<filename>Python/NLP/FeatureEngineering/n-gram-models.py
# Building n-gram models using new features

# Generate n-grams upto n=1
vectorizer_ng1 = CountVectorizer(ngram_range=(1,1))
ng1 = vectorizer_ng1.fit_transform(corpus)

# Generate n-grams upto n=2
vectorizer_ng2 = CountVectorizer(ngram_range=(1,2))
ng2 = vectorizer_ng2.fit_transform(corpus)

# Generate n-grams upto n=3
vectorizer_ng3 = CountVectorizer(ngram_range=(1, 3))
ng3 = vectorizer_ng3.fit_transform(corpus)

# Print the number of features for each model
print("ng1, ng2 and ng3 have %i, %i and %i features respectively" % (ng1.shape[1], ng2.shape[1], ng3.shape[1]))

# Define an instance of MultinomialNB 
clf_ng = MultinomialNB()

# Fit the classifier 
clf_ng.fit(X_train_ng, y_train)

# Measure the accuracy 
accuracy = clf_ng.score(X_test_ng, y_test)
print("The accuracy of the classifier on the test set is %.3f" % accuracy)

# Predict the sentiment of a negative review
review = "The movie was not good. The plot had several holes and the acting lacked panache."
prediction = clf_ng.predict(ng_vectorizer.transform([review]))[0]
print("The sentiment predicted by the classifier is %i" % (prediction))


# Comparison of uni-gram and n-gram (3) models. Overall model performance not to different even with much higer dimensionality
start_time = time.time()
# Splitting the data into training and test sets
train_X, test_X, train_y, test_y = train_test_split(df['review'], df['sentiment'], test_size=0.5, random_state=42, stratify=df['sentiment'])

# Generating ngrams
vectorizer = CountVectorizer(ngram_range=(1, 3))
train_X = vectorizer.fit_transform(train_X)
test_X = vectorizer.transform(test_X)

# Fit classifier
clf = MultinomialNB()
clf.fit(train_X, train_y)

# Print accuracy, time and number of dimensions
print("The program took %.3f seconds to complete. The accuracy on the test set is %.2f. The ngram representation had %i features." % (time.time() - start_time, clf.score(test_X, test_y), train_X.shape[1]))

<filename>Python/NLP/FeatureEngineering/n-gram-models.py
# Building n-gram models using new features

# Generate n-grams upto n=1
vectorizer_ng1 = CountVectorizer(ngram_range=(1,1))
ng1 = vectorizer_ng1.fit_transform(corpus)

# Generate n-grams upto n=2
vectorizer_ng2 = CountVectorizer(ngram_range=(1,2))
ng2 = vectorizer_ng2.fit_transform(corpus)

# Generate n-grams upto n=3
vectorizer_ng3 = CountVectorizer(ngram_range=(1, 3))
ng3 = vectorizer_ng3.fit_transform(corpus)

# Print the number of features for each model
print("ng1, ng2 and ng3 have %i, %i and %i features respectively" % (ng1.shape[1], ng2.shape[1], ng3.shape[1]))

# Define an instance of MultinomialNB 
clf_ng = MultinomialNB()

# Fit the classifier 
clf_ng.fit(X_train_ng, y_train)

# Measure the accuracy 
accuracy = clf_ng.score(X_test_ng, y_test)
print("The accuracy of the classifier on the test set is %.3f" % accuracy)

# Predict the sentiment of a negative review
review = "The movie was not good. The plot had several holes and the acting lacked panache."
prediction = clf_ng.predict(ng_vectorizer.transform([review]))[0]
print("The sentiment predicted by the classifier is %i" % (prediction))


# Comparison of uni-gram and n-gram (3) models. Overall model performance not to different even with much higer dimensionality
start_time = time.time()
# Splitting the data into training and test sets
train_X, test_X, train_y, test_y = train_test_split(df['review'], df['sentiment'], test_size=0.5, random_state=42, stratify=df['sentiment'])

# Generating ngrams
vectorizer = CountVectorizer(ngram_range=(1, 3))
train_X = vectorizer.fit_transform(train_X)
test_X = vectorizer.transform(test_X)

# Fit classifier
clf = MultinomialNB()
clf.fit(train_X, train_y)

# Print accuracy, time and number of dimensions
print("The program took %.3f seconds to complete. The accuracy on the test set is %.2f. The ngram representation had %i features." % (time.time() - start_time, clf.score(test_X, test_y), train_X.shape[1]))

# Building n-gram models using new features # Generate n-grams upto n=1 # Generate n-grams upto n=2 # Generate n-grams upto n=3 # Print the number of features for each model # Define an instance of MultinomialNB # Fit the classifier # Measure the accuracy # Predict the sentiment of a negative review # Comparison of uni-gram and n-gram (3) models. Overall model performance not to different even with much higer dimensionality # Splitting the data into training and test sets # Generating ngrams # Fit classifier # Print accuracy, time and number of dimensions