Commit Live PR

q01_bagging/build.py

@@ -1,18 +1,43 @@
 import pandas as pd
 from sklearn.model_selection import train_test_split
-from sklearn.tree import DecisionTreeClassifier
 from sklearn.ensemble import BaggingClassifier
+from sklearn.tree import DecisionTreeClassifier
 import matplotlib.pyplot as plt
 from sklearn.metrics import accuracy_score
 
-# Data Loading
 dataframe = pd.read_csv('data/loan_prediction.csv')
 
 X = dataframe.iloc[:, :-1]
 y = dataframe.iloc[:, -1]
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=9)
 
-
-# Write your code here
+scores1=[]
+scores2=[]
+def bagging(X_train, X_test, y_train, y_test,n_est):
+    n_est=51
+    estimators=range(1,n_est)
+    decision_clf = DecisionTreeClassifier()
+
+    for est in estimators:
+        bagging_clf = BaggingClassifier(decision_clf, n_estimators=est, max_samples=0.67,max_features=0.67, 
+                                    bootstrap=True, random_state=9)
+        bagging_clf.fit(X_train, y_train)
+        # test line
+        y_pred_bagging1 = bagging_clf.predict(X_test)
+        score_bc_dt1 = accuracy_score(y_test, y_pred_bagging1)
+        scores1.append(score_bc_dt1)
+        # train line
+        y_pred_bagging2 = bagging_clf.predict(X_train)
+        score_bc_dt2 = accuracy_score(y_train, y_pred_bagging2)
+        scores2.append(score_bc_dt2)
+
+    plt.figure(figsize=(10, 6))
+    plt.title('Bagging Info')
+    plt.xlabel('Estimators')
+    plt.ylabel('Scores')
+    plt.plot(estimators,scores1,'g',label='test line', linewidth=3)
+    plt.plot(estimators,scores2,'c',label='train line', linewidth=3)
+    plt.legend()
+    plt.show()
 
 

q02_stacking_clf/build.py

@@ -14,5 +14,37 @@
 
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=9)
 
+# Solution
 # Write your code here
 
+clf1 = LogisticRegression(random_state=9)
+clf2 = DecisionTreeClassifier(random_state=9)
+clf3 = DecisionTreeClassifier(max_depth=9, random_state=9)
+
+bagging_clf1 = BaggingClassifier(clf2, n_estimators=100, max_samples=100,
+                                 bootstrap=True, random_state=9, oob_score=True)
+bagging_clf2 = BaggingClassifier(clf1, n_estimators=100, max_samples=100,
+                                 bootstrap=True, random_state=9, oob_score=True)
+bagging_clf3 = BaggingClassifier(clf3, n_estimators=100, max_samples=100,
+                                 bootstrap=True, random_state=9, oob_score=True)
+
+model = [bagging_clf1, bagging_clf2, bagging_clf3]
+
+
+def stacking_clf(model, X_train, y_train, X_test, y_test):
+    predictions = []
+    for i in model:
+        i.fit(X_train, y_train)
+        pred = np.array(i.predict_proba(X_train))
+        predictions.append(pred)
+    X_bag_train = np.concatenate((predictions[0], predictions[1], predictions[2]), axis=1)
+    predictions_test = []
+    for j in model:
+        pred = np.array(j.predict_proba(X_test))
+        predictions_test.append(pred)
+    X_bag_test = np.concatenate((predictions_test[0], predictions_test[1], predictions_test[2]), axis=1)
+    predictions_bag_final = clf1.fit(X_bag_train, y_train).predict(X_bag_test)
+    return accuracy_score(y_test, predictions_bag_final)
+
+
+