From 9eed72b038d4ce5db8537f506565a332abd94a59 Mon Sep 17 00:00:00 2001
From: HardikaB <hardi.2607@gmail.com>
Date: Tue, 28 Aug 2018 11:04:14 +0000
Subject: [PATCH] Done

---
 __pycache__/__init__.cpython-36.pyc           | Bin 0 -> 150 bytes
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 162 bytes
 q01_bagging/__pycache__/build.cpython-36.pyc  | Bin 0 -> 1302 bytes
 q01_bagging/build.py                          |  30 ++++++++++++++-
 .../tests/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 168 bytes
 .../test_q01_bagging.cpython-36.pyc           | Bin 0 -> 959 bytes
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 167 bytes
 .../__pycache__/build.cpython-36.pyc          | Bin 0 -> 1752 bytes
 q02_stacking_clf/build.py                     |  35 ++++++++++++++++++
 .../tests/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 173 bytes
 .../test_q02_stacking_clf.cpython-36.pyc      | Bin 0 -> 2381 bytes
 11 files changed, 64 insertions(+), 1 deletion(-)
 create mode 100644 __pycache__/__init__.cpython-36.pyc
 create mode 100644 q01_bagging/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q01_bagging/__pycache__/build.cpython-36.pyc
 create mode 100644 q01_bagging/tests/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q01_bagging/tests/__pycache__/test_q01_bagging.cpython-36.pyc
 create mode 100644 q02_stacking_clf/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q02_stacking_clf/__pycache__/build.cpython-36.pyc
 create mode 100644 q02_stacking_clf/tests/__pycache__/__init__.cpython-36.pyc
 create mode 100644 q02_stacking_clf/tests/__pycache__/test_q02_stacking_clf.cpython-36.pyc

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diff --git a/q01_bagging/build.py b/q01_bagging/build.py
index 19f8726..3333f92 100644
--- a/q01_bagging/build.py
+++ b/q01_bagging/build.py
@@ -1,10 +1,11 @@
+# %load q01_bagging/build.py
 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.ensemble import BaggingClassifier
 import matplotlib.pyplot as plt
 from sklearn.metrics import accuracy_score
-plt.switch_backend('agg')
+import numpy as np
 
 # Data Loading
 dataframe = pd.read_csv('data/loan_prediction.csv')
@@ -17,3 +18,30 @@
 # Write your code here
 
 
+def bagging(X_train, X_test, y_train, y_test,n_est):
+    
+    i=1
+    dict1=dict()
+    dict2=dict()
+    
+    while (i<=50):        
+        # Fitting bagging classifier with Logisitc Regression
+        bagging_clf2 = BaggingClassifier(DecisionTreeClassifier(), n_estimators=i, max_samples=0.67, 
+                                        bootstrap=True, random_state=9,max_features=0.67)
+
+        bagging_clf2.fit(X_train, y_train)
+        y_pred_bagging_t = bagging_clf2.predict(X_train)
+        score_bc_dt_t = accuracy_score(y_train, y_pred_bagging_t)
+        y_pred_bagging = bagging_clf2.predict(X_test)
+        score_bc_dt = accuracy_score(y_test, y_pred_bagging)
+        dict1[i]=score_bc_dt_t
+        dict2[i]=score_bc_dt
+        i+=1
+    
+    plt.plot(np.arange(1,51),dict1.values())
+    plt.plot(np.arange(1,51),dict2.values())
+    plt.show()
+
+
+
+
diff --git a/q01_bagging/tests/__pycache__/__init__.cpython-36.pyc b/q01_bagging/tests/__pycache__/__init__.cpython-36.pyc
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diff --git a/q02_stacking_clf/build.py b/q02_stacking_clf/build.py
index 7b1c5f8..88b099c 100644
--- a/q02_stacking_clf/build.py
+++ b/q02_stacking_clf/build.py
@@ -1,11 +1,14 @@
+# %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
+from sklearn.ensemble import VotingClassifier
 
 # Loading data
 dataframe = pd.read_csv('data/loan_prediction.csv')
@@ -14,5 +17,37 @@
 
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=9)
 
+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]
 # Write your code here
+def stacking_clf(model, X_train, y_train, X_test, y_test):
+    voting_clf_hard = VotingClassifier(estimators = [('Logistic Regression', model[0]),
+                                                 ('Decision Tree 1', model[1]),
+                                                 ('Decision Tree 2', model[2])],
+                                   voting = 'hard')
+
+    voting_clf_hard.fit(X_train, y_train)
+    y_pred_hard = voting_clf_hard.predict(X_train)
+    y_pred_hard_test = voting_clf_hard.predict(X_test)
+    X_trainN = np.concatenate((X_train,pd.DataFrame(y_pred_hard)), axis=1)
+    X_testN = np.concatenate((X_test,pd.DataFrame(y_pred_hard_test)), axis=1)
+
+    stacking_clf1 = StackingClassifier(classifiers = model,
+                                 meta_classifier = LogisticRegression(random_state=9))
+    stacking_clf1.fit(X_trainN, y_train)
+    y_pred = stacking_clf1.predict(X_testN)
+    accuracy = accuracy_score(y_test, y_pred)
+    return accuracy
+
+
 
diff --git a/q02_stacking_clf/tests/__pycache__/__init__.cpython-36.pyc b/q02_stacking_clf/tests/__pycache__/__init__.cpython-36.pyc
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