "Requirement already satisfied: Jinja2 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (2.11.3)\n", - "Requirement already satisfied: numpy==1.18.2 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (1.18.2)\n", - "Requirement already satisfied: simplejson==3.12.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (3.12.0)\n", - "Requirement already satisfied: scikit-learn==0.22.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (0.22)\n", - "Requirement already satisfied: statsmodels==0.11.1 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (0.11.1)\n", - "Requirement already satisfied: PyWavelets>=1.1.1 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (1.1.1)\n", - "Requirement already satisfied: pillow==7.1.2 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (7.1.2)\n", - "Requirement already satisfied: tensorflow==2.2 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (2.2.0)\n", - "Requirement already satisfied: keras in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (2.4.3)\n", - "Requirement already satisfied: pyod in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (0.8.7)\n", - "Requirement already satisfied: nimfa==1.4.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (1.4.0)\n", - "Requirement already satisfied: stumpy==1.4.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (1.4.0)\n", - "Requirement already satisfied: more-itertools==8.5.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tods) (8.5.0)\n", - "Requirement already satisfied: MarkupSafe>=0.23 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from Jinja2->tods) (1.1.1)\n", - "Requirement already satisfied: joblib>=0.11 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from scikit-learn==0.22.0->tods) (1.0.1)\n", - "Requirement already satisfied: scipy>=0.17.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from scikit-learn==0.22.0->tods) (1.4.1)\n", - "Requirement already satisfied: patsy>=0.5 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from statsmodels==0.11.1->tods) (0.5.1)\n", - "Requirement already satisfied: pandas>=0.21 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from statsmodels==0.11.1->tods) (1.0.3)\n", - "Requirement already satisfied: google-pasta>=0.1.8 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tensorflow==2.2->tods) (0.2.0)\n", - "Requirement already satisfied: protobuf>=3.8.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tensorflow==2.2->tods) (3.15.8)\n", - "Requirement already satisfied: six>=1.12.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tensorflow==2.2->tods) (1.15.0)\n", - "Requirement already satisfied: grpcio>=1.8.6 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tensorflow==2.2->tods) (1.37.0)\n", - "Requirement already satisfied: keras-preprocessing>=1.1.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tensorflow==2.2->tods) (1.1.2)\n", - "Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tensorflow==2.2->tods) (2.10.0)\n", - "Requirement already satisfied: opt-einsum>=2.3.2 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tensorflow==2.2->tods) (3.3.0)\n", - "Requirement already satisfied: termcolor>=1.1.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from tensorflow==2.2->tods) (1.1.0)\n", - "Requirement already satisfied: wheel>=0.26; 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python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2->tods) (3.4.1)\n", - "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2->tods) (3.7.4.3)\n", - "Requirement already satisfied: oauthlib>=3.0.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2->tods) (3.1.0)\n", - "Requirement already satisfied: pyasn1>=0.1.3 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2->tods) (0.4.8)\n", - "Installing collected packages: tods\n", - " Attempting uninstall: tods\n", - " Found existing installation: tods 0.0.2\n", - " Uninstalling tods-0.0.2:\n", - " Successfully uninstalled tods-0.0.2\n", - " Running setup.py develop for tods\n", - "Successfully installed tods\n" - ] - } - ], - "source": [ - "!pip install -e git+https://github.com/datamllab/tods.git#egg=tods" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/wangyanghe/Desktop/Research/Tods Notebook/src/tods\n" - ] - } - ], - "source": [ - "%cd src/tods" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Branch 'wangyanghe' set up to track remote branch 'wangyanghe' from 'origin'.\r\n", - "Switched to a new branch 'wangyanghe'\r\n" - ] - } - ], - "source": [ - "!git checkout wangyanghe" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/wangyanghe/Desktop/Research/Tods Notebook/src/tods/examples/sk_examples\n" - ] - } - ], - "source": [ - "%cd examples/sk_examples" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt\r\n", - "DeepLog_test.py\r\n", - "IsolationForest_test.py\r\n", - "MatrixProfile_test.py\r\n", - "Telemanom_test.py\r\n" - ] - } - ], - "source": [ - "!ls" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import argparse\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "from sklearn.metrics import precision_recall_curve\n", - "from sklearn.metrics import accuracy_score\n", - "from sklearn.metrics import confusion_matrix\n", - "from sklearn.metrics import classification_report\n", - "import matplotlib.pyplot as plt\n", - "from sklearn import metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from tods.tods_skinterface.primitiveSKI.detection_algorithm.DeepLog_skinterface import DeepLogSKI" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.preprocessing.data module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.preprocessing. Anything that cannot be imported from sklearn.preprocessing is now part of the private API.\n", - " warnings.warn(message, FutureWarning)\n", - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.decomposition.truncated_svd module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.decomposition. Anything that cannot be imported from sklearn.decomposition is now part of the private API.\n", - " warnings.warn(message, FutureWarning)\n", - "d3m.primitives.tods.detection_algorithm.LSTMODetector: Primitive is not providing a description through its docstring.\n" - ] - } - ], - "source": [ - "from tods.tods_skinterface.primitiveSKI.detection_algorithm.Telemanom_skinterface import TelemanomSKI" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "from d3m import index\n", - "from d3m.metadata.base import ArgumentType\n", - "from d3m.metadata.pipeline import Pipeline, PrimitiveStep\n", - "from axolotl.backend.simple import SimpleRunner\n", - "from tods import generate_dataset, generate_problem\n", - "from tods.searcher import BruteForceSearch" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "from tods import generate_dataset, load_pipeline, evaluate_pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### UCR Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "data = np.loadtxt(\"./500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape: (20000,)\n", - "datatype of data: float64\n", - "First 5 rows:\n", - " [0.145299 0.128205 0.094017 0.076923 0.111111]\n" - ] - } - ], - "source": [ - "print(\"shape:\", data.shape)\n", - "print(\"datatype of data:\",data.dtype)\n", - "print(\"First 5 rows:\\n\", data[:5])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "X_train = np.expand_dims(data[:10000], axis=1)\n", - "X_test = np.expand_dims(data[10000:], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "First 5 rows train:\n", - " [[0.145299]\n", - " [0.128205]\n", - " [0.094017]\n", - " [0.076923]\n", - " [0.111111]]\n", - "First 5 rows test:\n", - " [[0.076923]\n", - " [0.076923]\n", - " [0.076923]\n", - " [0.094017]\n", - " [0.145299]]\n" - ] - } - ], - "source": [ - "print(\"First 5 rows train:\\n\", X_train[:5])\n", - "print(\"First 5 rows test:\\n\", X_test[:5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Yahoo Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "data_yahoo = pd.read_csv('../../datasets/anomaly/raw_data/yahoo_sub_5.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape: (1400, 7)\n", - "First 5 rows:\n", - " timestamp value_0 value_1 value_2 value_3 value_4 anomaly\n", - "0 1 12183 0.000000 3.716667 5 2109 0\n", - "1 2 12715 0.091758 3.610833 60 3229 0\n", - "2 3 12736 0.172297 3.481389 88 3637 0\n", - "3 4 12716 0.226219 3.380278 84 1982 0\n", - "4 5 12739 0.176358 3.193333 111 2751 0\n" - ] - } - ], - "source": [ - "print(\"shape:\", data_yahoo.shape)\n", - "print(\"First 5 rows:\\n\", data_yahoo[:5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SK Example 1: DeepLog" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "282/282 [==============================] - 1s 5ms/step - loss: 0.4239 - val_loss: 0.2694\n", - "Epoch 2/10\n", - "282/282 [==============================] - 1s 2ms/step - loss: 0.3344 - val_loss: 0.2818\n", - "Epoch 3/10\n", - "282/282 [==============================] - 1s 2ms/step - loss: 0.3444 - val_loss: 0.2806\n", - "Epoch 4/10\n", - "282/282 [==============================] - 1s 2ms/step - loss: 0.3575 - val_loss: 0.2731\n", - "Epoch 5/10\n", - "282/282 [==============================] - 1s 2ms/step - loss: 0.3364 - val_loss: 0.2783\n", - "Epoch 6/10\n", - "282/282 [==============================] - 1s 2ms/step - loss: 0.3447 - val_loss: 0.2742\n", - "Epoch 7/10\n", - "282/282 [==============================] - 1s 2ms/step - loss: 0.3357 - val_loss: 0.2586\n", - "Epoch 8/10\n", - "282/282 [==============================] - 1s 2ms/step - loss: 0.3392 - val_loss: 0.2804\n", - "Epoch 9/10\n", - "282/282 [==============================] - 1s 2ms/step - loss: 0.3442 - val_loss: 0.2691\n", - "Epoch 10/10\n", - "282/282 [==============================] - 1s 2ms/step - loss: 0.3475 - val_loss: 0.2683\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n", - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n", - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n" - ] - } - ], - "source": [ - "transformer = DeepLogSKI()\n", - "transformer.fit(X_train)\n", - "prediction_labels_train = transformer.predict(X_train)\n", - "prediction_labels_test = transformer.predict(X_test)\n", - "prediction_score = transformer.predict_score(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Primitive: d3m.primitives.tods.detection_algorithm.deeplog(hyperparams=Hyperparams({'contamination': 0.1, 'window_size': 1, 'step_size': 1, 'return_subseq_inds': False, 'use_columns': (), 'exclude_columns': (), 'return_result': 'new', 'use_semantic_types': False, 'add_index_columns': False, 'error_on_no_input': True, 'return_semantic_type': 'https://metadata.datadrivendiscovery.org/types/Attribute', 'hidden_size': 64, 'loss': 'mean_squared_error', 'optimizer': 'Adam', 'epochs': 10, 'batch_size': 32, 'dropout_rate': 0.2, 'l2_regularizer': 0.1, 'validation_size': 0.1, 'features': 1, 'stacked_layers': 1, 'preprocessing': True, 'verbose': 1}), random_seed=0)\n", - "Prediction Labels\n", - " [[0]\n", - " [0]\n", - " [0]\n", - " ...\n", - " [0]\n", - " [0]\n", - " [0]]\n", - "Prediction Score\n", - " [[0. ]\n", - " [0.3569443 ]\n", - " [0.3569443 ]\n", - " ...\n", - " [0.77054234]\n", - " [0.4575615 ]\n", - " [0.17499346]]\n" - ] - } - ], - "source": [ - "print(\"Primitive: \", transformer.primitive)\n", - "print(\"Prediction Labels\\n\", prediction_labels_test)\n", - "print(\"Prediction Score\\n\", prediction_score)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "y_true = prediction_labels_train\n", - "y_pred = prediction_labels_test\n", - "precision, recall, thresholds = precision_recall_curve(y_true, y_pred)\n", - "f1_scores = 2*recall*precision/(recall+precision)\n", - "fpr, tpr, threshold = metrics.roc_curve(y_true, y_pred)\n", - "roc_auc = metrics.auc(fpr, tpr)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy Score: 0.903\n" - ] - } - ], - "source": [ - "print('Accuracy Score: ', accuracy_score(y_true, y_pred))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[8646, 358],\n", - " [ 612, 384]])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion_matrix(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.93 0.96 0.95 9004\n", - " 1 0.52 0.39 0.44 996\n", - "\n", - " accuracy 0.90 10000\n", - " macro avg 0.73 0.67 0.69 10000\n", - "weighted avg 0.89 0.90 0.90 10000\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_true, y_pred))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best threshold: 1\n", - "Best F1-Score: 0.4418872266973533\n" - ] - } - ], - "source": [ - "print('Best threshold: ', thresholds[np.argmax(f1_scores)])\n", - "print('Best F1-Score: ', np.max(f1_scores))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.title('ROC')\n", - "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n", - "plt.legend(loc = 'lower right')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.xlabel('False Positive Rate')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SK Example 2: Telemanom" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "125/125 [==============================] - 1s 8ms/step - loss: 0.0112 - val_loss: 0.0046\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n", - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n" - ] - } - ], - "source": [ - "transformer = TelemanomSKI(l_s= 2, n_predictions= 1)\n", - "transformer.fit(X_train)\n", - "prediction_labels_train = transformer.predict(X_train)\n", - "prediction_labels_test = transformer.predict(X_test)\n", - "prediction_score = transformer.predict_score(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Primitive: d3m.primitives.tods.detection_algorithm.telemanom(hyperparams=Hyperparams({'contamination': 0.1, 'window_size': 1, 'step_size': 1, 'return_subseq_inds': False, 'use_columns': (), 'exclude_columns': (), 'return_result': 'new', 'use_semantic_types': False, 'add_index_columns': False, 'error_on_no_input': True, 'return_semantic_type': 'https://metadata.datadrivendiscovery.org/types/Attribute', 'smoothing_perc': 0.05, 'window_size_': 100, 'error_buffer': 50, 'batch_size': 70, 'dropout': 0.3, 'validation_split': 0.2, 'optimizer': 'Adam', 'lstm_batch_size': 64, 'loss_metric': 'mean_squared_error', 'layers': [10, 10], 'epochs': 1, 'patience': 10, 'min_delta': 0.0003, 'l_s': 2, 'n_predictions': 1, 'p': 0.05}), random_seed=0)\n", - "Prediction Labels\n", - " [[1]\n", - " [1]\n", - " [1]\n", - " ...\n", - " [1]\n", - " [1]\n", - " [1]]\n", - "Prediction Score\n", - " [[0.08822848]\n", - " [0.07965706]\n", - " [0.05999164]\n", - " ...\n", - " [0.05911084]\n", - " [0.05963569]\n", - " [0.06003137]]\n" - ] - } - ], - "source": [ - "print(\"Primitive: \", transformer.primitive)\n", - "print(\"Prediction Labels\\n\", prediction_labels_test)\n", - "print(\"Prediction Score\\n\", prediction_score)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "y_true = prediction_labels_train\n", - "y_pred = prediction_labels_test\n", - "precision, recall, thresholds = precision_recall_curve(y_true, y_pred)\n", - "f1_scores = 2*recall*precision/(recall+precision)\n", - "fpr, tpr, threshold = metrics.roc_curve(y_true, y_pred)\n", - "roc_auc = metrics.auc(fpr, tpr)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy Score: 0.1839551865559668\n" - ] - } - ], - "source": [ - "print('Accuracy Score: ', accuracy_score(y_true, y_pred))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 990, 8007],\n", - " [ 151, 849]])" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "confusion_matrix(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.87 0.11 0.20 8997\n", - " 1 0.10 0.85 0.17 1000\n", - "\n", - " accuracy 0.18 9997\n", - " macro avg 0.48 0.48 0.18 9997\n", - "weighted avg 0.79 0.18 0.19 9997\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_true, y_pred))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best threshold: 0\n", - "Best F1-Score: 0.18186778212239701\n" - ] - } - ], - "source": [ - "print('Best threshold: ', thresholds[np.argmax(f1_scores)])\n", - "print('Best F1-Score: ', np.max(f1_scores))" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.title('ROC')\n", - "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n", - "plt.legend(loc = 'lower right')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.xlabel('False Positive Rate')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pipline Example: AutoEncoder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'inputs.0'" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Creating pipeline\n", - "pipeline_description = Pipeline()\n", - "pipeline_description.add_input(name='inputs')" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "While loading primitive 'tods.data_processing.dataset_to_dataframe', an error has been detected: (networkx 2.5 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('networkx==2.4'), {'tamu-axolotl'})\n", - "Attempting to load primitive 'tods.data_processing.dataset_to_dataframe' without checking requirements.\n" - ] - } - ], - "source": [ - "# Step 0: dataset_to_dataframe\n", - "step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))\n", - "step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')\n", - "step_0.add_output('produce')\n", - "pipeline_description.add_step(step_0)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "While loading primitive 'tods.data_processing.column_parser', an error has been detected: (networkx 2.5 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('networkx==2.4'), {'tamu-axolotl'})\n", - "Attempting to load primitive 'tods.data_processing.column_parser' without checking requirements.\n" - ] - } - ], - "source": [ - "# Step 1: column_parser\n", - "step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))\n", - "step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')\n", - "step_1.add_output('produce')\n", - "pipeline_description.add_step(step_1)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "While loading primitive 'tods.data_processing.extract_columns_by_semantic_types', an error has been detected: (networkx 2.5 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('networkx==2.4'), {'tamu-axolotl'})\n", - "Attempting to load primitive 'tods.data_processing.extract_columns_by_semantic_types' without checking requirements.\n" - ] - } - ], - "source": [ - "# Step 2: extract_columns_by_semantic_types(attributes)\n", - "step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))\n", - "step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')\n", - "step_2.add_output('produce')\n", - "step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,\n", - "\t\t\t\t\t\t\t data=['https://metadata.datadrivendiscovery.org/types/Attribute'])\n", - "pipeline_description.add_step(step_2)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "# Step 3: extract_columns_by_semantic_types(targets)\n", - "step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))\n", - "step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')\n", - "step_3.add_output('produce')\n", - "step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,\n", - "\t\t\t\t\t\t\tdata=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])\n", - "pipeline_description.add_step(step_3)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "attributes = 'steps.2.produce'\n", - "targets = 'steps.3.produce'" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "While loading primitive 'tods.feature_analysis.statistical_maximum', an error has been detected: (networkx 2.5 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('networkx==2.4'), {'tamu-axolotl'})\n", - "Attempting to load primitive 'tods.feature_analysis.statistical_maximum' without checking requirements.\n" - ] - } - ], - "source": [ - "# Step 4: processing\n", - "step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_maximum'))\n", - "step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)\n", - "step_4.add_output('produce')\n", - "pipeline_description.add_step(step_4)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "While loading primitive 'tods.detection_algorithm.pyod_ae', an error has been detected: (networkx 2.5 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('networkx==2.4'), {'tamu-axolotl'})\n", - "Attempting to load primitive 'tods.detection_algorithm.pyod_ae' without checking requirements.\n" - ] - } - ], - "source": [ - "# Step 5: algorithm`\n", - "step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_ae'))\n", - "step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce')\n", - "step_5.add_output('produce')\n", - "pipeline_description.add_step(step_5)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "While loading primitive 'tods.data_processing.construct_predictions', an error has been detected: (networkx 2.5 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('networkx==2.4'), {'tamu-axolotl'})\n", - "Attempting to load primitive 'tods.data_processing.construct_predictions' without checking requirements.\n" - ] - } - ], - "source": [ - "# Step 6: Predictions\n", - "step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions'))\n", - "step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce')\n", - "step_6.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')\n", - "step_6.add_output('produce')\n", - "pipeline_description.add_step(step_6)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'outputs.0'" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Final Output\n", - "pipeline_description.add_output(name='output predictions', data_reference='steps.6.produce')" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\"id\": \"5ea6f8e5-e938-43e3-9dd4-4c9451bb8821\", \"schema\": \"https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json\", \"created\": \"2021-04-14T16:15:48.973138Z\", \"inputs\": [{\"name\": \"inputs\"}], \"outputs\": [{\"data\": \"steps.6.produce\", \"name\": \"output predictions\"}], \"steps\": [{\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"4b42ce1e-9b98-4a25-b68e-fad13311eb65\", \"version\": \"0.3.0\", \"python_path\": \"d3m.primitives.tods.data_processing.dataset_to_dataframe\", \"name\": \"Extract a DataFrame from a Dataset\", \"digest\": \"fb5cd27ebf69b9587b23940618071ba9ffe9f47ebd7772797d61ae0521f92515\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"inputs.0\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"d510cb7a-1782-4f51-b44c-58f0236e47c7\", \"version\": \"0.6.0\", \"python_path\": \"d3m.primitives.tods.data_processing.column_parser\", \"name\": \"Parses strings into their types\", \"digest\": \"62af3e97e2535681a0b1320e4ac97edeba15895862a46244ab079c47ce56958d\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.0.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"4503a4c6-42f7-45a1-a1d4-ed69699cf5e1\", \"version\": \"0.4.0\", \"python_path\": \"d3m.primitives.tods.data_processing.extract_columns_by_semantic_types\", \"name\": \"Extracts columns by semantic type\", \"digest\": \"d4c8204514d840de1b5acad9831f9d5581b41f425df3d14051336abdeacdf1b2\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.1.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"semantic_types\": {\"type\": \"VALUE\", \"data\": [\"https://metadata.datadrivendiscovery.org/types/Attribute\"]}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"4503a4c6-42f7-45a1-a1d4-ed69699cf5e1\", \"version\": \"0.4.0\", \"python_path\": \"d3m.primitives.tods.data_processing.extract_columns_by_semantic_types\", \"name\": \"Extracts columns by semantic type\", \"digest\": \"d4c8204514d840de1b5acad9831f9d5581b41f425df3d14051336abdeacdf1b2\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.0.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"semantic_types\": {\"type\": \"VALUE\", \"data\": [\"https://metadata.datadrivendiscovery.org/types/TrueTarget\"]}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"3b448057-ac26-4f1b-96b6-141782f16a54\", \"version\": \"0.1.0\", \"python_path\": \"d3m.primitives.tods.feature_analysis.statistical_maximum\", \"name\": \"Time Series Decompostional\", \"digest\": \"922b594bd6c0894d57f6ebf5a54ccae6d69dab67326bd591c8c25e3a3dea6781\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.2.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"67e7fcdf-d645-3417-9aa4-85cd369487d9\", \"version\": \"0.0.1\", \"python_path\": \"d3m.primitives.tods.detection_algorithm.pyod_ae\", \"name\": \"TODS.anomaly_detection_primitives.AutoEncoder\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.4.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"8d38b340-f83f-4877-baaa-162f8e551736\", \"version\": \"0.3.0\", \"python_path\": \"d3m.primitives.tods.data_processing.construct_predictions\", \"name\": \"Construct pipeline predictions output\", \"digest\": \"33d90bfb7f97f47a6de5372c5f912c26fca8da2d2777661651c69687ad6f9950\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.5.produce\"}, \"reference\": {\"type\": \"CONTAINER\", \"data\": \"steps.1.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}], \"digest\": \"fc87321fbbe0b4faa956958d39d41d2cafd02700a3ed7ba80b01e80cace8d07e\"}\n" - ] - } - ], - "source": [ - "# Output to json\n", - "data = pipeline_description.to_json()\n", - "with open('autoencoder_pipeline.json', 'w') as f:\n", - " f.write(data)\n", - " print(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "this_path = os.path.dirname(os.path.abspath(\"__file__\"))\n", - "default_data_path = os.path.join(this_path, '../../datasets/anomaly/raw_data/yahoo_sub_5.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "_StoreAction(option_strings=['--pipeline_path'], dest='pipeline_path', nargs=None, const=None, default='/Users/wangyanghe/Desktop/Research/Tods Notebook/src/tods/examples/sk_examples/autoencoder_pipeline.json', type=None, choices=None, help='Input the path of the pre-built pipeline description', metavar=None)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parser = argparse.ArgumentParser(description='Arguments for running predefined pipelin.')\n", - "parser.add_argument('--table_path', type=str, default=default_data_path,\n", - " help='Input the path of the input data table')\n", - "parser.add_argument('--target_index', type=int, default=6,\n", - " help='Index of the ground truth (for evaluation)')\n", - "parser.add_argument('--metric',type=str, default='F1_MACRO',\n", - " help='Evaluation Metric (F1, F1_MACRO)')\n", - "parser.add_argument('--pipeline_path', \n", - " default=os.path.join(this_path, 'autoencoder_pipeline.json'),\n", - " help='Input the path of the pre-built pipeline description')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "args, unknown = parser.parse_known_args()\n", - "table_path = args.table_path \n", - "target_index = args.target_index # what column is the target\n", - "pipeline_path = args.pipeline_path\n", - "metric = args.metric # F1 on both label 0 and 1" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "# Read data and generate dataset\n", - "df = pd.read_csv(table_path)\n", - "dataset = generate_dataset(df, target_index)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the default pipeline\n", - "pipeline = load_pipeline(pipeline_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_2\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_2 (Dense) (None, 12) 156 \n", - "_________________________________________________________________\n", - "dropout_2 (Dropout) (None, 12) 0 \n", - "_________________________________________________________________\n", - "dense_3 (Dense) (None, 12) 156 \n", - "_________________________________________________________________\n", - "dropout_3 (Dropout) (None, 12) 0 \n", - "_________________________________________________________________\n", - "dense_4 (Dense) (None, 1) 13 \n", - "_________________________________________________________________\n", - "dropout_4 (Dropout) (None, 1) 0 \n", - "_________________________________________________________________\n", - "dense_5 (Dense) (None, 4) 8 \n", - "_________________________________________________________________\n", - "dropout_5 (Dropout) (None, 4) 0 \n", - "_________________________________________________________________\n", - "dense_6 (Dense) (None, 1) 5 \n", - "_________________________________________________________________\n", - "dropout_6 (Dropout) (None, 1) 0 \n", - "_________________________________________________________________\n", - "dense_7 (Dense) (None, 12) 24 \n", - "=================================================================\n", - "Total params: 362\n", - "Trainable params: 362\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "None\n", - "Epoch 1/100\n", - "40/40 [==============================] - 0s 6ms/step - loss: 2.1020 - val_loss: 1.3966\n", - "Epoch 2/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.8250 - val_loss: 1.2834\n", - "Epoch 3/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.7095 - val_loss: 1.2056\n", - "Epoch 4/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.6036 - val_loss: 1.1504\n", - "Epoch 5/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.5416 - val_loss: 1.1075\n", - "Epoch 6/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.4905 - val_loss: 1.0713\n", - "Epoch 7/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.4248 - val_loss: 1.0404\n", - "Epoch 8/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.4080 - val_loss: 1.0133\n", - "Epoch 9/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.3664 - val_loss: 0.9888\n", - "Epoch 10/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.3319 - val_loss: 0.9664\n", - "Epoch 11/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.2825 - val_loss: 0.9456\n", - "Epoch 12/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.2695 - val_loss: 0.9260\n", - "Epoch 13/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.2545 - val_loss: 0.9075\n", - "Epoch 14/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.2153 - val_loss: 0.8899\n", - "Epoch 15/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.2071 - val_loss: 0.8733\n", - "Epoch 16/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.1693 - val_loss: 0.8575\n", - "Epoch 17/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.1569 - val_loss: 0.8424\n", - "Epoch 18/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.1470 - val_loss: 0.8280\n", - "Epoch 19/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.1229 - val_loss: 0.8143\n", - "Epoch 20/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.1088 - val_loss: 0.8011\n", - "Epoch 21/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0923 - val_loss: 0.7885\n", - "Epoch 22/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0745 - val_loss: 0.7764\n", - "Epoch 23/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0592 - val_loss: 0.7648\n", - "Epoch 24/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0476 - val_loss: 0.7537\n", - "Epoch 25/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.0341 - val_loss: 0.7430\n", - "Epoch 26/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.0216 - val_loss: 0.7328\n", - "Epoch 27/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.0110 - val_loss: 0.7230\n", - "Epoch 28/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9972 - val_loss: 0.7136\n", - "Epoch 29/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.9867 - val_loss: 0.7046\n", - "Epoch 30/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.9769 - val_loss: 0.6959\n", - "Epoch 31/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.9668 - val_loss: 0.6876\n", - "Epoch 32/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9543 - val_loss: 0.6797\n", - "Epoch 33/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9452 - val_loss: 0.6720\n", - "Epoch 34/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.9357 - val_loss: 0.6647\n", - "Epoch 35/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.9327 - val_loss: 0.6576\n", - "Epoch 36/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.9219 - val_loss: 0.6509\n", - "Epoch 37/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9144 - val_loss: 0.6443\n", - "Epoch 38/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.9061 - val_loss: 0.6381\n", - "Epoch 39/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8996 - val_loss: 0.6321\n", - "Epoch 40/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8924 - val_loss: 0.6263\n", - "Epoch 41/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8858 - val_loss: 0.6207\n", - "Epoch 42/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8793 - val_loss: 0.6153\n", - "Epoch 43/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8744 - val_loss: 0.6102\n", - "Epoch 44/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8671 - val_loss: 0.6052\n", - "Epoch 45/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8610 - val_loss: 0.6004\n", - "Epoch 46/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8574 - val_loss: 0.5958\n", - "Epoch 47/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8517 - val_loss: 0.5913\n", - "Epoch 48/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8469 - val_loss: 0.5870\n", - "Epoch 49/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8431 - val_loss: 0.5829\n", - "Epoch 50/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8371 - val_loss: 0.5789\n", - "Epoch 51/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8338 - val_loss: 0.5750\n", - "Epoch 52/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8288 - val_loss: 0.5713\n", - "Epoch 53/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8245 - val_loss: 0.5677\n", - "Epoch 54/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8207 - val_loss: 0.5642\n", - "Epoch 55/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8179 - val_loss: 0.5608\n", - "Epoch 56/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8163 - val_loss: 0.5576\n", - "Epoch 57/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8105 - val_loss: 0.5545\n", - "Epoch 58/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8069 - val_loss: 0.5514\n", - "Epoch 59/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8037 - val_loss: 0.5485\n", - "Epoch 60/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8004 - val_loss: 0.5457\n", - "Epoch 61/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7974 - val_loss: 0.5429\n", - "Epoch 62/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7941 - val_loss: 0.5403\n", - "Epoch 63/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7915 - val_loss: 0.5377\n", - "Epoch 64/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7891 - val_loss: 0.5352\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 65/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7860 - val_loss: 0.5328\n", - "Epoch 66/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7836 - val_loss: 0.5305\n", - "Epoch 67/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7811 - val_loss: 0.5282\n", - "Epoch 68/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7792 - val_loss: 0.5260\n", - "Epoch 69/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7770 - val_loss: 0.5238\n", - "Epoch 70/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7745 - val_loss: 0.5218\n", - "Epoch 71/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7725 - val_loss: 0.5197\n", - "Epoch 72/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7704 - val_loss: 0.5178\n", - "Epoch 73/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7683 - val_loss: 0.5159\n", - "Epoch 74/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7663 - val_loss: 0.5141\n", - "Epoch 75/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7644 - val_loss: 0.5123\n", - "Epoch 76/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7627 - val_loss: 0.5106\n", - "Epoch 77/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7608 - val_loss: 0.5089\n", - "Epoch 78/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7591 - val_loss: 0.5073\n", - "Epoch 79/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7576 - val_loss: 0.5057\n", - "Epoch 80/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7559 - val_loss: 0.5041\n", - "Epoch 81/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7544 - val_loss: 0.5026\n", - "Epoch 82/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7529 - val_loss: 0.5011\n", - "Epoch 83/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7514 - val_loss: 0.4997\n", - "Epoch 84/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7500 - val_loss: 0.4983\n", - "Epoch 85/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7486 - val_loss: 0.4970\n", - "Epoch 86/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7473 - val_loss: 0.4957\n", - "Epoch 87/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7460 - val_loss: 0.4944\n", - "Epoch 88/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7448 - val_loss: 0.4931\n", - "Epoch 89/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7436 - val_loss: 0.4919\n", - "Epoch 90/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7424 - val_loss: 0.4907\n", - "Epoch 91/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7412 - val_loss: 0.4896\n", - "Epoch 92/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7402 - val_loss: 0.4885\n", - "Epoch 93/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7390 - val_loss: 0.4874\n", - "Epoch 94/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7380 - val_loss: 0.4863\n", - "Epoch 95/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7370 - val_loss: 0.4853\n", - "Epoch 96/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7360 - val_loss: 0.4842\n", - "Epoch 97/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7350 - val_loss: 0.4833\n", - "Epoch 98/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7341 - val_loss: 0.4823\n", - "Epoch 99/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7332 - val_loss: 0.4814\n", - "Epoch 100/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7323 - val_loss: 0.4804\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n", - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'method_called': 'evaluate',\n", - " 'outputs': \"[{'outputs.0': d3mIndex anomaly\"\n", - " '0 0 1'\n", - " '1 1 0'\n", - " '2 2 1'\n", - " '3 3 1'\n", - " '4 4 1'\n", - " '... ... ...'\n", - " '1395 1395 1'\n", - " '1396 1396 0'\n", - " '1397 1397 1'\n", - " '1398 1398 1'\n", - " '1399 1399 1'\n", - " ''\n", - " \"[1400 rows x 2 columns]}, {'outputs.0': d3mIndex anomaly\"\n", - " '0 0 1'\n", - " '1 1 0'\n", - " '2 2 1'\n", - " '3 3 1'\n", - " '4 4 1'\n", - " '... ... ...'