completed lesson 7 - ensemble methods

python/Supervised Learning/Ensemble Methods/spam_ensembles.py

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+# Import our libraries
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import accuracy_score, precision_score
+from sklearn.metrics import recall_score, f1_score
+from sklearn.ensemble import BaggingClassifier, RandomForestClassifier
+from sklearn.ensemble import AdaBoostClassifier
+
+# Read in our dataset
+df = pd.read_csv('smsspamcollection_SMSSpamCollection',
+                 header=None,
+                 names=['label', 'sms_message'])
+
+# Fix our response value
+df['label'] = df.label.map({'ham': 0, 'spam': 1})
+
+# Split our dataset into training and testing data
+X_train, X_test, y_train, y_test = train_test_split(df['sms_message'],
+                                                    df['label'],
+                                                    random_state=1)
+
+# Instantiate the CountVectorizer method
+count_vector = CountVectorizer()
+
+# Fit the training data and then return the matrix
+training_data = count_vector.fit_transform(X_train)
+
+# Transform testing data and return the matrix. Note we are not fitting the
+# testing data into the CountVectorizer()
+testing_data = count_vector.transform(X_test)
+
+# Instantiate our model
+naive_bayes = MultinomialNB()
+
+# Fit our model to the training data
+naive_bayes.fit(training_data, y_train)
+
+# Predict on the test data
+predictions = naive_bayes.predict(testing_data)
+
+# Score our model
+print('Accuracy score: ', format(accuracy_score(y_test, predictions)))
+print('Precision score: ', format(precision_score(y_test, predictions)))
+print('Recall score: ', format(recall_score(y_test, predictions)))
+print('F1 score: ', format(f1_score(y_test, predictions)))
+
+
+
+
+# Instantiate a BaggingClassifier with:
+# 200 weak learners (n_estimators) and everything else as default values
+bagging = BaggingClassifier(n_estimators=200)
+
+
+# Instantiate a RandomForestClassifier with:
+# 200 weak learners (n_estimators) and everything else as default values
+forest = RandomForestClassifier(n_estimators=200)
+
+# Instantiate an a AdaBoostClassifier with:
+# With 300 weak learners (n_estimators) and a learning_rate of 0.2
+ada = AdaBoostClassifier(n_estimators=300, learning_rate=0.2)
+
+# Fit your BaggingClassifier to the training data
+bagging.fit(training_data, y_train)
+
+# Fit your RandomForestClassifier to the training data
+forest.fit(training_data, y_train)
+
+# Fit your AdaBoostClassifier to the training data
+ada.fit(training_data, y_train)
+
+
+# Predict using BaggingClassifier on the test data
+bag_result = bagging.predict(testing_data)
+
+# Predict using RandomForestClassifier on the test data
+forest_result = forest.predict(testing_data)
+
+# Predict using AdaBoostClassifier on the test data
+ada_result = ada.predict(testing_data)
+
+
+def print_metrics(y_true, preds, model_name=None):
+    '''
+    INPUT:
+    y_true - the y values that are actually true in the dataset (numpy array
+    or pandas series)
+    preds - the predictions for those values from some model (numpy array or
+    pandas series)
+    model_name - (str - optional) a name associated with the model if you
+    would like to add it to the print statements
+
+    OUTPUT:
+    None - prints the accuracy, precision, recall, and F1 score
+    '''
+    if model_name is None:
+        print('Accuracy score: ', format(accuracy_score(y_true, preds)))
+        print('Precision score: ', format(precision_score(y_true, preds)))
+        print('Recall score: ', format(recall_score(y_true, preds)))
+        print('F1 score: ', format(f1_score(y_true, preds)))
+        print('\n\n')
+
+    else:
+        print('Accuracy score for ' + model_name + ' :',
+              format(accuracy_score(y_true, preds)))
+        print('Precision score ' + model_name + ' :',
+              format(precision_score(y_true, preds)))
+        print('Recall score ' + model_name + ' :',
+              format(recall_score(y_true, preds)))
+        print('F1 score ' + model_name + ' :', format(f1_score(y_true, preds)))
+        print('\n\n')
+
+
+# Print Bagging scores
+print_metrics(y_test, bag_result, model_name='Bagging')
+
+# Print Random Forest scores
+print_metrics(y_test, forest_result, model_name='Random Forest')
+
+# Print AdaBoost scores
+print_metrics(y_test, ada_result, model_name='AdaBoost')
+
+# Naive Bayes Classifier scores
+print_metrics(y_test, predictions, model_name='NB')