Commit Live PR

q01_grid_search/build.py

@@ -1,22 +1,27 @@
-# Default imports
-
-import warnings
-warnings.filterwarnings("ignore")
 import pandas as pd
+import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import GridSearchCV
 
 loan_data = pd.read_csv('data/loan_prediction.csv')
-X_bal = loan_data.iloc[:, :-1]
-y_bal = loan_data.iloc[:, -1]
+X = loan_data.iloc[:, :-1]
+y = loan_data.iloc[:, -1]
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=9)
+
+param_grid1 = {'max_features': ['sqrt', 4, 'log2'],
+              'n_estimators': [10, 50, 120],
+              'max_depth': [40, 20, 10],
+              'max_leaf_nodes': [5, 10, 2]}
 
-X_train, X_test, y_train, y_test = train_test_split(X_bal, y_bal, test_size=0.33, random_state=9)
-param_grid = {"max_features": ['sqrt', 4, "log2"],
-              "n_estimators": [10, 50, 120],
-              "max_depth": [40, 20, 10],
-              "max_leaf_nodes": [5, 10, 2]}
+rfc = RandomForestClassifier(oob_score=True ,random_state=9)
 
+def grid_search(X_train1, y_train1,modelR,params,cv=3): 
+    GSCV_rfc = GridSearchCV(estimator=modelR, param_grid=params, cv=cv)
+    GSCV_rfc.fit(X_train1,y_train1)
+    param_list = GSCV_rfc.cv_results_['params']
+    score1 = GSCV_rfc.cv_results_['mean_test_score']
+    return GSCV_rfc,param_list,score1
 
-# Write your solution here :
 

q02_fit/build.py

@@ -1,27 +1,41 @@
-# Default imports
-
 import pandas as pd
-from greyatomlib.random_forest_project.q01_grid_search.build import grid_search
-from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
+import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
-
+from sklearn.model_selection import GridSearchCV
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import classification_report
+from sklearn.metrics import accuracy_score
 
 loan_data = pd.read_csv('data/loan_prediction.csv')
-X_bal = loan_data.iloc[:, :-1]
-y_bal = loan_data.iloc[:, -1]
+X = loan_data.iloc[:, :-1]
+y = loan_data.iloc[:, -1]
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=9)
+
+param_grid1 = {'max_features': ['sqrt', 4, 'log2'],
+              'n_estimators': [10, 50, 120],
+              'max_depth': [40, 20, 10],
+              'max_leaf_nodes': [5, 10, 2]}
 
-X_train, X_test, y_train, y_test = train_test_split(X_bal, y_bal, test_size=0.33, random_state=9)
-rfc = RandomForestClassifier(oob_score=True, random_state=9)
-param_grid = {"max_features": ['sqrt', 4, "log2"],
-              "n_estimators": [10, 50, 120],
-              "max_depth": [40, 20, 10],
-              "max_leaf_nodes": [5, 10, 2]}
+rfc = RandomForestClassifier(oob_score = True,random_state=9)
 
-grid, grid_param, grid_score = grid_search(X_train, y_train, rfc, param_grid, cv=3)
+def grid_search(X_train1, y_train1,modelR,params,cv): 
+    GSCV_rfc = GridSearchCV(estimator=modelR, param_grid=params, cv=3)
+    GSCV_rfc.fit(X_train1,y_train1)
+    param_list = GSCV_rfc.cv_results_['params']
+    scoreA = GSCV_rfc.cv_results_['mean_test_score']
+    return GSCV_rfc,param_list,scoreA
 
+GSCV_rfc1,param_list1,score1 = grid_search(X_train, y_train,rfc,param_grid1,3)
 
-# Write your solution here :
+model = GSCV_rfc1.best_estimator_
+y_pred = model.fit(X_train, y_train).predict(X_test)
 
+def fit(X_test,y_test):
+    acc_score = accuracy_score(y_test, y_pred)
+    conf_matrix = confusion_matrix(y_test, y_pred)
+    c_report = classification_report(y_test, y_pred)
+    return conf_matrix,c_report,acc_score