\n", - " '1395 1395 1'\n", - " '1396 1396 0'\n", - " '1397 1397 1'\n", - " '1398 1398 1'\n", - " '1399 1399 1'\n", - " ''\n", - " '[1400 rows x 2 columns]}]',\n", - " 'pipeline': '',\n", - " 'scores': ' metric value normalized randomSeed fold'\n", - " '0 F1_MACRO 0.509059 0.509059 0 0',\n", - " 'status': 'COMPLETED'}\n" - ] - } - ], - "source": [ - "# Run the pipeline\n", - "pipeline_result = evaluate_pipeline(dataset, pipeline, metric)\n", - "print(pipeline_result)\n", - "#raise pipeline_result.error[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Searcher Example:" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "table_path = '../../datasets/anomaly/raw_data/yahoo_sub_5.csv'\n", - "target_index = 6 # what column is the target\n", - "time_limit = 30 # How many seconds you wanna search" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "metric = 'F1_MACRO' # F1 on both label 0 and 1" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# Read data and generate dataset and problem\n", - "df = pd.read_csv(table_path)\n", - "dataset = generate_dataset(df, target_index=target_index)\n", - "problem_description = generate_problem(dataset, metric)" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "# Start backend\n", - "backend = SimpleRunner(random_seed=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [], - "source": [ - "# Start search algorithm\n", - "search = BruteForceSearch(problem_description=problem_description,\n", - " backend=backend)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_3\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_8 (Dense) (None, 12) 156 \n", - "_________________________________________________________________\n", - "dropout_7 (Dropout) (None, 12) 0 \n", - "_________________________________________________________________\n", - "dense_9 (Dense) (None, 12) 156 \n", - "_________________________________________________________________\n", - "dropout_8 (Dropout) (None, 12) 0 \n", - "_________________________________________________________________\n", - "dense_10 (Dense) (None, 1) 13 \n", - "_________________________________________________________________\n", - "dropout_9 (Dropout) (None, 1) 0 \n", - "_________________________________________________________________\n", - "dense_11 (Dense) (None, 4) 8 \n", - "_________________________________________________________________\n", - "dropout_10 (Dropout) (None, 4) 0 \n", - "_________________________________________________________________\n", - "dense_12 (Dense) (None, 1) 5 \n", - "_________________________________________________________________\n", - "dropout_11 (Dropout) (None, 1) 0 \n", - "_________________________________________________________________\n", - "dense_13 (Dense) (None, 12) 24 \n", - "=================================================================\n", - "Total params: 362\n", - "Trainable params: 362\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "None\n", - "Epoch 1/100\n", - "40/40 [==============================] - 0s 4ms/step - loss: 1.5944 - val_loss: 1.2184\n", - "Epoch 2/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.3842 - val_loss: 1.1148\n", - "Epoch 3/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.2952 - val_loss: 1.0463\n", - "Epoch 4/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.2214 - val_loss: 0.9919\n", - "Epoch 5/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.1586 - val_loss: 0.9538\n", - "Epoch 6/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.1199 - val_loss: 0.9192\n", - "Epoch 7/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0858 - val_loss: 0.8896\n", - "Epoch 8/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0449 - val_loss: 0.8645\n", - "Epoch 9/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0290 - val_loss: 0.8419\n", - "Epoch 10/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.0031 - val_loss: 0.8217\n", - "Epoch 11/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9821 - val_loss: 0.8030\n", - "Epoch 12/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9619 - val_loss: 0.7847\n", - "Epoch 13/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9446 - val_loss: 0.7676\n", - "Epoch 14/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9184 - val_loss: 0.7520\n", - "Epoch 15/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9076 - val_loss: 0.7376\n", - "Epoch 16/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8856 - val_loss: 0.7240\n", - "Epoch 17/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8732 - val_loss: 0.7110\n", - "Epoch 18/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8554 - val_loss: 0.6987\n", - "Epoch 19/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8449 - val_loss: 0.6868\n", - "Epoch 20/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8318 - val_loss: 0.6762\n", - "Epoch 21/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8222 - val_loss: 0.6654\n", - "Epoch 22/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8087 - val_loss: 0.6556\n", - "Epoch 23/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7972 - val_loss: 0.6465\n", - "Epoch 24/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7934 - val_loss: 0.6375\n", - "Epoch 25/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7774 - val_loss: 0.6290\n", - "Epoch 26/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7675 - val_loss: 0.6209\n", - "Epoch 27/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7598 - val_loss: 0.6133\n", - "Epoch 28/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7520 - val_loss: 0.6057\n", - "Epoch 29/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7446 - val_loss: 0.5991\n", - "Epoch 30/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7358 - val_loss: 0.5924\n", - "Epoch 31/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7289 - val_loss: 0.5861\n", - "Epoch 32/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7225 - val_loss: 0.5800\n", - "Epoch 33/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7172 - val_loss: 0.5745\n", - "Epoch 34/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7099 - val_loss: 0.5689\n", - "Epoch 35/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7043 - val_loss: 0.5637\n", - "Epoch 36/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6980 - val_loss: 0.5589\n", - "Epoch 37/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6935 - val_loss: 0.5542\n", - "Epoch 38/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6877 - val_loss: 0.5498\n", - "Epoch 39/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6828 - val_loss: 0.5454\n", - "Epoch 40/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6788 - val_loss: 0.5413\n", - "Epoch 41/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6742 - val_loss: 0.5376\n", - "Epoch 42/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6695 - val_loss: 0.5338\n", - "Epoch 43/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6655 - val_loss: 0.5303\n", - "Epoch 44/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6616 - val_loss: 0.5269\n", - "Epoch 45/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6578 - val_loss: 0.5238\n", - "Epoch 46/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6542 - val_loss: 0.5207\n", - "Epoch 47/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6505 - val_loss: 0.5178\n", - "Epoch 48/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6471 - val_loss: 0.5150\n", - "Epoch 49/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6442 - val_loss: 0.5124\n", - "Epoch 50/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6410 - val_loss: 0.5098\n", - "Epoch 51/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6386 - val_loss: 0.5073\n", - "Epoch 52/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6354 - val_loss: 0.5050\n", - "Epoch 53/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6326 - val_loss: 0.5028\n", - "Epoch 54/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6302 - val_loss: 0.5006\n", - "Epoch 55/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6287 - val_loss: 0.4986\n", - "Epoch 56/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6254 - val_loss: 0.4966\n", - "Epoch 57/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6231 - val_loss: 0.4947\n", - "Epoch 58/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6210 - val_loss: 0.4929\n", - "Epoch 59/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6195 - val_loss: 0.4911\n", - "Epoch 60/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6169 - val_loss: 0.4894\n", - "Epoch 61/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6153 - val_loss: 0.4878\n", - "Epoch 62/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6132 - val_loss: 0.4863\n", - "Epoch 63/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6114 - val_loss: 0.4848\n", - "Epoch 64/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6097 - val_loss: 0.4834\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 65/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6080 - val_loss: 0.4820\n", - "Epoch 66/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6065 - val_loss: 0.4806\n", - "Epoch 67/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6050 - val_loss: 0.4794\n", - "Epoch 68/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6035 - val_loss: 0.4781\n", - "Epoch 69/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6020 - val_loss: 0.4770\n", - "Epoch 70/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6007 - val_loss: 0.4758\n", - "Epoch 71/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5994 - val_loss: 0.4747\n", - "Epoch 72/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5981 - val_loss: 0.4736\n", - "Epoch 73/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5969 - val_loss: 0.4726\n", - "Epoch 74/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5958 - val_loss: 0.4716\n", - "Epoch 75/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5947 - val_loss: 0.4706\n", - "Epoch 76/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5937 - val_loss: 0.4697\n", - "Epoch 77/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5926 - val_loss: 0.4688\n", - "Epoch 78/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5918 - val_loss: 0.4679\n", - "Epoch 79/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5907 - val_loss: 0.4671\n", - "Epoch 80/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5897 - val_loss: 0.4663\n", - "Epoch 81/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5890 - val_loss: 0.4655\n", - "Epoch 82/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5880 - val_loss: 0.4647\n", - "Epoch 83/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5872 - val_loss: 0.4640\n", - "Epoch 84/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5864 - val_loss: 0.4632\n", - "Epoch 85/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5856 - val_loss: 0.4626\n", - "Epoch 86/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5849 - val_loss: 0.4619\n", - "Epoch 87/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5841 - val_loss: 0.4612\n", - "Epoch 88/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5834 - val_loss: 0.4606\n", - "Epoch 89/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5828 - val_loss: 0.4600\n", - "Epoch 90/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5821 - val_loss: 0.4594\n", - "Epoch 91/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5815 - val_loss: 0.4588\n", - "Epoch 92/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5809 - val_loss: 0.4582\n", - "Epoch 93/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5803 - val_loss: 0.4577\n", - "Epoch 94/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5797 - val_loss: 0.4572\n", - "Epoch 95/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5792 - val_loss: 0.4567\n", - "Epoch 96/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5786 - val_loss: 0.4562\n", - "Epoch 97/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5781 - val_loss: 0.4557\n", - "Epoch 98/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5776 - val_loss: 0.4552\n", - "Epoch 99/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5771 - val_loss: 0.4548\n", - "Epoch 100/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5766 - val_loss: 0.4543\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n", - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n", - "Traceback (most recent call last):\n", - " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", - " for error in pipeline_result.error:\n", - "TypeError: 'NoneType' object is not iterable\n", - "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_4\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_14 (Dense) (None, 12) 156 \n", - "_________________________________________________________________\n", - "dropout_12 (Dropout) (None, 12) 0 \n", - "_________________________________________________________________\n", - "dense_15 (Dense) (None, 12) 156 \n", - "_________________________________________________________________\n", - "dropout_13 (Dropout) (None, 12) 0 \n", - "_________________________________________________________________\n", - "dense_16 (Dense) (None, 1) 13 \n", - "_________________________________________________________________\n", - "dropout_14 (Dropout) (None, 1) 0 \n", - "_________________________________________________________________\n", - "dense_17 (Dense) (None, 4) 8 \n", - "_________________________________________________________________\n", - "dropout_15 (Dropout) (None, 4) 0 \n", - "_________________________________________________________________\n", - "dense_18 (Dense) (None, 1) 5 \n", - "_________________________________________________________________\n", - "dropout_16 (Dropout) (None, 1) 0 \n", - "_________________________________________________________________\n", - "dense_19 (Dense) (None, 12) 24 \n", - "=================================================================\n", - "Total params: 362\n", - "Trainable params: 362\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "None\n", - "Epoch 1/100\n", - "40/40 [==============================] - 0s 5ms/step - loss: 1.6224 - val_loss: 1.0535\n", - "Epoch 2/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.4768 - val_loss: 0.9671\n", - "Epoch 3/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.3657 - val_loss: 0.9039\n", - "Epoch 4/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.2853 - val_loss: 0.8548\n", - "Epoch 5/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.2478 - val_loss: 0.8155\n", - "Epoch 6/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.1850 - val_loss: 0.7841\n", - "Epoch 7/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.1566 - val_loss: 0.7577\n", - "Epoch 8/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.1255 - val_loss: 0.7338\n", - "Epoch 9/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0796 - val_loss: 0.7136\n", - "Epoch 10/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0545 - val_loss: 0.6954\n", - "Epoch 11/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0291 - val_loss: 0.6783\n", - "Epoch 12/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0136 - val_loss: 0.6627\n", - "Epoch 13/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9984 - val_loss: 0.6483\n", - "Epoch 14/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.9704 - val_loss: 0.6347\n", - "Epoch 15/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9495 - val_loss: 0.6222\n", - "Epoch 16/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9450 - val_loss: 0.6098\n", - "Epoch 17/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.9221 - val_loss: 0.5983\n", - "Epoch 18/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.9060 - val_loss: 0.5875\n", - "Epoch 19/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8961 - val_loss: 0.5772\n", - "Epoch 20/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8758 - val_loss: 0.5674\n", - "Epoch 21/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8635 - val_loss: 0.5580\n", - "Epoch 22/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8520 - val_loss: 0.5492\n", - "Epoch 23/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8367 - val_loss: 0.5407\n", - "Epoch 24/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8265 - val_loss: 0.5328\n", - "Epoch 25/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8198 - val_loss: 0.5251\n", - "Epoch 26/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8084 - val_loss: 0.5180\n", - "Epoch 27/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7999 - val_loss: 0.5108\n", - "Epoch 28/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7929 - val_loss: 0.5042\n", - "Epoch 29/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7812 - val_loss: 0.4979\n", - "Epoch 30/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7739 - val_loss: 0.4918\n", - "Epoch 31/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7629 - val_loss: 0.4861\n", - "Epoch 32/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7554 - val_loss: 0.4807\n", - "Epoch 33/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7480 - val_loss: 0.4754\n", - "Epoch 34/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7435 - val_loss: 0.4704\n", - "Epoch 35/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7358 - val_loss: 0.4656\n", - "Epoch 36/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7309 - val_loss: 0.4610\n", - "Epoch 37/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7260 - val_loss: 0.4567\n", - "Epoch 38/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7171 - val_loss: 0.4525\n", - "Epoch 39/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7124 - val_loss: 0.4485\n", - "Epoch 40/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7065 - val_loss: 0.4447\n", - "Epoch 41/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7011 - val_loss: 0.4412\n", - "Epoch 42/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6965 - val_loss: 0.4377\n", - "Epoch 43/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6914 - val_loss: 0.4343\n", - "Epoch 44/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6886 - val_loss: 0.4311\n", - "Epoch 45/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6872 - val_loss: 0.4280\n", - "Epoch 46/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6799 - val_loss: 0.4251\n", - "Epoch 47/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6760 - val_loss: 0.4223\n", - "Epoch 48/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6714 - val_loss: 0.4196\n", - "Epoch 49/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6685 - val_loss: 0.4171\n", - "Epoch 50/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6642 - val_loss: 0.4146\n", - "Epoch 51/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6605 - val_loss: 0.4123\n", - "Epoch 52/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6584 - val_loss: 0.4100\n", - "Epoch 53/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6540 - val_loss: 0.4078\n", - "Epoch 54/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6517 - val_loss: 0.4057\n", - "Epoch 55/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6498 - val_loss: 0.4037\n", - "Epoch 56/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6457 - val_loss: 0.4018\n", - "Epoch 57/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6431 - val_loss: 0.3999\n", - "Epoch 58/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6410 - val_loss: 0.3982\n", - "Epoch 59/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6376 - val_loss: 0.3964\n", - "Epoch 60/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6357 - val_loss: 0.3948\n", - "Epoch 61/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6334 - val_loss: 0.3932\n", - "Epoch 62/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6313 - val_loss: 0.3917\n", - "Epoch 63/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6290 - val_loss: 0.3902\n", - "Epoch 64/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6271 - val_loss: 0.3888\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 65/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6252 - val_loss: 0.3874\n", - "Epoch 66/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6233 - val_loss: 0.3861\n", - "Epoch 67/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6213 - val_loss: 0.3848\n", - "Epoch 68/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6195 - val_loss: 0.3836\n", - "Epoch 69/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6180 - val_loss: 0.3824\n", - "Epoch 70/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6162 - val_loss: 0.3813\n", - "Epoch 71/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6150 - val_loss: 0.3802\n", - "Epoch 72/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6135 - val_loss: 0.3791\n", - "Epoch 73/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6118 - val_loss: 0.3781\n", - "Epoch 74/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6104 - val_loss: 0.3771\n", - "Epoch 75/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6094 - val_loss: 0.3761\n", - "Epoch 76/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6080 - val_loss: 0.3752\n", - "Epoch 77/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6068 - val_loss: 0.3743\n", - "Epoch 78/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6055 - val_loss: 0.3734\n", - "Epoch 79/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6045 - val_loss: 0.3726\n", - "Epoch 80/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6033 - val_loss: 0.3717\n", - "Epoch 81/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6023 - val_loss: 0.3710\n", - "Epoch 82/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6012 - val_loss: 0.3702\n", - "Epoch 83/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6002 - val_loss: 0.3694\n", - "Epoch 84/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5992 - val_loss: 0.3687\n", - "Epoch 85/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5983 - val_loss: 0.3680\n", - "Epoch 86/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5973 - val_loss: 0.3674\n", - "Epoch 87/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5964 - val_loss: 0.3667\n", - "Epoch 88/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5956 - val_loss: 0.3661\n", - "Epoch 89/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5948 - val_loss: 0.3655\n", - "Epoch 90/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5941 - val_loss: 0.3649\n", - "Epoch 91/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5933 - val_loss: 0.3643\n", - "Epoch 92/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5926 - val_loss: 0.3637\n", - "Epoch 93/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5920 - val_loss: 0.3632\n", - "Epoch 94/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5913 - val_loss: 0.3626\n", - "Epoch 95/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5906 - val_loss: 0.3621\n", - "Epoch 96/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5900 - val_loss: 0.3616\n", - "Epoch 97/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5894 - val_loss: 0.3611\n", - "Epoch 98/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5888 - val_loss: 0.3607\n", - "Epoch 99/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5883 - val_loss: 0.3602\n", - "Epoch 100/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5878 - val_loss: 0.3598\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n", - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n", - "Traceback (most recent call last):\n", - " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", - " for error in pipeline_result.error:\n", - "TypeError: 'NoneType' object is not iterable\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_5\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_20 (Dense) (None, 12) 156 \n", - "_________________________________________________________________\n", - "dropout_17 (Dropout) (None, 12) 0 \n", - "_________________________________________________________________\n", - "dense_21 (Dense) (None, 12) 156 \n", - "_________________________________________________________________\n", - "dropout_18 (Dropout) (None, 12) 0 \n", - "_________________________________________________________________\n", - "dense_22 (Dense) (None, 1) 13 \n", - "_________________________________________________________________\n", - "dropout_19 (Dropout) (None, 1) 0 \n", - "_________________________________________________________________\n", - "dense_23 (Dense) (None, 4) 8 \n", - "_________________________________________________________________\n", - "dropout_20 (Dropout) (None, 4) 0 \n", - "_________________________________________________________________\n", - "dense_24 (Dense) (None, 1) 5 \n", - "_________________________________________________________________\n", - "dropout_21 (Dropout) (None, 1) 0 \n", - "_________________________________________________________________\n", - "dense_25 (Dense) (None, 12) 24 \n", - "=================================================================\n", - "Total params: 362\n", - "Trainable params: 362\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "None\n", - "Epoch 1/100\n", - "40/40 [==============================] - 0s 4ms/step - loss: 1.4693 - val_loss: 1.5144\n", - "Epoch 2/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.3518 - val_loss: 1.4134\n", - "Epoch 3/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.2985 - val_loss: 1.3370\n", - "Epoch 4/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.2306 - val_loss: 1.2773\n", - "Epoch 5/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.1788 - val_loss: 1.2243\n", - "Epoch 6/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.1182 - val_loss: 1.1844\n", - "Epoch 7/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.0755 - val_loss: 1.1497\n", - "Epoch 8/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.0492 - val_loss: 1.1189\n", - "Epoch 9/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.0257 - val_loss: 1.0919\n", - "Epoch 10/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.0068 - val_loss: 1.0675\n", - "Epoch 11/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9761 - val_loss: 1.0451\n", - "Epoch 12/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9635 - val_loss: 1.0221\n", - "Epoch 13/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9525 - val_loss: 1.0028\n", - "Epoch 14/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9207 - val_loss: 0.9840\n", - "Epoch 15/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9108 - val_loss: 0.9668\n", - "Epoch 16/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8925 - val_loss: 0.9508\n", - "Epoch 17/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8695 - val_loss: 0.9353\n", - "Epoch 18/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.8591 - val_loss: 0.9214\n", - "Epoch 19/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8465 - val_loss: 0.9071\n", - "Epoch 20/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8336 - val_loss: 0.8959\n", - "Epoch 21/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8194 - val_loss: 0.8821\n", - "Epoch 22/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8122 - val_loss: 0.8705\n", - "Epoch 23/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7998 - val_loss: 0.8596\n", - "Epoch 24/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7871 - val_loss: 0.8494\n", - "Epoch 25/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7770 - val_loss: 0.8404\n", - "Epoch 26/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7678 - val_loss: 0.8301\n", - "Epoch 27/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7575 - val_loss: 0.8213\n", - "Epoch 28/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7487 - val_loss: 0.8130\n", - "Epoch 29/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7393 - val_loss: 0.8051\n", - "Epoch 30/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7329 - val_loss: 0.7975\n", - "Epoch 31/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7236 - val_loss: 0.7904\n", - "Epoch 32/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7174 - val_loss: 0.7836\n", - "Epoch 33/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7104 - val_loss: 0.7772\n", - "Epoch 34/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7031 - val_loss: 0.7711\n", - "Epoch 35/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6954 - val_loss: 0.7651\n", - "Epoch 36/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6895 - val_loss: 0.7599\n", - "Epoch 37/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6836 - val_loss: 0.7544\n", - "Epoch 38/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6809 - val_loss: 0.7494\n", - "Epoch 39/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6726 - val_loss: 0.7447\n", - "Epoch 40/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6698 - val_loss: 0.7402\n", - "Epoch 41/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6631 - val_loss: 0.7359\n", - "Epoch 42/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6581 - val_loss: 0.7320\n", - "Epoch 43/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6547 - val_loss: 0.7279\n", - "Epoch 44/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6490 - val_loss: 0.7241\n", - "Epoch 45/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6449 - val_loss: 0.7206\n", - "Epoch 46/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6427 - val_loss: 0.7173\n", - "Epoch 47/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6378 - val_loss: 0.7140\n", - "Epoch 48/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6354 - val_loss: 0.7109\n", - "Epoch 49/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6302 - val_loss: 0.7080\n", - "Epoch 50/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6279 - val_loss: 0.7052\n", - "Epoch 51/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6244 - val_loss: 0.7025\n", - "Epoch 52/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6204 - val_loss: 0.6999\n", - "Epoch 53/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6177 - val_loss: 0.6976\n", - "Epoch 54/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6146 - val_loss: 0.6953\n", - "Epoch 55/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6117 - val_loss: 0.6929\n", - "Epoch 56/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6094 - val_loss: 0.6909\n", - "Epoch 57/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6072 - val_loss: 0.6888\n", - "Epoch 58/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6045 - val_loss: 0.6868\n", - "Epoch 59/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6026 - val_loss: 0.6850\n", - "Epoch 60/100\n", - "40/40 [==============================] - 0s 3ms/step - loss: 0.5997 - val_loss: 0.6833\n", - "Epoch 61/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5977 - val_loss: 0.6815\n", - "Epoch 62/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5954 - val_loss: 0.6798\n", - "Epoch 63/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5939 - val_loss: 0.6782\n", - "Epoch 64/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5917 - val_loss: 0.6767\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 65/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5898 - val_loss: 0.6753\n", - "Epoch 66/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5880 - val_loss: 0.6739\n", - "Epoch 67/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5865 - val_loss: 0.6726\n", - "Epoch 68/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5848 - val_loss: 0.6713\n", - "Epoch 69/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5832 - val_loss: 0.6700\n", - "Epoch 70/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5817 - val_loss: 0.6689\n", - "Epoch 71/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5804 - val_loss: 0.6677\n", - "Epoch 72/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5790 - val_loss: 0.6666\n", - "Epoch 73/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5777 - val_loss: 0.6655\n", - "Epoch 74/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5764 - val_loss: 0.6645\n", - "Epoch 75/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5750 - val_loss: 0.6635\n", - "Epoch 76/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5739 - val_loss: 0.6626\n", - "Epoch 77/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5728 - val_loss: 0.6617\n", - "Epoch 78/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5716 - val_loss: 0.6608\n", - "Epoch 79/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5706 - val_loss: 0.6599\n", - "Epoch 80/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5696 - val_loss: 0.6591\n", - "Epoch 81/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5686 - val_loss: 0.6584\n", - "Epoch 82/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5676 - val_loss: 0.6576\n", - "Epoch 83/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5667 - val_loss: 0.6569\n", - "Epoch 84/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5659 - val_loss: 0.6561\n", - "Epoch 85/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5651 - val_loss: 0.6554\n", - "Epoch 86/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5642 - val_loss: 0.6548\n", - "Epoch 87/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5635 - val_loss: 0.6541\n", - "Epoch 88/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5627 - val_loss: 0.6535\n", - "Epoch 89/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5620 - val_loss: 0.6529\n", - "Epoch 90/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5613 - val_loss: 0.6523\n", - "Epoch 91/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5606 - val_loss: 0.6518\n", - "Epoch 92/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5599 - val_loss: 0.6512\n", - "Epoch 93/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5593 - val_loss: 0.6507\n", - "Epoch 94/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5587 - val_loss: 0.6502\n", - "Epoch 95/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5581 - val_loss: 0.6497\n", - "Epoch 96/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5575 - val_loss: 0.6492\n", - "Epoch 97/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5570 - val_loss: 0.6487\n", - "Epoch 98/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5564 - val_loss: 0.6483\n", - "Epoch 99/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5559 - val_loss: 0.6478\n", - "Epoch 100/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5554 - val_loss: 0.6474\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n" - ] - } - ], - "source": [ - "# Find the best pipeline\n", - "best_runtime, best_pipeline_result = search.search_fit(input_data=[dataset], time_limit=time_limit)\n", - "best_pipeline = best_runtime.pipeline\n", - "best_output = best_pipeline_result.output" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_6\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_26 (Dense) (None, 12) 156 \n", - "_________________________________________________________________\n", - "dropout_22 (Dropout) (None, 12) 0 \n", - "_________________________________________________________________\n", - "dense_27 (Dense) (None, 12) 156 \n", - "_________________________________________________________________\n", - "dropout_23 (Dropout) (None, 12) 0 \n", - "_________________________________________________________________\n", - "dense_28 (Dense) (None, 1) 13 \n", - "_________________________________________________________________\n", - "dropout_24 (Dropout) (None, 1) 0 \n", - "_________________________________________________________________\n", - "dense_29 (Dense) (None, 4) 8 \n", - "_________________________________________________________________\n", - "dropout_25 (Dropout) (None, 4) 0 \n", - "_________________________________________________________________\n", - "dense_30 (Dense) (None, 1) 5 \n", - "_________________________________________________________________\n", - "dropout_26 (Dropout) (None, 1) 0 \n", - "_________________________________________________________________\n", - "dense_31 (Dense) (None, 12) 24 \n", - "=================================================================\n", - "Total params: 362\n", - "Trainable params: 362\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "None\n", - "Epoch 1/100\n", - "40/40 [==============================] - 0s 4ms/step - loss: 1.5860 - val_loss: 1.0422\n", - "Epoch 2/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.4206 - val_loss: 0.9430\n", - "Epoch 3/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.3349 - val_loss: 0.8805\n", - "Epoch 4/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.2650 - val_loss: 0.8352\n", - "Epoch 5/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.1954 - val_loss: 0.7995\n", - "Epoch 6/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.1571 - val_loss: 0.7708\n", - "Epoch 7/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.1171 - val_loss: 0.7457\n", - "Epoch 8/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0848 - val_loss: 0.7238\n", - "Epoch 9/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 1.0527 - val_loss: 0.7043\n", - "Epoch 10/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.0312 - val_loss: 0.6868\n", - "Epoch 11/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 1.0008 - val_loss: 0.6706\n", - "Epoch 12/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9809 - val_loss: 0.6556\n", - "Epoch 13/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9630 - val_loss: 0.6415\n", - "Epoch 14/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9388 - val_loss: 0.6283\n", - "Epoch 15/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.9264 - val_loss: 0.6162\n", - "Epoch 16/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.9051 - val_loss: 0.6044\n", - "Epoch 17/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8931 - val_loss: 0.5934\n", - "Epoch 18/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8782 - val_loss: 0.5829\n", - "Epoch 19/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8635 - val_loss: 0.5730\n", - "Epoch 20/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8542 - val_loss: 