def SVCClassifier():
df, bal_df = prepareDataset()
print "\n##############################################################"
print "\nFor preprocessed dataset - SVC"
print "\n##############################################################\n"
train_ten, test_ten, train_starsTen, test_starsTen = train_test_split(df.TEXT_REVIEW, df.STARS, test_size=0.20, random_state=42)
"""Most common features vectorizer."""
bow_vectorizer = CountVectorizer(analyzer = "word", tokenizer = None, preprocessor = None,
ngram_range = (1, 1), binary = False,strip_accents='unicode', max_features=700)
"""Feature Matrix for training and test sets."""
bow_Feature_train = bow_vectorizer.fit_transform(train_ten)
bow_Feature_test = bow_vectorizer.transform(test_ten)
bow_Feature_train, bow_Feature_test
bow_clf = SVC(C=1.0, kernel='linear')
bow_clf.fit(bow_Feature_train, train_starsTen)
bow_clf_prediction = bow_clf.predict(bow_Feature_test)
print bow_clf_prediction
"""BiGrams vectorizer."""
biGram_vectorizer = CountVectorizer(analyzer = "word", tokenizer = None, preprocessor = None,
ngram_range = (2, 2), strip_accents='unicode')
biGramFeatMatrain = biGram_vectorizer.fit_transform(train_ten)
biGramFeatMatest = biGram_vectorizer.transform(test_ten)
biGramFeatMatrain, biGramFeatMatest
biGram_clf = SVC(C=1.0, kernel='linear')
biGram_clf.fit(biGramFeatMatrain, train_starsTen)
biGram_clf_prediction = biGram_clf.predict(biGramFeatMatest)
print biGram_clf_prediction
"""TriGrams vectorizer."""
trigram_vectorizer = CountVectorizer(analyzer = "word", tokenizer = None, preprocessor = None,
ngram_range = (3, 3), strip_accents='unicode')
triGramFeatMatrain = trigram_vectorizer.fit_transform(train_ten)
triGramFeatMatest = trigram_vectorizer.transform(test_ten)
triGramFeatMatrain, triGramFeatMatest
triGram_clf = SVC(C=1.0, kernel='linear')
triGram_clf.fit(triGramFeatMatrain, train_starsTen)
triGram_clf_prediction = triGram_clf.predict(triGramFeatMatest)
print triGram_clf_prediction
def SVCEvaluationsPreprocessed(name, predictions):
target_names = ['*', '**', '***', '****', '*****']
print "MODEL: %s" % name
print
print 'Precision: ' + str(metrics.precision_score(test_starsTen, predictions))
print 'Recall: ' + str(metrics.recall_score(test_starsTen, predictions))
print 'F1: ' + str(metrics.f1_score(test_starsTen, predictions))
print 'Accuracy: ' + str(metrics.accuracy_score(test_starsTen, predictions))
print
print 'Classification Report:'
print classification_report(test_starsTen, predictions, target_names=target_names)
SVCEvaluationsPreprocessed('Most Common Features SVC - preprocessed', bow_clf_prediction)
SVCEvaluationsPreprocessed('BiGram SVC - preprocessed', biGram_clf_prediction)
SVCEvaluationsPreprocessed('TriGram SVC - preprocessed', triGram_clf_prediction)
print "\n##############################################################"
print "\nFor balanced dataset - SVC"
print "\n##############################################################\n"
train_bal, test_bal, train_bal_stars, test_bal_stars = train_test_split(bal_df.TEXT_REVIEW, bal_df.STARS, test_size=0.20, random_state=42)
"""Most common features vectorizer."""
bow_vectorizer = CountVectorizer(analyzer = "word", tokenizer = None, preprocessor = None,
ngram_range = (1, 1), binary = False,strip_accents='unicode', max_features=700)
"""Feature Matrix for training and test sets."""
bow_Feature_bal_train = bow_vectorizer.fit_transform(train_bal)
bow_Feature_bal_test = bow_vectorizer.transform(test_bal)
bow_Feature_bal_train, bow_Feature_bal_test
bow_bal_clf = SVC(C=1.0, kernel='linear')
bow_bal_clf.fit(bow_Feature_bal_train, train_bal_stars)
bow_bal_clf_prediction = bow_bal_clf.predict(bow_Feature_bal_test)
print bow_bal_clf_prediction
"""BiGrams vectorizer."""
biGram_bal_vectorizer = CountVectorizer(analyzer = "word", tokenizer = None, preprocessor = None,
ngram_range = (2, 2), strip_accents='unicode')
biGram_Feature_bal_train = biGram_bal_vectorizer.fit_transform(train_bal)
biGram_Feature_bal_test = biGram_bal_vectorizer.transform(test_bal)
biGram_Feature_bal_train, biGram_Feature_bal_test
biGram_bal_clf = SVC(C=1.0, kernel='linear')
biGram_bal_clf.fit(biGram_Feature_bal_train, train_bal_stars)
biGram_bal_clf_prediction = biGram_bal_clf.predict(biGram_Feature_bal_test)
print biGram_bal_clf_prediction
"""TriGrams vectorizer."""
trigram_bal_vectorizer = CountVectorizer(analyzer = "word", tokenizer = None, preprocessor = None,
ngram_range = (3, 3), strip_accents='unicode')
triGram_Feature_bal_train = trigram_bal_vectorizer.fit_transform(train_bal)
triGram_Feature_bal_test = trigram_bal_vectorizer.transform(test_bal)
triGram_Feature_bal_train, triGram_Feature_bal_test
triGram_bal_clf = SVC(C=1.0, kernel='linear')
triGram_bal_clf.fit(triGram_Feature_bal_train, train_bal_stars)
triGram_bal_clf_prediction = triGram_bal_clf.predict(triGram_Feature_bal_test)
print triGram_bal_clf_prediction
def SVCEvaluationsBalanced(name, predictions):
target_names = ['*', '**', '***', '****', '*****']
print "MODEL: %s" % name
print
print 'Precision: ' + str(metrics.precision_score(test_bal_stars, predictions))
print 'Recall: ' + str(metrics.recall_score(test_bal_stars, predictions))
print 'F1: ' + str(metrics.f1_score(test_bal_stars, predictions))
print 'Accuracy: ' + str(metrics.accuracy_score(test_bal_stars, predictions))
print
print 'Classification Report:'
print classification_report(test_bal_stars, predictions, target_names=target_names)
SVCEvaluationsBalanced('Most Common Features SVC - balanced', bow_bal_clf_prediction)
SVCEvaluationsBalanced('BiGram SVC - balanced', biGram_bal_clf_prediction)
SVCEvaluationsBalanced('TriGram SVC - balanced', triGram_bal_clf_prediction)
