Commit Live PR

q01_bagging/build.py

@@ -1,3 +1,4 @@
+# %load q01_bagging/build.py
 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
@@ -12,8 +13,19 @@
 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
+def bagging(X_train, X_test, y_train, y_test,n_est):
+    score_dt =[]#create empty list to store scores for different value of estimators
+    score_dt1 =[]
+    array1=[10,20,30,40,50]
+    for i in [0,1,2,3,4]:
+        bagging_clf2 = BaggingClassifier(DecisionTreeClassifier(),n_estimators=array1[i],max_samples=0.67,max_features=0.67,bootstrap=True, random_state=9)
+        bagging_clf2.fit(X_train, y_train)
+        y_pred_decision=bagging_clf2.predict(X_test)
+        score_dt.append(accuracy_score(y_test, y_pred_decision))
+        y_pred_decision1=bagging_clf2.predict(X_train)
+        score_dt1.append(accuracy_score(y_train, y_pred_decision1))
+        plt.plot(array1[i],score_dt[i])
+        plt.plot(array1[i],score_dt1[i])
+        plt.show()
 
 

q02_stacking_clf/build.py

@@ -1,9 +1,11 @@
+# %load q02_stacking_clf/build.py
 # Default imports
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.linear_model import LogisticRegression
 from sklearn.ensemble import BaggingClassifier
 from sklearn.metrics import accuracy_score
+from mlxtend.classifier import StackingClassifier
 import pandas as pd
 import numpy as np
 
@@ -13,6 +15,44 @@
 y = dataframe.iloc[:, -1]
 
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=9)
+LR=LogisticRegression(random_state=9)
+DT1=DecisionTreeClassifier(random_state=9)
+DT2=DecisionTreeClassifier(max_depth=9,random_state=9)
+bagging_clf1 = BaggingClassifier(LR, n_estimators=100, max_samples=100, 
+                                bootstrap=True, random_state=9,oob_score=True)
+bagging_clf2 = BaggingClassifier(DT1, n_estimators=100, max_samples=100, 
+                                bootstrap=True,oob_score=True)
 
-# Write your code here
+bagging_clf3 = BaggingClassifier(DT2, n_estimators=100, max_samples=100, 
+                                bootstrap=True, random_state=9,oob_score=True)
+
+
+
+def ModelUse(ModelToUse):
+    ModelToUse.fit(X_train, y_train)
+    y_pred_decision=ModelToUse.predict(X_test)    
+    score=accuracy_score(y_test.reshape(-1,1),y_pred_decision)
+    y_pred_decision=y_pred_decision.reshape(185,1)
+    NewXtest=np.concatenate((X_test, y_pred_decision), axis=1)
+    y_pred_decision1=ModelToUse.predict(X_train)    
+    y_pred_decision1=y_pred_decision1.reshape(429,1)
+    NewXtrain=np.concatenate((X_train, y_pred_decision1), axis=1)
+    return(NewXtest,NewXtrain)
+
+model=[bagging_clf1,bagging_clf2,bagging_clf3]
+
+def stacking_clf(model,Xtrain,y_train,Xtest,y_test):
+    stacking_clf = StackingClassifier(classifiers = model,
+                                 meta_classifier = LR)
+    stacking_clf.fit(NewXtrain, y_train)
+    y_pred2 = stacking_clf.predict(NewXtest)
+    accuracy = accuracy_score(y_test, y_pred2)
+    return(accuracy)
+
+NewXtest,NewXtrain=ModelUse(bagging_clf1)
+stacking_clf(model,NewXtrain,y_train,NewXtest,y_test)
+NewXtest,NewXtrain=ModelUse(bagging_clf2)
+stacking_clf(model,NewXtrain,y_train,NewXtest,y_test)
+NewXtest,NewXtrain=ModelUse(bagging_clf3)
+stacking_clf(model,NewXtrain,y_train,NewXtest,y_test)