0.5636\n", - "Epoch 21/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8429 - val_loss: 0.5546\n", - "Epoch 22/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8253 - val_loss: 0.5461\n", - "Epoch 23/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8158 - val_loss: 0.5379\n", - "Epoch 24/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.8028 - val_loss: 0.5302\n", - "Epoch 25/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7972 - val_loss: 0.5228\n", - "Epoch 26/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7844 - val_loss: 0.5158\n", - "Epoch 27/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7754 - val_loss: 0.5091\n", - "Epoch 28/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7669 - val_loss: 0.5026\n", - "Epoch 29/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7594 - val_loss: 0.4966\n", - "Epoch 30/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7521 - val_loss: 0.4907\n", - "Epoch 31/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7435 - val_loss: 0.4852\n", - "Epoch 32/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.7363 - val_loss: 0.4799\n", - "Epoch 33/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7316 - val_loss: 0.4748\n", - "Epoch 34/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7238 - val_loss: 0.4699\n", - "Epoch 35/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7178 - val_loss: 0.4653\n", - "Epoch 36/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7112 - val_loss: 0.4609\n", - "Epoch 37/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7055 - val_loss: 0.4567\n", - "Epoch 38/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.7002 - val_loss: 0.4526\n", - "Epoch 39/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6961 - val_loss: 0.4487\n", - "Epoch 40/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6912 - val_loss: 0.4450\n", - "Epoch 41/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6860 - val_loss: 0.4415\n", - "Epoch 42/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6834 - val_loss: 0.4381\n", - "Epoch 43/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6771 - val_loss: 0.4348\n", - "Epoch 44/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6734 - val_loss: 0.4317\n", - "Epoch 45/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6708 - val_loss: 0.4287\n", - "Epoch 46/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6658 - val_loss: 0.4258\n", - "Epoch 47/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6623 - val_loss: 0.4230\n", - "Epoch 48/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6587 - val_loss: 0.4204\n", - "Epoch 49/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6559 - val_loss: 0.4179\n", - "Epoch 50/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6526 - val_loss: 0.4154\n", - "Epoch 51/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6512 - val_loss: 0.4131\n", - "Epoch 52/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6469 - val_loss: 0.4109\n", - "Epoch 53/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6446 - val_loss: 0.4087\n", - "Epoch 54/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6413 - val_loss: 0.4067\n", - "Epoch 55/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6389 - val_loss: 0.4047\n", - "Epoch 56/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6367 - val_loss: 0.4027\n", - "Epoch 57/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6341 - val_loss: 0.4009\n", - "Epoch 58/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6321 - val_loss: 0.3991\n", - "Epoch 59/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6300 - val_loss: 0.3974\n", - "Epoch 60/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6279 - val_loss: 0.3957\n", - "Epoch 61/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6264 - val_loss: 0.3941\n", - "Epoch 62/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6243 - val_loss: 0.3926\n", - "Epoch 63/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6226 - val_loss: 0.3911\n", - "Epoch 64/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6214 - val_loss: 0.3897\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 65/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6192 - val_loss: 0.3883\n", - "Epoch 66/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6176 - val_loss: 0.3870\n", - "Epoch 67/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6158 - val_loss: 0.3857\n", - "Epoch 68/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6143 - val_loss: 0.3845\n", - "Epoch 69/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6130 - val_loss: 0.3833\n", - "Epoch 70/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6116 - val_loss: 0.3821\n", - "Epoch 71/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6103 - val_loss: 0.3810\n", - "Epoch 72/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6090 - val_loss: 0.3799\n", - "Epoch 73/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6079 - val_loss: 0.3789\n", - "Epoch 74/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6068 - val_loss: 0.3779\n", - "Epoch 75/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6058 - val_loss: 0.3769\n", - "Epoch 76/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6044 - val_loss: 0.3760\n", - "Epoch 77/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6034 - val_loss: 0.3751\n", - "Epoch 78/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.6024 - val_loss: 0.3742\n", - "Epoch 79/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6016 - val_loss: 0.3733\n", - "Epoch 80/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.6005 - val_loss: 0.3725\n", - "Epoch 81/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5996 - val_loss: 0.3717\n", - "Epoch 82/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5987 - val_loss: 0.3709\n", - "Epoch 83/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5979 - val_loss: 0.3702\n", - "Epoch 84/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5971 - val_loss: 0.3694\n", - "Epoch 85/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5963 - val_loss: 0.3687\n", - "Epoch 86/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5956 - val_loss: 0.3680\n", - "Epoch 87/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5948 - val_loss: 0.3674\n", - "Epoch 88/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5941 - val_loss: 0.3667\n", - "Epoch 89/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5934 - val_loss: 0.3661\n", - "Epoch 90/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5928 - val_loss: 0.3655\n", - "Epoch 91/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5922 - val_loss: 0.3649\n", - "Epoch 92/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5915 - val_loss: 0.3644\n", - "Epoch 93/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5910 - val_loss: 0.3638\n", - "Epoch 94/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5903 - val_loss: 0.3633\n", - "Epoch 95/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5898 - val_loss: 0.3627\n", - "Epoch 96/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5892 - val_loss: 0.3622\n", - "Epoch 97/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5887 - val_loss: 0.3617\n", - "Epoch 98/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5882 - val_loss: 0.3613\n", - "Epoch 99/100\n", - "40/40 [==============================] - 0s 1ms/step - loss: 0.5877 - val_loss: 0.3608\n", - "Epoch 100/100\n", - "40/40 [==============================] - 0s 2ms/step - loss: 0.5872 - val_loss: 0.3603\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n", - "/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages/sklearn/utils/validation.py:933: FutureWarning: Passing attributes to check_is_fitted is deprecated and will be removed in 0.23. The attributes argument is ignored.\n", - " \"argument is ignored.\", FutureWarning)\n" - ] - } - ], - "source": [ - "# Evaluate the best pipeline\n", - "best_scores = search.evaluate(best_pipeline).scores" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Search History:\n", - "----------------------------------------------------\n", - "Pipeline id: 108e1dce-67e7-45f2-962c-1965d988710a\n", - " metric value normalized randomSeed fold\n", - "0 F1_MACRO 0.708549 0.708549 0 0\n", - "----------------------------------------------------\n", - "Pipeline id: 2a42a07c-0263-427c-b6c8-d9ce45ac0b21\n", - " metric value normalized randomSeed fold\n", - "0 F1_MACRO 0.616695 0.616695 0 0\n" - ] - } - ], - "source": [ - "print('Search History:')\n", - "for pipeline_result in search.history:\n", - " print('-' * 52)\n", - " print('Pipeline id:', pipeline_result.pipeline.id)\n", - " print(pipeline_result.scores)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best pipeline:\n", - "----------------------------------------------------\n", - "Pipeline id: 108e1dce-67e7-45f2-962c-1965d988710a\n", - "Pipeline json: {\"id\": \"108e1dce-67e7-45f2-962c-1965d988710a\", \"schema\": \"https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json\", \"created\": \"2021-04-14T16:38:58.226503Z\", \"inputs\": [{\"name\": \"inputs\"}], \"outputs\": [{\"data\": \"steps.7.produce\", \"name\": \"output predictions\"}], \"steps\": [{\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"4b42ce1e-9b98-4a25-b68e-fad13311eb65\", \"version\": \"0.3.0\", \"python_path\": \"d3m.primitives.tods.data_processing.dataset_to_dataframe\", \"name\": \"Extract a DataFrame from a Dataset\", \"digest\": \"fb5cd27ebf69b9587b23940618071ba9ffe9f47ebd7772797d61ae0521f92515\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"inputs.0\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"d510cb7a-1782-4f51-b44c-58f0236e47c7\", \"version\": \"0.6.0\", \"python_path\": \"d3m.primitives.tods.data_processing.column_parser\", \"name\": \"Parses strings into their types\", \"digest\": \"62af3e97e2535681a0b1320e4ac97edeba15895862a46244ab079c47ce56958d\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.0.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"4503a4c6-42f7-45a1-a1d4-ed69699cf5e1\", \"version\": \"0.4.0\", \"python_path\": \"d3m.primitives.tods.data_processing.extract_columns_by_semantic_types\", \"name\": \"Extracts columns by semantic type\", \"digest\": \"d4c8204514d840de1b5acad9831f9d5581b41f425df3d14051336abdeacdf1b2\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.1.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"semantic_types\": {\"type\": \"VALUE\", \"data\": [\"https://metadata.datadrivendiscovery.org/types/Attribute\"]}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"4503a4c6-42f7-45a1-a1d4-ed69699cf5e1\", \"version\": \"0.4.0\", \"python_path\": \"d3m.primitives.tods.data_processing.extract_columns_by_semantic_types\", \"name\": \"Extracts columns by semantic type\", \"digest\": \"d4c8204514d840de1b5acad9831f9d5581b41f425df3d14051336abdeacdf1b2\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.0.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"semantic_types\": {\"type\": \"VALUE\", \"data\": [\"https://metadata.datadrivendiscovery.org/types/TrueTarget\"]}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"642de2e7-5590-3cab-9266-2a53c326c461\", \"version\": \"0.0.1\", \"python_path\": \"d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler\", \"name\": \"Axis_wise_scale\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.2.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"eaff2f35-978c-4530-a12e-061a5f0beacd\", \"version\": \"0.1.0\", \"python_path\": \"d3m.primitives.tods.feature_analysis.statistical_mean\", \"name\": \"Time Series Decompostional\", \"digest\": \"86f8a7a74cc872b09ec7dbec5910f9613c918255ba618731aa7f1ff9b42e37ba\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.4.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"67e7fcdf-d645-3417-9aa4-85cd369487d9\", \"version\": \"0.0.1\", \"python_path\": \"d3m.primitives.tods.detection_algorithm.pyod_ae\", \"name\": \"TODS.anomaly_detection_primitives.AutoEncoder\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.5.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"contamination\": {\"type\": \"VALUE\", \"data\": 0.01}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"8d38b340-f83f-4877-baaa-162f8e551736\", \"version\": \"0.3.0\", \"python_path\": \"d3m.primitives.tods.data_processing.construct_predictions\", \"name\": \"Construct pipeline predictions output\", \"digest\": \"33d90bfb7f97f47a6de5372c5f912c26fca8da2d2777661651c69687ad6f9950\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.6.produce\"}, \"reference\": {\"type\": \"CONTAINER\", \"data\": \"steps.1.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}], \"digest\": \"a4ba790aa8c5ad34057cd97135f67edc8ccdc79d0bec0c4660fea0d2dfc82eb3\"}\n", - "Output:\n", - " d3mIndex anomaly\n", - "0 0 0\n", - "1 1 0\n", - "2 2 0\n", - "3 3 0\n", - "4 4 0\n", - "... ... ...\n", - "1395 1395 0\n", - "1396 1396 0\n", - "1397 1397 1\n", - "1398 1398 1\n", - "1399 1399 0\n", - "\n", - "[1400 rows x 2 columns]\n", - "Scores:\n", - " metric value normalized randomSeed fold\n", - "0 F1_MACRO 0.708549 0.708549 0 0\n" - ] - } - ], - "source": [ - "print('Best pipeline:')\n", - "print('-' * 52)\n", - "print('Pipeline id:', best_pipeline.id)\n", - "print('Pipeline json:', best_pipeline.to_json())\n", - "print('Output:')\n", - "print(best_output)\n", - "print('Scores:')\n", - "print(best_scores)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Demo Notebook/TODSBlockchainNotebook.ipynb b/examples/Demo Notebook/TODSBlockchainNotebook.ipynb new file mode 100644 index 00000000..fc309e8e --- /dev/null +++ b/examples/Demo Notebook/TODSBlockchainNotebook.ipynb @@ -0,0 +1,2127 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Anomaly Detection in Blockchain System with TODS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction Summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is TODS?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODS, developed by DATA Lab @ Texas A&M University, is a full-stack automated machine learning system for outlier detection on multivariate time-series data. TODS provides exhaustive modules for building machine learning-based outlier detection systems, including: data processing, time series processing, feature analysis (extraction), detection algorithms, and reinforcement module." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is the Blockchain System?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"A blockchain is, in the simplest of terms, a time-stamped series of immutable records of data that is managed by a cluster of computers not owned by any single entity. Each of these blocks of data (i.e. block) is secured and bound to each other using cryptographic principles (i.e. chain).\" \n", + "\n", + "More information about blockchain can be found at https://blockgeeks.com/guides/what-is-blockchain-technology/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How does TODS detect anomalies from Blockchain System?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODS system can perform three common outlier detection scenarios on time-series data: point-wise detection (time points as outliers), pattern-wise detection (subsequences as outliers), and system-wise detection (sets of time series as outliers).\n", + "\n", + "In this notebook, we use Google BigQuery Bitcoin Blockchain Dataset, which contains information about dates, transactions, blocks and prices of Bitcoin. According to Google, this dataset updates every 10 minutes in the following link:\n", + "https://bigquery.cloud.google.com/dataset/bigquery-public-data:bitcoin_blockchain\n", + "\n", + "With TODS, we first collect and process the data to put in a dataframe. Next, we use two primitives from TODS to fit and predict on all dimensions of the data, to get the prediction labels and scores. Last, we use the prediction information from TODS primitives to plot and visualize the outliers within the data.\n", + "\n", + "The searcher of TODS will take the dataset and automatically build a pipeline to find the best possible model with its scores. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zV8tJwO6-U9h" + }, + "source": [ + "## Packages and Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "mnF3OkUJ-U9i", + "outputId": "af2dc0fd-9b87-4a0a-d95c-285f7101f654" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: google-cloud-bigquery in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (2.20.0)\n", + "Requirement already satisfied: google-api-core[grpc]<2.0.0dev,>=1.29.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-cloud-bigquery) (1.30.0)\n", + "Requirement already satisfied: google-cloud-core<2.0dev,>=1.4.1 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-cloud-bigquery) (1.6.0)\n", + "Requirement already satisfied: packaging>=14.3 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-cloud-bigquery) (20.9)\n", + "Requirement already satisfied: protobuf>=3.12.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-cloud-bigquery) (3.15.8)\n", + "Requirement already satisfied: google-resumable-media<2.0dev,>=0.6.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-cloud-bigquery) (1.3.0)\n", + "Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-cloud-bigquery) (2.23.0)\n", + "Requirement already satisfied: proto-plus>=1.10.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-cloud-bigquery) (1.18.1)\n", + "Requirement already satisfied: six>=1.13.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-api-core[grpc]<2.0.0dev,>=1.29.0->google-cloud-bigquery) (1.16.0)\n", + "Requirement already satisfied: setuptools>=40.3.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-api-core[grpc]<2.0.0dev,>=1.29.0->google-cloud-bigquery) (57.0.0)\n", + "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-api-core[grpc]<2.0.0dev,>=1.29.0->google-cloud-bigquery) (1.53.0)\n", + "Requirement already satisfied: pytz in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-api-core[grpc]<2.0.0dev,>=1.29.0->google-cloud-bigquery) (2021.1)\n", + "Requirement already satisfied: google-auth<2.0dev,>=1.25.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-api-core[grpc]<2.0.0dev,>=1.29.0->google-cloud-bigquery) (1.28.1)\n", + "Requirement already satisfied: grpcio<2.0dev,>=1.29.0; 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python_version >= \"3.6\" in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-auth<2.0dev,>=1.25.0->google-api-core[grpc]<2.0.0dev,>=1.29.0->google-cloud-bigquery) (4.7.2)\n", + "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-auth<2.0dev,>=1.25.0->google-api-core[grpc]<2.0.0dev,>=1.29.0->google-cloud-bigquery) (4.2.1)\n", + "Requirement already satisfied: pyasn1-modules>=0.2.1 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-auth<2.0dev,>=1.25.0->google-api-core[grpc]<2.0.0dev,>=1.29.0->google-cloud-bigquery) (0.2.8)\n", + "Requirement already satisfied: cffi>=1.0.0 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from google-crc32c<2.0dev,>=1.0; python_version >= \"3.5\"->google-resumable-media<2.0dev,>=0.6.0->google-cloud-bigquery) (1.14.0)\n", + "Requirement already satisfied: pyasn1>=0.1.3 in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2.0dev,>=1.25.0->google-api-core[grpc]<2.0.0dev,>=1.29.0->google-cloud-bigquery) (0.4.8)\n", + "Requirement already satisfied: pycparser in /Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages (from cffi>=1.0.0->google-crc32c<2.0dev,>=1.0; python_version >= \"3.5\"->google-resumable-media<2.0dev,>=0.6.0->google-cloud-bigquery) (2.20)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install google-cloud-bigquery\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "hI2JIaHC-U9i", + "outputId": "4424ee78-f84f-4e1c-9cdf-c0086ce3dca5", + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d3m.primitives.tods.detection_algorithm.LSTMODetector: Primitive is not providing a description through its docstring.\n" + ] + } + ], + "source": [ + "from google.cloud import bigquery\n", + "from scipy.stats.mstats import zscore\n", + "from sklearn.metrics import precision_recall_curve\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import classification_report\n", + "from tods.sk_interface.detection_algorithm.Telemanom_skinterface import TelemanomSKI\n", + "from tods.sk_interface.detection_algorithm.DeepLog_skinterface import DeepLogSKI\n", + "from sklearn.preprocessing import MinMaxScaler, QuantileTransformer\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.manifold import TSNE\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('fivethirtyeight')\n", + "import matplotlib as mpl\n", + "from pathlib import Path\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.preprocessing import StandardScaler\n", + "import seaborn as sns\n", + "import datetime as dt\n", + "from datetime import datetime,tzinfo\n", + "import scipy, json, csv, time, pytz\n", + "from pytz import timezone\n", + "import numpy as np\n", + "import pandas as pd\n", + "seed = 135\n", + "%config InlineBackend.figure_format = 'retina'\n", + "%matplotlib inline\n", + "import os\n", + "# os.listdir('./')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oFJ3d5MW-U9j" + }, + "source": [ + "## Processing Data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "1Laxlepi-U9j" + }, + "outputs": [], + "source": [ + "#Connecting to Google datastore (use path to ur private key)\n", + "os.environ['GOOGLE_APPLICATION_CREDENTIALS']=\"blockchain-316820-a21123305db7.json\"\n", + "client = bigquery.Client()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "ZkCH3LR5-U9k" + }, + "outputs": [], + "source": [ + "# The query to get date, number of transactions from Google BigQuery bitcoin blockchain dataset \n", + "# Select records from the last three years and group them with respect to date\n", + "query_1 = \"\"\"\n", + "SELECT \n", + " DATE(TIMESTAMP_MILLIS(timestamp)) AS Date,\n", + " COUNT(transactions) AS Transactions\n", + "FROM `bigquery-public-data.bitcoin_blockchain.blocks`\n", + "GROUP BY date\n", + "HAVING date >= '2009-01-09' AND date <= '2020-06-12'\n", + "ORDER BY date\n", + "\"\"\"\n", + "query_job_1 = client.query(query_1)\n", + "# Waits for the query to finish\n", + "iterator_1 = query_job_1.result(timeout=30)\n", + "rows_1 = list(iterator_1)\n", + "df_1 = pd.DataFrame(data=[list(x.values()) for x in rows_1], columns=list(rows_1[0].keys()))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "NXpxzJ5R-U9k" + }, + "outputs": [], + "source": [ + "# The query to get sum of all satoshis spent each day and number of blocks\n", + "query_2 = \"\"\"\n", + "SELECT\n", + " o.Date,\n", + " COUNT(o.block) AS Blocks,\n", + " SUM(o.output_price) AS Output_Satoshis\n", + "FROM (\n", + " SELECT\n", + " DATE(TIMESTAMP_MILLIS(timestamp)) AS Date,\n", + " output.output_satoshis AS output_price,\n", + " block_id AS block\n", + " FROM\n", + " `bigquery-public-data.bitcoin_blockchain.transactions`,\n", + " UNNEST(outputs) AS output ) AS o\n", + "GROUP BY\n", + " o.date\n", + "HAVING o.date >= '2009-01-09' AND o.date <= '2020-06-12'\n", + "ORDER BY o.date, blocks\n", + "\"\"\"\n", + "query_job_2 = client.query(query_2)\n", + "# Waits for the query to finish\n", + "iterator_2 = query_job_2.result(timeout=30)\n", + "rows_2 = list(iterator_2)\n", + "df_2 = pd.DataFrame(data=[list(x.values()) for x in rows_2], columns=list(rows_2[0].keys()))\n", + "\n", + "df_2[\"Output_Satoshis\"]= df_2[\"Output_Satoshis\"].apply(lambda x: float(x/100000000))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "TQzZev9C-U9k", + "outputId": "c643c8e9-b222-4ce9-f306-ae3a8812988d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateBlocksOutput_Satoshis
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DateTransactionsBlocksOutput_Satoshis
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TransactionsBlocksOutput_Satoshis
count3532.0000003.532000e+033.532000e+03
mean153.0532282.623631e+051.358730e+06
std37.9784112.799358e+052.193636e+06
min2.0000004.000000e+002.000000e+02
25%138.0000001.161975e+043.047457e+05
50%153.0000001.586145e+059.436964e+05
75%169.0000005.145980e+051.835461e+06
max692.0000002.035035e+066.735430e+07
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" + ], + "text/plain": [ + " Transactions Blocks Output_Satoshis\n", + "count 3532.000000 3.532000e+03 3.532000e+03\n", + "mean 153.053228 2.623631e+05 1.358730e+06\n", + "std 37.978411 2.799358e+05 2.193636e+06\n", + "min 2.000000 4.000000e+00 2.000000e+02\n", + "25% 138.000000 1.161975e+04 3.047457e+05\n", + "50% 153.000000 1.586145e+05 9.436964e+05\n", + "75% 169.000000 5.145980e+05 1.835461e+06\n", + "max 692.000000 2.035035e+06 6.735430e+07" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "eaiHITgY-U9m", + "outputId": "6200f972-cfec-49aa-dd8b-dc873c959b17" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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FDVQ0AwAAQGYQZrCDl7d2q8djY9qysGn/Me8EmL18wgwjMwAAAMgUwgx24Lde5uBxRSrqV51snk+YWd7cq85eqmYDAAAg/Qgz2IFfJbPDJ5bs8PPu5SHVFCcvvRx10tImRmcAAMhn+bJvIdIrG9cFYQbbOef0ny0+62XG77i/jJn5js4sZN0MAAB5KxQKKRplKwbsLBqNKhTKbNwgzGC7pm6npu7kCTpk0sHjd94s0z/MMDIDAEC+KisrU3t7+0g3Azmovb1dZWVlGX0Nwgy229Du/anK7uVhVRXtfMn4lWdeRJgBACBvVVVVqaWlRc3Nzert7WXKWYFzzqm3t1fNzc1qaWlRVVVVRl/PuxeaAjMbI+kjkk6StK+kSZK6Jb0h6VZJtzrnPOpj7XCuVZKmJXl4k3Nu4nDbi+RSCTO7slct08wAAChURUVFGj9+vFpaWrRp0ybFYil1+3JST0/8A9iiIu++DbyFQiGVlZVp/PjxGf9dDjvMSDpd0o2SNkiaL2mNpAmSPirpd5Leb2anu9RjepOk63dxf+vwmwovfmFmt4pdD+T5TTNb1x5VY1dMtSUMBAIAkI+Kioo0evTokW7GsNXV1UmSZsyYMcItQarSEWaWSvqQpAf7j8CY2TclvSjpVMWDzb0pnq/ROXdVGtqFQdrQ7v1JysSyXY/M1JaENKk8rHUeYWhRY88Om20CAAAAwzXsj8qdc0865/4+cCqZc26jpJsSPx493NdB5vlOM6vYdZiRpL181s1QBAAAAADplo6RGS99PdjBLJooMbNPSZoqqU3S65Keds5R8y/D1rf5TDNLsmZGik81+8e65HvUsG4GAAAA6ZaxMGNmEUmfTvz4yCCeOlHSHQPuW2lm5zrnnhpsO/rmPsLfqoYSSckDS6x+g+qSLOob0x2WlHwa2Svrm1VXt2WYLURQ8d8hhoprB8PB9YOh4trJjlmzZg37HJlckf1DSftIesg592iKz7lV0nGKB5oKxauj/UbSdEkPm9n+GWgnEjZ3eV8O40qS13CYWe693mZZW0hUagQAAEA6WSZqgZvZlyXdIGmxpCOdc/XDPN9PJF0m6W/OuY/s6pimpia6ysPQG3Mad/t6ef0S131qN1XsYp8ZSersdZp053pFPU7w1scmapLHuhvkn75PttLxyQsKC9cOhoPrB0PFtZMbampqLNVj0z4yY2YXKR5kFko6ZrhBJqGvkMBRaTgXdmFTR8wzyFQXW9IgI0mlEdPMajbPBAAAQPakNcyY2SWSfiHpTcWDzMY0nbpvsUVFms6HAYa6YWZ/VDQDAABANqUtzJjZ1yRdJ2mB4kFmc7rOLemwxO2KNJ4T/Qynklkfv80z3yLMAAAAII3SEmbM7ErFF/y/LOk459xWj2OLzGyumc0ccP9eZrbTyIuZTZf0y8SPd6ajvdiZ38hMOsLMIsozAwAAII2GXZrZzM6WdI2kqKR/Sfqy2U5rdlY5525LfD9J0iJJqxWvUtbnDEmXmdnTicdaJM2UdJKkUkkPSfrJcNuLXdvoG2b8c++8Wu8ws6SpR70xp0go5TVdAAAAQFLp2Gdmj8RtWNIlSY55StJtPueZL2mOpAMlHan4+phGSc8ovu/MHS4TpdcgSVqfhpGZ6VVhlYVNHUlKmnVFpTWtUc3wKRQAAAAApGLYvUrn3FWSrhrE8ask7fTRfGJDzEFvion02NDuvU9MKmEmHDLNqono9frka2PWtvYSZgAAAJAWmdw0EwHiW80sxf1hplV5H7e61ft1AAAAgFQRZiBJ2pCGamaSNLXSe9RlTQthBgAAAOlBmIFaemJq7U2+HCksp3GlqV0q0yq9Q8+aViqaAQAAID0IM/AdlRlT7BROsQLZVKaZAQAAIEsIM/BdLzOuOPUicr7TzBiZAQAAQJoQZqD1PpXMxpUMJsx4j8xsaI+pK0npZgAAAGAwCDPwHZkZP4iRmcqikMaUeF9WbzPVDAAAAGlAmIFvmBk7iDAjpbJuhqlmAAAAGD7CDHwLAAxmZEbyn2q2hpEZAAAApAFhBv4FAAaxZkaSplEEAAAAAFlAmEFa18xIjMwAAAAgOwgzBS4ac9rU4VPNbNBhxntkZnULIzMAAAAYPsJMgdvSGZNXpeTysFOFdzbZiV8BAEZmAAAAkA6EmQKXzg0z+/hNM9vUEVNHL3vNAAAAYHgIMwVuvU8ls6GEmfJISONKvS+ttRQBAAAAwDARZgpcuhf/96EIAAAAADKNMFPg0l2Wuc+0Kr/yzIQZAAAADA9hpsBtaE9vJbM+/iMzTDMDAADA8BBmClwmCgBIqZRnZmQGAAAAw0OYKXC+a2aGOM2MkRkAAABkGmGmwK3P0MjMNPaaAQAAQIYRZgpYW09Mzd3Jw4pJGjPEMDPZZ6fNLZ0xtfV4r9cBAAAAvBBmCthGn8X/48tCitjQzl0WMU0o89lrxmePGwAAAMALYaaA+U0x263ce6qYH991MxQBAAAAwDAQZgqY3+L/4YYZ/71mKAIAAACAoSPMFDC/MLN7RWZHZlZTBAAAAADDQJgpYOt91qwMf5oZIzMAAADIHMJMAdvY4R1mJpYP7/KY5rvXDCMzAAAAGDrCTAHb0OZdzWz3DI/MrKYAAAAAAIaBMFPAMl3NbLLPyEx9V0wt7DUDAACAISLMFKiYc9qY4TBTEjbt5jNVbS1TzQAAADBEhJkCtbUzpl6X/PHyiKmmeIg7ZvYzjSIAAAAAyBDCTIHyq2Q2sSwks+GHGd/yzKybAQAAwBARZgqUXyWz3Ya5x0wf//LMhBkAAAAMDWGmQG1qz2wlsz5Tq/zKMzPNDAAAAENDmClQ27q8w8y4svRcGuw1AwAAgEwhzBSo+k7vMDOmJDvTzFa3MDIDAACAoSHMFCi/kZnRJem5NCZVhOVVRqCx26mpm71mAAAAMHiEmQJV3+k9vWt0aXoujeKwaZJPMQH2mgEAAMBQEGYKVL3PyMyYNIUZSZriW56ZqWYAAAAYPMJMgdrms2YmXdPMJP+9ZigCAAAAgKEgzBQo35GZtIYZv71mGJkBAADA4BFmClBvzKmx23keMyqNYWaa714zjMwAAABg8AgzBajBZ1SmushUHPaqQTY4vuWZCTMAAAAYAsJMAfKbYpauSmZ9/NfMMM0MAAAAg0eYKUDZXPwvSZMrwvIa6Gnudmr0CVgAAADAQISZAuS3YWY6F/9LUiRk2t1nr5nVjM4AAABgkAgzBchvzUy6p5lJlGcGAABA+hFmClC2p5lJqZRnJswAAABgcAgzBch3j5lS71GUoZjmNzLTwjQzAAAADA5hpgCNzMiM35oZRmYAAAAwOISZAuRbACATa2aq/KaZMTIDAACAwSHMFKCGERiZ8ZtmtrY1Kudc2l8XAAAA+YswU4C2dXlP6cpEmNmtPKyIx14zLT1Ojd2EGQAAAKSOMFOA/NbMZGKaWSRkmuS31wxFAAAAADAIhJkC0xtzavIZAcnEyIxEEQAAAACkF2GmwDR2x+QVZaqKTMVhj/lgwzCNIgAAAABII8JMgRmJssx9/EZm1rQwMgMAAIDUEWYKjN+GmaMzsF6mz9RKRmYAAACQPoSZAuO7+H8kR2ZYMwMAAIBBIMwUmJEcmfFfM8NeMwAAAEgdYabA1I/gmpmJZSEVeZy+rddpm0/YAgAAAPoMu+dqZmPM7LNmdp+ZLTOzDjNrMrNnzOwzZjao1zCzyWZ2i5mtN7MuM1tlZteb2ajhthXyDQuZnGYWDpkm++w1QxEAAAAApCodPdfTJd0s6VBJ/5Z0vaR7Je0j6XeS/p+ZpVTr18xmSnpZ0rmSXpR0naQVki6W9LyZjUlDewua3zSzMaXeYWO4UplqBgAAAKTCu2eZmqWSPiTpQefc9p6ymX1T8UByqqSPKh5w/Pxa0nhJX3bO/aLfuX4m6VJJ35N0QRraXLBGsjSzlMrGmVQ0AwAAQGqG3XN1zj3pnPt7/yCTuH+jpJsSPx7td57EqMzxklZJ+tWAh78jqU3SWWZWMdw2FzLfNTMZLAAgpVKemZEZAAAApCbTBQB6ErepfNx+TOL2sV0EoxZJz0oql3RY+ppXeHyrmY3wyMyaFkZmAAAAkJp0TDPbJTOLSPp04sdHUnjKnMTt0iSP1yk+cjNb0hOptqOuri7VQwvClvYyScmXMDWvX626bTuXR07X7zHUHJJUmvTxZfUdvGd5iPcUQ8W1g+Hg+sFQce1kx6xZs4Z9jkx+DP9DxYsAPOScezSF42sSt01JHu+7v3aY7SpYUSc1+wx81BRldp+X3Uu8z7++y8RWMwAAAEhFRkZmzOzLki6TtFjSWZl4jVSlI/Hli22dUblnNyZ9vDJi2nvOjr+vvk8m0vV7nOmcSl5Zr64kS2O6YqbaKTM0viyzVdWQHem+flA4uHYwHFw/GCquneBJ+8iMmV0k6QZJCyUd45yrT/GpfSMvNUke77u/ceitK2y+lcwyvPhfkkJmmlJBEQAAAAAMX1p7r2Z2iaRfSHpT8SCTfBhgZ0sSt7OTPN4XkZOtqYEP/z1mMh9mpBTKM1MEAAAAAClIW+/VzL6m+CaXCxQPMpsHeYr5idvjzWyHdplZlaQjJbVLemGYTS1YI73HTB/fimaMzAAAACAFaem9mtmVii/4f1nScc65rR7HFpnZ3MS+Mts555ZLekzSdElfHPC0qyVVSLrDOdeWjjYXom1+IzPZCjNVftPMGJkBAACAv2EXADCzsyVdIykq6V+Svmy2U+nfVc652xLfT5K0SNJqxYNLf1+Q9Jykn5vZcYnjDlV8D5qlkv5nuO0tZA0+YWZUlsLMNEZmAAAAkAbpqGa2R+I2LOmSJMc8Jek2vxM555ab2cGKh6MTJX1A0gbFCwpc7ZxrGG5jC5nfNLPsrZnxvuxWtxBmAAAA4G/YYcY5d5WkqwZx/Cp57NronFsr6dzhtgs7851mliMFANa29SrmnEI7j/ABAAAA22Wn94qcUJ8jBQDGl4VU6pFnuqLS5g7vtgIAAACEmQLiV5p5dEl2Nqo0M9+pZhQBAAAAgB/CTAHJlX1mpFT2mmHdDAAAALwRZgpIruwzI/kXAaCiGQAAAPwQZgpENOZ8SzNnM8xMq/Irz8w0MwAAAHgjzBSIpu6YnMfjFRFTaSR71cN8p5kxMgMAAAAfhJkC4VeWeXQW18tIqew1w8gMAAAAvBFmCoTvhplZnGImpTLNLKruqNdYEgAAAAodYaZA+Jdlzu6lMKYkpOri5NPaok5axegMAAAAPBBmCoTvyEyWp5mZmWZVe081W9ZMmAEAAEByhJkCkUuVzPrMrPEJM02EGQAAACRHmCkQvnvMZHlkRhIjMwAAABgWwkyB8Ktmlu0CAJK0p8/ITB0jMwAAAPBAmCkQuVYAQJL2rCnyfJxpZgAAAPBCmCkQ9TlWAECSZviUZ97SGVOjTwgDAABA4SLMFAj/TTO9g0UmVBSFNLnC+3WXs24GAAAASRBmCoTfyMxITDOTpJkUAQAAAMAQEWYKQDTm1NCdm2FmFkUAAAAAMESEmQLQ1B1TzCV/vCJiKotY9hrUj9/IzHLCDAAAAJIgzBQAv0pmo0ZoVEZKYWSGaWYAAABIgjBTAPw2zByJSmZ9/PaaWdHcq5jzGFYCAABAwSLMFIBc3GOmz5SKsIo9Xr6912l9WzR7DQIAAEBgEGYKgF9Z5pEcmQmHTDP81s0w1QwAAAC7QJgpALlalrnPnj5hhopmAAAA2BXCTAHI5Wlmkv+6GfaaAQAAwK4QZgpALhcAkFIIM4zMAAAAYBcIMwXAd83MSI/M+EwzY2QGAAAAu0KYKQANftPMRnhkxm+vmTWtUXVFKc8MAACAHRFmCoBfAYCR3DRTiq/ZqS22pI/HnLSyhdEZAAAA7IgwUwAaunO7AICZ+Y7OUNEMAAAAAxFm8pxzznea2UiPzEjSTL+9ZggzAAAAGGDke7HIqNZepx6PLFMUkioiyad4ZcusmiLPx+soAgAAAIABCDN5znfxf0lIZiMfZvzKMzMyAwAAgIEIM3kuCFPMJP/yzKyZAQAAwEC50ZNFxgQlzMyojshrfGhbV8z33wIAAIDCkhs9WWRMUMJMWcQ0uTLsecwyRmcAAADQT270ZJExDV3em03mSpiR/KeaLaMIAAAAAPrJnZ4sMqI+hQIAucKvCMCypp4stQQAAABBkDs9WWREUKaZSRQBAAAAwODkTk8WGeE3MjOqOHcugVl+IzNMMwMAAEA/udOTRUb47jNTmjuXgN80sxXNvYo57zVAAAAAKBy505NFRjT6hJnaHBqZmVwRVqlHQbPOqPR2WzR7DQIAAEBOy52eLDLCtwBADo3MhMw0w2fdzKIGppoBAAAgLnd6ssgI3wIAxV5bVWbf7Joiz8cXbOvOUksAAACQ6wgzecw5F6hqZpK0/xjvMPPqVsozAwAAIC63erJIq5Yep16P9fIlYak8klsjMweO9Qsz3XIUAQAAAIAIM3nNf4pZSGa5FWYOGFPs+fimjpg2tHv/uwAAAFAYCDN5LGhTzCSptiSkPao8SpopPjoDAAAA5F5vFmkTxDAjSQeO9R6deXUb62YAAABAmMlrgQ0zPkUAFjAyAwAAABFm8prvHjO5GmbG+YzMbO2hCAAAAAAIM/ksqCMz+40ukldZgm1dMa1ti2atPQAAAMhNudmbRVo0dAczzFQXhzSrJuJ5DPvNAAAAIDd7s0iL+s5gTjOTpAN89pth3QwAAABytzeLYWvo9l5XUpvDYeZAn/1mqGgGAACA3O3NYtgaA7pmRpIO9BmZeXVrN0UAAAAAClzu9mYxbEGtZiZJ+44uUsijCkBTt9PKFooAAAAAFLLc7c1i2IJazUySKopCmutbBIB1MwAAAIUsd3uzGBbnnG+YyeWRGUk6YKz/fjMAAAAoXLndm8WQNfc4RT2WlJSGpbKI124uI8933cw2RmYAAAAKGWEmTwV5ilmfd/mMzLy2tUcxigAAAAAUrNzv0WJI8iHM7D2qSF6DR629TsuaerPXIAAAAOSUtPRozew0M/uFmf3LzJrNzJnZnUM4z6rEc3f1tTEdbS0U+RBmSiOmeaP8ppqxbgYAAKBQeZeLSt23JO0vqVXS25LmDuNcTZKu38X9rcM4Z8HxDTPFuR9mpPi6mdfrkweWV7d264yZ5VlsEQAAAHJFusLMpYqHmGWS3itp/jDO1eicuyodjSpkvnvMlAYlzBTr9qXtSR+nohkAAEDhSkuYcc5tDy9muV0hq1Dky8jMAWO8p5m9vq1HvTGniNcOmwAAAMhL6RqZSacSM/uUpKmS2iS9Lulp5xzbvQ+C7x4zARmZmTeqSMUhqTvJP6cj6rSksVd7j/YOPQAAAMg/uRhmJkq6Y8B9K83sXOfcU4M9WV1dXXpaFTCrtxTL6+3tbtyiurrUayqM5O9xz/ISLWwNJ338kYVrVTyBrJvLCvW/Qwwf1w6Gg+sHQ8W1kx2zZs0a9jly7eP5WyUdp3igqZC0r6TfSJou6WEz23/kmhYszb3e066qczHGJrFXpfco0+vNyYMOAAAA8ldOdWmdc1cPuOtNSReYWaukyyRdJekjgzlnOhJfEHUt2Swp+eL4vfeYpFkTS3zP0/fJxEj+Ho92bbp3Y2PSx19qKdGee05lvVYOyoXrB8HEtYPh4PrBUHHtBE+ujcwkc1Pi9qgRbUWANHQ5z8eDUgBAkg6bUOz5+Lr2qBY2sHkmAABAoQlKj3ZL4rZiRFsRIPmwaWafPasjml7lPZXs8bc7s9QaAAAA5Iqg9GgPS9yuGNFWBETMOTUkK/+VEKQwY2Z63+RSz2MeJcwAAAAUnKz3aM2syMzmmtnMAffvZWY7jbyY2XRJv0z8eGcWmhh4zd1OMY9ZZmVhU1kkWOtLjvcJMy9u7lajz2gUAAAA8ktaCgCY2SmSTkn8ODFxe7iZ3Zb4fqtz7vLE95MkLZK0WvEqZX3OkHSZmT2deKxF0kxJJ0kqlfSQpJ+ko735rtFnVGZ0gEZl+vzXxBKVhqXOJBWYo06av75TH9mjPLsNAwAAwIhJVzWzAySdPeC+GYkvKR5OLpe3+ZLmSDpQ0pGKr49plPSM4vvO3OGc817VDklSfad3mKktCdaojCSVRUxH7Vaix97uSnrMY293EWYAAAAKSFrCjHPuKsXLJqdy7CpJO/WmExtiDnpTTOwsn9bL9Pe+yaWeYeYfb3cq5pxClGgGAAAoCMHs1cKTXyWzIE4zk+RbBGBLZ0yvbUu+tw4AAADySzB7tfDkN80sqCMz06simlPjPZj4GFXNAAAACkYwe7XwlK/TzCT/0ZnH1hJmAAAACkVwe7VIym9kJqjTzCT/MPPK1h5t6UhS8gwAAAB5Jbi9WiTlNzJTG+Awc/iEYlV67JHjJD2xLnmRAAAAAOSP4PZqkZTf5pFBHpkpDpuO3r3E85jHWTcDAABQEILbq0VS9T5hJshrZiTp+CneU82eWNep3hhbEgEAAOS7YPdqsUt+pZmDHmb+e5J3mGnsdnppS3eWWgMAAICREuxeLXapoct7VCLI08wkafeKsPYdXeR5DFPNAAAA8l+we7XYScw5NfoVACgO/tt+/GTvdTMPryHMAAAA5Lvg92qxg+ZuJ6/lIuURU6lHNbCg8CvRvLCxVwu2MtUMAAAgnxFm8ozfepmgTzHrc/C4YtUWe4eyO+ras9QaAAAAjIT86NliO79KZkHeY6a/SMj0wWllnsfcs6Jd7b3evw8AAAAEV370bLFdoYzMSNKnZ5d7Pt7c7fR/q1g7AwAAkK/yp2cLSansMRP89TJ9DhlXrLm1Ec9j/rC0LUutAQAAQLYRZvKM7x4zeVDJrI+Z6VOzvEdnntvUrWVNPVlqEQAAALIpf3q2kJTCNLPS/HrLP75nuYp8/kl3UggAAAAgL+VXzxb+08zyaGRGksaWhnXSVO9CAH9a1q4er3rVAAAACKT86tlCjQVSzaw/v0IAmztienQthQAAAADyTf71bAtcIVUz63P07iWaUhn2POYOCgEAAADknfzr2RY4/2pm+feWh8x05p7eozOPr+vS+rZolloEAACAbMi/nm2BK7QCAH3OnFUur6LTMRdfOwMAAID8kZ892wLW0F1YBQD6TKmM6LhJJZ7H3LG0TTFHIQAAAIB8kZ892wIVjTk1dnl31vNxmlmfs2ZXeD6+ujWqf7zdlaXWAAAAINPyt2dbgJp7nLyiTGXEVBz2mowVbO+fUqqxPtPofr2wNUutAQAAQKYRZvJIfWfhlWXurzhs+vhM70IA/1zfpTfqe7LUIgAAAGRSfvduC8y2Lu9qXflYlnmgc+dUeBYCkKRfvdmSlbYAAAAgs/K/d1tAtvqMzPhNwcoHM2siOnFKqecx967s0IZ2yjQDAAAEXf73bgvINp8wM6YAwowkXbRPpefjPTHp5kWsnQEAAAi6wujdFgjCTNwRE4p1wJgiz2NuWdymth7v3xcAAAByW2H0bgvENp8NM8cUwJoZSTIz39GZxm7HJpoAAAABVxi92wLhv2YmnKWWjLwPTy/TpHLvf++Nb7UqGmMTTQAAgKAizOSR+k6famYFMs1MkopCpgvmeW+iuaIlqofXdmapRQAAAEi3wundFgCqme3orNkVqox4F2r+1VsUAgAAAAiqwurd5jnfNTMFFmZqS0I6a7b3JprPb+rWK1u6s9QiAAAApFNh9W7znF81s0IbmZGkz8+rVMhnF01GZwAAAIKp8Hq3eaor6tTSk3wxu0kaVVx4b/f0qog+NK3M85i/rerQ2tbeLLUIAAAA6VJ4vds85TcqM6okpLDfEEWe+uLe3mWao076zcK2LLUGAAAA6UKYyROsl0nukPHFOnR8secxf1japuZuNtEEAAAIksLt4eaZbT5lmQtxvUx/X/AZnWnucbqjjk00AQAAgqSwe7h5xG+a2eiSwn6rPzi1VNMqvTfRvGlhq3rZRBMAACAwCruHm0fYY8ZbOGS60Gd0Zm1rVH9f3ZGlFgEAAGC4CruHm0f81swUepiRpE/NKld1sXcRhF++2SrnGJ0BAAAIAnq4ecJ3mlmp9xSrQlBZFNK5sys8j3l5a4/+vZlNNAEAAIKAMJMn2DAzNZ+bV6mIT4XqX77JJpoAAABBQA83T2z1qWY2psALAPSZVBHWR/fw3kTzwTWdWtHMJpoAAAC5jh5unqhnZCZlfmWanaQbFzI6AwAAkOvo4eaJrT4FAEYTZrY7YGyx/mui9yaaf6prVxObaAIAAOQ0erh5wDnHmplBumgf79GZtl6nPy9jE00AAIBcRg83DzR1O0U9qgmXhU3lEd7q/o6fXKpZNRHPY363qE0xyjQDAADkLHq4ecBvVGYMozI7CZnpgnneZZqXNffqqfVdWWoRAAAABotebh7wrWRGmNmlM2aWq7rIu07zbxe1Zak1AAAAGCx6uXlgm8/if9bL7FplUUgf37Pc85hH3+7UmlbKNAMAAOQierl5YKvfNDP2mEnqs3O9p5rFnHTrYkZnAAAAchG93Dzgt8cM08ySm11bpKN3L/E85val7erspRAAAABArqGXmwd8R2ZKw1lqSTD5jc7Ud8V036qOLLUGAAAAqSLM5AHWzAzPiVNKNbnCO/D9blFrlloDAACAVNHLzQPbfKqZjWbNjKdIyHSez+jMy1t79MqW7iy1CAAAAKmgl5sH/PaZYWTG36dnl6vY59d0M4UAAAAAcgq93Dzgv2aGt9nP2NKwPrJHmecxf13Z7jsKBgAAgOyhl5sHGJlJj/P3qvR8vCsq3VnXnqXWAAAAwE9aerlmdpqZ/cLM/mVmzWbmzOzOIZ5rspndYmbrzazLzFaZ2fVmNiodbc03nb1OrR5lg01Srd/8KUiSDhpbpAPGFHkec9uSNsUcZZoBAAByQbp6ud+SdJGkAyStG+pJzGympJclnSvpRUnXSVoh6WJJz5vZmGG3NM/4VTIbXRJSOGRZak2wmZnO38u7EMDKlqj+ub4rSy0CAACAl3SFmUslzZZULenCYZzn15LGS/qyc+4U59zXnXPHKh5q5kj63rBbmmf81nCwXmZwPrpHuWqLvcPf7ykEAAAAkBPS0tN1zs13ztU5N/T5N4lRmeMlrZL0qwEPf0dSm6SzzMz7o/MC47dehjAzOGUR0ydnlXse88jaTq1roxAAAADASMulnu4xidvHnHM79NCdcy2SnpVULumwbDcsl/lNMxvDHjODdu4c77wcddIdSxmdAQAAGGmRkW5AP3MSt0uTPF6n+MjNbElPpHrSurq6YTYrty1aH5FUnPTxou4W1dXVD/t18v33ONDBNSV6qSmc9PFbFjbpwxWbFGE5UkoK7fpB+nDtYDi4fjBUXDvZMWvWrGGfI5fCTE3itinJ433312a+KcHR2OPdm67NpXc4QE7drdczzGzuDumZ+rCOHsN0s2yp75b+sTWi+dvCWtkeUlnY6b9GR3Xm7r2aWEqFOQAAClHed3XTkfhymdvSqPhyol2btftYzZrlvX+Kl75PJvL99zjQ9JlO16/eqE0dyafxPdJcrfMPG5vFVgXPcK+ftp6YHlzTqXuWt+vJ9V2K9s8sPaa71of0141FOm9uhS7br0rjypIHUARLof7tQXpw/WCouHaCJ5cWVPSNvNQkebzv/sbMNyU4tnVRzSwTikKms2Z5r515Yl2XVjb3ZqlFhaWz1+lbLzZp9l0b9bmnG/T4ugFBpp/umHTTwjYd8JdN+u4rzWr0WUcGAADyRy71dJckbmcnebwvIidbU1OQtvpUMxtLmBmys+eUy2+LntuWUAgg3XpiTh9/Ypt++Var2jw2hB2ordfpJ6+16IC/bNT9qzoy2EIAAJArcqmnOz9xe7yZ7dAuM6uSdKSkdkkvZLthuazerzQz1cyGbEplRO+bXOp5zJ117epKNmSAIfnmv5uGtTFpY7fT2fPr9fvFrWlsFQAAyEVZ7+maWZGZzU3sK7Odc265pMckTZf0xQFPu1pShaQ7nHN8FN6P38gM08yG5zyfMs3bumL6P0YB0uYPS9t0c5o2Jf3qC016ZuPQQxEAAMh9aSkAYGanSDol8ePExO3hZnZb4vutzrnLE99PkrRI0mrFg0t/X5D0nKSfm9lxieMOVXwPmqWS/icd7c0XMedU77fPDGFmWP57UommVIa1tjX52qTfL27T6TO9N9qEvxc3d+my5xvTdr6ok86ZX6/5J4/TlMq8r3UCAEBBSldP9wBJZye+TkjcN6PffaelcpLE6MzBkm5TPMRcJmmmpBskHeac25am9uaFpm6XdFG0JJVHTOURwsxwhEOmc2Z7j868sLlbr2/rzlKL8tP6tqjOerJePWleu7+1M6aznqxXxyDW3gAAgOBIS0/XOXeVc848vqb3O3bVwPsGnGutc+5c59xuzrli59w059wlzrmGdLQ1n2zr9K5kNpr1MmnxqVnlvptj/i5NU6MKUWev01lPbvMsg90nbNJxk0r0rXdVa3JFamWYF2zr0aXPNcg5Ag0AAPmG3m6AUcksOyaUh/Wh6WWex9yzvEMNlAQeNOecLnuhUS9v7fE99oNTS7XojIm69/ixunz/Kr186gT96NAajUvhOr9reYd+s4jACQBAvqG3G2DbCDNZc/5e3lPNOqJOd9bRWR6se1Z06I917b7H7T0qot8cNUrj+22KWRI2fX5epf75ofEpBZr/ebFJ/9pAQQAAAPIJvd0A2+YzEjCaMJM2h40v1j6jizyP+f3iNkVjTGVKVVfU6ZqXm32PG1Vi+uNxY1RRtOvreVJFWLcdM9p3KmDUSZ95qp5NNQEAyCP0dgOMkZnsMTN9zmd0ZlVLVP9Yxyf/qfrD0ja93ea97its0m1Hj9b0Ku9qZEdOLNH3313j+5qbO2L64QL/AAUAAIKB3m6A+e4xU5LaAmmk5rQZZaop9v74/+ZFbNSYio5ep5+93uJ73LWH1Oi9u3tvXNrn/L0q9Mk9/Utk37yoTYsb/dfoAACA3EeYCTC/amaMzKRXeSSks2Z5j878Y12Xljf1ZqlFwXXrkjZtaPcO4x+bWaYL53n/vvszM/3s8Fq9a6z3dMCok7757yaqmwEAkAfo7QaY3zQz1syk32fmVshnaYZ+t5jRGS9tPTFd5zMqUxExff/dNTLz+23vqDRiuuPYMb5B/sn1XXr07c5BnRsAAOQeersB5lcAgJGZ9NujOqLjJ5d4HvPHZe1qTffuj3nk94vbtMUniH9urwqNLR3aNMlJFWFde4j/+plv/rtJ3V67zgIAgJxHbzfA/NfM8PZmwvl7VXo+3tztdM/yjiy1JlhaemK6/g3vkauqItOX9vH+Hfs5Y2aZDvKZbraiJarfLGQUDQCAIKO3G2D1VDMbEcdOKtGMKu9Rg98uamVNxi78dmGb6n1GFC/cu1Kjhzgq0ydkph8eWut73P++1qLNHd5rzwAAQO6itxtQnb1Orb3JO8shk2oZmcmIkJk+6zM6s6ixV/PXU6a5v6bumH7xpvdamZpi0xfmDW9Ups8h44t1xswyz2NaepyuTWGvGwAAkJvo7QaU74aZJSGFBrl4Gqn75J7lKvfZpdFvOlWhufGtVjV2e49WXbR3ZVpD+HcOqlGFz/t0Z127FmztTttrAgCA7CHMBNRWn7LMrJfJrNqSkO+n/k9v6NKrdJIlSc3dMf36Le9wN6rE9Pk0jcr02b0irK/sV+V5jJN0FaMzAAAEEj3egPJbLzOG9TIZd0EKHe/r3/DfGLIQ3LWsXc093qMyF+9Tperi9F+3X9y7UlMrvdfg/HN9l57dyLRAAACChh5vQPlWMiPMZNyc2iK9f4r37vT/t6pTK5oLexNN5+LlmL2MLQ3p/L1S3yBzMEojpu+mUKr5e680U7QBAICAoccbUOwxkxsu2dd7dMZJvove893LTSEtafIOdBfvW6mKosxdsydPK9V7JhZ7HvPcpm49tYHRGQAAgoQeb0D57zEzvNK2SM2hE0p02HjvTvKflrVrU3vhlv+9Z0PE8/HKiOns2ZkZleljZvrOwYzOAACQbwgzAbXRp3M8toy3Nlsu9hmd6YrG950pRJu7TE9t8w7WH9+zPCNrZQY6eFyxTphc4nnMf7b06PG3GZ0BACAo6PEG1AafMLNbOSMz2XLClFLNrfUeffjd4ja19HiPpuWj+zZGFJV3aeTPzM3sqEx/3ziw2veY77/K6AwAAEFBmAko/zDDW5stITN9aR/v0Zmmbqfblngvgs833VGn+zZ6h7wjJxZrr1FFWWqRdMDYYp08zbtow4JtPXpwTWeWWgQAAIaDHm9AMTKTW06fUa5JPr/zG99qVXe0cD7xf3BNh7b1eI/KnD83vfvKpOIbB1b7jBXFR2dijM4AAJDzCDMB1Nnr1NDl3dGaUEaYyabisOnCvb2nS61vj+mu5e1ZatHIu3mR90jUxLKQTvIZJcmEeaOK9NE9vDc8XdjQq/tXdWSpRQAAYKgIMwG0scN7VGZcaUjFYb/PnpFuZ8+pUE2x9+/9fxe0qLM3/z/xf6u+R89t6vY85uw5FSoKjcx1+vUDq+T30j94tUXRWP6/VwAABBlhJoD8pphNZIrZiKgqCumzPovZ326L6tYCWDtzi8+/MWzSOXOyt/B/oFk1RfrYDO/RmaVNvbpnBaMzAADkMsJMAG1o8w4zu7P4f8R8fl6l/Lb4+enrLWrN48pmzd0x3b3MezrdB6eVjvi6rq8dUC2/AcwfLWhWD6MzAADkLHq9AbSekZmcNb4s7FtqeGtnTDctzN/RmbuXt6vVZyrdZ0dg4f9Ae1RHdOascs9jVrZE9WefYAYAAEYOYSaANrZ7f6o/0p94F7qv7Felyoj3R/4/f7NFDV35NzrjnPOdRje3NqL/mlicpRZ5u3z/KhX5/BX88WstBVWFDgCAICHMBBBlmXPb2NKwLtzbe+Shudvp52+0ZKlF2fPSlh4tbOj1POYzcytklhsFKqZWRnT2bO+RtLWtUd1Rl78jaQAABBlhJoAIM7nvon0qVetT2eymhW3a5PNeBs1tS707/RUR0xkzvad2Zdtl+1ep1G+d02uFUYUOAICgIcwEkG+YqSDMjLSa4pAu3a/K85iOqNNPXs+f0Zmm7pj+6lP96/QZZaouzq0/O7uVh3Wezzqn9e2xgqhCBwBA0ORWrwK+nHMprJnhbc0F5+9VoQll3u/FbUvatLrFe1pWUNyzvF0dPmtLRrIcs5dL961Suc86p+veaFF7b/6tcwIAIMjo9QZMU7fz7DAWhaQxJbytuaA8EtIV+3uPzvTEpO+/2pylFmVOKgv/9x9TpAPG5sbC/4HGlYX1ub28g9bmjph+t4jRGQAAcgm93oBJZcPMXFlcDenTsys0tdJ72t/dyzv00pbuLLUoM17Z2qO3fBb+n+Oz0H6kfXmfSlUVef+3c/0brWrJ4z2CAAAIGsJMwPiFmd1Z/J9TisOmbxxY7XvcV19oVMwFd4H5bT6jMhUR06kzyrLUmqEZXRrWBfO8q9DVd8V001utWWoRAADwQ5gJGP8NM3lLc83HZpRpTk3E85hXtvboTwHdnLG5O6Z7V3ov/D81Bxf+78oX965UjU8Vul++1arGPNwjCACAIMr93gV2wIaZwRMOmb5zsP/ozNUvNaupO3id5HtWtKvdp2xxrk8x61NbEtJFPnsENXU7/YrRGQAAcgJhJmDYYyaY3j+lVMfuXuJ5zJbOmH68IFilmuML/71HlOZUxHTg2KIstWj4Lti7UqN9imjctLBV9Z35tUcQAABBRJgJGMJMMJmZfnBojXyq/+qmha1a2tiTnUalwatbe/RmvXd7PzKxN1BFKaqKQrpkX+/RmZYep5+/yegMAAAjjTATMKlUM0NumlNbpM/N855u1eukb7zYJBeQYgC3LfVe+F8acjphXPD20fnsXhUa77NH0G8XtWlzB6MzAACMJMJMwGz0rWbGW5rLvnZAtcaVer9HT6zr0iNrO7PUoqFr6IrpLyu8F/6fMC6qSu/aBzmpPBLSpft67xHU3ut0/RvBmhYIAEC+oecbIL0xp00d3gvEGZnJbTXFIV15kH8xgG+82KQOn0X1I+0PS9t8F/5/ZGLwRmX6nDunwvfDgVsWt/mOlgIAgMwhzATIls6YYh59x+oiU2URb2mu+9Ssct8F8ataovrxa81ZatHg9cacbl7kPcVsn9FFmlcZvOpsfUojpsv29x6d6YxKP3uN0RkAAEYKPd8A2dDG4v98EDLTjw6t8T3u52+0+i6uHykPrO7U2z7X42fnVihA6/536axZFZpS6f3f1e1L27S2NbgjUAAABBlhJkD8N8wkzATFu8eX6IyZZZ7H9Drp4mcbFPUajhshNy70ruQ1qsT0MZ9/XxAUh01X+IzOdMeknzA6AwDAiCDMBIjf4v/dWPwfKFcfXKOqIu+hi5e39uh3i72nc2Xby1u69e/N3Z7HnDunQuWR/LgeP7Fnufao8v6g4M66dq1sZnQGAIBsy4/eRoFgj5n8MrE8rCvf5V8M4NqXm/V2Dk1juslnVCZi0mfmeu/TEiRFIdPXDvB+n6JO+tGC3F3jBABAviLMBMiGdu/F1ISZ4PnM3AodMs67GEBrr9NlL+TG3jMb2qO6b6V3OeZT9ijTpIr8uhZPn1Gm2TXeNab/34qOQG14CgBAPiDMBAgbZuafcMh0w5GjFPFZKP/o2k7dv2rk9575/aI2+VWMvmBe/ozK9AmHTN840HvtTMxJP1rA2hkAALKJMBMgvhtm5tmn4YVi3qgiXeKzQaMkffXfjWrsGrlSxx29Trcs8V6/c8i4Ih08rjhLLcquD08v096jvEdn/rqyQwsbGJ0BACBbCDMB4lvNrIy3M6gu379KM6u9w+jmjpi++kJjdhq0C/esaFe9T5i6MA9HZfqEzPSNA73XzjhJP3iVtTMAAGQLvd+AaO+Nqak7+fwekzSBaWaBVRoxXX/EKN/j/t+KDt23sj0LLdqRc043vuW98H9SeVgnTw9+OWYvJ00t1QFjvNc4/X11p17b5l3tDQAApAdhJiA2+iz+H18WUlEo4DsUFrj37Fais2aV+x536XONvuun0u3BNZ1a1OhdUe2ze1Xk/TVoZvqmz+iMJH3/VdbOAACQDYSZgGDDzMJwzSE1Glfq/Z9lY7fTRc80ZK26WU/M6aqXvKdOlYVNZ8/2D2L54H2TS3wr0D26tlMvbWF0BgCATCPMBIT/hpmEmXwwqiSknxxe63vcE+u69PssbaZ5+5I2LfPZEPLje5ZpdGlhXINmpv9JYX+g77/C2hkAADKNMBMQG9r8wgxvZb748PQynTHTf+3Jlf9p1rKmzFbOau6O6Yc+5YZN+VmO2ct7dyvRERO8q7Y9ub5LT2/oylKLAAAoTPSAA2JDByMzheR/D6vVZJ9S2x1Rp88/3aDeWOamm93wRou2dnqv1/rEnuWaU+s97SrfpDo6c9VLubHZKQAA+YowExAb2rw7lKyZyS81xSH9+j3+1c1e3tqTsVLA69qi+pVPBbOycGqd+nx05MQSHbN7iecxr2zt0d9WdWSpRQAAFB7CTEBs9BmZ2Z0wk3eO2q1EX9zbf/rWT19vzUi55u++0qxOn6JpX9i7QpMKeLPWVILcNS83qyeDo2cAABQywkxArPdZM8PITH668l3V2qvWe9d5SfrCvxq1YGv6qme9Ud+ju5Z5B6SxpSFdvG9V2l4ziA4eV6wPTi31PGZlS1S3LclOsQYAAAoNYSYAnHMpjMzwVuaj0ojppqNGqcjn7e2IOp35RL02+1wnqfr2f5rkN5bw9QOqVF3Mdfftg6oV9tle50cLWtTS4z1VFAAADB49kQBo6Iqpy6OPWhKOl/RFftp/TLG+kcJGjevao/rUE/Xqig5vStM9y9s1f713Fa49qyM6e07FsF4nX8yuLfLd7HRrZ0y/eNN7/REAABg8esABsL7dZ/F/WVhm+b3zeqG7eJ9KHe2z2FySXtzSrUufaxxyBa0XNnXpomcbfI+76uBqFYW45vp8/cBqlUe8fx+/erNVm3z2iwIAAIOTtjBjZpPN7BYzW29mXWa2ysyuNzP/kkzvnOOfZuY8vrwnp+cpvw0zdy/gBdiFIhwy3Xr0aM2o8n+v/7SsXT9+rWXQgWZFc68++US95yigJB0+oVgn+awTKTQTy8P6gk+xhrZep/99zXvPHgAAMDj+K4tTYGYzJT0nabyk+yUtlvRuSRdLOtHMjnTObRvEKa9Ocr/3NuR5ar1PmJlYRpgpBKNKQvrzf4/R+x7YouYe76Dy/VdbtKolquuOqFWJ34IOSfWdUZ3++FbVd/mv6/juITWMBO7Cl/ep1K2L27TN43d425I2XTivQnvWFNa+PAAAZEpawoykXyseZL7snPtF351m9jNJl0r6nqQLUj2Zc+6qNLUrL/iNzOxWwWzBQjGntki/e+9onfGPbb4L9P+0rF3Lm3t1x7GjNd4j8Hb2Op35ZL2WN/tPgfrYjDIdNK54kK0uDNXFIV1xQJW+/u+mpMdEnXTVS82687gxWWwZAAD5a9i94MSozPGSVkn61YCHvyOpTdJZZsZq4SHa4BdmGJkpKMdPKdXVB6e2UeW/N3fr2L9v0Wvbdl22eU1rrz7zVL2e3+Rf1nlmdVg/Oqx2ME0tOOfNqdB0n6mAD6zp1LMbvQssAACA1KTjI/1jErePOed2mF/hnGuR9KykckmHpXpCMzvDzL5uZl8xs/ebmf/K5zy2wacAwG6smSk4X9qnUmfMLEvp2LfbojrhwS365BPbdNPCVi1s6NFzG7v06Se36YC/bNKDazp9zzGmJKR73jeWqnk+isOmK1PYSPNb/2lSbIhFGgAAwDvSMc1sTuJ2aZLH6xQfuZkt6YkUz3nXgJ83m9kXnXN/GWzj6urqBvuUnLOqvlReubO3foPqopndwyIffo/55ksTpLc2l+jNFv8w2xmVHlrTqYdSCC4DFZvTj+a0K7pppeo2DaWlhXX97OOkvSpLtKg1+fvy6tYe/fK5lXr/eKqb+Smkawfpx/WDoeLayY5Zs2YN+xzp+Ji1JnGbbKJ43/21KZzrfkknS5osqUzSXEk/SDz3bjM7ccitDLCNXd6LrccX8wlvISoJSdfN69K+VZntEF81u1v7V7PhY6pCJl08vcf3uF+tLlInWQYAgGFJVwGAtHDOXTfgriWSvmlm6yX9QvFg88hgzpmOxDeSGrpianxmQ9LHTdLh82aqzGePi6Hq+2Qi6L/HfPb4bKeLn2vQ3cs70n7u7xxUrS/sVzXk5xfq9TNL0v1N2/Tw2uQjYZu6Qnq8e6K+Mozfbz4r1GsH6cH1g6Hi2gmedIzM9I281CR5vO/+xmG8xu8UL8t8gJkV1P/565q8P+GdXBnOWJBBMJRGTDe9Z5SuObha6bwSPj27XJfs6713CpK75pBq+f2ned3rLdrSwfAMAABDlY4wsyRxOzvJ433RNtmaGl/OuU5JfbvNFVRVtLom7611ZlXn1OAaRoiZ6cv7Vumu/x6jqqLhR5qP7lGmnx5ey34ywzCrpkjnzvX+c9XS4/SDV9lIEwCAoUpHmJmfuD3ezHY4X2IU5UhJ7ZJeGOoLmNkcSaMUDzRbh3qeIFrmF2ZqCDN4xwlTSvX4B8dphk954GSmVob1iyNr9fv3jlJRiCAzXF87oErVPuHytqVtWtzov8YGAADsbNhhxjm3XNJjkqZL+uKAh69WfCTlDudcW9+dZjbXzOb2P9DM9jCz0QPPb2bjJN2a+PEu55x37z7PLCXMYJDm1hbp+Y9M0P8eWqMDxhSlNPXsyInFuuPY0Xr11Ak6a3YFIzJpMrY0rMv2954ZG3PSlS8m32gTAAAkl66e8BckPSfp52Z2nKRFkg5VfA+apZL+Z8DxixK3/XtM75V0k5k9I2mFpHpJUyV9QPF1Ny9J+mqa2hsYjMxgKErCps/Nq9Tn5lWqoSumZzZ26ekNXfrXhi4tboxfUxUR0yl7lOnze1VovzHFI9zi/PX5vSr1u8VtWtuafG3M4+u69OjaTp0wpTSLLQMAIPjS0hN2zi03s4MlXSPpRMUDyAZJN0i62jnXkMJpXlZ8f5mDJB0oqVrxaWVvSPp/kn7jnPPfpjyP9MacVrR4h5k9a4qy1BoE1aiSkE6eVqaTp8U32Wzsiqm+K6YplWGmkmVBacR01UHV+sxT3n8Gv/lio47ZfYKKw7wnAACkKm0f6zvn1ko6N8Vjd/q/tXPuDUnnpKs9+WBNa1Q9Htt7VERMu5ezIzsGp7YkpNoSrpts+ugeZbpxYate2pJ8bczy5qhuWtiqL+9bUAUbAQAYFno0OWypT1nmPWsirG0AAsDM9L1DklWvf8f/LmjRxnZKNQMAkCrCTA7zLcvMehkgMA6dUKKPzSjzPKa11+mqlygGAABAqggzOcxv8f+e7DEDBMpVB9eowmcnzbuWd+g/mwtqeSAAAENGmMlhfiMzsxmZAQJl9wr/Us2S9NV/NyrmXBZaBABAsBFmcphfmNmTMAMEzhfmVWoPn01NX93aoz/WtWepRQAABBdhJkc1dsW0pdOjlJmkmUwzAwKnNGL6/rv9iwFc83KzGru8/wYAAFDoCDM5almz96jM5IqwKop4+4AgOnFKqY6bVOJ5zJbOmK5+mWIAAAB4oTeco5Y2epdlppIZEFxmph+8u0Y+tQB065J2Pb+pKzuNAgAggAgzOcpvZIb1MkCwza4t0gXzKn2Pu+TZRnVFKQYAAMCuEGZylO8eM6yXAQLvqwdUaXyZ95/hJU29uuGNliy1CACAYCHM5Ci/PWaYZgYEX3VxSD9IoRjAT15rUV2T99RTAAAKEWEmB0VjTst9ppkRZoD88NE9yvQ+n2IA3THpkuca5dh7BgCAHRBmctCa1qi6PSqylkdMu1d471MBIBjMTD85vFblPtUAnt3YrTvZewYAgB0QZnKQ33qZmdURhcynDBKAwJhWFdE3DqzyPe7K/zRpc0c0Cy0CACAYCDM5qM5nitlsppgBeefCeZXab3SR5zGN3Y7pZgAA9EOYyUF1PnvMUJYZyD+RkOmGI2sV8hl0fWhNp+5guhkAAJIIMznJb2SGxf9AfjpwbLEumFfhe9zX/92k5T7TUQEAKASEmRzkV5Z5T/aYAfLWNw+s1mSfAh/tvU7nP12vnhjTzQAAhY0wk2OaumPa1OFRykxMMwPyWWVRSNcdUet73Ctbe/S/C9hMEwBQ2AgzOcZvVGZSeViVRbxtQD573+RSfXau/3Szn77eohc2dWWhRQAA5CZ6xTnGrywzozJAYbjmkGrfyoUxJ33u6QY1e21MBQBAHiPM5Bi/kRkW/wOFoTwS0m+PGiW/gdg1rVFd9jzlmgEAhYkwk2OWNnmXZSbMAIXjgLHF+p8Dq32Pu2dFh371VmsWWgQAQG4hzOQYRmYA9PelfSp15MRi3+O+/VKz5q/rzEKLAADIHYSZHBKNOS1vYc0MgHeEQ6ab3jNK1cXeu2nGnHTuP+u10mefKgAA8glhJoesbYuqK5r88bKw+e4/ASD/TKmM6LrDa32Pa+x2+uQT29TSQ0EAAEBhIMzkkNe2ea+XmVkTUci8P50FkJ9OnVGuT88u9z1uUWOvLny6QTEKAgAACgBhJof8a4P3fhFzmGIGFLQfH1ard4/zXz/zwJpONtQEABQEwkwOedonzBw2wb8TAyB/lYRNfzh2tHYr9//T/cMFLbp9SVsWWgUAwMghzOSIDe1RLfWpZPae3Uqy1BoAuWpieVh3HDtGxSn89b7kuUbdu6I9840CAGCEEGZyhN8Us/FlIaaZAZAkHTyuWNcfUet7nJP0+acb9OhaSjYDAPITYSZH+IWZo3YrkbH4H0DCJ2dV6IJ5Fb7H9Trp7PnbfP/GAAAQRISZHOG3XuYoppgBGOC7h9Sk9LehMyp94h/b9PKW7iy0CgCA7CHM5IBVLb1a3eqxwYwIMwB2FgmZbj9mtOaN8p+C2trrdNrjW/UKgQYAkEcIMznAb/rH5IqwplWyWSaAnY0qCem+48dqjyr/vxENXU4femSrnlrPGhoAQH4gzOQA1ssAGI4J5WH97YSxmlTuH2hae51Of3yb7l/VkYWWAQCQWYSZEeac0782sl4GwPBMq4rovhPGaGyp/5/17ph0zvx63cY+NACAgCPMjLBlzb3a0B7zPIb9ZQCkYnZtke49foyqi/xHcp3i+9D85LUWOecy3zgAADKAMDPC/KqYzawOa1IF62UApGb/McW6+31jVBZObWrqd19p1sXPNaorSqABAAQPYWaEUZIZQLodPqEk5REaSfrD0nZ98OEt2tDuXVURAIBcQ5gZQTHn9MwG7zKphBkAQ3HExBI9+IFxGl+W2p/5/2zp0dH/t1kvbmZzTQBAcBBmRtDChl5t6/JeL/NfEwkzAIZm39FFevQD41Iu7b6pI6aTHt6qPyylMAAAIBgIMyPIb4rZvNqIxpWxXgbA0O1RHdGjJ43T3ilsrClJPTHpy8826qJnGtTa4/1hCwAAI40wM4L8wgxVzACkw8TysB58/zgdPqE45efcWdeu99y/WS9t8Z4KCwDASCLMjJDemNNz7C8DIEtqS0L66/FjdfqMspSfs7IlqhMe3KIfLWhWb4xqZwCA3EOYGSGvb+tRc0/yzkHIpCNZLwMgjcoipt8eNUrXHlKtUGqFzhR10g9ebdEHHtqqlc29mW0gAACDRJgZIY+83en5+H6ji1RbwtsDIL3MTF/ap0p/PX6MRpWkmGgkvbilW0f8bbN+8loLe9IAAHIGveUR0NoT0+8WeVcLYooZgEw6evdSzT95fMqFASSpI+r03VeadeTfNmv+Ou8PZAAAyAbCzAi4dUmb6n1KMhNmAGTa9KqIHjtpnE4bxDoaSVrW3KuPPLZN58yv17o2NtoEAIwcwkyWdfQ6/fLNVs9jxpSEqGQGICsqikK6+ahRuuk9o1RVlPq0M0n626oOHXzvJn3zxUZtbCfUAACyjzCTZXfWtWlTh/eozBf3qVRJeHCdCgAYKjPTx/cs1zMfHj+o8s1SfOrZr99q0/5/2aivvtCo9YzUAACyiDCTRd1Rpxve8B6VqSk2fXZuRZZaBADvmFYV0QMnjtWV76pWZJCfp3RFpd8uatMBf9moS59r0CtbuuUchQIAAJlFmMmiu5a3622fTy0/P69S1cW8LQBGRjhkumz/Kj3+wXHaqzb14gB9umPSrUvadewDW3TofZv109datKaVks4AgMyg15wlvTGn615v8TymMmK6cF5llloEAMkdOLZYT394vK49uFoVgx2mSVja1KtrX2nWfvds0vsf2qLvvdKsJ9d1qrnbe6otAACpGvzHbhiSe1d2aGWL96jMZ/eq0Cj2lgGQI4pCpi/tW6WPzijX/7zYpL+t6hjyuZ7f1K3nN3VLim8KvPeoIh0yrlh7VIU1pTKiKZVhTa0Ma2xpSGasGQQApIYwkwUx5/TT17xHZcrCpi/uzagMgNwzqSKs244ZrSfXdeqKFxq1vHl4i/xjTnqjvkdv1Pfs9FhJWKoqCqnYlao8LI1ZukUVEVNJ2FQclkpCpuJw4ueQVBI2FYXiP5eEpNKIaVRJSKP7vkpDGlMSUnnECEkAkIcIM1lwZ127ljZ5zxk/e065xpWFs9QiABi8YyeV6vlTJujPy9r109dbtKY1/ZXLuqJSVzSmvlnQy9q703LeqiJLjP5ENDUxCjS1MqK5tRHNqI4oEiLoAEAQEWYy7J7l7brkuUbPY4pD0pf2qcpOgwBgGIrDprPnVOiTs8p1VyLUrPKZQpsLWnqcFjb0amHDzh8slYalObVFmjeqSPNGRbT3qPj3E8qY8gYAuY4wk0F/rGvTRc80yq846ZmzyjWpglEZAMFRFDKdNbtCn9izXPes6NB1r7f4jkDnqs6o9Nq2Hr22bcdpb6NLQpo3KqK9RhVpn1FF2nt0kfaqjaiiiLWNAJArCDMZctuSNt8RGUkKm3TxvozKAAimSMj0iT3L9fGZZXppS4/uXt6ue1e2q6Er+HvM1HfF9MzGbj2z8Z2pbiZpj6qw9hkdDzdza4s0uyY+VY3NjgEg+wgzGfDbha366r+bUjr2E3uWa3oVbwOAYDMzHTK+WIeML9b3312jx97u1N3L2/XY253qyv1ZaClzkla0RLWiJar/W925/f6QSdMrw5pVW6SZ1f3X5sRv2T8MADKDXnSatfTEdMMbrSkdO7UyrO8eUpPhFgFAdhWHTR+cVqYPTitTR6/TK1u79cKmbv17c5de2Nyt5u7gj9oMFHPvhJxdqS42jS8Na3xZSOPLwhpXFtK40pBqikOqLDJVFoVUVWSqKur7+Z3vKU4AAMmlLcyY2WRJ10g6UdIYSRsk/U3S1c65hkGcZ7Skb0s6RdJukrZJekTSt51zb6ervZlSVRTS/504Vh94eIs2dSTfGG5qZVj/d+JY1bKvDIA8VhYxHTmxREdOLJFUpZiLL8R/o75Ha1t7tbY1qrVt0e3f5+t+ms3dTs3dvVrWPPjnloVN5RFTSTgeFIsT5al3KE0dkorClihdLRUnylUXDShfXRzqfw4pYqZIKH4bMm3/PhySIiaFQ6awvXNcqN/3fbPqnCTnErf9vo/13efc9vv7Hx9TfOuCqJN6Yzt+H3VOvS5+jmgs/n38MRe/L/F91MWPjQ54vL4+opCkce0tCll8emDIJDMpZLb955DiUyX7/j3hfr+PcOK+d/697/xeIiFTxBK/j8T3kVDid2j9z5n4/fX7PpRoAzASuqNOK1t6tbSpV8uaenX07iU6cGzxSDdrWNISZsxspqTnJI2XdL+kxZLeLeliSSea2ZHOuW0pnGdM4jyzJT0p6S5JcyWdK+kkMzvcObciHW3OpJk1Ed1/4lh98OGt2tq58/+Z96iKB5kplQyMASgsITPtM7pI+4wu2umxmHNq73Vq63F6a9lKtUel0btNUVuvU1fUqSfm1BWVumPxn7uiTt0xxW+jTl2x+HPru2Lxr8747dbOmKIBHgzqiDp1BPkfMCISnbM1Q0iPWRKyvgAV/+/inaATDz7vPG6yvuP0TsAMSQolvu8LXeF+oawvRIUGBKq+EBZOhLDwgOfFQ2z8eZG+cydeZ4cIZjvcxL/vF9JMu3p8p6fveGy/O/sCb//gqwH3ORe/v394HhiiY27gY+6d+wacJyapsalYMSdVbqgfELx3fn7fefv+DdY/OG//2XZ4bPtt4kmhxO9t4GM7f2/bz6tdHTfgtXpiTq09fV8xtfY4be6IanVrdIe/h07VhJmEXyseZL7snPtF351m9jNJl0r6nqQLUjjP9xUPMj9zzl3W7zxflnRD4nVOTFObM2pubZH+dsJYnfzIlh0Wws6qiej+E8Zqd6qXAcAOQmaJKVZSc1n87+asiSXDPm805rS+Pao1rX1fvVrZ3KvFjb1a3Nijzjxa04Pg6OsMx2voDQyrhNeRk+gab+4Y2WZkSV1Aq1D2Z84N7z+YxKjMMkmrJM10zsX6PVal+HQzkzTeOdfmcZ5KSZsVD8C7Oeda+j0WkrRC0rTEa+w0OtPU1MR/+QAAAEDA1dTUpDwXMx0LNo5J3D7WP8hIUiKQPCupXNJhPuc5TFKZpGf7B5nEeWKSHh3wegAAAAAKWDrCzJzE7dIkj9clbmdn6TwAAAAACkA6wkxfbeFkG6v03V+bpfMAAAAAKADUBQYAAAAQSOkIM30jJsl2f+y7vzFL5wEAAABQANJRmnlJ4jbZWpZZidtka2HScp7BVD0AAAAAEHxBKs28XNJ0JSnNDAAAAKCwDHuamXNuuaTHFA8aXxzw8NWSKiTd0T/ImNlcM5s74Dytku5IHH/VgPNclDj/owQZAAAAAFIaRmak7aMzz0kaL+l+SYskHar4njBLJR3hnNvW73gnSc45G3CeMYnzzJb0pKQXJe0l6cOKj9ockQhPAAAAAApcWsKMJJnZFEnXSDpR0hjFp5fdJ+lq51zDgGN3GWYSj42W9B1Jp0jaTdI2SQ9L+rZz7u20NBYAAABA4KWtNLNzbq1z7lzn3G7OuWLn3DTn3CUDg0ziWNtVkEk8Vu+cuzjx/OLE+c4jyKSPmU02s1vMbL2ZdZnZKjO73sxGDfI8oxPPW5U4z/rEeSdnqu0YWem4dszsn2bmPL5KM/lvQPaZ2Wlm9gsz+5eZNSfe5zuHeK60/P1CcKTr+klcK8n+7mzMRNsxssxsjJl91szuM7NlZtZhZk1m9oyZfSaxJnsw5+PvTw5KRzUzBMgupgQulvRuSRdLOtHMjuw/JdDjPAOnBN4laa6kcyWdZGaHs74pv6Tr2unn6iT39w6rochF35K0v6RWSW8r/rdi0DJwDSIY0nL9JDRJun4X97cO45zIXadLulHx2ULzJa2RNEHSRyX9TtL7zex0l8I0Jf7+5DDnHF8F9CXpUUlO0pcG3P+zxP03pXie3ySO/+mA+7+cuP+Rkf638pWz184/4396Rv7fxFd2vhRfPzlL8cqWRyeulzuHcJ60XIN8BesrjdfPKkmrRvrfw1f2viQdK+lkSaEB909UPNg4SaemeC7+/uToV9rWzCD3ZbGM9gpJ00QZ7byRrmsncfw/Jb3XJZlqivxmZkcr/gnpH51znxrE89J2DSK4hnr9JJ67SpKcc9PT3S4Ej5l9U9L3JP3SOfcln2P5+5PD0rZmBoFwTOL2sf7/IUpSIpA8K6lc0mE+5zlMUpmkZ/sHmcR5Yop/etH/9RB86bp2tjOzM8zs62b2FTN7v5mVpK+5yENpvwZRkErM7FNm9k0zu9jMjjGz8Eg3CiOiJ3GbytRm/v7kMMJMYZmTuF2a5PG6xO3sLJ0HwZGJ9/wuST+Q9FNJD0laY2anDa15KAD83UE6TFR8T7vvKb525klJdWb23pFsFLLLzCKSPp348ZEUnsLfnxxGmCksNYnbpiSP991fm6XzIDjS+Z7fr/gc5smKj/DNVTzU1Eq628xOHHIrkc/4u4PhulXScYoHmgpJ+yq+/nO6pIfNbP+Raxqy7IeS9pH0kHPuUb+Dxd+fnEY1MwBZ5Zy7bsBdSyR908zWS/qF4sEmlU/KACBlzrmBFRTflHSBmbVKukzSVZI+ku12IbvM7MuKv9+LJZ01ws1BGjAyU1j6PjmoSfJ43/2NWToPgiMb7/nvFJ+7fEBiQSXQH393kCk3JW6PGtFWIOPM7CJJN0haKOkY51x9ik/l708OI8wUliWJ22RzOmclbpPNCU33eRAcGX/PnXOdkvoKSlQM9TzIW/zdQaZsSdzydyePmdklio/+v6l4kBnMRqn8/clhhJnCMj9xe/zAXW8Tn4QfKald0gs+53lBUoekIwd+gp447/EDXg/Bl65rJykzmyNplOKBZutQz4O8lfFrEAWrrwIVWwnkKTP7mqTrJC1QPMhsHuQp+PuTwwgzBcQ5t1zSY4ovdvzigIevVvxTqTv610g3s7lmtsNuy865VsWrwVQoPse4v4sS53+UPWbyR7quHTPbw8xGDzy/mY1TfHGuJN3lnEulVCbykJkVJa6dmf3vH8o1iMKT7Poxs73MbKeRFzObLumXiR/vzEITkWVmdqXiC/5flnSccy7ph2X8/QkmNs0sMIn/QJ+TNF7xqlKLJB2qeA31pZKOcM5t63e8k6SBGxya2ZjEeWYrXtryRUl7Sfqw4htqHpH4jx95Ih3Xjpmdo/j89GcU/xS0XtJUSR9QfM7xS5Le55xrzPg/CFljZqdIOiXx40RJJyj+/v8rcd9W59zliWOnS1opafXAzQ0Hew0iP6Tj+jGzqxRf9P20pNWKjwDPlHSSpFLFy8N/xDnXncl/C7LLzM6WdJukqOJTzHZVjWyVc+62xPHTxd+fwCHMFCAzmyLpGkknShqj+M6190m62jnXMODYXYaZxGOjJX1H8f/J7CZpm6SHJX3bOfd2Bv8JGCHDvXbMbF/FOxQHSdpdUrXinYq3JP0/Sb+hM5F/Eh3J73gcsr3j4NWZSDye8jWI/JCO6yexj8wFkg7UO6WZGxWfdnSH4p+q0yHKMylcO5L0lHPu6MTx08Xfn8AhzAAAAAAIJNbMAAAAAAgkwgwAAACAQCLMAAAAAAgkwgwAAACAQCLMAAAAAAgkwgwAAACAQCLMAAAAAAgkwgwAAACAQCLMAAAAAAgkwgwAAACAQCLMAAAAAAXGzE4zs1+Y2b/MrNnMnJndmYHXeZeZ/cnM3jazLjPbZGZPmdmn03H+SDpOAgAAACBQviVpf0mtkt6WNDfdL2BmF0m6QVKDpAclrZM0WtI+kj4g6Q/DfQ3CDAAAAFB4LlU8xCyT9F5J89N5cjM7XtLPJT0u6TTnXMuAx4vS8TpMMwMAAAAKjHNuvnOuzjnnUn2OmX3CzOabWaOZdZrZIjP7lpmV7OLwH0vqkPTJgUEm8fo9w2j+dozMAAAAAPBkZrdIOlfx0Zx7JTVKOkzStZKOM7P3Oed6E8fuI2k/SX+TVG9mx0g6SJKTtEDSfOdcLB3tIswAAAAASMrMzlE8yNwn6UznXEe/x66S9B1JX1R8fYwkHZK43Szpn5KOGnDKN8zso865ZcNtG9PMAAAAAHi5WFKvpPP6B5mEayVtk3Rmv/vGJ24/I2m6pJMk1UiaLelOSftKetDMiofbMEZmAAAAAOySmZUrXvVsq6RLzGxXh3VJ2qvfz30DJmFJH3fOPZ/4uTlRknmupIMlnSrpz8NpH2EGAAAAQDKjJJmkcYpPJ0tFY+J2Y78gI0lyzjkzu1/xMPNuDTPMMM0MAAAAQDJNidtXnXPm9dXvOUsSt41JztmQuC0bbuMIMwAAAAB2yTnXKuktSXub2egUn/aCpDZJ082sYheP75O4XTnc9hFmAAAAAHj5maRiSbeYWe3AB81slJm9q+9n51y7pN9LKpX0Xeu30MbM9pV0juIFBf4y3IbZIPbJAQAAAJAHzOwUSackfpwo6QRJKyT9K3HfVufc5f2O/5WkL0iql/SopDWSRkvaQ/HSy7c65y7od3y1pKckHSDp35KelTRB0kcVn152iXOur5Tz0P8dhBkAAACgsPTbHyaZ1c656QOe80FJFyi+cL9W8WCzRtJjku50zi0ecHylpG9IOl3SNEkdkl6U9BPn3GNp+XcQZgAAAAAEEWtmAAAAAAQSYQYAAABAIBFmAAAAAAQSYQYAAABAIBFmAAAAAAQSYQYAAABAIBFmAAAAAAQSYQYAAABAIBFmAAAAAAQSYQYAAABAIBFmAAAAAAQSYQYAAABAIBFmAAAAAAQSYQYAAABAIBFmAAAAAAQSYQYAAABAIBFmAAAAAATS/wePJn3GEvg6jQAAAABJRU5ErkJggg==\n", 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\n", 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aBu2YdtBBB23x9SpVaI488kjOPPNMfvKTn/C//1t+bdirX/3qLZ7bcccdATauRRrKtGnTmD9/Pvfccw8HH3wwF154Ibfddhvr1q3L+MxqzzCTI9195Y+v60ukZKCRJKnZzZ49GyDzG9qB55SbRlUtpXEN1NbWxqxZs1i9evWYv38lJk2axLHHHss555zDtddeyxNPPMFFF11Ea2srF1100SZh4tJLL+WEE07gnnvu4cADD+T000/nH//xH/nEJz7BXnvtBUBX1+B7BQ5m1apVzJw5c9AGBKWv16pVqzY+d9xxx/HDH/6QffbZh6uuuor3ve99vOpVr+Lwww/nlltuGfQ9BlvTVJou1teX8U0nheYBn/jEJ9iwYQPnn38+xx13HLvssgsf+MAHeOGFF4b7qY4518zkSFZlpj9Bdz90tpY9TZIkjXMHHHAAt99+O//93//Ne9/73iHP6+vr27gBZKlDV2na0GDf0K5cuXJU43rhhRe2WJDf29vL0qVLN5mmVW4M1RjHSHR0dPD+97+f3/zmN1x99dXcdttt7LPPPvT29nLBBRew7bbbcuutt24RCn/zm99U/F7Tpk1j+fLl9PT00N7evsmxwb5eAEcffTRHH300a9eu5be//S033XQTl19+OSeccAK33XbbJh3VqmHixImcffbZnH322Tz99NPcddddfO973+Pqq6/mqaee4uc//3lV32+krMzkSPcwWi871UySJL3nPe+htbWV66+/nocffnjI86666iqeffZZ5s2bt3FKU2lNy9NPP73F+ffff/+g1yn9RL+/v3y3osHWm/z617+mr6+PffbZZ+Nz5cawatWqQTuNVVJVGI1SiCjNhlm6dCkrV65kv/322yLIrFmzht///vcVj3Wfffahv7+fu+66a4tjd955J319fZu0wR5o8uTJHHbYYfy///f/+NjHPkZ3dzf/9V//NfxPcAR23HFH3vnOd/KTn/yEXXbZhV//+te5aQJgZSZHujO6mUGho9mMzrEfiyRJjaoa+7gMpdQ9a8KECWP2HsOx884787GPfYyLLrqId7/73fzgBz/Y4ifz119/PZ/85CdpbW3lX/7lXzZWQ0pdrL773e/yrne9i7a2wreDTz/9NF/4whcGfb/Sgu+nnnqq7AaLF110EfPnz98YVjZs2MC5554LsHHxORQCw2677cY999zDn/70p41j7+vr49Of/jTr168fcgyDBaBKXH755ey999785V/+5RbHHn30Uf7jP/4DeGn9zzbbbMOkSZO4//77WbNmzcYF+z09PXzyk58cdK3MjBkziIghx3riiSdy6623cs4553D99dczadIkANatW7exAcBJJ5208fw777yT/ffff+O9KnnxxRcBNr6+WpYsWcLzzz+/RdvvtWvXsnbtWtra2raoKNWLYSZHrMxIkqThOvvss1m3bh1f/epXOfjggznyyCPZY4896Onp4d577+W3v/0tEydO5LLLLtukJfPrXvc6DjzwQO666y7e8IY3cOihh/LCCy9w4403cuSRRw76Dfhhhx3GV77yFT7ykY9w3HHHMWXKFKZPn84HPvCBTc7bbbfdOOCAAzbZZ+aJJ57g6KOP3qIt89///d/z93//9xx99NEcf/zxdHZ2cscdd9DT08Nee+3FQw89tMn58+bNY/vtt+cnP/kJ7e3tzJkzh76+Pt7+9rczb968YX/dbr75Zj72sY8xd+5cDjjgAHbYYQe6urp47LHH+NWvfkVPTw+nnnoqr33ta4HClLhTTz2VL33pSxx44IEcc8wx9PT0cPvtt7N8+fKN3cwGmjJlCq973ev49a9/zfvf/3523XVXWltbeeMb38hee+3FO97xDm644QauvfZaDjjgAI499lgigp/97GcsXryYt771rbzzne/ceL1PfOITPPvss+y///7MnTuXjo4O7r//fm677TbmzJnD2972tmF//sPxzDPPcOihh/LKV76Svfbaix122IHVq1dz44038vzzz3PqqacO2t2tHgwzOZK1ZgYKTQAkSZJaWlo477zzeOtb38o3v/lN7rrrLm699VZaW1uZO3cuH/rQhzj99NMHbd/8ve99j8997nPccMMN/Nu//Ru77ror5557LkcccQTXXnvtFucfeeSR/N//+3/5zne+w6WXXkp3dzdz5szZIsxcccUVXHTRRVx99dU899xzvOxlL+OTn/wkZ5555hYbZp500kmklPja177G97//fWbMmMExxxzD5z73uU2qEiWtra1cddVVLFiwgOuuu47Vq1eTUmK//farKMyce+65vP71r+fWW2/lN7/5Dddffz29vb3Mnj2bo48+mhNPPJH58+dv8ppPf/rTzJo1iyuvvJIrrriCadOmcfjhh/OZz3yG888/f9D3+cY3vsHZZ5/NzTffzI9+9CNSSmy//fYbGwZcdtllHHTQQVx11VVcccUVQCEMfuhDH+Jv//ZvN7nWWWedxfXXX899993HrbfeSktLCzvuuCNnnXUWp59++ibtsKth7ty5nH322dxxxx3cfvvtLF26lJkzZ/KKV7yCBQsWVD08jUaMl+5YK1eubKhPZOHChQCb/OU77bZl/OCxLcuqA/3i2K3Zb7bzzKppsHuh+vBe5Iv3Iz+8F5t66qmnKt71vVryMs0sb4499ljuvPPOzJa/1eb9yI+h7sVI/r5Onz49ss8qsAFAjgxnzYzTzCRJkqQCw0yODGfNzDrDjCRJkgS4ZiZXhrNmxsqMJEnSSx566KFhtyY+++yzx3g0qjXDTI4MqzWzDQAkSVLO/OxnP6vbez/00ENceOGFwzrXMDP+GGZypMvWzJIkSRV517vexcknn1zvYahOXDOTIz3Dac1smJEkSZIAw0yudPVln2OYkSRJkgoMMzkynMqM08wkSZKkAsNMjrhmRpKk4Rkvm35L41kt/p4aZnKkx25mkiRlamlpoa9vGHOzJdVVX18fLS1jGzcMMzliZUaSpGwTJ05k3bp19R6GpAzr1q1j4sSJY/oehpkc6bKbmSRJmaZOncrq1atZtWoVvb29TjmTciSlRG9vL6tWrWL16tVMnTp1TN/PfWZypGcYFXMrM5KkZtfe3s7s2bNZvXo1zz//PP39w5inXSU9PT0bx6D6837kx8B70dLSwsSJE5k9e/aY3xvDTI4MpzJjmJEkqfAN01ZbbVXz9124cCEAu+yyS83fW1vyfuRHve6F08xyoq8/MYwsYwMASZIkqcgwkxPDqcoArO+tXSldkiRJyjPDTE50D7PDpNPMJEmSpALDTE50D7MyYzczSZIkqaAqYSYiLoyIX0bEUxGxPiKWRcR9EfH5iJhVwXUWRUQa4tdz1RhrXnUPcy3MetfMSJIkSUD1upmdCfwP8F/AC8Bk4ABgAfCBiDggpfTUMK+1EvjyIM+vGf0w86t7mEthuvoKzQJaW2JsByRJkiTlXLXCzLSU0obNn4yI84BPAWcDZwzzWitSSguqNK6GMdxpZlCozkwxzEiSJKnJVWWa2WBBpujq4uO8arzPeNZVwfQxmwBIkiRJY79p5puKjw9U8JrOiDgRmAusLb72tpTSMPt9NabhdjMDmwBIkiRJAJFS9b4xjoiPA1OA6cDrgIMphJG/Sim9OIzXLwJ2GuTQE8ApKaVbh3rtypUrB/1ESruR5t3/rGzh1AcnDOvcH752PbtMMtBIkiSpcc2bN/jkrenTpw97PUW1KzMfB7Yd8PGNwMnDCTJF3wJuB/4ArAZ2AT4EfAD4eUS8PqX0+yqONzd6KtgLc0NfAIYZSZIkNbeqVmY2XjRiW+BA4AJgKvDXKaX/GcX1/hk4C/iPlNJbBjtnqMpMXpUqRqVEetNTGzjh5qXDeu3P3rg1B23XOWZjazab3wvVj/ciX7wf+eG9yA/vRb54P/KjmveiksrMmGyamVJ6PqV0LXAUMAv4zigv+fXi46GjvE5u2QBAkiRJqsyYhJmSlNJi4I/AqyJi61FcqjRNbfLoR5VPPRW0ZrYBgCRJkjTGYaZo++LjaLqRHVB8fHyUY8mtiiozFZwrSZIkjVejDjMRsVtETB/k+ZbippmzgbtSSsuLz7dHxB4Rsetm5+8ZEVtUXiJiZ+Bfix9eNdrx5lUlDQCcZiZJkiRVp5vZMcD5EXEHhRbKSyl0NDuMQjey54D3Dzh/B+BhYDGw84DnTwDOiojbisdWA7sCxwITgBuAf67CeHOpksqM08wkSZKk6oSZm4FXUNhT5jXADAqbXT4KXAl8JaW0bBjXuQXYvXiNgyisj1kB3FG8zpVpLFqv5US3a2YkSZKkiow6zKSUHqKwF8xwz18EbNFurbgh5pCbYo533RVNM6vgZEmSJGmcqkUDAA2D08wkSZKkyhhmcqKS1sw2AJAkSZIMM7nRVUHj6nW2ZpYkSZIMM3lRSQMAKzOSJEmSYSY3uivZNNMwI0mSJBlm8qKSbmY2AJAkSZIMM7lRUWXGNTOSJEmSYSYvXDMjSZIkVcYwkxOVVGacZiZJkiQZZnKjkjUzVmYkSZIkw0xudNnNTJIkSaqIYSYnKlkzs64vkZKBRpIkSc3NMJMT3X3DP7c/VTYtTZIkSRqPDDM5UUllBpxqJkmSJBlmcqKSbmZgRzNJkiTJMJMTlU4bszIjSZKkZmeYyYlKp5mtq7CSI0mSJI03hpmcqKQ1M8D6XjsASJIkqbkZZnKix2lmkiRJUkUMMzlRaWXGBgCSJElqdoaZHOjrT1S6BMbKjCRJkpqdYSYHRrIBpg0AJEmS1OwMMzlQ6RQzsDIjSZIkGWZyoKfCtsxgmJEkSZIMMzkwksrMWsOMJEmSmpxhJgcqbcsMVmYkSZIkw0wOdDnNTJIkSaqYYSYHukcwzcxuZpIkSWp2hpkcGElrZiszkiRJanaGmRwYUWXGMCNJkqQmZ5jJgW7XzEiSJEkVM8zkQFdf5a8xzEiSJKnZGWZyYCSVGRsASJIkqdkZZnJgJGtm1veOoGuAJEmSNI4YZnLAbmaSJElS5QwzOWA3M0mSJKlyhpkcGFE3M9fMSJIkqckZZnKgawTBpKsP+kYQgiRJkqTxwjCTAz0jXMtvdUaSJEnNrCphJiIujIhfRsRTEbE+IpZFxH0R8fmImFXhtXaMiMsj4pmI6IqIRRHx5YiYWY2x5tFIKjNgEwBJkiQ1t2pVZs4EJgP/BVwMfBfoBRYAD0TEnOFcJCJ2BX4HnALcC3wJeBz4CPDrSoNRo+gZ4XQxmwBIkiSpmbVV6TrTUkobNn8yIs4DPgWcDZwxjOt8DZgNfDildMmA63yRQmA6DzitKiPOka6+kb3OaWaSJElqZlWpzAwWZIquLj7Oy7pGsSpzFLAI+Opmhz8PrAVOiojJIxxmbnWNsDLjNDNJkiQ1s7FuAPCm4uMDwzj3iOLjL1JKmyyJTymtBu4EJgEHVG94+dAzwgqL08wkSZLUzKo1zQyAiPg4MAWYDrwOOJhCkLlgGC/fvfj46BDHF1Ko3OwG/HK4Y1q4cOFwT62LhQsX8uKKDkZyK/785NPMXj3CVmjaQt7/rDQT70W+eD/yw3uRH96LfPF+5Ecl92LevMzJW5mqGmaAjwPbDvj4RuDklNKLw3jt9OLjyiGOl56fMbKh5ddICywb+qK6A5EkSZIaSFXDTEppO4CI2BY4kEJF5r6I+OuU0v9U872GqxqJbyyUUuu8efPoeGopMNSyo6HNmL0d83adVOWRNZ+B90L15b3IF+9Hfngv8sN7kS/ej/yo170YkzUzKaXnU0rXUpgWNgv4zjBeVqq8TB/ieOn5FaMbXf50j3TTTNfMSJIkqYmNaQOAlNJi4I/AqyJi64zTHyk+7jbE8VLMG2pNTcPqtgGAJEmSVLGx7mYGsH3xMWs3lVuKj0dFxCbjioipwEHAOuDu6g6v/rrdNFOSJEmq2KjDTETsFhFbTA2LiJbippmzgbtSSsuLz7dHxB7FfWU2Sik9BvwC2Bn44GaXOweYDFyZUlo72jHnzUgrM+t77WQmSZKk5lWNBgDHAOdHxB3AE8BSCh3NDgN2AZ4D3j/g/B2Ah4HFFILLQGcAdwFfiYgji+ftT2EPmkeBT1dhvLkz0jUzVmYkSZLUzKoRZm4GXkFhT5nXUGidvJZC+LgS+EpKadlwLpRSeiwiXgecC8ynEJSeBS4GzilVd8abkVdmDDOSJElqXqMOMymlh4APVXD+ImDIDVJSSk8Bp4x2XI2ka6QNAEb4OkmSJGk8qEUDAGWwNbMkSZJUOcNMDoy0m5lhRpIkSc3MMJMD7jMjSZIkVc4wkwMjnmbmmhlJkiQ1McNMDtjNTJIkSaqcYabO+lNipJnEaWaSJElqZoaZOuvuG/lrrcxIkiSpmRlm6qxrhJ3MwDAjSZKk5maYqbOeUYSZdX2JlAw0kiRJak6GmTrrGsU0s/408k5okiRJUqMzzNTZSDuZlTjVTJIkSc3KMFNn3aOYZgZ2NJMkSVLzMszUWZeVGUmSJGlEDDN11jPKNS/rRhmGJEmSpEZlmKmz0Vdm7AAgSZKk5mSYqbPRtGYGp5lJkiSpeRlm6mw0rZnBBgCSJElqXoaZOhttNzMrM5IkSWpWhpk6G+0+MzYAkCRJUrMyzNRZ9yjX71uZkSRJUrMyzNSZ08wkSZKkkTHM1NloWzOvNcxIkiSpSRlm6sxpZpIkSdLIGGbqLKsBwITW8q83zEiSJKlZGWbqLGvNzPSO8rfIbmaSJElqVoaZOsuqzMzICDNWZiRJktSsDDN1lrVmJrMyY5iRJElSkzLM1FlWN7MZnVH2uJUZSZIkNSvDTJ31jHLNjGFGkiRJzcowU2ddfeWPZ62ZsQGAJEmSmpVhps4yKzOdWZWZUW5UI0mSJDUow0ydZa6Z6XDNjCRJkjQYw0yddWUUVmZkVGbsZiZJkqRmZZips56MykxmAwDXzEiSJKlJGWbqrCtjzcy09vK3qKsP+jKuIUmSJI1Hhpk668noZtbZCpPaMtbNWJ2RJElSEzLM1FlWZaazNZjYWj7MrOkxzEiSJKn5GGbqLGvNTHtLMKOzfJhZusH2zJIkSWo+hpk6y67MwDYTWsues2RDxlw1SZIkaRwadZiJiFkR8XcRcW1E/Dki1kfEyoi4IyL+NiKG/R4RsSgi0hC/nhvtWPOoO6Oo0t4SzJpQ/ku4xMqMJEmSmlBbFa7xDuBS4FngFuBJYFvgrcC/A2+MiHeklIa7sGMl8OVBnl8z+qHmT3fGNLPO1mBrw4wkSZK0hWqEmUeB44CfpZQ2flcdEZ8C7gXeRiHY/HiY11uRUlpQhXE1hO6MaWYdLRhmJEmSpEGMeppZSulXKaWfDgwyxeefA75e/PDw0b7PeNWdsdylozXY2jUzkiRJ0haqUZkpp6f42FvBazoj4kRgLrAWeAC4LaU0Lr9j78qYZtbR4jQzSZIkaTAx/KUsFV44og24D9gLmJ9SumkYr1kE7DTIoSeAU1JKtw712pUrVw76iSxcuHBY462H/gT73zmp7Dn3HrSOe1e08KE/TBjynNdM6+Pf9umq9vAkSZKkMTNv3rxBn58+fXr5fUkGGMvWzBdQCDI3DCfIFH0LOBLYDpgM7A18A9gZ+HlE7DsG46ybrL0u2yIRATPay5+4vGfY91uSJEkaN8ZkmllEfBg4C/gTcNJwX5dSOmezpx4CTouINcXrLQDeUslYhkp89bZw4UJ6MmaHTWhtYd68eUxZ1wf3D92ZemV/a24/z0ZQqt75Naw/70W+eD/yw3uRH96LfPF+5Ee97kXVKzMR8SHgYuCPwBEppWVVuGypkcChVbhWbmRVZjpaCxWXWZ3lb9PyrkRvRlc0SZIkabypapiJiI8Cl1CoqBxR7GhWDS8WHydX6Xq50NNffnpYR/HudLQG0zrKn7usyyYAkiRJai5VCzMR8QngS8D9FILMC9W6NnBA8fHxKl6z7rqHWZkB2DqjOmNHM0mSJDWbqoSZiPgshQX/vwOOTCktKXNue0TsERG7bvb8nhGxReUlInYG/rX44VXVGG9e9Gbkj46Wl8LMNhPL7zXz4nrDjCRJkprLqBsARMR7gXOBPuB24MMRW0yJWpRSuqL4+x2Ah4HFFLqUlZwAnBURtxWPrQZ2BY4FJgA3AP882vHmSXdWmBmQX2Zl7DWz1I0zJUmS1GSq0c3s5cXHVuCjQ5xzK3BFxnVuAXYHXgMcRGF9zArgDuBK4Mo0Vpvi1ElPylozM2CamRtnSpIkSZsYdZhJKS2g0DJ5uOcvArb4Lr64IeaQm2KOR1mtmTtbKwgzNgCQJElSkxnLTTOVIasBQPuAu7P1hPJrZpa4ZkaSJElNxjBTR1WtzLhmRpIkSU3GMFNHWWtm2l0zI0mSJA3JMFNH2ZWZl36f3c3MMCNJkqTmYpipo56sTTMH7jOTsWbmRcOMJEmSmoxhpo6yKjMdA9bMZFVmlnf109c/rjpXS5IkSWUZZuqoO2PNTOeAykxnazCtfejzE7DM9sySJElqIoaZOsqqzLRvdneyqjM2AZAkSVIzMczUUdaamYGtmcF1M5IkSdJAhpk66s5aM9OyaZjJ7mjmXjOSJElqHoaZOurNWDPTsVkhxr1mJEmSpJcYZuqo0sqMYUaSJEl6iWGmjippzQyw9cTya2YMM5IkSWomhpk6yt40c9OPsyszrpmRJElS8zDM1FFPf9aaGaeZSZIkSUMxzNRRd2ZlZrNuZp1Z3cwMM5IkSWoehpk66s3IHp2bLZHZJmPNzIvrDTOSJElqHoaZOsqqzLRX2M1sWVc/ff0ZF5UkSZLGCcNMHWWtmencbM1MZ2swtX3o1yRgeVa/Z0mSJGmcMMzUUaXdzABm2QRAkiRJAgwzdZW5aWbrllWYbTLCjOtmJEmS1CwMM3XUm1GZ6WzZMszMmlC+CYAdzSRJktQsDDN11J2xZqZ9kMqMG2dKkiRJBYaZOspaMzPYtjJunClJkiQVGGbqqGcEa2YMM5IkSVKBYaaOsruZDRZmyq+ZMcxIkiSpWRhm6ihrn5mOQXKLa2YkSZKkAsNMHY2sMlP+ltnNTJIkSc3CMFNHmfvMjCDMvGiYkSRJUpMwzNRJStCbRjLNrPyamWVd/fSnjJKPJEmSNA4YZuoka4pZW0BLbBl2JrQFU9qGDkH9CZZ3WZ2RJEnS+GeYqZOsKWadg7RlLplle2ZJkiTJMFMvvRmVmfYyd2abia6bkSRJkgwzddKd0Za5fGWm/LoZO5pJkiSpGRhm6iSzLXOZMONeM5IkSZJhpm56MtsyD31s607XzEiSJEmGmTrJqsx0DrLHTMnWGWtmlqw3zEiSJGn8M8zUSdaamfay08zKr5mxMiNJkqRmYJipk6xuZmUrM66ZkSRJkkYfZiJiVkT8XURcGxF/joj1EbEyIu6IiL+NiIreIyJ2jIjLI+KZiOiKiEUR8eWImDnaseZJ1j4z7WWKL1lhxm5mkiRJagZtVbjGO4BLgWeBW4AngW2BtwL/DrwxIt6RUsqoRUBE7ArcBcwGrgP+BOwHfASYHxEHpZSWVmHMdTeqNTMZYcZ9ZiRJktQMqhFmHgWOA36WUtr4XXREfAq4F3gbhWDz42Fc62sUgsyHU0qXDLjWF4EzgfOA06ow5rrrGcM1M8u6+ulPiZYo/x6SJElSIxv1NLOU0q9SSj8dGGSKzz8HfL344eFZ1ylWZY4CFgFf3ezw54G1wEkRMXm0Y86DrGlm5bovT2wLJrcNHVT6EqzosjojSZKk8a0alZlyeoqPvcM494ji4y8GCUarI+JOCmHnAOCXwx3AwoULh3tqTfWk8tWVrnVrWLhw2ZDHp7dOYG3v0Innd488wc6TMmf2aYC8/llpRt6LfPF+5If3Ij+8F/ni/ciPSu7FvHnzRv1+Y9bNLCLagL8pfnjjMF6ye/Hx0SGOl74yu41mXHmRtWlme8YMsZnt5YPKsh6nmEmSJGl8G8vKzAXAXsANKaWbhnH+9OLjyiGOl56fUckgqpH4xkLPs0+UPb71jGnMmzd0A7cdFy3hD2u6hjw+YevtmbfzxBGPr5mUfoKQ1z8rzcR7kS/ej/zwXuSH9yJfvB/5Ua97MSaVmYj4MHAWhW5kJ43FezS6rDUzHWUaAADMymgCYHtmSZIkjXdVDzMR8SHgYuCPwBEppaEXfmyqVHmZPsTx0vMrRj66/MjaNLOjTGtmGM5eM26cKUmSpPGtqmEmIj4KXAI8RCHIPFfByx8pPg61JqZUsxpqTU1DyexmVr7wwqyMMLPMbmaSJEka56oWZiLiE8CXgPspBJkXKrzELcXHoyJik3FFxFTgIGAdcPcoh5oLPal85SVrmtnMcr2bgeWGGUmSJI1zVQkzEfFZCgv+fwccmVJaUubc9ojYo7ivzEYppceAXwA7Ax/c7GXnAJOBK1NKa6sx5nrL6maWNc3MMCNJkqRmN+puZhHxXuBcoA+4HfhwbLnz/KKU0hXF3+8APAwsphBcBjoDuAv4SkQcWTxvfwp70DwKfHq0482Lnqw1M6OuzLjHjCRJksa3arRmfnnxsRX46BDn3ApckXWhlNJjEfE6CuFoPnAM8CyFhgLnpJSWj3aweZFdmSl/fKuMMOOaGUmSJI13ow4zKaUFwIIKzl8EDFl2SCk9BZwy2nHlXXd/+cpLp2tmJEmSpLLGZJ8ZZcuaZtaetWYmo3SzvLuf/uRUM0mSJI1fhpk6yZpmllF4YUJbMKlt6MDTn2BVt2FGkiRJ45dhpk6yckZ7xjQzyF4341QzSZIkjWeGmTrpyVozkzHNDLLXzdgEQJIkSeOZYaZOstbMdLZmX8MmAJIkSWpmhpk66c5qzVyFaWZWZiRJkjSeGWbqpDrTzMqfY2VGkiRJ45lhpk5q0QDAyowkSZLGM8NMnfSOsjUzwAzDjCRJkpqYYaZOsioz1Vgzs8IwI0mSpHHMMFMn3VlrZoYRZmzNLEmSpGZmmKmTnipMM3PTTEmSJDUzw0yd2ABAkiRJGh3DTJ1kNwAY/TQzKzOSJEkazwwzddDXn+ijfFhpH8adyQozK7sTvf0ZJSBJkiSpQRlm6qArI2B0tkJEdmWmrSWY1l7+vBXdVmckSZI0Phlm6qC7r/zx4UwxK3GqmSRJkpqVYaYOuvrKV2aGs8dMSWZ75g2GGUmSJI1Phpk6yJpm1lHBXclsz+w0M0mSJI1Thpk66MmYZlZJZWarCVZmJEmS1JwMM3WQ2QCgkjUzGWWc5Vkb2kiSJEkNyjBTB93VXDOTUZlZbmVGkiRJ45Rhpg6yGgB0tg7/WtmVGcOMJEmSxifDTB1kdUtur2CamWtmJEmS1KwMM3XQk7lpZvXWzCxznxlJkiSNU4aZOsicZlZJa+asNTOGGUmSJI1Thpk66K5ia2YrM5IkSWpWhpk6yGzNXMV9ZlYYZiRJkjROGWbqIGuaWUcFDQCmdwTlzl7TmzJbQUuSJEmNyDBTB5n7zFRwV1oimNFZPvy4bkaSJEnjkWGmDrK2fqlkzQzAVhkdA1w3I0mSpPHIMFMHWZWZStbMAMw0zEiSJKkJGWbqILMBQAVrZiC7MuM0M0mSJI1Hhpk6yFwz01rZ9WZYmZEkSVITMszUQVfGPjOVTjPLqszYnlmSJEnjkWGmDrozppm1VzjNzDUzkiRJakaGmTrIbgBQ2fVcMyNJkqRmZJipg6xsUcmmmWBrZkmSJDWnqoSZiHh7RFwSEbdHxKqISBFx1Qius6j42sF+PVeNseZBrVszW5mRJEnSeNRWpet8BtgXWAM8DewximutBL48yPNrRnHNXOmqcpixMiNJkqRmVK0wcyaFEPNn4DDgllFca0VKaUE1BpVXPZkNACq7XlZrZiszkiRJGo+qEmZSShvDS0RlVYVmlJUtql2ZMcxIkiRpPKpWZaaaOiPiRGAusBZ4ALgtpZSxO0vjyJpmVmkDgKntQVtA7xCX3dAH63r7mdRmvwdJkiSNH5FS+W+sK75gxOEUppl9N6V0YoWvXQTsNMihJ4BTUkq3DvXalStXDvqJLFy4sJIh1MTJ93fyhzVD91++bJ8N7DOtsmrK0fdMZFnP0CHo+r9cz7ad1b3XkiRJ0kjNmzdv0OenT58+7J/s5+1H9d8CjgS2AyYDewPfAHYGfh4R+9ZvaNXTncrfn46WykPHtLbyr1nZU/ElJUmSpFzL1TSzlNI5mz31EHBaRKwBzgIWAG+p5JpDJb56igefB3qHPP6KnXdi3sz2iq653aMvsmh995DHp243h3kv66zoms2iVL3L45+VZuO9yBfvR354L/LDe5Ev3o/8qNe9yFtlZihfLz4eWtdRVEl3RjezStfMgHvNSJIkqfk0Sph5sfg4ua6jqJKsTTM7hl5OM6SsMLNsg2FGkiRJ40ujhJkDio+P13UUVdKV0Zet0tbMMIz2zN2GGUmSJI0vNQ8zEdEeEXtExK6bPb9nRGxReYmInYF/LX54VQ2GOObqMc3MyowkSZLGm6o0AIiI44Hjix9uV3x8fURcUfz9kpTSx4u/3wF4GFhMoUtZyQnAWRFxW/HYamBX4FhgAnAD8M/VGG+9Ze0zY2VGkiRJylatbmavBt672XO7FH9BIZx8nPJuAXYHXgMcRGF9zArgDuBK4MpU7U1x6iClRFau6BhBvczKjCRJkppNVcJMSmkBhbbJwzl3EbBF6aG4IeaQm2KOFz0ZmaI1oHUMppmtsDIjSZKkcaZRGgCMG10Z62VGMsUMYGZn+ddZmZEkSdJ4Y5ipscy2zCO8I66ZkSRJUrMxzNTYWLRlBthqQvaamXGw5EiSJEnayDBTY5ltmUcYZia2Bp1lNtvsTbC6xzAjSZKk8cMwU2NjNc0sIrKnmnU51UySJEnjh2GmxrLyROcIOpmVzMxIQoYZSZIkjSeGmRrLrMyMcJoZwMyMdTOGGUmSJI0nhpka68oIMyNtAADZlZllhhlJkiSNI4aZGstsADCKO5LV0czKjCRJksYTw0yNdWe0Zh7VNDMrM5IkSWoihpka68qszIw8zGTuNWOYkSRJ0jhimKmxrAYAo1ozY2tmSZIkNRHDTI1lNQDoKLPxZRbDjCRJkpqJYabGusdwnxk3zZQkSVIzMczUWD2nmblmRpIkSeOJYabGshoAtI+mNbNhRpIkSU3EMFNjWa2ZR1OZyQozK7oSvRlhSpIkSWoUhpkay2zNPIow09EaTGsf+vUJWJG1aEeSJElqEIaZGstcMzOKBgAwjHUzGwwzkiRJGh8MMzU2lq2ZAWZlbJy51HUzkiRJGicMMzXWk5ElOkZZmclsAmBlRpIkSeOEYabGsiozo2kAALCVlRlJkiQ1CcNMjWU2ABjlHXHjTEmSJDULw0yNjWVrZoBZGWFmqdPMJEmSNE4YZmosuwHA2E4zc+NMSZIkjReGmRrryZhmNtrWzLM6y7dDszIjSZKk8cIwU2Nj3Zo5c58ZKzOSJEkaJwwzNdY9xq2Zs/aZMcxIkiRpvDDM1NiYt2a2AYAkSZKahGGmxrrHugFARphZ0d1PX8a6HUmSJKkRGGZqLGufmYwskmlCWzClbehA1J9gZdZcN0mSJKkBGGZqLHPNzCgrMwAzXTcjSZKkJmCYqbHMaWajbAAAbpwpSZKk5mCYqbHsBgCjf4+sdTNWZiRJkjQeGGZqrBbTzLLaMy81zEiSJGkcMMzUWGZlpgrTzDI3znSamSRJksYBw0wN9fUnMrIM7VW4I26cKUmSpGZgmKmhzClmLRAx+sqMG2dKkiSpGRhmaih78f/ogwxkdzOzMiNJkqTxoCphJiLeHhGXRMTtEbEqIlJEXDXCa+0YEZdHxDMR0RURiyLiyxExsxpjrafujA0zq9GWGWArp5lJkiSpCbRV6TqfAfYF1gBPA3uM5CIRsStwFzAbuA74E7Af8BFgfkQclFJaWpUR10Et2jLDMFozO81MkiRJ40C1ppmdCewGTANOH8V1vkYhyHw4pXR8SumTKaU3AF8CdgfOG/VI66inBm2ZAWZNKJ+KbM0sSZKk8aAqYSaldEtKaWFKKaNX19CKVZmjgEXAVzc7/HlgLXBSREwe8UDrrBZtmSG7MrO8q5/+kd8qSZIkKRfy1ADgiOLjL1JKm5QOUkqrgTuBScABtR5YtWSFmfYqVWYmtgWT2oa+Vl+CVd2GGUmSJDW2aq2ZqYbdi4+PDnF8IYXKzW7AL4d70YULF45yWNXz2KoWYMKQx1P3hqqNd2rrBNb1Dp1V/+eRx5kz0UAzUJ7+rDQ770W+eD/yw3uRH96LfPF+5Ecl92LevHmjfr88VWamFx9XDnG89PyMsR/K2MhcM1PFuzEjI6au6KlOFUiSJEmqlzxVZsZENRJftTz5vxvgoaGbsU2fMol58+ZW5b1e9vgSHlnbNeTxSbO3Z96ciVV5r0ZX+glCnv6sNCvvRb54P/LDe5Ef3ot88X7kR73uRZ4qM6XKy/QhjpeeXzH2Qxkb3ZkNAKr3XpkbZ9qeWZIkSQ0uT2HmkeLjbkMcL8W8odbU5F53Rn5or1I3M8juaGZ7ZkmSJDW6PIWZW4qPR0XEJuOKiKnAQcA64O5aD6xasjfNrGKYmZDdnlmSJElqZDUPMxHRHhF7FPeV2Sil9BjwC2Bn4IObvewcYDJwZUppbU0GOgaywky1Ns2EYVRmnGYmSZKkBleVBgARcTxwfPHD7YqPr4+IK4q/X5JS+njx9zsADwOLKQSXgc4A7gK+EhFHFs/bn8IeNI8Cn67GeOulu7+Ga2YyKjPLrMxIkiSpwVWrm9mrgfdu9twuxV9QCC4fJ0NK6bGIeB1wLjAfOAZ4FrgYOCeltLxK462L7r7yx63MSJIkScNXlTCTUloALBjmuYuAIb9rTyk9BZxSjXHlTWZlpoZhxjUzkiRJanR5agAw7mWumanhNDO7mUmSJKnRGWZqKE/TzJZt6Cel8uFKkiRJyjPDTA11ZTYAqF6YmdQWTGgd+nhvglU9hhlJkiQ1LsNMDdWyNXNEDKs6I0mSJDUqw0wN9WQ2AKju+21VrjSD7ZklSZLU2AwzNdSVsWamvYrTzMD2zJIkSRrfDDM1VMvWzACzsqaZWZmRJElSAzPM1FDWmplqNgAA2Mr2zJIkSRrHDDM11J3ZAKC675e5cabTzCRJktTADDM11J2RHao9zSxzzUzWIh5JkiQpxwwzNZTZmrnK08xmZU0zszIjSZKkBmaYqaGsBgAdVb4bWWHGBgCSJElqZIaZGsqa1VXraWZumilJkqRGZpipoewGADUOM1ZmJEmS1MAMMzWUuc9MHVozp1R+TJIkSVJeGWZqqDtjmlm1WzNPaYuy63B6+mFNr2FGkiRJjckwU0NdmQ0AqluZiYjs9syum5EkSVKDMszUUNaamWo3AIDsqWbLXTcjSZKkBmWYqaHMykyVp5nBMDbOtDIjSZKkBmWYqZGUUnZr5ipPM4NhbJxpZUaSJEkNyjBTI1nr7FsDWscgzLjXjCRJksYrw0yNdNVhvQzArM7yc9eszEiSJKlRGWZqJGvxf/sY3QkbAEiSJGm8MszUSFZmGKvKjA0AJEmSNF4ZZmoka5pZtfeYKclqALDMyowkSZIalGGmRnoy2jJnLG0ZsezKTEaLNUmSJCmnDDM1Uo+2zJBdmXHNjCRJkhqVYaZGMhsAjNGamZlZlZmuflLK6BstSZIk5ZBhpka6sqaZjVFlZlp70Fbm0l19sC5rExxJkiQphwwzNZJVmekYozUzEZHZntm9ZiRJktSIDDM1krlmZoymmQHMyphq9uJ6w4wkSZIaj2GmRrozppmNVWtmgK0zKjMPr+gZs/eWJEmSxophpkayppmNZWVmj5ntZY8/sNQwI0mSpMZjmKmRrGUpHWN4J/beqnyYeXCZYUaSJEmNxzBTI9kNAMauMrNPRph5aFkP/bZnliRJUoMxzNRIVz2nmc1oL9ueeXVPYvHqjA4FkiRJUs4YZmokuwHA2L33hLZgtxltZc95wKlmkiRJajCGmRrpzlgzM5aVGcieavagTQAkSZLUYAwzNZI1zax9DFszA+w9q6Ps8QeXdY/p+0uSJEnVVrUwExE7RsTlEfFMRHRFxKKI+HJEzKzgGv8dEanMrwnVGm+t1bM1MwyjMuM0M0mSJDWY8gsphikidgXuAmYD1wF/AvYDPgLMj4iDUkpLK7jkOUM83zuqgdZRV8aamc4xrpFltWd+Zl0/Szb0sfWE1rEdiCRJklQlVQkzwNcoBJkPp5QuKT0ZEV8EzgTOA04b7sVSSguqNK7c6MloFjaWrZkBZnS2MGdKK0+tGXogDy7t4YgdDDOSJElqDKOuBxSrMkcBi4Cvbnb488Ba4KSImDza92pkmZWZMQ4zkD3VzI5mkiRJaiTVmNx0RPHxFymlTXp2pZRWA3cCk4ADhnvBiDghIj4ZER+LiDdGRGcVxllXWQ0AxrI1c0nWVDPXzUiSJKmRVGOa2e7Fx0eHOL6QQuVmN+CXw7zmDzb7+IWI+GBK6UeVDm7hwoWVvmRMLF3ZQbkv97IXn2chY7tx5dZdrcDQufB3z65l4cJKljaNL3n5syLvRd54P/LDe5Ef3ot88X7kRyX3Yt68eaN+v2rUA6YXH1cOcbz0/IxhXOs64E3AjsBEYA/g/OJrfxgR80c8yjrrydhnpn3sZ5mx++Tyg1i8PtgwtnlKkiRJqppqNQCoipTSlzZ76hHgUxHxDHAJhWBzYyXXrEbiq4b2J5YAXUMe33nH7Zk3Z2w7T78iJWY88Cwrugef8tZP0DVrJ/bepvyeNONN6ScIefmz0sy8F/ni/cgP70V+eC/yxfuRH/W6F9WozJQqL9OHOF56fsUo3uPfKbRlfnVETB3FdeqmO6My01mDJmIRwT4Zm2c+sNR1M5IkSWoM1QgzjxQfdxvieCmeDbWmJlNKaQOwuvhhQ3ZFy9o0s6OlBvPMGE4TgO6ajEOSJEkarWqEmVuKj0dFxCbXK1ZRDgLWAXeP9A0iYndgJoVAs2Sk16mnPLRmBthnlh3NJEmSND6MOsyklB4DfgHsDHxws8PnUKikXJlSWlt6MiL2iIg9Bp4YES+PiK02v35EbAN8q/jhD1JKvaMdcz1ktWZur0FrZsiuzPxhWS99GcFLkiRJyoNqNQA4A7gL+EpEHAk8DOxPYQ+aR4FPb3b+w8XHgeWIw4CvR8QdwOPAMmAucAyFdTe/Bf6xSuOtue6MLmG1qszMm95GZyt0DTGe9X2JP6/qZfcZ5UOPJEmSVG9VqQcUqzOvA66gEGLOAnYFLgYOSCkNZ/OS31HYX2Zb4G3Fa8wHHgQ+DByUUlpRjfHWQ3dOppm1twSvnFk+qNgEQJIkSY2gaq2ZU0pPAacM89wtvnNPKT0InFyt8eRNXhoAQGGq2X1Lhg4sDy7r4R271mw4kiRJ0ojUaKWGshsA1GggwD6ZHc2szEiSJCn/DDM1krVmpr3GlZlyHljaQ0o2AZAkSVK+GWZqJKubWa3WzAC8aqt2yr3b0q5+nl2XscunJEmSVGeGmRroT4nejEJHRw3vxJT2FnadVn651ANunilJkqScM8zUQNYUs44WiKhdZQayp5r9T5kGAZIkSVIeGGZqIHvxf22DDMA+s8qHmesXr6/RSCRJkqSRMczUQJ7aMpe8duvyYeaPy3t5ZIXVGUmSJOWXYaYGshb/d9SwLXPJ67ftZHpH+RD1kyeszkiSJCm/DDM10J3RGKwelZmO1uCvd5pY9pyfPLHeFs2SJEnKLcNMDXTncM0MwNteXj7MLFzZy0PLe2s0GkmSJKkyhpkayJ5mVp8wc+jLOpnVWf6PwLVPrKvRaCRJkqTKGGZqIKs1c0aeGDNtLcFxO08oe45TzSRJkpRXhpkayGrNXK/KDMBbXj6p7PFFq/u4f6ldzSRJkpQ/hpkayGNr5pKDtu1g24nl/xjY1UySJEl5ZJipgewGADUayCBaW4I371y+EcC1T6yn36lmkiRJyhnDTA10ZayZqWdlBuCtGV3Nnl7bx29e6K7RaCRJkqThMczUQNY0s3q1Zi7Zb3YHO0wqXx5yqpkkSZLyxjBTA3luAADQEsHxGdWZ6xatpy/j85AkSZJqyTBTA1mtmTtycBeyppo9t76fXzvVTJIkSTmSg2+jx7+8bpo50Gu3bmenKRlTzR53qpkkSZLywzBTAz1Z3czq3AAAICIyqzPXLlrHhl6nmkmSJCkfDDM1kFWZqWdr5oHekhFmlnclfrrY6owkSZLywTBTA1395Y/nYZoZwN5btTNvelvZc654dG2NRiNJkiSVZ5ipgazWzPXeZ6YkInjPKyaVPefO57r588qeGo1IkiRJGpphpgYaoQFAyf83bxJtGcP59qPrajMYSZIkqQzDTA30ZEwz68zRXZg9sZVj5k4oe873Fq7LDGiSJEnSWMvRt9HjV943zdzcybtPLnt8aVc/NzxpIwBJkiTVl2GmBrLWzHTmLMwcvn0nczP2nHGqmSRJkurNMFMDma2Zc9IAoKQlgr/ZrXx15r+f6eKJVb01GpEkSZK0JcNMDWS1Zm7P4V14z7xJZBWMrlxom2ZJkiTVTw6/jR5/Gm2aGcDLJrVy9JzyjQCuWriOnoz1QJIkSdJYKb9Doqqiu8EaAJScvNtkbnhyw5DHX1jfzxt++iI7TWllu0mt7DGjjaPmTGDuFP9YSZIkaez5XWcNZFZmclofO3KHTnac3MrTa/uGPOfBZT08uOylTTRb7lnJ214+kbP2ncoeM9prMUxJkiQ1qZx+Gz2+dA2dBYB8TjMDaG0JTpw3qaLX9Ce45vH1vP7aF3jvLUs3CTqSJElSNRlmaiBrmll7zrqZDXTivEmMZHgJuG7RBg657gXeffNSbn2mi5RcXyNJkqTqMczUQGZr5vJbutTVjlPa+D87dI7qGj9/agNvvmkJf/mTF/jqH9awPKu9myRJkjQMhpka6Mn43j2vDQBKTn/VlKpc58+revn0vSvZ84fP8oFbl3HFI2u5b0k3G3qt2EiSJKlyNgCogUbbNHNzh28/gZPmTeLKheuqcr0NfXD14+u5+vH1ALQF7DmznT1ntDG9s4Xp7S1M6wimtrcwvSPYYXIrc6e2se3EFloi318rSZIk1Y5hpga6MtbM5LUBwEBfOWgGr926gx88to4HlvawPiOgVaI3bdkVbTAdLbBjMdjMntDClPYWprRH8VcLk9qCloAAIgplx5Z46bmWKP0KZnS0sNuMNrabuGVxsrc/8ejKXh5Y2sOLG/qY1BbMndLGTlNamTOljYlt+b9fkiRJzaBqYSYidgTOBeYDs4Bngf8AzkkpLa/gOlsBnwOOB14GLAVuBD6XUnq6WuOtha88uJqLfr+a1T1Z+8zUaECjEBGcssdkTtljMiklVvUknlvXx3Pr+lm8ppfL/7SW+5eObeey7n54fHUfj6/OaA9XgWkdwU6dnewyKTHrxRU8sKybh5b1sKHMW2w7sYWdprQxb0Ybu01vY970Nnaf3s5OU1tpK1NlSynx5Jo+Fq7sZW1vorMVJrTGxl8tAcu7Esu6+liyoZ+lG/pZ0d3PpLZgl2mF99ptejszBvTy7utPLNnQz3Pr+1jVnZjZ2cKu02obuDb0Jp5b38fz6wpftOmdLUzvKFTVJrYGkcNqWk9/orUYbCVJGu9SSmzogwmt5PL/5dGoSpiJiF2Bu4DZwHXAn4D9gI8A8yPioJTS0mFcZ1bxOrsBvwJ+AOwBnAIcGxGvTyk9Xo0x10JPP5lBBqAj59PMNhcRTO8Ipne0sPsMgE5OmjeJm/+3iy/cv4rfvNg47ZhXdSce7G7lwdXA82uH9Zrn1/fz/Ppu7n2xe5PnO1pg+8mtvGzSS79mTWhh8epe/ri8hz+t6B3Wn4cs20xoYbtJrSzZ0McL6/vZvEgWwM5TW9l9Rju7T29jakcLz63r45l1fTy7ro9n1/axrjcxo7OF7UtjndzCyya2sr4v8cL6fp5f37fxsasvMbE1mNgWTGoLJra10BLwwvrC9ZZ3Df05tbfAlPagPxXadvcl6EuJ/gQTW4NpHS1MbX/pccO6Trr6IR55kXV9iQ29iZ7+wlhnT2hh64mtzJ7QwjbFitqansTa3sSann7W9CQ29KWN75OK79OTYHV3P6t6Eiu7+1nV3c+GvkKVbvtJrew4uZU5UwqPL5vUSqJQLeztT/T0Fx5botB1sC2grSVoayl8nXtTIRj19Rd+35cSrRG0t0Br8TUtAUs39PPsuj6eWVu6D/109yW2ntjCyya1sv2k1o1/djpaStcqhNXS/W0LaCmOoTWgNYLh/n+UEvRT+HoUfl8Y9+qexMquflZ297OyO7G6p59E4d5MaA161nfQ2QLbLllBZ2swoa0QUDtbC5/bkg39xR9q9PHs+n5eWN9HSoU/o9tOamXbiYU/q9tMbKWUr9OAMW0cH1v+frDuh6X/gIOXKrBDfrzx90Hpy7Tx2sXfpbTleNLm527+92vAe2zyGJv+vvTeA8/Z9PN8aQyDvd/m43jxxcJ/19v0rBn0vQf/eNP3L11vOO891Odfuv5wxjDU+1Mcw3A+96Hef5P3GuL9Cx8Pfg82f/9y7735EJYU78XsIe5Fua/B5l//zd9n8I83HUFEbPG5D/W12HwMpfcf+N4D33fge6biIAb+eWih8O9Q64B/hxKFfwMH/vvenwrHNz+3PxX+TetN0N+fKC2ZbS3OnGgrnt8SbLxmb3rp+hHFfwtL/xZH4e9GX4KZ3Wvo7U/09hdeM/Df4tK5wMZ/10v/fvenl463tRR+395SeP+e4v8F3X2JnuK/n+0the/b2lqK/y+0QG9/oXNtT39hb8Ge/kRE0DHg3I7i/wc9/YX37Sm+pvB/TPHc1qC9+P6l/2Ne+pwKX9+2iI3jLI25P730eZWuv6K7n+fWFf7vebb473R3f6Hp1I6TW5k7pY05U1qZM7mV418+kXnTG3dvwKhGu9yIuAk4CvhwSumSAc9/ETgT+EZK6bRhXOcbwAeAL6aUzhrw/IeBi4GbUkrzB3vtypUrc7eK/OIHV/P5364qe05LwLKTd6jRiMZeSolbn+3in3+/mjue685+gSRJkurmh381i6PnTBj1dRYuXAjAvHnzRn2t6dOnD/sn/aMOM8WqzJ+BRcCuKaX+AcemUphuFsDslNKQP/qOiCnACxR+aPiylNLqAcdagMeBnYrvsUV1Jo9hRpIkSVJlKgkz1WjNfETx8RcDgwxAMZDcCUwCDsi4zgHARODOgUGmeJ1+4KbN3k+SJElSE6tGmNm9+PjoEMcXFh93q9F1JEmSJDWBaoSZ6cXHlUMcLz0/o0bXkSRJktQEqhFmJEmSJKnmqhFmShWT6UMcLz2/okbXkSRJktQEqrHPzCPFx6HWspT6sw21FqYq16mk64EkSZKkxtdIrZkfA3ZmiNbMkiRJkprLqKeZpZQeA35BIWh8cLPD5wCTgSsHBpmI2CMi9tjsOmuAK4vnL9jsOh8qXv8mg4wkSZIkqEJlBjZWZ+4CZgPXAQ8D+1PYE+ZR4MCU0tIB5yeAlFJsdp1ZxevsBvwKuBfYE3gzharNgcXwJEmSJKnJVSXMAETEHOBcYD4wi8L0smuBc1JKyzc7d9AwUzy2FfB54HjgZcBS4OfA51JKT1dlsJIkSZIaXtVaM6eUnkopnZJSellKqSOltFNK6aObB5niuTFYkCkeW5ZS+kjx9R3F671vPASZiNgxIi6PiGcioisiFkXElyNiZr3H1mwi4u0RcUlE3B4RqyIiRcRV9R5Xs4mIWRHxdxFxbUT8OSLWR8TKiLgjIv62uF5ONRQRF0bELyPiqeL9WBYR90XE54vVc9VRRJxY/PcqRcTf1Xs8zaT4f3Ya4tdz9R5fM4qII4v/fzxX/L7qmYi4KSKOqffYmkFEnFzm70TpV99Yj6Ma3cw0DINMxfsTsB/wEWB+RBw0cCqextxngH2BNcDTwB7lT9cYeQdwKYVK7i3Ak8C2wFuBfwfeGBHvSNUqIWs4zgT+B/gvCtN7JwMHUFjL+IGIOCCl9FT9hte8ijMg/pXCv1tT6jycZrUS+PIgz6+p8TiaXkR8AfgHCv+H/yewBNgG+AvgcOCGug2uedxPYX38YA4B3kBhdtWYMszUztcoBJkPp5QuKT0ZEV+k8M3DecBpdRpbMzqTwj+AfwYOo/CNtGrvUeA44GebdUL8FIU1c2+jEGx+XJ/hNaVpKaUNmz8ZEecBnwLOBs6o+aiaXEQE8C0KU69/Any8viNqWitSSgvqPYhmFxHvpxBkvg18IKXUvdnx9roMrMmklO6nEGi2EBG/Lv7238Z6HE7hqIFiVeYoCu2rv7rZ4c8Da4GTImJyjYfWtFJKt6SUFvoT//pKKf0qpfTTgUGm+PxzwNeLHx5e84E1scGCTNHVxcd5QxzX2PowhZ9ynkLh/wypKUVEJ4UfAD/JIEEGIKXUU/OBaaOI2JtCRf9/gZ+N9ftZmamNI4qPvxjkm7bVEXEnhbBzAPDLWg9OyqnSf0a9dR2FSt5UfHygrqNoQhGxJ3ABcHFK6baIeEO9x9TEOiPiRGAuhVD5AHBbSmnM1wVoo/9DYTrZl4H+iDgW2AvYANybUvp1mdeqNj5QfLysFn83DDO1sXvx8dEhji+kEGZ2wzAjERFtwN8UP7yxnmNpVhHxcQrrMqYDrwMOpvCN2wX1HFezKf5duJLCT6E/VefhCLajcD8GeiIiTkkp3VqPATWhvyw+bgDuoxBkNoqI24C3p5RerPXABBExETgR6KOw9nXMOc2sNqYXH1cOcbz0/IyxH4rUEC6g8B/UDSmlm+o9mCb1cQrTYD9KIcjcCBzlNwg19zngNcDJKaX19R5Mk/sWcCSFQDMZ2Bv4BoVNvX8eEfvWb2hNZXbx8R+ARGGh+VRgHwqbuB8KXFOfoQl4J4XvZ2+sVbMYw4ykXImIDwNnUej4d1Kdh9O0UkrbFVvob0ehCcMuwH0R8dr6jqx5RMT+FKox/+LUmfpLKZ1TXOf3fEppXUrpoZTSacAXgYkUOv5p7JW+d+0Fjksp3ZFSWpNSehB4C4XmPodFxOvrNsLmVppi9o1avaFhpjZKlZfpQxwvPb9i7Ici5VdEfAi4GPgjcERKaVmdh9T0it+4XUthKuws4Dt1HlJTKE4v+w6F6cmfrfNwVF6pWcmhdR1F81hRfLwvpbRo4IGU0jqgVM3fr4ZjEhARrwIOpBAoa9Ya2zBTG48UH3cb4nipO9BQa2qkcS8iPgpcAjxEIci4CV2OpJQWUwiZr4qIres9niYwhcL/GXsCGwZuQkdh+h/AN4vPfblegxQApamXdiStjdL3VCuGOF7arH3i2A9Fm6npwv8SGwDURmkPk6MiomWz/TSmAgcB64C76zE4qd4i4hMU1sncD/yflNKS+o5IQ9i++GjnprHXBVw2xLHXUlhHcweFb+ycglZfBxQfH6/rKJrHLymslXnl5t9TFZUaAjxR22E1t4iYQGFqeB9D/9s1JgwzNZBSeiwifkFhmsYHKfz0ueQcCj/N+UZKyb0D1HQi4rPAucDvKCwwd2pZnUTEbsDzKaWVmz3fAvwThYW3d6WUlg/2elVPcbH/3w12LCIWUAgz304p1aRbULMrtsd+cvP/pyNiZ+Bfix9eVetxNaOU0uKI+CmFDZc/AnypdCwijgKOplC1sRNmbb0DmAlcX6uF/yWGmdo5A7gL+EpEHAk8DOxPYQ+aR4FP13FsTScijgeOL364XfHx9RFxRfH3S1JK7rA9xiLivRSCTB9wO/Dhwkbnm1iUUrqixkNrVscA50fEHRR+qrkU2BY4jEIDgOeA99dveFLdnACcVWz7uxhYDewKHAtMoLA+4J/rN7ym80EKgf6LxX1m7gNeTuH/9T7g7zb/oYzGXGmK2b/V+o0NMzVSrM68jsI3bvMpfNPwLIXFzuf4k86aezXw3s2e26X4Cwr/WRlmxt7Li4+tFFoAD+ZW4IpaDEbcDLyCQivm11Bor7mWwg9crgS+YuVMTeoWCnvGvYbC1PDJFH76fweFvxtXppRS3UbXZFJKT0fEX1BoXX4cheYLq4CfAuenlO6t5/iaTbFyeTA1Xvi/8f39uydJkiSpEdnNTJIkSVJDMsxIkiRJakiGGUmSJEkNyTAjSZIkqSEZZiRJkiQ1JMOMJEmSpIZkmJEkSZLUkAwzkiRJkhqSYUaSJElSQzLMSJIkSWpIhhlJkiSpyUTE2yPikoi4PSJWRUSKiKuqdO3Di9fL+jVntO/VVo0BS5IkSWoonwH2BdYATwN7VPHai4Bzhji2N/BW4KGU0lOjfSPDjCRJktR8zqQQYv4MHAbcUq0Lp5QWAQsGOxYR3y/+9pvVeC+nmUmSJElNJqV0S0ppYUopDfc1EfHuiLglIlZExIaIeDgiPhMRncN8/dbAW4D1wHdGOPRNGGYkSZIklRURlwPfA14B/Bj4KrAM+CfgxogYzoyv9wKdwDUppRXVGJfTzCRJkiQNKSJOBk4BrgXek1JaP+DYAuDzwAeBizMu9f7i4zeqNTYrM5IkSZLK+QjQC7xvYJAp+idgKfCecheIiMOA3Sks/L+rWgOzMiNJkiRpUBExiULXsyXARyNisNO6gD0zLvWB4uO/VW90hhlJkiRJQ5sJBLANhelkFYuIrYC3UVj4f2X1huY0M0mSJElDW1l8vC+lFOV+lblGaeH/1dVa+F9imJEkSZI0qJTSGuAPwKuKFZaRKC38r+oUMzDMSJIkSSrvi0AHcHlEzNj8YETMjIjXDvbCiDiEwnqaqi78L3HNjCRJktRkIuJ44Pjih9sVH18fEVcUf78kpfRxgJTS5RHxF8AZwGMRcRPwJLAV8HLgUOBbwGmDvNWYLPwviQo2/ZQkSZI0DgzYH2Yoi1NKO2/2mr+mEFj2A2ZQ2DTzSeAXwFUppT9tdv5M4BkgAdtXe70MGGYkSZIkNSjXzEiSJElqSIYZSZIkSQ3JMCNJkiSpIRlmJEmSJDUkw4wkSZKkhmSYkSRJktSQDDOSJEmSGpJhRpIkSVJDMsxIkiRJakiGGUmSJEkNyTAjSZIkqSEZZiRJkiQ1JMOMJEmSpIZkmJEkSZLUkAwzkiRJkhqSYUaSJElSQzLMSJIkSWpI/z8A7ItlBoZhfAAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "image/png": { + "height": 267, + "width": 409 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.kdeplot(result['Output_Satoshis'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "XM2VzO-O-U9n", + "outputId": "419acb1d-bf9a-4170-b4b3-c95c977dbe4a" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Transactions per day')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 570, + "width": 1329 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "plt.style.use('ggplot')\n", + "sns.set_context(\"notebook\", font_scale=1.5, rc={\"lines.linewidth\": 1.5})\n", + "\n", + "g = plt.subplots(figsize=(20,9))\n", + "g = sns.lineplot(x='Date', y='Transactions', data=result, palette='Blues_d')\n", + "plt.title('Transactions per day')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "UVdkiHb0-U9n", + "outputId": "b5fec5cd-72b0-4761-85b0-bb7b07289277" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Blocks per day')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 570, + "width": 1334 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "plt.style.use('ggplot')\n", + "sns.set_context(\"notebook\", font_scale=1.5, rc={\"lines.linewidth\": 1.5})\n", + "\n", + "g = plt.subplots(figsize=(20,9))\n", + "g = sns.lineplot(x='Date', y='Blocks', data=result, palette='Blues_d')\n", + "plt.title('Blocks per day')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "LX2ltABE-U9n", + "outputId": "4fa594b6-7545-4661-b309-4eb859979c06" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Sum of all satoshis spent each day')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 570, + "width": 1309 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "g = plt.subplots(figsize=(20,9))\n", + "g = sns.lineplot(x='Date', y='Output_Satoshis', data=result, palette='BuGn_r')\n", + "plt.title('Sum of all satoshis spent each day')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "2X_TZjMu-U9o", + "outputId": "64d1558a-cde7-43be-a812-06537976beb2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 551, + "width": 547 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# check the relation among the features of data\n", + "sns.set(style=\"ticks\")\n", + "sns.pairplot(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "CvTtBZiH-U9o", + "outputId": "95b1d249-17fc-4bb5-809d-ec2f6f212429" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-0.61916492 -0.93730866 -3.66189407]\n", + " [-0.61809349 -0.93714074 -2.42417364]\n", + " [-0.617364 -0.93702641 -1.58147036]\n", + " ...\n", + " [-0.29173402 0.96100967 -0.08040515]\n", + " [-0.29118499 0.83351596 0.26194305]\n", + " [-0.37942782 0.12385633 -2.08182543]]\n" + ] + } + ], + "source": [ + "# select the three most important features (Transactions, Blocks, Output Satoshis) from the data\n", + "data = result[['Output_Satoshis','Blocks','Transactions']]\n", + "outliers_fraction=0.05\n", + "scaler = StandardScaler()\n", + "np_scaled = scaler.fit_transform(data)\n", + "data = pd.DataFrame(np_scaled).to_numpy()\n", + "print(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PZ2vf0nY-U9o" + }, + "source": [ + "## Example 1: DeepLog" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "7QMpoT-0-U9o", + "outputId": "a498ffd9-df43-4dd7-f4e8-0859ec874e06", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "100/100 [==============================] - 1s 8ms/step - loss: 0.7798 - val_loss: 0.4070\n", + "Epoch 2/10\n", + "100/100 [==============================] - 0s 3ms/step - loss: 0.4824 - val_loss: 0.2307\n", + "Epoch 3/10\n", + "100/100 [==============================] - 0s 3ms/step - loss: 0.4781 - val_loss: 0.1939\n", + "Epoch 4/10\n", + "100/100 [==============================] - 0s 3ms/step - loss: 0.4621 - val_loss: 0.1893\n", + "Epoch 5/10\n", + "100/100 [==============================] - 0s 3ms/step - loss: 0.4242 - val_loss: 0.1795\n", + "Epoch 6/10\n", + "100/100 [==============================] - 0s 2ms/step - loss: 0.4443 - val_loss: 0.1818\n", + "Epoch 7/10\n", + "100/100 [==============================] - 0s 2ms/step - loss: 0.4472 - val_loss: 0.1904\n", + "Epoch 8/10\n", + "100/100 [==============================] - 0s 3ms/step - loss: 0.4068 - val_loss: 0.1940\n", + "Epoch 9/10\n", + "100/100 [==============================] - 0s 3ms/step - loss: 0.3949 - val_loss: 0.1972\n", + "Epoch 10/10\n", + "100/100 [==============================] - 0s 3ms/step - loss: 0.4296 - val_loss: 0.1947\n" + ] + } + ], + "source": [ + "transformer_DL = DeepLogSKI()\n", + "transformer_DL.fit(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "FFVMEusH-U9p", + "outputId": "2d599283-e295-42c2-cc15-636fb0b5670d", + "scrolled": true + }, + "outputs": [], + "source": [ + "prediction_labels_DL = transformer_DL.predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "0ZdcjuJ3-U9p", + "outputId": "b8842503-07cf-4577-8a19-e14c9f05dad0" + }, + "outputs": [], + "source": [ + "prediction_score_DL = transformer_DL.predict_score(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "QcjVBJsv-U9p", + "outputId": "9cc9feec-5f44-4e09-c72b-09122806a82e", + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction Labels\n", + " [[0]\n", + " [0]\n", + " [0]\n", + " ...\n", + " [0]\n", + " [0]\n", + " [1]]\n", + "Prediction Score\n", + " [[0. ]\n", + " [0.30808094]\n", + " [0.28474334]\n", + " ...\n", + " [0.55789091]\n", + " [0.4687671 ]\n", + " [2.28331122]]\n" + ] + } + ], + "source": [ + "print(\"Prediction Labels\\n\", prediction_labels_DL)\n", + "print(\"Prediction Score\\n\", prediction_score_DL)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "B6y-VhoA-U9p", + "outputId": "6024cee9-2195-4a46-eb38-4bcb860b2c76" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3178\n", + "1 354\n", + "Name: anomaly_DeepLog, dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a new column for storing the results of DeepLog method\n", + "result['anomaly_DeepLog'] = pd.Series(prediction_labels_DL.flatten())\n", + "result['anomaly_DeepLog'] = result['anomaly_DeepLog'].apply(lambda x: x == 1)\n", + "result['anomaly_DeepLog'] = result['anomaly_DeepLog'].astype(int)\n", + "result['anomaly_DeepLog'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "76MRjCtk-U9p", + "outputId": "012428da-86b8-4891-844c-6dc46e37b072" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 390, + "width": 684 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,6))\n", + "\n", + "#anomaly\n", + "a = result.loc[result['anomaly_DeepLog'] == 1]\n", + "ax.plot(result['Transactions'], color='black', label = 'Normal', linewidth=1.5)\n", + "ax.scatter(a.index ,a['Transactions'], color='red', label = 'Anomaly', s=16)\n", + "plt.legend()\n", + "plt.title(\"Anamoly Detection Using DeepLog\")\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Transactions')\n", + "plt.show();" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "UQkOGQWt-U9q", + "outputId": "760eea07-80e9-457c-88c4-9a6bcda69aea" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 390, + "width": 687 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,6))\n", + "\n", + "#anomaly\n", + "a = result.loc[result['anomaly_DeepLog'] == 1]\n", + "ax.plot(result['Blocks'], color='black', label = 'Normal', linewidth=1.5)\n", + "ax.scatter(a.index ,a['Blocks'], color='red', label = 'Anomaly', s=16)\n", + "plt.legend()\n", + "plt.title(\"Anamoly Detection Using DeepLog\")\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Blocks')\n", + "plt.show();" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "_ZTX7H7L-U9q", + "outputId": "2301e3a8-8d7d-44f9-9097-0f9761535322" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 390, + "width": 671 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,6))\n", + "\n", + "#anomaly\n", + "a = result.loc[result['anomaly_DeepLog'] == 1]\n", + "ax.plot(result['Output_Satoshis'], color='black', label = 'Normal', linewidth=1.5)\n", + "ax.scatter(a.index ,a['Output_Satoshis'], color='red', label = 'Anomaly', s=16)\n", + "plt.legend()\n", + "plt.title(\"Anamoly Detection Using DeepLog\")\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Output_Satoshis')\n", + "plt.show();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2vfa0ibf-U9q" + }, + "source": [ + "## Example 2: Telemanom" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "N2RStdU_-U9q", + "outputId": "77422b8f-6c24-4e7a-c477-2bf612f9061f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "43/43 [==============================] - 3s 70ms/step - loss: 1.0232 - val_loss: 0.9895\n" + ] + } + ], + "source": [ + "transformer_TL = TelemanomSKI()\n", + "transformer_TL.fit(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "I3nvHUCb-U9q", + "outputId": "5f8a61bf-7887-4943-b70e-6df03019f464" + }, + "outputs": [], + "source": [ + "prediction_labels_TL = transformer_TL.predict(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "SplHaWJJ-U9r", + "outputId": "edc47217-038a-4e11-b95b-bf885e677a58" + }, + "outputs": [], + "source": [ + "prediction_score_TL = transformer_TL.predict_score(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "5mYPYtGA-U9r", + "outputId": "fd17642a-c12b-4dd2-d375-bf5070232764" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction Labels\n", + " [[0]\n", + " [0]\n", + " [0]\n", + " ...\n", + " [0]\n", + " [0]\n", + " [0]]\n", + "Prediction Score\n", + " [[24.42881933]\n", + " [24.31431483]\n", + " [24.17330598]\n", + " ...\n", + " [18.83590403]\n", + " [18.81861319]\n", + " [18.80372313]]\n" + ] + } + ], + "source": [ + "print(\"Prediction Labels\\n\", prediction_labels_TL)\n", + "print(\"Prediction Score\\n\", prediction_score_TL)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "NC2EYAbV-U9r", + "outputId": "1cbd7eb4-ce20-43b0-c086-7a58b27a773e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3178\n", + "1 354\n", + "Name: anomaly_Telemanom, dtype: int64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a new column for storing the results of Telemanom method\n", + "result['anomaly_Telemanom'] = pd.Series(prediction_labels_DL.flatten()) #somehow make into 1d\n", + "result['anomaly_Telemanom'] = result['anomaly_Telemanom'].apply(lambda x: x == 1)\n", + "result['anomaly_Telemanom'] = result['anomaly_Telemanom'].astype(int)\n", + "result['anomaly_Telemanom'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "-HBPP9RT-U9r", + "outputId": "6d0b93d3-61bc-4379-d048-e0bbd8a5bd0a" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 390, + "width": 684 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,6))\n", + "\n", + "#anomaly\n", + "a = result.loc[result['anomaly_Telemanom'] == 1]\n", + "ax.plot(result['Transactions'], color='black', label = 'Normal', linewidth=1.5)\n", + "ax.scatter(a.index ,a['Transactions'], color='red', label = 'Anomaly', s=16)\n", + "plt.legend()\n", + "plt.title(\"Anamoly Detection Using Telemanom\")\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Transactions')\n", + "plt.show();" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "Cpvf08eP-U9r", + "outputId": "dc1dae6e-4b8b-4b33-c727-fdbb4cda1643" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 390, + "width": 687 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,6))\n", + "\n", + "#anomaly\n", + "a = result.loc[result['anomaly_Telemanom'] == 1]\n", + "ax.plot(result['Blocks'], color='black', label = 'Normal', linewidth=1.5)\n", + "ax.scatter(a.index ,a['Blocks'], color='red', label = 'Anomaly', s=16)\n", + "plt.legend()\n", + "plt.title(\"Anamoly Detection Using Telemanom\")\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Blocks')\n", + "plt.show();" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "id": "hC2jpBrZ-U9s", + "outputId": "60f16bb0-a3ce-4ab4-bb37-f208b2c18706" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 390, + "width": 671 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,6))\n", + "\n", + "#anomaly\n", + "a = result.loc[result['anomaly_Telemanom'] == 1]\n", + "ax.plot(result['Output_Satoshis'], color='black', label = 'Normal', linewidth=1.5)\n", + "ax.scatter(a.index ,a['Output_Satoshis'], color='red', label = 'Anomaly', s=16)\n", + "plt.legend()\n", + "plt.title(\"Anamoly Detection Using Telemanom\")\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Output_Satoshis')\n", + "plt.show();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "evax-5IF-U9s" + }, + "source": [ + "## Searcher Example" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "id": "qG2gYe9H-U9s", + "outputId": "7bd27e67-0bc2-4d6f-9cac-15c9cf97b3a5" + }, + "outputs": [], + "source": [ + "from d3m import index\n", + "from d3m.metadata.base import ArgumentType\n", + "from d3m.metadata.pipeline import Pipeline, PrimitiveStep\n", + "from axolotl.backend.simple import SimpleRunner\n", + "from tods import generate_dataset, generate_problem\n", + "from tods.searcher import BruteForceSearch\n", + "from tods import generate_dataset, load_pipeline, evaluate_pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "id": "J_WmjBBC-U9s" + }, + "outputs": [], + "source": [ + "table_path = 'nodateblock.csv'\n", + "target_index = 3 # what column is the target\n", + "time_limit = 30 # How many seconds you wanna search" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "id": "0txdW5de-U9t" + }, + "outputs": [], + "source": [ + "metric = 'F1_MACRO' # F1 on both label 0 and 1" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "8VowONRq-U9t" + }, + "outputs": [], + "source": [ + "# Read data and generate dataset and problem\n", + "df = pd.read_csv(table_path)\n", + "dataset = generate_dataset(df, target_index=target_index)\n", + "problem_description = generate_problem(dataset, metric)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "id": "BIg-SdEw-U9t" + }, + "outputs": [], + "source": [ + "# Start backend\n", + "backend = SimpleRunner(random_seed=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "id": "a42_XF4v-T_E" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "While loading primitive 'tods.data_processing.dataset_to_dataframe', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.data_processing.dataset_to_dataframe' without checking requirements.\n", + "While loading primitive 'tods.data_processing.column_parser', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.data_processing.column_parser' without checking requirements.\n", + "While loading primitive 'tods.data_processing.extract_columns_by_semantic_types', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.data_processing.extract_columns_by_semantic_types' without checking requirements.\n", + "While loading primitive 'tods.timeseries_processing.transformation.axiswise_scaler', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.timeseries_processing.transformation.axiswise_scaler' without checking requirements.\n", + "While loading primitive 'tods.feature_analysis.statistical_mean', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.feature_analysis.statistical_mean' without checking requirements.\n", + "While loading primitive 'tods.detection_algorithm.pyod_ae', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.detection_algorithm.pyod_ae' without checking requirements.\n", + "While loading primitive 'tods.data_processing.construct_predictions', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.data_processing.construct_predictions' without checking requirements.\n", + "While loading primitive 'tods.detection_algorithm.pyod_vae', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.detection_algorithm.pyod_vae' without checking requirements.\n", + "While loading primitive 'tods.detection_algorithm.pyod_cof', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.detection_algorithm.pyod_cof' without checking requirements.\n", + "While loading primitive 'tods.detection_algorithm.pyod_sod', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.detection_algorithm.pyod_sod' without checking requirements.\n", + "While loading primitive 'tods.detection_algorithm.pyod_abod', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.detection_algorithm.pyod_abod' without checking requirements.\n", + "While loading primitive 'tods.detection_algorithm.pyod_hbos', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.detection_algorithm.pyod_hbos' without checking requirements.\n", + "While loading primitive 'tods.detection_algorithm.pyod_iforest', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.detection_algorithm.pyod_iforest' without checking requirements.\n", + "While loading primitive 'tods.feature_analysis.statistical_median', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.feature_analysis.statistical_median' without checking requirements.\n", + "While loading primitive 'tods.feature_analysis.statistical_g_mean', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.feature_analysis.statistical_g_mean' without checking requirements.\n", + "While loading primitive 'tods.feature_analysis.statistical_abs_energy', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.feature_analysis.statistical_abs_energy' without checking requirements.\n", + "While loading primitive 'tods.feature_analysis.statistical_abs_sum', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.feature_analysis.statistical_abs_sum' without checking requirements.\n", + "While loading primitive 'tods.feature_analysis.statistical_h_mean', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.feature_analysis.statistical_h_mean' without checking requirements.\n", + "While loading primitive 'tods.feature_analysis.statistical_maximum', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.feature_analysis.statistical_maximum' without checking requirements.\n", + "While loading primitive 'tods.timeseries_processing.transformation.standard_scaler', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.timeseries_processing.transformation.standard_scaler' without checking requirements.\n", + "While loading primitive 'tods.timeseries_processing.transformation.power_transformer', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.timeseries_processing.transformation.power_transformer' without checking requirements.\n", + "While loading primitive 'tods.timeseries_processing.transformation.quantile_transformer', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.timeseries_processing.transformation.quantile_transformer' without checking requirements.\n", + "While loading primitive 'tods.timeseries_processing.transformation.moving_average_transform', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.timeseries_processing.transformation.moving_average_transform' without checking requirements.\n", + "While loading primitive 'tods.timeseries_processing.transformation.simple_exponential_smoothing', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.timeseries_processing.transformation.simple_exponential_smoothing' without checking requirements.\n" + ] + } + ], + "source": [ + "# Start search algorithm\n", + "search = BruteForceSearch(problem_description=problem_description,\n", + " backend=backend)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "id": "GLVMi4pg-T_F", + "outputId": "a86d614b-ef69-4502-a73d-a234e03864ca" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_2\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_2 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_2 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_3 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_3 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_4 (Dense) (None, 1) 7 \n", + "_________________________________________________________________\n", + "dropout_4 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_5 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_5 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_6 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_6 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_7 (Dense) (None, 6) 12 \n", + "=================================================================\n", + "Total params: 116\n", + "Trainable params: 116\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "69/69 [==============================] - 0s 3ms/step - loss: 1.5420 - val_loss: 1.2671\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n", + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_3\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_8 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_7 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_9 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_8 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_10 (Dense) (None, 1) 7 \n", + "_________________________________________________________________\n", + "dropout_9 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_11 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_10 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_12 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_11 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_13 (Dense) (None, 6) 12 \n", + "=================================================================\n", + "Total params: 116\n", + "Trainable params: 116\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "69/69 [==============================] - 0s 3ms/step - loss: 1.4295 - val_loss: 1.2573\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n", + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_4\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_14 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_12 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_15 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_13 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_16 (Dense) (None, 1) 7 \n", + "_________________________________________________________________\n", + "dropout_14 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_17 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_15 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_18 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_16 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_19 (Dense) (None, 6) 12 \n", + "=================================================================\n", + "Total params: 116\n", + "Trainable params: 116\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "69/69 [==============================] - 0s 3ms/step - loss: 2.0883 - val_loss: 1.7401\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n", + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_5\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_20 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_17 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_21 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_18 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_22 (Dense) (None, 1) 7 \n", + "_________________________________________________________________\n", + "dropout_19 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_23 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_20 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_24 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_21 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_25 (Dense) (None, 6) 12 \n", + "=================================================================\n", + "Total params: 116\n", + "Trainable params: 116\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "69/69 [==============================] - 0s 4ms/step - loss: 1.4033 - val_loss: 1.1445\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n", + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_6\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_26 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_22 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_27 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_23 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_28 (Dense) (None, 1) 7 \n", + "_________________________________________________________________\n", + "dropout_24 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_29 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_25 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_30 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_26 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_31 (Dense) (None, 6) 12 \n", + "=================================================================\n", + "Total params: 116\n", + "Trainable params: 116\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "69/69 [==============================] - 0s 3ms/step - loss: 1.3410 - val_loss: 1.1344\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n", + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_7\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_32 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_27 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_33 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_28 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_34 (Dense) (None, 1) 7 \n", + "_________________________________________________________________\n", + "dropout_29 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_35 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_30 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_36 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_31 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_37 (Dense) (None, 6) 12 \n", + "=================================================================\n", + "Total params: 116\n", + "Trainable params: 116\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "69/69 [==============================] - 0s 3ms/step - loss: 1.4389 - val_loss: 1.2434\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_8\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_38 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_32 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_39 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_33 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_40 (Dense) (None, 1) 7 \n", + "_________________________________________________________________\n", + "dropout_34 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_41 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_35 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_42 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_36 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_43 (Dense) (None, 6) 12 \n", + "=================================================================\n", + "Total params: 116\n", + "Trainable params: 116\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "69/69 [==============================] - 0s 3ms/step - loss: 1.8007 - val_loss: 1.4152\n" + ] + } + ], + "source": [ + "# Find the best pipeline\n", + "best_runtime, best_pipeline_result = search.search_fit(input_data=[dataset], time_limit=time_limit)\n", + "# print(best_runtime)\n", + "best_pipeline = best_runtime.pipeline\n", + "best_output = best_pipeline_result.output" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "id": "31nHAeqc-T_F", + "outputId": "3131a52a-1a9d-41fd-8484-0a0dae1dc3e4" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_9\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_44 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_37 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_45 (Dense) (None, 6) 42 \n", + "_________________________________________________________________\n", + "dropout_38 (Dropout) (None, 6) 0 \n", + "_________________________________________________________________\n", + "dense_46 (Dense) (None, 1) 7 \n", + "_________________________________________________________________\n", + "dropout_39 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_47 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_40 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_48 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_41 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_49 (Dense) (None, 6) 12 \n", + "=================================================================\n", + "Total params: 116\n", + "Trainable params: 116\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "69/69 [==============================] - 0s 3ms/step - loss: 1.5298 - val_loss: 1.2598\n" + ] + } + ], + "source": [ + "# Evaluate the best pipeline\n", + "best_scores = search.evaluate(best_pipeline).scores" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "E9xPN5L1-T_F", + "outputId": "1fa638b1-aad0-42df-a142-74163d7c7bd9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Search History:\n", + "----------------------------------------------------\n", + "Pipeline id: 9164b8de-70f0-40ca-a734-9f8ed180b1a5\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.571701 0.571701 0 0\n", + "----------------------------------------------------\n", + "Pipeline id: 55715e68-efbe-4187-a7ab-3e59936ca38d\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.534343 0.534343 0 0\n", + "----------------------------------------------------\n", + "Pipeline id: 267eff7f-861a-4861-b5d5-7d99ef33b589\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.503497 0.503497 0 0\n", + "----------------------------------------------------\n", + "Pipeline id: a4782ac4-7a1c-43e3-8737-eca42de86271\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.493586 0.493586 0 0\n", + "----------------------------------------------------\n", + "Pipeline id: 253adf70-ffa5-4a47-89e8-0b158d222c57\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.481985 0.481985 0 0\n", + "----------------------------------------------------\n", + "Pipeline id: 7491c037-e280-48c4-bbb5-0e893592a7eb\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.465044 0.465044 0 0\n" + ] + } + ], + "source": [ + "print('Search History:')\n", + "for pipeline_result in search.history:\n", + " print('-' * 52)\n", + " print('Pipeline id:', pipeline_result.pipeline.id)\n", + " print(pipeline_result.scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "id": "hwzKSMDJ-T_F", + "outputId": "80915d93-d46b-4471-fed6-b11001980f68" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best pipeline:\n", + "----------------------------------------------------\n", + "Pipeline id: 9164b8de-70f0-40ca-a734-9f8ed180b1a5\n", + "Pipeline json: {\"id\": \"9164b8de-70f0-40ca-a734-9f8ed180b1a5\", \"schema\": \"https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json\", \"created\": \"2021-07-26T16:42:39.368833Z\", \"inputs\": [{\"name\": \"inputs\"}], \"outputs\": [{\"data\": \"steps.7.produce\", \"name\": \"output predictions\"}], \"steps\": [{\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"c78138d9-9377-31dc-aee8-83d9df049c60\", \"version\": \"0.3.0\", \"python_path\": \"d3m.primitives.tods.data_processing.dataset_to_dataframe\", \"name\": \"Extract a DataFrame from a Dataset\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"inputs.0\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"81235c29-aeb9-3828-911a-1b25319b6998\", \"version\": \"0.6.0\", \"python_path\": \"d3m.primitives.tods.data_processing.column_parser\", \"name\": \"Parses strings into their types\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.0.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"a996cd89-ddf0-367f-8e7f-8c013cbc2891\", \"version\": \"0.4.0\", \"python_path\": \"d3m.primitives.tods.data_processing.extract_columns_by_semantic_types\", \"name\": \"Extracts columns by semantic type\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.1.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"semantic_types\": {\"type\": \"VALUE\", \"data\": [\"https://metadata.datadrivendiscovery.org/types/Attribute\"]}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"a996cd89-ddf0-367f-8e7f-8c013cbc2891\", \"version\": \"0.4.0\", \"python_path\": \"d3m.primitives.tods.data_processing.extract_columns_by_semantic_types\", \"name\": \"Extracts columns by semantic type\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.0.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"semantic_types\": {\"type\": \"VALUE\", \"data\": [\"https://metadata.datadrivendiscovery.org/types/TrueTarget\"]}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"642de2e7-5590-3cab-9266-2a53c326c461\", \"version\": \"0.0.1\", \"python_path\": \"d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler\", \"name\": \"Axis_wise_scale\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.2.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"30bc7cec-2ccc-34bc-9df8-2095bf3b1ae2\", \"version\": \"0.1.0\", \"python_path\": \"d3m.primitives.tods.feature_analysis.statistical_mean\", \"name\": \"Time Series Decompostional\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.4.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"67e7fcdf-d645-3417-9aa4-85cd369487d9\", \"version\": \"0.0.1\", \"python_path\": \"d3m.primitives.tods.detection_algorithm.pyod_ae\", \"name\": \"TODS.anomaly_detection_primitives.AutoEncoder\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.5.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"contamination\": {\"type\": \"VALUE\", \"data\": 0.01}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"2530840a-07d4-3874-b7d8-9eb5e4ae2bf3\", \"version\": \"0.3.0\", \"python_path\": \"d3m.primitives.tods.data_processing.construct_predictions\", \"name\": \"Construct pipeline predictions output\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.6.produce\"}, \"reference\": {\"type\": \"CONTAINER\", \"data\": \"steps.1.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}], \"digest\": \"22316bcba55ae1f9a02760920d825aa7b1af94e55011400e18bf377f88a46230\"}\n", + "Output:\n", + " d3mIndex anomaly\n", + "0 0 0\n", + "1 1 0\n", + "2 2 0\n", + "3 3 0\n", + "4 4 0\n", + "... ... ...\n", + "2432 2432 0\n", + "2433 2433 0\n", + "2434 2434 0\n", + "2435 2435 0\n", + "2436 2436 1\n", + "\n", + "[2437 rows x 2 columns]\n", + "Scores:\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.571701 0.571701 0 0\n" + ] + } + ], + "source": [ + "print('Best pipeline:')\n", + "print('-' * 52)\n", + "print('Pipeline id:', best_pipeline.id)\n", + "print('Pipeline json:', best_pipeline.to_json())\n", + "print('Output:')\n", + "print(best_output)\n", + "print('Scores:')\n", + "print(best_scores)" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "Blockchain.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/Demo Notebook/TODSOfficialNotebook.ipynb b/examples/Demo Notebook/TODSOfficialNotebook.ipynb new file mode 100644 index 00000000..f4add53d --- /dev/null +++ b/examples/Demo Notebook/TODSOfficialNotebook.ipynb @@ -0,0 +1,2028 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TODS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction Summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data. TODS provides exhaustive modules for building machine learning-based outlier detection systems, including: data processing, time series processing, feature analysis (extraction), detection algorithms, and reinforcement module. The functionalities provided via these modules include data preprocessing for general purposes, time series data smoothing/transformation, extracting features from time/frequency domains, various detection algorithms, and involving human expertise to calibrate the system. Three common outlier detection scenarios on time-series data can be performed: point-wise detection (time points as outliers), pattern-wise detection (subsequences as outliers), and system-wise detection (sets of time series as outliers), and a wide-range of corresponding algorithms are provided in TODS. This package is developed by DATA Lab @ Texas A&M University." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.6.10 :: Anaconda, Inc.\r\n" + ] + } + ], + "source": [ + "!python -V\n", + "# Make sure python version is 3.6" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.4.1'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import scipy\n", + "scipy.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TODS Notebook Master-Branch.ipynb TODSBlockchainDemo.ipynb\r\n", + "TODS Official Demo Notebook.ipynb\r\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import argparse\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.metrics import precision_recall_curve\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import classification_report\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import metrics\n", + "from d3m import index\n", + "from d3m.metadata.base import ArgumentType\n", + "from d3m.metadata.pipeline import Pipeline, PrimitiveStep\n", + "from axolotl.backend.simple import SimpleRunner\n", + "from tods import generate_dataset, generate_problem\n", + "from tods.searcher import BruteForceSearch\n", + "from tods import generate_dataset, load_pipeline, evaluate_pipeline\n", + "from tods.sk_interface.detection_algorithm.DeepLog_skinterface import DeepLogSKI\n", + "from tods.sk_interface.detection_algorithm.Telemanom_skinterface import TelemanomSKI\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### UCR Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "data_UCR = np.loadtxt(\"../../datasets/anomaly/raw_data/500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (20000,)\n", + "datatype of data: float64\n", + "First 5 rows:\n", + " [0.145299 0.128205 0.094017 0.076923 0.111111]\n" + ] + } + ], + "source": [ + "print(\"shape:\", data_UCR.shape)\n", + "print(\"datatype of data:\",data_UCR.dtype)\n", + "print(\"First 5 rows:\\n\", data_UCR[:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = np.expand_dims(data_UCR[:10000], axis=1)\n", + "X_test = np.expand_dims(data_UCR[10000:], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First 5 rows train:\n", + " [[0.145299]\n", + " [0.128205]\n", + " [0.094017]\n", + " [0.076923]\n", + " [0.111111]]\n", + "First 5 rows test:\n", + " [[0.076923]\n", + " [0.076923]\n", + " [0.076923]\n", + " [0.094017]\n", + " [0.145299]]\n" + ] + } + ], + "source": [ + "print(\"First 5 rows train:\\n\", X_train[:5])\n", + "print(\"First 5 rows test:\\n\", X_test[:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Yahoo Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "data_yahoo = pd.read_csv('../../datasets/anomaly/raw_data/yahoo_sub_5.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (1400, 7)\n", + "First 5 rows:\n", + " timestamp value_0 value_1 value_2 value_3 value_4 anomaly\n", + "0 1 12183 0.000000 3.716667 5 2109 0\n", + "1 2 12715 0.091758 3.610833 60 3229 0\n", + "2 3 12736 0.172297 3.481389 88 3637 0\n", + "3 4 12716 0.226219 3.380278 84 1982 0\n", + "4 5 12739 0.176358 3.193333 111 2751 0\n" + ] + } + ], + "source": [ + "print(\"shape:\", data_yahoo.shape)\n", + "print(\"First 5 rows:\\n\", data_yahoo[:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SK Example 1: DeepLog" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "282/282 [==============================] - 1s 5ms/step - loss: 0.4261 - val_loss: 0.2868\n", + "Epoch 2/10\n", + "282/282 [==============================] - 1s 3ms/step - loss: 0.3442 - val_loss: 0.2581\n", + "Epoch 3/10\n", + "282/282 [==============================] - 1s 3ms/step - loss: 0.3438 - val_loss: 0.2641\n", + "Epoch 4/10\n", + "282/282 [==============================] - 1s 3ms/step - loss: 0.3572 - val_loss: 0.2442\n", + "Epoch 5/10\n", + "282/282 [==============================] - 1s 3ms/step - loss: 0.3504 - val_loss: 0.2714\n", + "Epoch 6/10\n", + "282/282 [==============================] - 1s 3ms/step - loss: 0.3372 - val_loss: 0.2573\n", + "Epoch 7/10\n", + "282/282 [==============================] - 1s 3ms/step - loss: 0.3484 - val_loss: 0.2845\n", + "Epoch 8/10\n", + "282/282 [==============================] - 1s 3ms/step - loss: 0.3360 - val_loss: 0.2547\n", + "Epoch 9/10\n", + "282/282 [==============================] - 1s 3ms/step - loss: 0.3454 - val_loss: 0.2384\n", + "Epoch 10/10\n", + "282/282 [==============================] - 1s 3ms/step - loss: 0.3426 - val_loss: 0.2661\n" + ] + } + ], + "source": [ + "transformer = DeepLogSKI()\n", + "transformer.fit(X_train)\n", + "prediction_labels_train = transformer.predict(X_train)\n", + "prediction_labels_test = transformer.predict(X_test)\n", + "prediction_score = transformer.predict_score(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction Labels\n", + " [[0]\n", + " [0]\n", + " [0]\n", + " ...\n", + " [0]\n", + " [0]\n", + " [0]]\n", + "Prediction Score\n", + " [[0. ]\n", + " [0.28977011]\n", + " [0.28977011]\n", + " ...\n", + " [0.68693455]\n", + " [0.36795402]\n", + " [0.09138567]]\n" + ] + } + ], + "source": [ + "print(\"Prediction Labels\\n\", prediction_labels_test)\n", + "print(\"Prediction Score\\n\", prediction_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "y_true = prediction_labels_train\n", + "y_pred = prediction_labels_test\n", + "precision, recall, thresholds = precision_recall_curve(y_true, y_pred)\n", + "f1_scores = 2*recall*precision/(recall+precision)\n", + "fpr, tpr, threshold = metrics.roc_curve(y_true, y_pred)\n", + "roc_auc = metrics.auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy Score: 0.9056\n" + ] + } + ], + "source": [ + "print('Accuracy Score: ', accuracy_score(y_true, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.93 0.96 0.95 9007\n", + " 1 0.53 0.38 0.44 993\n", + "\n", + " accuracy 0.91 10000\n", + " macro avg 0.73 0.67 0.70 10000\n", + "weighted avg 0.89 0.91 0.90 10000\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_true, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best threshold: 1\n", + "Best F1-Score: 0.44405182567726736\n" + ] + } + ], + "source": [ + "print('Best threshold: ', thresholds[np.argmax(f1_scores)])\n", + "print('Best F1-Score: ', np.max(f1_scores))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title('ROC')\n", + "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n", + "plt.legend(loc = 'lower right')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SK Example 2: Telemanom" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "125/125 [==============================] - 1s 8ms/step - loss: 0.0130 - val_loss: 0.0058\n" + ] + } + ], + "source": [ + "transformer = TelemanomSKI(l_s= 2, n_predictions= 1)\n", + "transformer.fit(X_train)\n", + "prediction_labels_train = transformer.predict(X_train)\n", + "prediction_labels_test = transformer.predict(X_test)\n", + "prediction_score = transformer.predict_score(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction Labels\n", + " [[1]\n", + " [1]\n", + " [1]\n", + " ...\n", + " [1]\n", + " [1]\n", + " [1]]\n", + "Prediction Score\n", + " [[0.10077949]\n", + " [0.09220807]\n", + " [0.0722706 ]\n", + " ...\n", + " [0.06649317]\n", + " [0.06706881]\n", + " [0.06753459]]\n" + ] + } + ], + "source": [ + "print(\"Prediction Labels\\n\", prediction_labels_test)\n", + "print(\"Prediction Score\\n\", prediction_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "y_true = prediction_labels_train\n", + "y_pred = prediction_labels_test\n", + "precision, recall, thresholds = precision_recall_curve(y_true, y_pred)\n", + "f1_scores = 2*recall*precision/(recall+precision)\n", + "fpr, tpr, threshold = metrics.roc_curve(y_true, y_pred)\n", + "roc_auc = metrics.auc(fpr, tpr)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy Score: 0.18095428628588578\n" + ] + } + ], + "source": [ + "print('Accuracy Score: ', accuracy_score(y_true, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 951, 8046],\n", + " [ 142, 858]])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion_matrix(y_true, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.11 0.19 8997\n", + " 1 0.10 0.86 0.17 1000\n", + "\n", + " accuracy 0.18 9997\n", + " macro avg 0.48 0.48 0.18 9997\n", + "weighted avg 0.79 0.18 0.19 9997\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_true, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best threshold: 0\n", + "Best F1-Score: 0.18186778212239701\n" + ] + } + ], + "source": [ + "print('Best threshold: ', thresholds[np.argmax(f1_scores)])\n", + "print('Best F1-Score: ', np.max(f1_scores))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title('ROC')\n", + "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n", + "plt.legend(loc = 'lower right')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pipline Example: AutoEncoder" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'inputs.0'" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Creating pipeline\n", + "pipeline_description = Pipeline()\n", + "pipeline_description.add_input(name='inputs')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "While loading primitive 'tods.data_processing.dataset_to_dataframe', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.data_processing.dataset_to_dataframe' without checking requirements.\n" + ] + } + ], + "source": [ + "# Step 0: dataset_to_dataframe\n", + "step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))\n", + "step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')\n", + "step_0.add_output('produce')\n", + "pipeline_description.add_step(step_0)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "While loading primitive 'tods.data_processing.column_parser', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.data_processing.column_parser' without checking requirements.\n" + ] + } + ], + "source": [ + "# Step 1: column_parser\n", + "step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))\n", + "step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')\n", + "step_1.add_output('produce')\n", + "pipeline_description.add_step(step_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "While loading primitive 'tods.data_processing.extract_columns_by_semantic_types', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.data_processing.extract_columns_by_semantic_types' without checking requirements.\n" + ] + } + ], + "source": [ + "# Step 2: extract_columns_by_semantic_types(attributes)\n", + "step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))\n", + "step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')\n", + "step_2.add_output('produce')\n", + "step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,\n", + "\t\t\t\t\t\t\t data=['https://metadata.datadrivendiscovery.org/types/Attribute'])\n", + "pipeline_description.add_step(step_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Step 3: extract_columns_by_semantic_types(targets)\n", + "step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))\n", + "step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')\n", + "step_3.add_output('produce')\n", + "step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,\n", + "\t\t\t\t\t\t\tdata=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])\n", + "pipeline_description.add_step(step_3)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "attributes = 'steps.2.produce'\n", + "targets = 'steps.3.produce'" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "While loading primitive 'tods.feature_analysis.statistical_maximum', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.feature_analysis.statistical_maximum' without checking requirements.\n" + ] + } + ], + "source": [ + "# Step 4: processing\n", + "step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_maximum'))\n", + "step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)\n", + "step_4.add_output('produce')\n", + "pipeline_description.add_step(step_4)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "While loading primitive 'tods.detection_algorithm.pyod_ae', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.detection_algorithm.pyod_ae' without checking requirements.\n" + ] + } + ], + "source": [ + "# Step 5: algorithm`\n", + "step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_ae'))\n", + "step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce')\n", + "step_5.add_output('produce')\n", + "pipeline_description.add_step(step_5)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "While loading primitive 'tods.data_processing.construct_predictions', an error has been detected: (PyYAML 5.3.1 (/Users/wangyanghe/anaconda3/envs/tods2/lib/python3.6/site-packages), Requirement.parse('PyYAML<=5.3,>=5.1'), {'tamu-d3m'})\n", + "Attempting to load primitive 'tods.data_processing.construct_predictions' without checking requirements.\n" + ] + } + ], + "source": [ + "# Step 6: Predictions\n", + "step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions'))\n", + "step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce')\n", + "step_6.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')\n", + "step_6.add_output('produce')\n", + "pipeline_description.add_step(step_6)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'outputs.0'" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Final Output\n", + "pipeline_description.add_output(name='output predictions', data_reference='steps.6.produce')" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"id\": \"91d30ee2-ec20-4a30-8fb4-70122182f075\", \"schema\": \"https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json\", \"created\": \"2021-07-26T16:45:59.782462Z\", \"inputs\": [{\"name\": \"inputs\"}], \"outputs\": [{\"data\": \"steps.6.produce\", \"name\": \"output predictions\"}], \"steps\": [{\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"c78138d9-9377-31dc-aee8-83d9df049c60\", \"version\": \"0.3.0\", \"python_path\": \"d3m.primitives.tods.data_processing.dataset_to_dataframe\", \"name\": \"Extract a DataFrame from a Dataset\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"inputs.0\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"81235c29-aeb9-3828-911a-1b25319b6998\", \"version\": \"0.6.0\", \"python_path\": \"d3m.primitives.tods.data_processing.column_parser\", \"name\": \"Parses strings into their types\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.0.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"a996cd89-ddf0-367f-8e7f-8c013cbc2891\", \"version\": \"0.4.0\", \"python_path\": \"d3m.primitives.tods.data_processing.extract_columns_by_semantic_types\", \"name\": \"Extracts columns by semantic type\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.1.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"semantic_types\": {\"type\": \"VALUE\", \"data\": [\"https://metadata.datadrivendiscovery.org/types/Attribute\"]}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"a996cd89-ddf0-367f-8e7f-8c013cbc2891\", \"version\": \"0.4.0\", \"python_path\": \"d3m.primitives.tods.data_processing.extract_columns_by_semantic_types\", \"name\": \"Extracts columns by semantic type\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.0.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"semantic_types\": {\"type\": \"VALUE\", \"data\": [\"https://metadata.datadrivendiscovery.org/types/TrueTarget\"]}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"f07ce875-bbc7-36c5-9cc1-ba4bfb7cf48e\", \"version\": \"0.1.0\", \"python_path\": \"d3m.primitives.tods.feature_analysis.statistical_maximum\", \"name\": \"Time Series Decompostional\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.2.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"67e7fcdf-d645-3417-9aa4-85cd369487d9\", \"version\": \"0.0.1\", \"python_path\": \"d3m.primitives.tods.detection_algorithm.pyod_ae\", \"name\": \"TODS.anomaly_detection_primitives.AutoEncoder\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.4.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"2530840a-07d4-3874-b7d8-9eb5e4ae2bf3\", \"version\": \"0.3.0\", \"python_path\": \"d3m.primitives.tods.data_processing.construct_predictions\", \"name\": \"Construct pipeline predictions output\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.5.produce\"}, \"reference\": {\"type\": \"CONTAINER\", \"data\": \"steps.1.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}], \"digest\": \"92a8442d64d1547e13386ee02e73cb61ef154ea607e48b14853c7678921377da\"}\n" + ] + } + ], + "source": [ + "# Output to json\n", + "data = pipeline_description.to_json()\n", + "with open('autoencoder_pipeline.json', 'w') as f:\n", + " f.write(data)\n", + " print(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "this_path = os.path.dirname(os.path.abspath(\"__file__\"))\n", + "default_data_path = os.path.join(this_path, '../../datasets/anomaly/raw_data/yahoo_sub_5.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "_StoreAction(option_strings=['--pipeline_path'], dest='pipeline_path', nargs=None, const=None, default='/Users/wangyanghe/Desktop/Research/tods/examples/Demo Notebook/autoencoder_pipeline.json', type=None, choices=None, help='Input the path of the pre-built pipeline description', metavar=None)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parser = argparse.ArgumentParser(description='Arguments for running predefined pipelin.')\n", + "parser.add_argument('--table_path', type=str, default=default_data_path,\n", + " help='Input the path of the input data table')\n", + "parser.add_argument('--target_index', type=int, default=6,\n", + " help='Index of the ground truth (for evaluation)')\n", + "parser.add_argument('--metric',type=str, default='F1_MACRO',\n", + " help='Evaluation Metric (F1, F1_MACRO)')\n", + "parser.add_argument('--pipeline_path', \n", + " default=os.path.join(this_path, 'autoencoder_pipeline.json'),\n", + " help='Input the path of the pre-built pipeline description')" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "args, unknown = parser.parse_known_args()\n", + "table_path = args.table_path \n", + "target_index = args.target_index # what column is the target\n", + "pipeline_path = args.pipeline_path\n", + "metric = args.metric # F1 on both label 0 and 1" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# Read data and generate dataset\n", + "df = pd.read_csv(table_path)\n", + "dataset = generate_dataset(df, target_index)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the default pipeline\n", + "pipeline = load_pipeline(pipeline_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_2\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_2 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_2 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_3 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_3 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_4 (Dense) (None, 1) 13 \n", + "_________________________________________________________________\n", + "dropout_4 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_5 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_5 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_6 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_6 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_7 (Dense) (None, 12) 24 \n", + "=================================================================\n", + "Total params: 362\n", + "Trainable params: 362\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "40/40 [==============================] - 0s 6ms/step - loss: 2.4658 - val_loss: 1.6924\n", + "{'method_called': 'evaluate',\n", + " 'outputs': \"[{'outputs.0': d3mIndex anomaly\"\n", + " '0 0 1'\n", + " '1 1 0'\n", + " '2 2 0'\n", + " '3 3 1'\n", + " '4 4 0'\n", + " '... ... ...'\n", + " '1395 1395 1'\n", + " '1396 1396 0'\n", + " '1397 1397 1'\n", + " '1398 1398 1'\n", + " '1399 1399 0'\n", + " ''\n", + " \"[1400 rows x 2 columns]}, {'outputs.0': d3mIndex anomaly\"\n", + " '0 0 1'\n", + " '1 1 0'\n", + " '2 2 0'\n", + " '3 3 1'\n", + " '4 4 0'\n", + " '... ... ...'\n", + " '1395 1395 1'\n", + " '1396 1396 0'\n", + " '1397 1397 1'\n", + " '1398 1398 1'\n", + " '1399 1399 0'\n", + " ''\n", + " '[1400 rows x 2 columns]}]',\n", + " 'pipeline': '',\n", + " 'scores': ' metric value normalized randomSeed fold'\n", + " '0 F1_MACRO 0.501786 0.501786 0 0',\n", + " 'status': 'COMPLETED'}\n" + ] + } + ], + "source": [ + "# Run the pipeline\n", + "pipeline_result = evaluate_pipeline(dataset, pipeline, metric)\n", + "print(pipeline_result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Searcher Example:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "table_path = '../../datasets/anomaly/raw_data/yahoo_sub_5.csv'\n", + "target_index = 6 # column of the target label\n", + "time_limit = 30 # How many seconds you wanna search" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "metric = 'F1_MACRO' # F1 on both label 0 and 1" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "# Read data and generate dataset and problem\n", + "df = pd.read_csv(table_path)\n", + "dataset = generate_dataset(df, target_index=target_index)\n", + "problem_description = generate_problem(dataset, metric)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# Start backend\n", + "backend = SimpleRunner(random_seed=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "# Start search algorithm\n", + "search = BruteForceSearch(problem_description=problem_description,\n", + " backend=backend)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_3\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_8 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_7 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_9 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_8 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_10 (Dense) (None, 1) 13 \n", + "_________________________________________________________________\n", + "dropout_9 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_11 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_10 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_12 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_11 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_13 (Dense) (None, 12) 24 \n", + "=================================================================\n", + "Total params: 362\n", + "Trainable params: 362\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "40/40 [==============================] - 0s 4ms/step - loss: 1.2581 - val_loss: 2.9452\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n", + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_4\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_14 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_12 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_15 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_13 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_16 (Dense) (None, 1) 13 \n", + "_________________________________________________________________\n", + "dropout_14 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_17 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_15 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_18 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_16 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_19 (Dense) (None, 12) 24 \n", + "=================================================================\n", + "Total params: 362\n", + "Trainable params: 362\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "40/40 [==============================] - 0s 5ms/step - loss: 1.3265 - val_loss: 1.9382\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n", + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_5\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_20 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_17 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_21 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_18 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_22 (Dense) (None, 1) 13 \n", + "_________________________________________________________________\n", + "dropout_19 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_23 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_20 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_24 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_21 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_25 (Dense) (None, 12) 24 \n", + "=================================================================\n", + "Total params: 362\n", + "Trainable params: 362\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "40/40 [==============================] - 0s 5ms/step - loss: 1.6087 - val_loss: 1.0320\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n", + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_6\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_26 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_22 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_27 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_23 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_28 (Dense) (None, 1) 13 \n", + "_________________________________________________________________\n", + "dropout_24 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_29 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_25 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_30 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_26 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_31 (Dense) (None, 12) 24 \n", + "=================================================================\n", + "Total params: 362\n", + "Trainable params: 362\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "40/40 [==============================] - 0s 5ms/step - loss: 1.5175 - val_loss: 2.2118\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n", + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_7\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_32 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_27 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_33 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_28 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_34 (Dense) (None, 1) 13 \n", + "_________________________________________________________________\n", + "dropout_29 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_35 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_30 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_36 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_31 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_37 (Dense) (None, 12) 24 \n", + "=================================================================\n", + "Total params: 362\n", + "Trainable params: 362\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "40/40 [==============================] - 0s 4ms/step - loss: 1.2544 - val_loss: 2.2630\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n", + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_8\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_38 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_32 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_39 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_33 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_40 (Dense) (None, 1) 13 \n", + "_________________________________________________________________\n", + "dropout_34 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_41 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_35 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_42 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_36 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_43 (Dense) (None, 12) 24 \n", + "=================================================================\n", + "Total params: 362\n", + "Trainable params: 362\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "40/40 [==============================] - 0s 5ms/step - loss: 1.5134 - val_loss: 2.4912\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n", + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_9\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_44 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_37 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_45 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_38 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_46 (Dense) (None, 1) 13 \n", + "_________________________________________________________________\n", + "dropout_39 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_47 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_40 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_48 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_41 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_49 (Dense) (None, 12) 24 \n", + "=================================================================\n", + "Total params: 362\n", + "Trainable params: 362\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "40/40 [==============================] - 0s 4ms/step - loss: 1.4981 - val_loss: 1.0899\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n", + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) [(None, 12)] 0 \n", + "__________________________________________________________________________________________________\n", + "dense_50 (Dense) (None, 12) 156 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_51 (Dense) (None, 1) 13 dense_50[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_42 (Dropout) (None, 1) 0 dense_51[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_52 (Dense) (None, 4) 8 dropout_42[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_43 (Dropout) (None, 4) 0 dense_52[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_53 (Dense) (None, 1) 5 dropout_43[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_44 (Dropout) (None, 1) 0 dense_53[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_54 (Dense) (None, 2) 4 dropout_44[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_55 (Dense) (None, 2) 4 dropout_44[0][0] \n", + "__________________________________________________________________________________________________\n", + "lambda (Lambda) (None, 2) 0 dense_54[0][0] \n", + " dense_55[0][0] \n", + "==================================================================================================\n", + "Total params: 190\n", + "Trainable params: 190\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n", + "Model: \"model_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_2 (InputLayer) [(None, 2)] 0 \n", + "_________________________________________________________________\n", + "dense_56 (Dense) (None, 2) 6 \n", + "_________________________________________________________________\n", + "dense_57 (Dense) (None, 4) 12 \n", + "_________________________________________________________________\n", + "dropout_45 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_58 (Dense) (None, 4) 20 \n", + "_________________________________________________________________\n", + "dropout_46 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_59 (Dense) (None, 4) 20 \n", + "_________________________________________________________________\n", + "dropout_47 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_60 (Dense) (None, 12) 60 \n", + "=================================================================\n", + "Total params: 118\n", + "Trainable params: 118\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Model: \"model_2\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) [(None, 12)] 0 \n", + "__________________________________________________________________________________________________\n", + "model (Model) [(None, 2), (None, 2 190 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "model_1 (Model) (None, 12) 118 model[1][2] \n", + "__________________________________________________________________________________________________\n", + "dense_50 (Dense) (None, 12) 156 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_51 (Dense) (None, 1) 13 dense_50[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_42 (Dropout) (None, 1) 0 dense_51[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_52 (Dense) (None, 4) 8 dropout_42[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_43 (Dropout) (None, 4) 0 dense_52[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_53 (Dense) (None, 1) 5 dropout_43[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_44 (Dropout) (None, 1) 0 dense_53[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_55 (Dense) (None, 2) 4 dropout_44[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_54 (Dense) (None, 2) 4 dropout_44[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_AddV2 (TensorFlowOp [(None, 2)] 0 dense_55[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_Square (TensorFlowO [(None, 2)] 0 dense_54[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_Sub (TensorFlowOpLa [(None, 2)] 0 tf_op_layer_AddV2[0][0] \n", + " tf_op_layer_Square[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_Exp (TensorFlowOpLa [(None, 2)] 0 dense_55[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_Sub_1 (TensorFlowOp [(None, 2)] 0 tf_op_layer_Sub[0][0] \n", + " tf_op_layer_Exp[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_Sum (TensorFlowOpLa [(None,)] 0 tf_op_layer_Sub_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_Mul_1 (TensorFlowOp [(None,)] 0 tf_op_layer_Sum[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_SquaredDifference ( [(None, 12)] 0 model_1[1][0] \n", + " input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_Sub_2 (TensorFlowOp [(None,)] 0 tf_op_layer_Mul_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_Mean (TensorFlowOpL [(None,)] 0 tf_op_layer_SquaredDifference[0][\n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_Abs (TensorFlowOpLa [(None,)] 0 tf_op_layer_Sub_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_Mul (TensorFlowOpLa [(None,)] 0 tf_op_layer_Mean[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_Mul_2 (TensorFlowOp [(None,)] 0 tf_op_layer_Abs[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_AddV2_1 (TensorFlow [(None,)] 0 tf_op_layer_Mul[0][0] \n", + " tf_op_layer_Mul_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf_op_layer_Mean_1 (TensorFlowO [()] 0 tf_op_layer_AddV2_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "add_loss (AddLoss) () 0 tf_op_layer_Mean_1[0][0] \n", + "==================================================================================================\n", + "Total params: 308\n", + "Trainable params: 308\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n", + "Epoch 1/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40/40 [==============================] - 0s 8ms/step - loss: 8.4644 - val_loss: 12.1816\n", + "Epoch 2/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 8.2609 - val_loss: 11.9437\n", + "Epoch 3/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 7.9611 - val_loss: 11.5748\n", + "Epoch 4/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 7.4981 - val_loss: 11.0452\n", + "Epoch 5/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 7.0250 - val_loss: 10.4739\n", + "Epoch 6/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 6.5004 - val_loss: 10.0370\n", + "Epoch 7/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 6.1546 - val_loss: 9.7352\n", + "Epoch 8/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 6.0062 - val_loss: 9.6098\n", + "Epoch 9/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.8159 - val_loss: 9.5476\n", + "Epoch 10/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.6725 - val_loss: 9.5159\n", + "Epoch 11/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.6656 - val_loss: 9.5018\n", + "Epoch 12/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.6096 - val_loss: 9.4962\n", + "Epoch 13/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5999 - val_loss: 9.4889\n", + "Epoch 14/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5292 - val_loss: 9.4657\n", + "Epoch 15/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5649 - val_loss: 9.4886\n", + "Epoch 16/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5469 - val_loss: 9.4826\n", + "Epoch 17/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.6645 - val_loss: 9.4725\n", + "Epoch 18/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5034 - val_loss: 9.4725\n", + "Epoch 19/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5282 - val_loss: 9.4751\n", + "Epoch 20/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5231 - val_loss: 9.4735\n", + "Epoch 21/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5046 - val_loss: 9.4731\n", + "Epoch 22/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5434 - val_loss: 9.4760\n", + "Epoch 23/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5236 - val_loss: 9.4645\n", + "Epoch 24/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5161 - val_loss: 9.4741\n", + "Epoch 25/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4853 - val_loss: 9.4739\n", + "Epoch 26/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5102 - val_loss: 9.4755\n", + "Epoch 27/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5882 - val_loss: 9.4745\n", + "Epoch 28/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5261 - val_loss: 9.4748\n", + "Epoch 29/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5632 - val_loss: 9.4725\n", + "Epoch 30/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5333 - val_loss: 9.4727\n", + "Epoch 31/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5254 - val_loss: 9.4699\n", + "Epoch 32/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5061 - val_loss: 9.4731\n", + "Epoch 33/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5201 - val_loss: 9.4732\n", + "Epoch 34/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4940 - val_loss: 9.4728\n", + "Epoch 35/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5049 - val_loss: 9.4727\n", + "Epoch 36/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4827 - val_loss: 9.4727\n", + "Epoch 37/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4982 - val_loss: 9.4728\n", + "Epoch 38/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4899 - val_loss: 9.4727\n", + "Epoch 39/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5173 - val_loss: 9.4726\n", + "Epoch 40/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5445 - val_loss: 9.4729\n", + "Epoch 41/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4936 - val_loss: 9.4730\n", + "Epoch 42/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.6540 - val_loss: 9.4724\n", + "Epoch 43/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4761 - val_loss: 9.4727\n", + "Epoch 44/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 6.0112 - val_loss: 9.4729\n", + "Epoch 45/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 6.0335 - val_loss: 9.4727\n", + "Epoch 46/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4839 - val_loss: 9.4729\n", + "Epoch 47/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5024 - val_loss: 9.4727\n", + "Epoch 48/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4854 - val_loss: 9.4727\n", + "Epoch 49/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.6187 - val_loss: 9.4725\n", + "Epoch 50/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4893 - val_loss: 9.4727\n", + "Epoch 51/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4781 - val_loss: 9.4722\n", + "Epoch 52/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4876 - val_loss: 9.4726\n", + "Epoch 53/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5166 - val_loss: 9.4726\n", + "Epoch 54/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5078 - val_loss: 9.4726\n", + "Epoch 55/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5348 - val_loss: 9.4726\n", + "Epoch 56/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4919 - val_loss: 9.4726\n", + "Epoch 57/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5030 - val_loss: 9.4727\n", + "Epoch 58/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4790 - val_loss: 9.4726\n", + "Epoch 59/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4879 - val_loss: 9.4726\n", + "Epoch 60/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4971 - val_loss: 9.4726\n", + "Epoch 61/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4645 - val_loss: 9.4726\n", + "Epoch 62/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4850 - val_loss: 9.4725\n", + "Epoch 63/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4789 - val_loss: 9.4726\n", + "Epoch 64/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4758 - val_loss: 9.4726\n", + "Epoch 65/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4922 - val_loss: 9.4726\n", + "Epoch 66/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4887 - val_loss: 9.4726\n", + "Epoch 67/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4730 - val_loss: 9.4726\n", + "Epoch 68/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4766 - val_loss: 9.4726\n", + "Epoch 69/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4721 - val_loss: 9.4726\n", + "Epoch 70/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4897 - val_loss: 9.4726\n", + "Epoch 71/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4831 - val_loss: 9.4726\n", + "Epoch 72/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.9874 - val_loss: 9.4726\n", + "Epoch 73/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4919 - val_loss: 9.4726\n", + "Epoch 74/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.6101 - val_loss: 9.4726\n", + "Epoch 75/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5194 - val_loss: 9.4726\n", + "Epoch 76/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4838 - val_loss: 9.4726\n", + "Epoch 77/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4781 - val_loss: 9.4726\n", + "Epoch 78/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4686 - val_loss: 9.4726\n", + "Epoch 79/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4799 - val_loss: 9.4726\n", + "Epoch 80/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4692 - val_loss: 9.4726\n", + "Epoch 81/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4739 - val_loss: 9.4726\n", + "Epoch 82/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4837 - val_loss: 9.4726\n", + "Epoch 83/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40/40 [==============================] - 0s 2ms/step - loss: 5.4878 - val_loss: 9.4726\n", + "Epoch 84/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4727 - val_loss: 9.4726\n", + "Epoch 85/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4667 - val_loss: 9.4726\n", + "Epoch 86/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 7.0072 - val_loss: 9.4726\n", + "Epoch 87/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4939 - val_loss: 9.4726\n", + "Epoch 88/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4939 - val_loss: 9.4726\n", + "Epoch 89/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4861 - val_loss: 9.4726\n", + "Epoch 90/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4777 - val_loss: 9.4726\n", + "Epoch 91/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4696 - val_loss: 9.4726\n", + "Epoch 92/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4884 - val_loss: 9.4726\n", + "Epoch 93/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.5034 - val_loss: 9.4726\n", + "Epoch 94/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.6430 - val_loss: 9.4726\n", + "Epoch 95/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4715 - val_loss: 9.4726\n", + "Epoch 96/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4797 - val_loss: 9.4726\n", + "Epoch 97/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4834 - val_loss: 9.4726\n", + "Epoch 98/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4964 - val_loss: 9.4726\n", + "Epoch 99/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4756 - val_loss: 9.4726\n", + "Epoch 100/100\n", + "40/40 [==============================] - 0s 2ms/step - loss: 5.4850 - val_loss: 9.4726\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/wangyanghe/Desktop/Research/tods/tods/searcher/brute_force_search.py\", line 62, in _search\n", + " for error in pipeline_result.error:\n", + "TypeError: 'NoneType' object is not iterable\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_10\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_61 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_48 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_62 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_49 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_63 (Dense) (None, 1) 13 \n", + "_________________________________________________________________\n", + "dropout_50 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_64 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_51 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_65 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_52 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_66 (Dense) (None, 12) 24 \n", + "=================================================================\n", + "Total params: 362\n", + "Trainable params: 362\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "40/40 [==============================] - 0s 4ms/step - loss: 1.4520 - val_loss: 1.0567\n" + ] + } + ], + "source": [ + "# Find the best pipeline\n", + "best_runtime, best_pipeline_result = search.search_fit(input_data=[dataset], time_limit=time_limit)\n", + "best_pipeline = best_runtime.pipeline\n", + "best_output = best_pipeline_result.output" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Not all provided hyper-parameters for the data preparation pipeline 79ce71bd-db96-494b-a455-14f2e2ac5040 were used: ['method', 'number_of_folds', 'randomSeed', 'shuffle', 'stratified']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_11\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_67 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_53 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_68 (Dense) (None, 12) 156 \n", + "_________________________________________________________________\n", + "dropout_54 (Dropout) (None, 12) 0 \n", + "_________________________________________________________________\n", + "dense_69 (Dense) (None, 1) 13 \n", + "_________________________________________________________________\n", + "dropout_55 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_70 (Dense) (None, 4) 8 \n", + "_________________________________________________________________\n", + "dropout_56 (Dropout) (None, 4) 0 \n", + "_________________________________________________________________\n", + "dense_71 (Dense) (None, 1) 5 \n", + "_________________________________________________________________\n", + "dropout_57 (Dropout) (None, 1) 0 \n", + "_________________________________________________________________\n", + "dense_72 (Dense) (None, 12) 24 \n", + "=================================================================\n", + "Total params: 362\n", + "Trainable params: 362\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "40/40 [==============================] - 0s 4ms/step - loss: 1.4804 - val_loss: 1.0427\n" + ] + } + ], + "source": [ + "# Evaluate the best pipeline\n", + "best_scores = search.evaluate(best_pipeline).scores" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Search History:\n", + "----------------------------------------------------\n", + "Pipeline id: ac70b74c-94e1-4bb7-bebf-096153b21f0e\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.708549 0.708549 0 0\n", + "----------------------------------------------------\n", + "Pipeline id: 49f8da0e-07ec-409e-be54-e193c73f5eaf\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.616695 0.616695 0 0\n", + "----------------------------------------------------\n", + "Pipeline id: 7526cb7c-e1a2-4634-81f2-b7e13e172aa5\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.54104 0.54104 0 0\n", + "----------------------------------------------------\n", + "Pipeline id: b8d7afa8-ca41-44d3-9672-681889c3ad23\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.521223 0.521223 0 0\n", + "----------------------------------------------------\n", + "Pipeline id: 5ece54b5-9425-4037-8a7e-1ce75645b6b5\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.501786 0.501786 0 0\n", + "----------------------------------------------------\n", + "Pipeline id: 65871cd6-acfb-4791-8301-f392d9d617b2\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.483604 0.483604 0 0\n", + "----------------------------------------------------\n", + "Pipeline id: c27403b5-35ec-48e5-9dbc-e99e3c8bd89a\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.462872 0.462872 0 0\n", + "----------------------------------------------------\n", + "Pipeline id: 93756abe-ccd0-4751-927e-5af7025a2cae\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.708549 0.708549 0 0\n" + ] + } + ], + "source": [ + "print('Search History:')\n", + "for pipeline_result in search.history:\n", + " print('-' * 52)\n", + " print('Pipeline id:', pipeline_result.pipeline.id)\n", + " print(pipeline_result.scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best pipeline:\n", + "----------------------------------------------------\n", + "Pipeline id: ac70b74c-94e1-4bb7-bebf-096153b21f0e\n", + "Pipeline json: {\"id\": \"ac70b74c-94e1-4bb7-bebf-096153b21f0e\", \"schema\": \"https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json\", \"created\": \"2021-07-26T16:46:05.719490Z\", \"inputs\": [{\"name\": \"inputs\"}], \"outputs\": [{\"data\": \"steps.7.produce\", \"name\": \"output predictions\"}], \"steps\": [{\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"c78138d9-9377-31dc-aee8-83d9df049c60\", \"version\": \"0.3.0\", \"python_path\": \"d3m.primitives.tods.data_processing.dataset_to_dataframe\", \"name\": \"Extract a DataFrame from a Dataset\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"inputs.0\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"81235c29-aeb9-3828-911a-1b25319b6998\", \"version\": \"0.6.0\", \"python_path\": \"d3m.primitives.tods.data_processing.column_parser\", \"name\": \"Parses strings into their types\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.0.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"a996cd89-ddf0-367f-8e7f-8c013cbc2891\", \"version\": \"0.4.0\", \"python_path\": \"d3m.primitives.tods.data_processing.extract_columns_by_semantic_types\", \"name\": \"Extracts columns by semantic type\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.1.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"semantic_types\": {\"type\": \"VALUE\", \"data\": [\"https://metadata.datadrivendiscovery.org/types/Attribute\"]}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"a996cd89-ddf0-367f-8e7f-8c013cbc2891\", \"version\": \"0.4.0\", \"python_path\": \"d3m.primitives.tods.data_processing.extract_columns_by_semantic_types\", \"name\": \"Extracts columns by semantic type\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.0.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"semantic_types\": {\"type\": \"VALUE\", \"data\": [\"https://metadata.datadrivendiscovery.org/types/TrueTarget\"]}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"642de2e7-5590-3cab-9266-2a53c326c461\", \"version\": \"0.0.1\", \"python_path\": \"d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler\", \"name\": \"Axis_wise_scale\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.2.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"30bc7cec-2ccc-34bc-9df8-2095bf3b1ae2\", \"version\": \"0.1.0\", \"python_path\": \"d3m.primitives.tods.feature_analysis.statistical_mean\", \"name\": \"Time Series Decompostional\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.4.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"67e7fcdf-d645-3417-9aa4-85cd369487d9\", \"version\": \"0.0.1\", \"python_path\": \"d3m.primitives.tods.detection_algorithm.pyod_ae\", \"name\": \"TODS.anomaly_detection_primitives.AutoEncoder\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.5.produce\"}}, \"outputs\": [{\"id\": \"produce\"}], \"hyperparams\": {\"contamination\": {\"type\": \"VALUE\", \"data\": 0.01}}}, {\"type\": \"PRIMITIVE\", \"primitive\": {\"id\": \"2530840a-07d4-3874-b7d8-9eb5e4ae2bf3\", \"version\": \"0.3.0\", \"python_path\": \"d3m.primitives.tods.data_processing.construct_predictions\", \"name\": \"Construct pipeline predictions output\"}, \"arguments\": {\"inputs\": {\"type\": \"CONTAINER\", \"data\": \"steps.6.produce\"}, \"reference\": {\"type\": \"CONTAINER\", \"data\": \"steps.1.produce\"}}, \"outputs\": [{\"id\": \"produce\"}]}], \"digest\": \"8d7e53142f54c8d22a81dc462cb5f4124b4566aafcfcc46ee8f50d3d966c2e95\"}\n", + "Output:\n", + " d3mIndex anomaly\n", + "0 0 0\n", + "1 1 0\n", + "2 2 0\n", + "3 3 0\n", + "4 4 0\n", + "... ... ...\n", + "1395 1395 0\n", + "1396 1396 0\n", + "1397 1397 1\n", + "1398 1398 1\n", + "1399 1399 0\n", + "\n", + "[1400 rows x 2 columns]\n", + "Scores:\n", + " metric value normalized randomSeed fold\n", + "0 F1_MACRO 0.708549 0.708549 0 0\n" + ] + } + ], + "source": [ + "print('Best pipeline:')\n", + "print('-' * 52)\n", + "print('Pipeline id:', best_pipeline.id)\n", + "print('Pipeline json:', best_pipeline.to_json())\n", + "print('Output:')\n", + "print(best_output)\n", + "print('Scores:')\n", + "print(best_scores)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 6c92ca0a70b219f8c74d7e0f0201238c30c65aaa Mon Sep 17 00:00:00 2001 From: hwy893747147 Date: Thu, 29 Jul 2021 22:11:43 -0500 Subject: [PATCH 6/8] in progress of XGBOD --- tods/detection_algorithm/PyodXGBOD.py | 396 +++++++++++++++++++++++++- 1 file changed, 381 insertions(+), 15 deletions(-) diff --git a/tods/detection_algorithm/PyodXGBOD.py b/tods/detection_algorithm/PyodXGBOD.py index 4eab9092..ec662798 100644 --- a/tods/detection_algorithm/PyodXGBOD.py +++ b/tods/detection_algorithm/PyodXGBOD.py @@ -12,8 +12,25 @@ import numpy as np from sklearn.utils import check_array from sklearn.exceptions import NotFittedError +from sklearn.utils.validation import check_X_y +from xgboost.sklearn import XGBClassifier # conda install -c conda-forge xgboost +from sklearn.utils.validation import check_is_fitted +from sklearn.metrics import roc_auc_score # from numba import njit from pyod.utils.utility import argmaxn +from pyod.models.base import BaseDetector +from pyod.models.base import BaseDetector +from pyod.models.knn import KNN +from pyod.models.lof import LOF +from pyod.models.iforest import IForest +from pyod.models.hbos import HBOS +from pyod.models.ocsvm import OCSVM +from pyod.models.loda import LODA + +from pyod.utils.utility import check_parameter +from pyod.utils.utility import check_detector +from pyod.utils.utility import standardizer +from pyod.utils.utility import precision_n_scores from d3m.container.numpy import ndarray as d3m_ndarray from d3m.container import DataFrame as d3m_dataframe @@ -395,31 +412,380 @@ def set_params(self, *, params: Params) -> None: ### The Implementation of your algorithm ### -class DetectionAlgorithm(BaseDetector): - """ +class XGBOD(BaseDetector): + r"""XGBOD class for outlier detection. + It first uses the passed in unsupervised outlier detectors to extract + richer representation of the data and then concatenates the newly + generated features to the original feature for constructing the augmented + feature space. An XGBoost classifier is then applied on this augmented + feature space. Read more in the :cite:`zhao2018xgbod`. + Parameters + ---------- + estimator_list : list, optional (default=None) + The list of pyod detectors passed in for unsupervised learning + standardization_flag_list : list, optional (default=None) + The list of boolean flags for indicating whether to perform + standardization for each detector. + max_depth : int + Maximum tree depth for base learners. + learning_rate : float + Boosting learning rate (xgb's "eta") + n_estimators : int + Number of boosted trees to fit. + silent : bool + Whether to print messages while running boosting. + objective : string or callable + Specify the learning task and the corresponding learning objective or + a custom objective function to be used (see note below). + booster : string + Specify which booster to use: gbtree, gblinear or dart. + n_jobs : int + Number of parallel threads used to run xgboost. (replaces ``nthread``) + gamma : float + Minimum loss reduction required to make a further partition on a leaf + node of the tree. + min_child_weight : int + Minimum sum of instance weight(hessian) needed in a child. + max_delta_step : int + Maximum delta step we allow each tree's weight estimation to be. + subsample : float + Subsample ratio of the training instance. + colsample_bytree : float + Subsample ratio of columns when constructing each tree. + colsample_bylevel : float + Subsample ratio of columns for each split, in each level. + reg_alpha : float (xgb's alpha) + L1 regularization term on weights. + reg_lambda : float (xgb's lambda) + L2 regularization term on weights. + scale_pos_weight : float + Balancing of positive and negative weights. + base_score: + The initial prediction score of all instances, global bias. + random_state : int + Random number seed. (replaces seed) + # missing : float, optional + # Value in the data which needs to be present as a missing value. If + # None, defaults to np.nan. + importance_type: string, default "gain" + The feature importance type for the ``feature_importances_`` + property: either "gain", + "weight", "cover", "total_gain" or "total_cover". + \*\*kwargs : dict, optional + Keyword arguments for XGBoost Booster object. Full documentation of + parameters can be found here: + https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst. + Attempting to set a parameter via the constructor args and \*\*kwargs + dict simultaneously will result in a TypeError. + Note: \*\*kwargs is unsupported by scikit-learn. We do not + guarantee that parameters passed via this argument will interact + properly with scikit-learn. Attributes ---------- + n_detector_ : int + The number of unsupervised of detectors used. + clf_ : object + The XGBoost classifier. decision_scores_ : numpy array of shape (n_samples,) The outlier scores of the training data. The higher, the more abnormal. Outliers tend to have higher - scores. This value is available once the detector is - fitted. - threshold_ : float - The threshold is based on ``contamination``. It is the - ``n_samples * contamination`` most abnormal samples in - ``decision_scores_``. The threshold is calculated for generating - binary outlier labels. + scores. This value is available once the detector is fitted. labels_ : int, either 0 or 1 The binary labels of the training data. 0 stands for inliers and 1 for outliers/anomalies. It is generated by applying ``threshold_`` on ``decision_scores_``. """ - def __init__(): - pass + def __init__(self, estimator_list=None, standardization_flag_list=None, + max_depth=3, learning_rate=0.1, + n_estimators=100, silent=True, + objective="binary:logistic", booster='gbtree', + n_jobs=1, nthread=None, gamma=0, min_child_weight=1, + max_delta_step=0, subsample=1, colsample_bytree=1, + colsample_bylevel=1, + reg_alpha=0, reg_lambda=1, scale_pos_weight=1, + base_score=0.5, random_state=0, + # missing=None, + **kwargs): + super(XGBOD, self).__init__() + self.estimator_list = estimator_list + self.standardization_flag_list = standardization_flag_list + self.max_depth = max_depth + self.learning_rate = learning_rate + self.n_estimators = n_estimators + self.silent = silent + self.objective = objective + self.booster = booster + self.n_jobs = n_jobs + self.nthread = nthread + self.gamma = gamma + self.min_child_weight = min_child_weight + self.max_delta_step = max_delta_step + self.subsample = subsample + self.colsample_bytree = colsample_bytree + self.colsample_bylevel = colsample_bylevel + self.reg_alpha = reg_alpha + self.reg_lambda = reg_lambda + self.scale_pos_weight = scale_pos_weight + self.base_score = base_score + self.random_state = random_state + # self.missing = missing + self.kwargs = kwargs + + def _init_detectors(self, X): + """initialize unsupervised detectors if no predefined detectors is + provided. + Parameters + ---------- + X : numpy array of shape (n_samples, n_features) + The train data + Returns + ------- + estimator_list : list of object + The initialized list of detectors + standardization_flag_list : list of boolean + The list of bool flag to indicate whether standardization is needed + """ + estimator_list = [] + standardization_flag_list = [] + + # predefined range of n_neighbors for KNN, AvgKNN, and LOF + k_range = [1, 3, 5, 10, 20, 30, 40, 50] + + # validate the value of k + k_range = [k for k in k_range if k < X.shape[0]] + + for k in k_range: + estimator_list.append(KNN(n_neighbors=k, method='largest')) + # estimator_list.append(KNN(n_neighbors=k, method='mean')) + estimator_list.append(LOF(n_neighbors=k)) + # standardization_flag_list.append(True) + standardization_flag_list.append(True) + standardization_flag_list.append(True) + + n_bins_range = [5, 10, 15, 20, 25, 30, 50] + for n_bins in n_bins_range: + estimator_list.append(HBOS(n_bins=n_bins)) + standardization_flag_list.append(False) + + # predefined range of nu for one-class svm + nu_range = [0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99] + for nu in nu_range: + estimator_list.append(OCSVM(nu=nu)) + standardization_flag_list.append(True) + + # predefined range for number of estimators in isolation forests + n_range = [10, 20, 50, 70, 100, 150, 200] + for n in n_range: + estimator_list.append( + IForest(n_estimators=n, random_state=self.random_state)) + standardization_flag_list.append(False) + + # # predefined range for number of estimators in LODA + # n_bins_range = [3, 5, 10, 15, 20, 25, 30, 50] + # for n_bins in n_bins_range: + # estimator_list.append(LODA(n_bins=n_bins)) + # standardization_flag_list.append(False) + + return estimator_list, standardization_flag_list + + def _validate_estimator(self, X): + if self.estimator_list is None: + self.estimator_list, \ + self.standardization_flag_list = self._init_detectors(X) + + # perform standardization for all detectors by default + if self.standardization_flag_list is None: + self.standardization_flag_list = [True] * len(self.estimator_list) + + # validate two lists length + if len(self.estimator_list) != len(self.standardization_flag_list): + raise ValueError( + "estimator_list length ({0}) is not equal " + "to standardization_flag_list length ({1})".format( + len(self.estimator_list), + len(self.standardization_flag_list))) + + # validate the estimator list is not empty + check_parameter(len(self.estimator_list), low=1, + param_name='number of estimators', + include_left=True, include_right=True) + + for estimator in self.estimator_list: + check_detector(estimator) + + return len(self.estimator_list) + + def _generate_new_features(self, X): + X_add = np.zeros([X.shape[0], self.n_detector_]) + + # keep the standardization scalar for test conversion + X_norm = self._scalar.transform(X) + + for ind, estimator in enumerate(self.estimator_list): + if self.standardization_flag_list[ind]: + X_add[:, ind] = estimator.decision_function(X_norm) + + else: + X_add[:, ind] = estimator.decision_function(X) + return X_add + + def fit(self, X, y): + """Fit the model using X and y as training data. + Parameters + ---------- + X : numpy array of shape (n_samples, n_features) + Training data. + y : numpy array of shape (n_samples,) + The ground truth (binary label) + - 0 : inliers + - 1 : outliers + Returns + ------- + self : object + """ + + # Validate inputs X and y + X, y = check_X_y(X, y) + X = check_array(X) + self._set_n_classes(y) + self.n_detector_ = self._validate_estimator(X) + self.X_train_add_ = np.zeros([X.shape[0], self.n_detector_]) + + # keep the standardization scalar for test conversion + X_norm, self._scalar = standardizer(X, keep_scalar=True) + + for ind, estimator in enumerate(self.estimator_list): + if self.standardization_flag_list[ind]: + estimator.fit(X_norm) + self.X_train_add_[:, ind] = estimator.decision_scores_ + + else: + estimator.fit(X) + self.X_train_add_[:, ind] = estimator.decision_scores_ + + # construct the new feature space + self.X_train_new_ = np.concatenate((X, self.X_train_add_), axis=1) + + # initialize, train, and predict on XGBoost + self.clf_ = clf = XGBClassifier(max_depth=self.max_depth, + learning_rate=self.learning_rate, + n_estimators=self.n_estimators, + silent=self.silent, + objective=self.objective, + booster=self.booster, + n_jobs=self.n_jobs, + nthread=self.nthread, + gamma=self.gamma, + min_child_weight=self.min_child_weight, + max_delta_step=self.max_delta_step, + subsample=self.subsample, + colsample_bytree=self.colsample_bytree, + colsample_bylevel=self.colsample_bylevel, + reg_alpha=self.reg_alpha, + reg_lambda=self.reg_lambda, + scale_pos_weight=self.scale_pos_weight, + base_score=self.base_score, + random_state=self.random_state, + # missing=self.missing, + **self.kwargs) + self.clf_.fit(self.X_train_new_, y) + self.decision_scores_ = self.clf_.predict_proba( + self.X_train_new_)[:, 1] + self.labels_ = self.clf_.predict(self.X_train_new_).ravel() + + return self + + def decision_function(self, X): + + check_is_fitted(self, ['clf_', 'decision_scores_', + 'labels_', '_scalar']) + + X = check_array(X) + + # construct the new feature space + X_add = self._generate_new_features(X) + X_new = np.concatenate((X, X_add), axis=1) + + pred_scores = self.clf_.predict_proba(X_new)[:, 1] + return pred_scores.ravel() + + def predict(self, X): + """Predict if a particular sample is an outlier or not. + Calling xgboost `predict` function. + Parameters + ---------- + X : numpy array of shape (n_samples, n_features) + The input samples. + Returns + ------- + outlier_labels : numpy array of shape (n_samples,) + For each observation, tells whether or not + it should be considered as an outlier according to the + fitted model. 0 stands for inliers and 1 for outliers. + """ + + check_is_fitted(self, ['clf_', 'decision_scores_', + 'labels_', '_scalar']) + + X = check_array(X) + + # construct the new feature space + X_add = self._generate_new_features(X) + X_new = np.concatenate((X, X_add), axis=1) + + pred_scores = self.clf_.predict(X_new) + return pred_scores.ravel() + + def predict_proba(self, X): + """Predict the probability of a sample being outlier. + Calling xgboost `predict_proba` function. + Parameters + ---------- + X : numpy array of shape (n_samples, n_features) + The input samples. + Returns + ------- + outlier_labels : numpy array of shape (n_samples,) + For each observation, tells whether or not + it should be considered as an outlier according to the + fitted model. Return the outlier probability, ranging + in [0,1]. + """ + return self.decision_function(X) + + def fit_predict(self, X, y): + self.fit(X, y) + return self.labels_ + + def fit_predict_score(self, X, y, scoring='roc_auc_score'): + """Fit the detector, predict on samples, and evaluate the model by + predefined metrics, e.g., ROC. + Parameters + ---------- + X : numpy array of shape (n_samples, n_features) + The input samples. + y : Ignored + Not used, present for API consistency by convention. + scoring : str, optional (default='roc_auc_score') + Evaluation metric: + - 'roc_auc_score': ROC score + - 'prc_n_score': Precision @ rank n score + Returns + ------- + score : float + """ + + self.fit(X, y) + + if scoring == 'roc_auc_score': + score = roc_auc_score(y, self.decision_scores_) + elif scoring == 'prc_n_score': + score = precision_n_scores(y, self.decision_scores_) + else: + raise NotImplementedError('PyOD built-in scoring only supports ' + 'ROC and Precision @ rank n') - def fit(): - pass + print("{metric}: {score}".format(metric=scoring, score=score)) - def decision_function(self): - pass + return score From be9a6c56925ce67eac2b80622a088db9cc6f2913 Mon Sep 17 00:00:00 2001 From: hwy893747147 Date: Mon, 30 Aug 2021 10:49:32 -0500 Subject: [PATCH 7/8] Autokears examples initial push (not finished) --- examples/sk_examples/AEAutokeras.py | 86 +++++++++++++++++++ examples/sk_examples/SubSeqAutokeras.py | 108 ++++++++++++++++++++++++ 2 files changed, 194 insertions(+) create mode 100644 examples/sk_examples/AEAutokeras.py create mode 100644 examples/sk_examples/SubSeqAutokeras.py diff --git a/examples/sk_examples/AEAutokeras.py b/examples/sk_examples/AEAutokeras.py new file mode 100644 index 00000000..866b8216 --- /dev/null +++ b/examples/sk_examples/AEAutokeras.py @@ -0,0 +1,86 @@ +from autokeras.engine.block import Block +import autokeras as ak +import tensorflow as tf +import numpy as np +import pandas as pd +from tensorflow.python.util import nest +from numpy import asarray +from sklearn.metrics import precision_recall_curve +from sklearn.metrics import accuracy_score +from sklearn.metrics import confusion_matrix +from sklearn.metrics import classification_report +# from sklearn.model_selection import train_test_split +from autokeras import StructuredDataClassifier +from autokeras import StructuredDataRegressor +from tods.sk_interface.detection_algorithm.AutoEncoder_skinterface import AutoEncoderSKI + +#how to split yahoo? +#is y true and y pred correct? +#show the notebook error +#how to get autokeras reports + +#dataset +dataset = pd.read_csv("./yahoo_sub_5.csv") +data = dataset.to_numpy() +labels = dataset.iloc[:,6] +value1 = dataset.iloc[:,2] # delete later +print(labels) + +#tods primitive +transformer = AutoEncoderSKI() +transformer.fit(data) +tods_output = transformer.predict(data) +prediction_score = transformer.predict_score(data) +print('result from AE primitive: \n', tods_output) #sk report +print('score from AE: \n', prediction_score) + +#sk report +y_true = labels +y_pred = tods_output + +print('Accuracy Score: ', accuracy_score(y_true, y_pred)) + +print('confusion matrix: \n', confusion_matrix(y_true, y_pred)) + +print(classification_report(y_true, y_pred)) + +precision, recall, thresholds = precision_recall_curve(y_true, y_pred) +f1_scores = 2*recall*precision/(recall+precision) + +print('Best threshold: ', thresholds[np.argmax(f1_scores)]) +print('Best F1-Score: ', np.max(f1_scores)) + + +#classifier +print('Classifier Starts here:') +search = StructuredDataClassifier(max_trials=15) +# perform the search +search.fit(x=data, y=labels, verbose=0) # y = data label colume +# evaluate the model +loss, acc = search.evaluate(data, labels, verbose=0) +print('Accuracy: %.3f' % acc) +# use the model to make a prediction +# row = [0.0200,0.0371,0.0428,0.0207,0.0954,0.0986,0.1539,0.1601,0.3109,0.2111,0.1609,0.1582,0.2238,0.0645,0.0660,0.2273,0.3100,0.2999,0.5078,0.4797,0.5783,0.5071,0.4328,0.5550,0.6711,0.6415,0.7104,0.8080,0.6791,0.3857,0.1307,0.2604,0.5121,0.7547,0.8537,0.8507,0.6692,0.6097,0.4943,0.2744,0.0510,0.2834,0.2825,0.4256,0.2641,0.1386,0.1051,0.1343,0.0383,0.0324,0.0232,0.0027,0.0065,0.0159,0.0072,0.0167,0.0180,0.0084,0.0090,0.0032] +# X_new = asarray([row]).astype('float32') +yhat = search.predict(data) +print('Predicted: %.3f' % yhat[0]) +# get the best performing model +model = search.export_model() +# summarize the loaded model +model.summary() + +#regressor +print('Regressor Starts here:') +search = StructuredDataRegressor(max_trials=15, loss='mean_absolute_error') +# perform the search +search.fit(x=data, y=labels, verbose=0) # y = data label +mae, _ = search.evaluate(data, labels, verbose=0) +print('MAE: %.3f' % mae) +# use the model to make a prediction +# X_new = asarray([[108]]).astype('float32') +yhat = search.predict(data) +print('Predicted: %.3f' % yhat[0]) +# get the best performing model +model = search.export_model() +# summarize the loaded model +model.summary() diff --git a/examples/sk_examples/SubSeqAutokeras.py b/examples/sk_examples/SubSeqAutokeras.py new file mode 100644 index 00000000..f7f49c58 --- /dev/null +++ b/examples/sk_examples/SubSeqAutokeras.py @@ -0,0 +1,108 @@ +import autokeras as ak +from autokeras.engine.block import Block +import tensorflow as tf +import numpy as np +import pandas as pd +from tensorflow.python.util import nest +from tods.sk_interface.timeseries_processing.SubsequenceSegmentation_skinterface import SubsequenceSegmentationSKI +# load dataset +dataset = pd.read_csv("./yahoo_sub_5.csv") +data = dataset.to_numpy() +labels = dataset.iloc[:,6] +print(labels) +transformer = SubsequenceSegmentationSKI() +tods_output = transformer.produce(data) +print('result from SubsequenceSegmentation primitive:', tods_output) +print(tods_output.shape) + +#autoregression + +class MLPInteraction(Block): + """Module for MLP operation. This block can be configured with different layer, unit, and other settings. + # Attributes: + units (int). The units of all layer in the MLP block. + num_layers (int). The number of the layers in the MLP block. + use_batchnorm (Boolean). Use batch normalization or not. + dropout_rate(float). The value of drop out in the last layer of MLP. + """ + + def __init__(self, + units=None, + num_layers=None, + use_batchnorm=None, + dropout_rate=None, + **kwargs): + super().__init__(**kwargs) + self.units = units + self.num_layers = num_layers + self.use_batchnorm = use_batchnorm + self.dropout_rate = dropout_rate + + def get_state(self): + state = super().get_state() + state.update({ + 'units': self.units, + 'num_layers': self.num_layers, + 'use_batchnorm': self.use_batchnorm, + 'dropout_rate': self.dropout_rate}) + return state + + def set_state(self, state): + super().set_state(state) + self.units = state['units'] + self.num_layers = state['num_layers'] + self.use_batchnorm = state['use_batchnorm'] + self.dropout_rate = state['dropout_rate'] + + def build(self, hp, inputs=None): + input_node = [tf.keras.layers.Flatten()(node) if len(node.shape) > 2 else node for node in nest.flatten(inputs)] + output_node = tf.concat(input_node, axis=1) + num_layers = self.num_layers or hp.Choice('num_layers', [1, 2, 3], default=2) + use_batchnorm = self.use_batchnorm + if use_batchnorm is None: + use_batchnorm = hp.Choice('use_batchnorm', [True, False], default=False) + dropout_rate = self.dropout_rate or hp.Choice('dropout_rate', + [0.0, 0.25, 0.5], + default=0) + + for i in range(num_layers): + units = self.units or hp.Choice( + 'units_{i}'.format(i=i), + [16, 32, 64, 128, 256, 512, 1024], + default=32) + output_node = tf.keras.layers.Dense(units)(output_node) + if use_batchnorm: + output_node = tf.keras.layers.BatchNormalization()(output_node) + output_node = tf.keras.layers.ReLU()(output_node) + output_node = tf.keras.layers.Dropout(dropout_rate)(output_node) + return output_node +inputs = ak.Input(shape=[7,]) #important!!! depends on shape above +print(inputs.shape) +print(inputs.dtype) +mlp_input = MLPInteraction()([inputs]) +mlp_output = MLPInteraction()([mlp_input]) + +# Step 2.3: Setup optimizer to handle the target task +output = ak.RegressionHead()(mlp_output) + +# Step 3: Build the searcher, which provides search algorithm +auto_model = ak.AutoModel(inputs=inputs,#produce + outputs=output, #final mlp out + objective='val_mean_squared_error', + max_trials=5 + ) +# Step 4: Use the searcher to search the recommender +auto_model.fit(x=[tods_output], + y=tods_output, #make new colume of labels of yahoo dataset # first element of next part + batch_size=32, + epochs=5) + +accuracy = auto_model.evaluate(x=[tods_output], + y=labels) + +print(accuracy) +# logger.info('Validation Accuracy (mse): {}'.format(auto_model.evaluate(x=[val_X_categorical], +# y=val_y))) +# # Step 5: Evaluate the searched model +# logger.info('Test Accuracy (mse): {}'.format(auto_model.evaluate(x=[tods_output], +# y=labels))) \ No newline at end of file From 9edd7292a3ea3f72680b3cf59de6c4c6bc168362 Mon Sep 17 00:00:00 2001 From: hwy893747147 Date: Tue, 28 Sep 2021 21:23:32 -0500 Subject: [PATCH 8/8] added files for SOD and updated XGBODPrimitive --- .../script/build_sod_pipeline.py | 78 +++ .../detection_algorithm/PyodXGBOD_pipeline.py | 57 +- tods/common/TODSBasePrimitives.py | 57 ++ tods/common/supervised_learning.py | 47 ++ tods/detection_algorithm/PyodXGBOD.py | 12 +- tods/detection_algorithm/SODBasePrimitive.py | 520 ++++++++++++++++++ .../SODPrimitive_template.py | 159 ++++++ .../core/SOD_algorithm_template.py | 64 +++ tods/resources/.entry_points.ini | 1 + 9 files changed, 974 insertions(+), 21 deletions(-) create mode 100644 examples/axolotl_interface/example_pipelines/script/build_sod_pipeline.py create mode 100644 tods/common/supervised_learning.py create mode 100755 tods/detection_algorithm/SODBasePrimitive.py create mode 100755 tods/detection_algorithm/SODPrimitive_template.py create mode 100644 tods/detection_algorithm/core/SOD_algorithm_template.py diff --git a/examples/axolotl_interface/example_pipelines/script/build_sod_pipeline.py b/examples/axolotl_interface/example_pipelines/script/build_sod_pipeline.py new file mode 100644 index 00000000..e88ae225 --- /dev/null +++ b/examples/axolotl_interface/example_pipelines/script/build_sod_pipeline.py @@ -0,0 +1,78 @@ +from d3m import index +from d3m.metadata.base import ArgumentType +from d3m.metadata.pipeline import Pipeline, PrimitiveStep + +# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest +# extract_columns_by_semantic_types(targets) -> ^ + +# Creating pipeline +pipeline_description = Pipeline() +pipeline_description.add_input(name='inputs') + +# Step 0: dataset_to_dataframe +step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')) +step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') +step_0.add_output('produce') +pipeline_description.add_step(step_0) + +# Step 1: column_parser +step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) +step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_1.add_output('produce') +pipeline_description.add_step(step_1) + +# Step 2: extract_columns_by_semantic_types(attributes) +step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_2.add_output('produce') +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +pipeline_description.add_step(step_2) + +# Step 3: extract_columns_by_semantic_types(targets) +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') +step_3.add_output('produce') +step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) +pipeline_description.add_step(step_3) + +attributes = 'steps.2.produce' +targets = 'steps.3.produce' + +# Step 4: processing +#step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler')) +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_maximum')) +#step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_minimum')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) +step_4.add_output('produce') +pipeline_description.add_step(step_4) + +# Step 5: supervised outlier detection algorithm +step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.sod_primitive')) +step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce') +step_5.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets) +step_5.add_output('produce') +pipeline_description.add_step(step_5) + +# step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_knn')) +# step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce') +# step_5.add_output('produce') +# pipeline_description.add_step(step_5) + +# Step 6: Predictions +step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce') +step_6.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_6.add_output('produce') +pipeline_description.add_step(step_6) + +# Final Output +pipeline_description.add_output(name='output predictions', data_reference='steps.6.produce') + +# Output to json +data = pipeline_description.to_json() +with open('./examples/axolotl_interface/example_pipelines/sod_pipeline.json', 'w') as f: + f.write(data) + print(data) + diff --git a/primitive_tests/detection_algorithm/PyodXGBOD_pipeline.py b/primitive_tests/detection_algorithm/PyodXGBOD_pipeline.py index d7969bad..884ed6f0 100644 --- a/primitive_tests/detection_algorithm/PyodXGBOD_pipeline.py +++ b/primitive_tests/detection_algorithm/PyodXGBOD_pipeline.py @@ -2,21 +2,21 @@ from d3m.metadata.base import ArgumentType from d3m.metadata.pipeline import Pipeline, PrimitiveStep +# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest +# extract_columns_by_semantic_types(targets) -> ^ # Creating pipeline pipeline_description = Pipeline() pipeline_description.add_input(name='inputs') # Step 0: dataset_to_dataframe -primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe') -step_0 = PrimitiveStep(primitive=primitive_0) +step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')) step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0') step_0.add_output('produce') pipeline_description.add_step(step_0) # Step 1: column_parser -primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser') -step_1 = PrimitiveStep(primitive=primitive_1) +step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser')) step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') step_1.add_output('produce') pipeline_description.add_step(step_1) @@ -25,30 +25,55 @@ step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') step_2.add_output('produce') -step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute']) +step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/Attribute']) pipeline_description.add_step(step_2) -# Step 3: SoGaal -step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_xgbod')) -step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) -step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) -step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) -step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce') +# Step 3: extract_columns_by_semantic_types(targets) +step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types')) +step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce') step_3.add_output('produce') +step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, + data=['https://metadata.datadrivendiscovery.org/types/TrueTarget']) pipeline_description.add_step(step_3) -# Step 4: Predictions -step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) -step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce') -step_4.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +attributes = 'steps.2.produce' +targets = 'steps.3.produce' + +# Step 4: processing +#step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler')) +step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_maximum')) +#step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_minimum')) +step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes) step_4.add_output('produce') pipeline_description.add_step(step_4) + +# Step 3: change +step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_xgbod')) +# step_5.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1) +# step_5.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True) +# step_5.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) +step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce') +step_5.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets) +step_5.add_output('produce') +pipeline_description.add_step(step_5) + +# Step 6: Predictions +step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions')) +step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce') +step_6.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce') +step_6.add_output('produce') +pipeline_description.add_step(step_6) + # Final Output -pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce') +pipeline_description.add_output(name='output predictions', data_reference='steps.6.produce') # Output to JSON data = pipeline_description.to_json() with open('example_pipeline.json', 'w') as f: f.write(data) print(data) + + + diff --git a/tods/common/TODSBasePrimitives.py b/tods/common/TODSBasePrimitives.py index cf844324..78d8cbd2 100644 --- a/tods/common/TODSBasePrimitives.py +++ b/tods/common/TODSBasePrimitives.py @@ -6,6 +6,7 @@ from d3m.primitive_interfaces import generator, transformer from d3m.primitive_interfaces.base import * from d3m.primitive_interfaces.unsupervised_learning import UnsupervisedLearnerPrimitiveBase +from tods.common.supervised_learning import SupervisedLearnerPrimitiveBase from d3m.metadata import base as metadata_base, hyperparams, params from d3m import container @@ -198,3 +199,59 @@ def _forward(self, data, method): out = produce_func(inputs=sys_data) data.iloc[i][col_name] = out.value return data + + +class TODSSupervisedLearnerPrimitiveBase(SupervisedLearnerPrimitiveBase[Inputs, Outputs, Params, Hyperparams]): + def __init__(self, *, hyperparams: Hyperparams, + random_seed: int = 0, + docker_containers: Dict[str, DockerContainer] = None) -> None: + super().__init__(hyperparams=hyperparams, random_seed=random_seed, docker_containers=docker_containers) + + def produce(self, *, inputs: container.DataFrame, timeout: float = None, iterations: int = None) -> CallResult[container.DataFrame]: + """ + A noop. + """ + return self._produce(inputs=inputs, timeout=timeout, iterations=iterations) + + def produce_score(self, *, inputs: container.DataFrame, timeout: float = None, iterations: int = None) -> CallResult[container.DataFrame]: + """ + A noop. + """ + return self._produce(inputs=inputs, timeout=timeout, iterations=iterations) + + def fit(self, *, timeout: float = None, iterations: int = None) -> CallResult[None]: + + """ + A noop. + """ + return self._fit(timeout=timeout, iterations=iterations) + + def fit_multi_produce(self, *, produce_methods: typing.Sequence[str], inputs: Inputs, outputs: Outputs, timeout: float = None, iterations: int = None) -> MultiCallResult: + + return self._fit_multi_produce(produce_methods=produce_methods, timeout=timeout, iterations=iterations, inputs=inputs, outputs=outputs) + + # def _produce(self, *, inputs: container.DataFrame, timeout: float = None, iterations: int = None) -> CallResult[container.DataFrame]: + # + # pass + # + # def _produce_score(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> CallResult[Outputs]: + # + # pass + # + # def _fit(self, *, timeout: float = None, iterations: int = None) -> CallResult[None]: + # + # pass + + def get_params(self) -> None: + """ + A noop. + """ + + return None + + def set_params(self, *, params: None) -> None: + """ + A noop. + """ + + return diff --git a/tods/common/supervised_learning.py b/tods/common/supervised_learning.py new file mode 100644 index 00000000..f0311ff4 --- /dev/null +++ b/tods/common/supervised_learning.py @@ -0,0 +1,47 @@ +from d3m.primitive_interfaces.base import * +import abc +import typing + +__all__ = ('SupervisedLearnerPrimitiveBase',) + + +class SupervisedLearnerPrimitiveBase(PrimitiveBase[Inputs, Outputs, Params, Hyperparams]): + """ + A base class for primitives which have to be fitted on both input and output data + before they can start producing (useful) outputs from inputs. + """ + @abc.abstractmethod + def set_training_data(self, *, inputs: Inputs, outputs: Outputs) -> None: # type: ignore + """ + Sets training data of this primitive. + + Parameters + ---------- + inputs: + inputs: Container DataFrame of instance feature + outputs: Container DataFrame of label + """ + + def fit_multi_produce(self, *, produce_methods: typing.Sequence[str], inputs: Inputs, outputs: Outputs, timeout: float = None, iterations: int = None) -> MultiCallResult: # type: ignore + """ + A method calling ``fit`` and after that multiple produce methods at once. + + Parameters + ---------- + produce_methods: + A list of names of produce methods to call. + inputs: + The inputs given to ``set_training_data`` and all produce methods. + timeout: + A maximum time this primitive should take to both fit the primitive and produce outputs + for all produce methods listed in ``produce_methods`` argument, in seconds. + iterations: + How many of internal iterations should the primitive do for both fitting and producing + outputs of all produce methods. + + Returns + ------- + A dict of values for each produce method wrapped inside ``MultiCallResult``. + """ + + return self._fit_multi_produce(produce_methods=produce_methods, timeout=timeout, iterations=iterations, inputs=inputs, outputs=outputs) diff --git a/tods/detection_algorithm/PyodXGBOD.py b/tods/detection_algorithm/PyodXGBOD.py index ec662798..fc2554af 100644 --- a/tods/detection_algorithm/PyodXGBOD.py +++ b/tods/detection_algorithm/PyodXGBOD.py @@ -50,7 +50,9 @@ import pandas from d3m import container, utils as d3m_utils +from .SODBasePrimitive import Params_SODBase, Hyperparams_SODBase, SupervisedOutlierDetectorBase +from ..common.TODSBasePrimitives import TODSSupervisedLearnerPrimitiveBase, TODSUnsupervisedLearnerPrimitiveBase from .UODBasePrimitive import Params_ODBase, Hyperparams_ODBase, UnsupervisedOutlierDetectorBase from pyod.models.xgbod import XGBOD # from typing import Union @@ -59,13 +61,13 @@ Outputs = d3m_dataframe -class Params(Params_ODBase): +class Params(Params_SODBase): ### Add more Attributes ### pass -class Hyperparams(Hyperparams_ODBase): +class Hyperparams(Hyperparams_SODBase): ### Add more Hyperparamters ### estimator_list = hyperparams.Union[Union[int, None]]( configuration=OrderedDict( @@ -207,7 +209,7 @@ class Hyperparams(Hyperparams_ODBase): # ) ### Name of your algorithm ### -class XGBODPrimitive(UnsupervisedOutlierDetectorBase[Inputs, Outputs, Params, Hyperparams]): +class XGBODPrimitive(SupervisedOutlierDetectorBase[Inputs, Outputs, Params, Hyperparams]): """ XGBOD class for outlier detection. It first uses the passed in unsupervised outlier detectors to extract @@ -342,7 +344,7 @@ def __init__(self, *, ) - def set_training_data(self, *, inputs: Inputs) -> None: + def set_training_data(self, *, inputs: Inputs, outputs: Outputs) -> None: """ Set training data for outlier detection. Args: @@ -351,7 +353,7 @@ def set_training_data(self, *, inputs: Inputs) -> None: Returns: None """ - super().set_training_data(inputs=inputs) + super().set_training_data(inputs=inputs, outputs = outputs) def fit(self, *, timeout: float = None, iterations: int = None) -> CallResult[None]: """ diff --git a/tods/detection_algorithm/SODBasePrimitive.py b/tods/detection_algorithm/SODBasePrimitive.py new file mode 100755 index 00000000..63b648a1 --- /dev/null +++ b/tods/detection_algorithm/SODBasePrimitive.py @@ -0,0 +1,520 @@ +from typing import Any, Callable, List, Dict, Union, Optional, Sequence, Tuple +from numpy import ndarray +from collections import OrderedDict +from scipy import sparse +import os +import sklearn +import numpy +# import typing +import abc +import typing +# +# # Custom import commands if any +import warnings +import numpy as np + +import copy + +from d3m.container.numpy import ndarray as d3m_ndarray +from d3m.container import DataFrame as d3m_dataframe +from d3m.metadata import hyperparams, params, base as metadata_base +from d3m import utils +from d3m.base import utils as base_utils +from d3m.exceptions import PrimitiveNotFittedError +from d3m.primitive_interfaces.base import CallResult, DockerContainer, PrimitiveBase, MultiCallResult, Params, Hyperparams + +from ..common.TODSBasePrimitives import TODSSupervisedLearnerPrimitiveBase, TODSUnsupervisedLearnerPrimitiveBase + +from d3m.primitive_interfaces.base import * + +Inputs = d3m_dataframe +# Inputs = container.Dataset +Outputs = d3m_dataframe + +# import abc +# import typing + +from d3m.primitive_interfaces.base import * + +__all__ = ('SupervisedOutlierDetectorBase',) + + +class Params_SODBase(params.Params): + clf_: Optional[Any] + fitted: Optional[bool] + + # D3M parameters + input_column_names: Optional[Any] + target_names_: Optional[Sequence[Any]] + training_indices_: Optional[Sequence[int]] + target_column_indices_: Optional[Sequence[int]] + target_columns_metadata_: Optional[List[OrderedDict]] + + + +class Hyperparams_SODBase(hyperparams.Hyperparams): + + # D3M hyperparameters + use_columns = hyperparams.Set( + elements=hyperparams.Hyperparameter[int](-1), + default=(), + semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], + description="A set of column indices to force primitive to operate on. If any specified column cannot be parsed, it is skipped.", + ) + exclude_columns = hyperparams.Set( + elements=hyperparams.Hyperparameter[int](-1), + default=(), + semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], + description="A set of column indices to not operate on. Applicable only if \"use_columns\" is not provided.", + ) + return_result = hyperparams.Enumeration( + values=['append', 'replace', 'new'], + default='new', + semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], + description="Should parsed columns be appended, should they replace original columns, or should only parsed columns be returned? This hyperparam is ignored if use_semantic_types is set to false.", + ) + use_semantic_types = hyperparams.UniformBool( + default=False, + semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], + description="Controls whether semantic_types metadata will be used for filtering columns in input dataframe. Setting this to false makes the code ignore return_result and will produce only the output dataframe" + ) + add_index_columns = hyperparams.UniformBool( + default=False, + semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], + description="Also include primary index columns if input data has them. Applicable only if \"return_result\" is set to \"new\".", + ) + error_on_no_input = hyperparams.UniformBool( + default=True, + semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], + description="Throw an exception if no input column is selected/provided. Defaults to true to behave like sklearn. To prevent pipelines from breaking set this to False.", + ) + + return_semantic_type = hyperparams.Enumeration[str]( + values=['https://metadata.datadrivendiscovery.org/types/Attribute', + 'https://metadata.datadrivendiscovery.org/types/ConstructedAttribute'], + default='https://metadata.datadrivendiscovery.org/types/Attribute', + description='Decides what semantic type to attach to generated attributes', + semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'] + ) + +class SupervisedOutlierDetectorBase(TODSSupervisedLearnerPrimitiveBase[Inputs, Outputs, Params, Hyperparams]): + """ + Attributes + ---------- + _clf: The Supervised Learner + + """ + __author__ = "DATALAB @Taxes A&M University" + metadata: metadata_base.PrimitiveMetadata = None + + def __init__(self, *, + hyperparams: Hyperparams, + random_seed: int = 0, + docker_containers: Dict[str, DockerContainer] = None) -> None: + super().__init__(hyperparams=hyperparams, random_seed=random_seed, docker_containers=docker_containers) + + self._clf = None + self._clf_fit_parameter = {} + self.primitiveNo = 0 + + self._inputs = None + self._outputs = None + self._training_inputs = None + self._training_outputs = None + self._target_names = None + self._training_indices = None + self._target_column_indices = None + self._target_columns_metadata: List[OrderedDict] = None + self._input_column_names = None + self._fitted = False + self._new_training_data = False +# + @abc.abstractmethod + def set_training_data(self, *, inputs: Inputs, outputs: Outputs) -> None: + """ + Set training data for outlier detection. + Args: + inputs: Container DataFrame of instance feature + outputs: Container DataFrame of label + + Returns: + None + """ + self._inputs = inputs + self._outputs = outputs + self._fitted = False + self._new_training_data = True + + def _fit(self, *, timeout: float = None, iterations: int = None) -> CallResult[None]: + """ + Fit model with training data. + Args: + *: Container DataFrame. Time series data up to fit. + + Returns: + None + """ + + if self._inputs is None or self._outputs is None: # pragma: no cover + raise ValueError("Missing training data.") + + if not self._new_training_data: # pragma: no cover + return CallResult(None) + self._new_training_data = False + + self._training_inputs, self._training_indices = self._get_columns_to_fit(self._inputs, self.hyperparams) + self._training_outputs, self._target_names, self._target_column_indices = self._get_targets(self._outputs, self.hyperparams) + self._input_column_names = self._training_inputs.columns + + if len(self._training_indices) > 0 and len(self._target_column_indices) > 0: + self._target_columns_metadata = self._get_target_columns_metadata(self._training_outputs.metadata, self.hyperparams) + sk_training_output = self._training_outputs.values + + shape = sk_training_output.shape + if len(shape) == 2 and shape[1] == 1: + sk_training_output = numpy.ravel(sk_training_output) + + self._clf.fit(X=self._training_inputs.values, y=sk_training_output, **self._clf_fit_parameter) + self._fitted = True + else: # pragma: no cover + if self.hyperparams['error_on_no_input']: + raise RuntimeError("No input columns were selected") + self.logger.warn("No input columns were selected") + + return CallResult(None) + + def _produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> CallResult[Outputs]: + """ + Process the testing data. + Args: + inputs: Container DataFrame. Time series data up to outlier detection. + + Returns: + Container DataFrame + 1 marks Outliers, 0 marks normal. + """ + sk_inputs, columns_to_use = self._get_columns_to_fit(inputs, self.hyperparams) + output = [] + + if len(sk_inputs.columns): + try: + sk_output = self._clf.predict(X=sk_inputs.values) + # print(sk_output) + except sklearn.exceptions.NotFittedError as error: + raise PrimitiveNotFittedError("Primitive not fitted.") from error + # For primitives that allow predicting without fitting like GaussianProcessRegressor + if not self._fitted: # pragma: no cover + raise PrimitiveNotFittedError("Primitive not fitted.") + if sparse.issparse(sk_output): + sk_output = sk_output.toarray() + output = self._wrap_predictions(inputs, sk_output) + output.columns = self._target_names + output = [output] + else: # pragma: no cover + if self.hyperparams['error_on_no_input']: + raise RuntimeError("No input columns were selected") + self.logger.warn("No input columns were selected") + outputs = base_utils.combine_columns(return_result=self.hyperparams['return_result'], + add_index_columns=self.hyperparams['add_index_columns'], + inputs=inputs, column_indices=self._target_column_indices, + columns_list=output) + + return CallResult(outputs) + + def get_params(self) -> Params_SODBase: + """ + Return parameters. + Args: + None + + Returns: + class Params_ODBase + """ + + if not self._fitted: + return Params_SODBase( + clf_=copy.copy(self._clf), + fitted=self._fitted, + + # D3M hyperparameters + input_column_names=self._input_column_names, + training_indices_=self._training_indices, + target_names_=self._target_names, + target_column_indices_=self._target_column_indices, + target_columns_metadata_=self._target_columns_metadata + ) + + return Params_SODBase( + clf_=copy.copy(self._clf), + fitted=self._fitted, + + # D3M hyperparameters + input_column_names=self._input_column_names, + training_indices_=self._training_indices, + target_names_=self._target_names, + target_column_indices_=self._target_column_indices, + target_columns_metadata_=self._target_columns_metadata + ) + # pass + + + def set_params(self, *, params: Params_SODBase) -> None: + """ + Set parameters for outlier detection. + Args: + params: class Params_ODBase + + Returns: + None + """ + + self._clf = copy.copy(params['clf_']) + self._fitted = params['fitted'] + + # D3M hyperparameters + self._input_column_names = params['input_column_names'] + self._training_indices = params['training_indices_'] + self._target_names = params['target_names_'] + self._target_column_indices = params['target_column_indices_'] + self._target_columns_metadata = params['target_columns_metadata_'] + + + @classmethod + def _get_columns_to_fit(cls, inputs: Inputs, hyperparams: Hyperparams): + """ + Select columns to fit. + Args: + inputs: Container DataFrame + hyperparams: d3m.metadata.hyperparams.Hyperparams + + Returns: + list + """ + if not hyperparams['use_semantic_types']: + return inputs, list(range(len(inputs.columns))) + + inputs_metadata = inputs.metadata + + def can_produce_column(column_index: int) -> bool: + return cls._can_produce_column(inputs_metadata, column_index, hyperparams) + + columns_to_produce, columns_not_to_produce = base_utils.get_columns_to_use(inputs_metadata, + use_columns=hyperparams[ + 'use_inputs_columns'], + exclude_columns=hyperparams[ + 'exclude_inputs_columns'], + can_use_column=can_produce_column) + return inputs.iloc[:, columns_to_produce], columns_to_produce + # return columns_to_produce + + @classmethod + def _can_produce_column(cls, inputs_metadata: metadata_base.DataMetadata, column_index: int, + hyperparams: Hyperparams) -> bool: + """ + Output whether a column can be processed. + Args: + inputs_metadata: d3m.metadata.base.DataMetadata + column_index: int + + Returns: + bool + """ + column_metadata = inputs_metadata.query((metadata_base.ALL_ELEMENTS, column_index)) + + accepted_structural_types = (int, float, numpy.integer, numpy.float64) + accepted_semantic_types = set() + accepted_semantic_types.add("https://metadata.datadrivendiscovery.org/types/Attribute") + if not issubclass(column_metadata['structural_type'], accepted_structural_types): + return False + + semantic_types = set(column_metadata.get('semantic_types', [])) + + if len(semantic_types) == 0: + cls.logger.warning("No semantic types found in column metadata") + return False + # Making sure all accepted_semantic_types are available in semantic_types + if len(accepted_semantic_types - semantic_types) == 0: + return True + + return False + + @classmethod + def _get_targets(cls, data: d3m_dataframe, hyperparams: Hyperparams): + + if not hyperparams['use_semantic_types']: + return data, list(data.columns), list(range(len(data.columns))) + + metadata = data.metadata + + def can_produce_column(column_index: int) -> bool: + accepted_semantic_types = set() + accepted_semantic_types.add("https://metadata.datadrivendiscovery.org/types/TrueTarget") + column_metadata = metadata.query((metadata_base.ALL_ELEMENTS, column_index)) + semantic_types = set(column_metadata.get('semantic_types', [])) + if len(semantic_types) == 0: + cls.logger.warning("No semantic types found in column metadata") + return False + # Making sure all accepted_semantic_types are available in semantic_types + if len(accepted_semantic_types - semantic_types) == 0: + return True + return False + + target_column_indices, target_columns_not_to_produce = base_utils.get_columns_to_use(metadata, + use_columns=hyperparams[ + 'use_outputs_columns'], + exclude_columns= + hyperparams[ + 'exclude_outputs_columns'], + can_use_column=can_produce_column) + targets = [] + if target_column_indices: + targets = data.select_columns(target_column_indices) + target_column_names = [] + for idx in target_column_indices: + target_column_names.append(data.columns[idx]) + return targets, target_column_names, target_column_indices + + @classmethod + def _get_target_columns_metadata(cls, outputs_metadata: metadata_base.DataMetadata, hyperparams) -> List[ + OrderedDict]: + """ + Output metadata of selected columns. + Args: + outputs_metadata: metadata_base.DataMetadata + hyperparams: d3m.metadata.hyperparams.Hyperparams + + Returns: + d3m.metadata.base.DataMetadata + """ + outputs_length = outputs_metadata.query((metadata_base.ALL_ELEMENTS,))['dimension']['length'] + + target_columns_metadata: List[OrderedDict] = [] + for column_index in range(outputs_length): + column_metadata = OrderedDict(outputs_metadata.query_column(column_index)) + + # Update semantic types and prepare it for predicted targets. + semantic_types = set(column_metadata.get('semantic_types', [])) + semantic_types_to_remove = set(["https://metadata.datadrivendiscovery.org/types/TrueTarget", + "https://metadata.datadrivendiscovery.org/types/SuggestedTarget", ]) + add_semantic_types = set(["https://metadata.datadrivendiscovery.org/types/PredictedTarget", ]) + add_semantic_types.add(hyperparams["return_semantic_type"]) + semantic_types = semantic_types - semantic_types_to_remove + semantic_types = semantic_types.union(add_semantic_types) + column_metadata['semantic_types'] = list(semantic_types) + + target_columns_metadata.append(column_metadata) + + return target_columns_metadata + + @classmethod + def _update_predictions_metadata(cls, inputs_metadata: metadata_base.DataMetadata, outputs: Optional[Outputs], + target_columns_metadata: List[OrderedDict]) -> metadata_base.DataMetadata: + """ + Updata metadata for selected columns. + Args: + inputs_metadata: metadata_base.DataMetadata + outputs: Container Dataframe + target_columns_metadata: list + + Returns: + d3m.metadata.base.DataMetadata + """ + outputs_metadata = metadata_base.DataMetadata().generate(value=outputs) + + for column_index, column_metadata in enumerate(target_columns_metadata): + column_metadata.pop("structural_type", None) + outputs_metadata = outputs_metadata.update_column(column_index, column_metadata) + + return outputs_metadata + + def _wrap_predictions(self, inputs: Inputs, predictions: ndarray) -> Outputs: + """ + Wrap predictions into dataframe + Args: + inputs: Container Dataframe + predictions: array-like data (n_samples, n_features) + + Returns: + Dataframe + """ + outputs = d3m_dataframe(predictions, generate_metadata=False) + outputs.metadata = self._update_predictions_metadata(inputs.metadata, outputs, self._target_columns_metadata) + return outputs + + @classmethod + def _add_target_columns_metadata(cls, outputs_metadata: metadata_base.DataMetadata): + """ + Add target columns metadata + Args: + outputs_metadata: metadata.base.DataMetadata + hyperparams: d3m.metadata.hyperparams.Hyperparams + + Returns: + List[OrderedDict] + """ + outputs_length = outputs_metadata.query((metadata_base.ALL_ELEMENTS,))['dimension']['length'] + + target_columns_metadata: List[OrderedDict] = [] + for column_index in range(outputs_length): + column_metadata = OrderedDict() + semantic_types = [] + semantic_types.append('https://metadata.datadrivendiscovery.org/types/PredictedTarget') + column_name = outputs_metadata.query((metadata_base.ALL_ELEMENTS, column_index)).get("name") + if column_name is None: + column_name = "output_{}".format(column_index) + column_metadata["semantic_types"] = semantic_types + column_metadata["name"] = str(column_name) + target_columns_metadata.append(column_metadata) + + return target_columns_metadata + + @classmethod + def _copy_inputs_metadata(cls, inputs_metadata: metadata_base.DataMetadata, input_indices: List[int], + outputs_metadata: metadata_base.DataMetadata, hyperparams): # pragma: no cover + """ + Updata metadata for selected columns. + Args: + inputs_metadata: metadata.base.DataMetadata + input_indices: list + outputs_metadata: metadata.base.DataMetadata + hyperparams: d3m.metadata.hyperparams.Hyperparams + + Returns: + d3m.metadata.base.DataMetadata + """ + outputs_length = outputs_metadata.query((metadata_base.ALL_ELEMENTS,))['dimension']['length'] + target_columns_metadata: List[OrderedDict] = [] + for column_index in input_indices: + column_name = inputs_metadata.query((metadata_base.ALL_ELEMENTS, column_index)).get("name") + if column_name is None: + column_name = "output_{}".format(column_index) + + column_metadata = OrderedDict(inputs_metadata.query_column(column_index)) + semantic_types = set(column_metadata.get('semantic_types', [])) + semantic_types_to_remove = set([]) + add_semantic_types = set() + add_semantic_types.add(hyperparams["return_semantic_type"]) + semantic_types = semantic_types - semantic_types_to_remove + semantic_types = semantic_types.union(add_semantic_types) + column_metadata['semantic_types'] = list(semantic_types) + + column_metadata["name"] = str(column_name) + target_columns_metadata.append(column_metadata) + + # If outputs has more columns than index, add Attribute Type to all remaining + if outputs_length > len(input_indices): + for column_index in range(len(input_indices), outputs_length): + column_metadata = OrderedDict() + semantic_types = set() + semantic_types.add(hyperparams["return_semantic_type"]) + column_name = "output_{}".format(column_index) + column_metadata["semantic_types"] = list(semantic_types) + column_metadata["name"] = str(column_name) + target_columns_metadata.append(column_metadata) + + return target_columns_metadata + + +SupervisedOutlierDetectorBase.__doc__ = SupervisedOutlierDetectorBase.__doc__ + + diff --git a/tods/detection_algorithm/SODPrimitive_template.py b/tods/detection_algorithm/SODPrimitive_template.py new file mode 100755 index 00000000..669a51d5 --- /dev/null +++ b/tods/detection_algorithm/SODPrimitive_template.py @@ -0,0 +1,159 @@ +from typing import Any, Callable, List, Dict, Union, Optional, Sequence, Tuple +from numpy import ndarray +from collections import OrderedDict +from scipy import sparse +import os +import sklearn +import numpy +import typing + +# Custom import commands if any +import warnings +import numpy as np +from sklearn.utils import check_array +from sklearn.exceptions import NotFittedError +# from numba import njit +from pyod.utils.utility import argmaxn + +from d3m.container.numpy import ndarray as d3m_ndarray +from d3m.container import DataFrame as d3m_dataframe +from d3m.metadata import hyperparams, params, base as metadata_base +from d3m import utils +from d3m.base import utils as base_utils +from d3m.exceptions import PrimitiveNotFittedError +from d3m.primitive_interfaces.base import CallResult, DockerContainer + +# from d3m.primitive_interfaces.supervised_learning import SupervisedLearnerPrimitiveBase +# from d3m.primitive_interfaces.unsupervised_learning import UnsupervisedLearnerPrimitiveBase +# from d3m.primitive_interfaces.transformer import TransformerPrimitiveBase + +from d3m.primitive_interfaces.base import ProbabilisticCompositionalityMixin, ContinueFitMixin +from d3m import exceptions +import pandas + +from d3m import container, utils as d3m_utils + +from .SODBasePrimitive import Params_SODBase, Hyperparams_SODBase, SupervisedOutlierDetectorBase +from .UODBasePrimitive import Params_ODBase, Hyperparams_ODBase, UnsupervisedOutlierDetectorBase +# from pyod.models.knn import KNN +from .core.SOD_algorithm_template import SODetector +from typing import Union +import uuid + +__all__ = ('SODPrimitive',) + +Inputs = d3m_dataframe +Outputs = d3m_dataframe + + +class Params(Params_SODBase): + ######## Add more Attributes ####### + + pass + + +class Hyperparams(Hyperparams_SODBase): + ######## Add more Hyperparamters ####### + + pass + + +class SODPrimitive(SupervisedOutlierDetectorBase[Inputs, Outputs, Params, Hyperparams]): + """ + Template of the wrapper of Supervised Oulier Detector + + Parameters + ---------- + Add the parameters here. + + Attributes + ---------- + Add the attributes here. + + """ + + metadata = metadata_base.PrimitiveMetadata({ + "__author__": "DATA Lab at Texas A&M University", + "name": "TODS.anomaly_detection_primitives.SODPrimitive", + "python_path": "d3m.primitives.tods.detection_algorithm.sod_primitive", + "source": { + 'name': "DATA Lab @Taxes A&M University", + 'contact': 'mailto:khlai037@tamu.edu', + }, + "hyperparams_to_tune": ['use_columns'], + "version": "0.0.1", + "algorithm_types": [ + metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, + ], + "primitive_family": metadata_base.PrimitiveFamily.ANOMALY_DETECTION, + "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'SODPrimitive')) + }) + + def __init__(self, *, + hyperparams: Hyperparams, # + random_seed: int = 0, + docker_containers: Dict[str, DockerContainer] = None) -> None: + super().__init__(hyperparams=hyperparams, random_seed=random_seed, docker_containers=docker_containers) + + self._clf = SODetector() + + return + + def set_training_data(self, *, inputs: Inputs, outputs: Outputs) -> None: + """ + Set training data for outlier detection. + Args: + inputs: Container DataFrame + outputs: Container DataFrame of label + + Returns: + None + """ + super().set_training_data(inputs=inputs, outputs=outputs) + + def fit(self, *, timeout: float = None, iterations: int = None) -> CallResult[None]: + """ + Fit model with training data. + Args: + *: Container DataFrame. Time series data up to fit. + + Returns: + None + """ + return super().fit() + + def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> CallResult[Outputs]: + """ + Process the testing data. + Args: + inputs: Container DataFrame. Time series data up to outlier detection. + + Returns: + Container DataFrame + 1 marks Outliers, 0 marks normal. + """ + return super().produce(inputs=inputs, timeout=timeout, iterations=iterations) + + def get_params(self) -> Params: + """ + Return parameters. + Args: + None + + Returns: + class Params + """ + return super().get_params() + + def set_params(self, *, params: Params) -> None: + """ + Set parameters for outlier detection. + Args: + params: class Params + + Returns: + None + """ + super().set_params(params=params) + + diff --git a/tods/detection_algorithm/core/SOD_algorithm_template.py b/tods/detection_algorithm/core/SOD_algorithm_template.py new file mode 100644 index 00000000..71787301 --- /dev/null +++ b/tods/detection_algorithm/core/SOD_algorithm_template.py @@ -0,0 +1,64 @@ +# -*- coding: utf-8 -*- +"""Autoregressive model for univariate time series outlier detection. +""" +import numpy as np +# from .CollectiveBase import CollectiveBaseDetector +from pyod.models.knn import KNN + + +class SODetector: # Add the class to inherit here + """ + Template of Supervised Oulier Detector + + """ + + def __init__(self): + + pass + + + + def _build_model(self): + model_ = KNN() + return model_ + + def fit(self, X: np.array, y: np.array, **kwargs) -> object: + """Fit detector. y is ignored in unsupervised methods. + + Parameters + ---------- + X : numpy array of shape (n_samples, n_features) + The input samples. + + y : numpy array of shape (n_samples, n_features) + The annotations of input samples. + For supervised outlier detection: + 0: normal sample. + 1: anomaly. + + For semi-supervised outlier detection: + 0: normal sample. + 1: anomaly. + -1: non-annotated. + + Returns + ------- + self : object + Fitted estimator. + """ + self.learner = self._build_model() + self.learner.fit(X) + # print("FIT Finished!") + + return self + + def predict(self, X: np.array) -> np.array: + + # print(X) + # y = np.array([0] * (X.shape[0]-1) + [1]).astype(np.int) + # y = np.zeros(X.shape[0]).astype(np.int) + y = self.learner.predict(X) + # print(y) + return y.astype('int').ravel() + + diff --git a/tods/resources/.entry_points.ini b/tods/resources/.entry_points.ini index 9b350525..17b86036 100644 --- a/tods/resources/.entry_points.ini +++ b/tods/resources/.entry_points.ini @@ -93,3 +93,4 @@ tods.evaluation.redact_columns = tods.common.RedactColumns:RedactColumnsPrimitiv tods.common.csv_reader = tods.common.CSVReader:CSVReaderPrimitive tods.common.denormalize = tods.common.Denormalize:DenormalizePrimitive +tods.detection_algorithm.sod_primitive = tods.detection_algorithm.SODPrimitive_template:SODPrimitive \ No newline at end of file