def build_model():
model = NLPModel()
with open('lib/data/train.tsv') as f:
data = pd.read_csv(f, sep='\t')
pos_neg = data[(data['Sentiment'] == 0) | (data['Sentiment'] == 4)]
pos_neg['Binary'] = pos_neg.apply(lambda x: 0 if x['Sentiment'] == 0 else 1, axis=1)
model.vectorizer_fit(pos_neg.loc[:, 'Phrase'])
print('Vectorizer fit complete')
X = model.vectorizer_transform(pos_neg.loc[:, 'Phrase'])
print('Vectorizer tranform complete')
y = pos_neg.loc[:, 'Binaryy']
X_train, X_test, y_train, y_test = train_test_split(X, y)
model.train(X_train, y_train)
Print('Model training complete')
model.pickle_clf()
model.pickle_vectorizer()
model.plot_roc(X_train, y_test)
def build_model():
model = NLPModel()
# filename = os.path.join(
# os.path.dirname(__file__), 'chalicelib', 'all/train.tsv')
with open('lib/data/train.tsv') as f:
data = pd.read_csv(f, sep='\t')
pos_neg = data[(data['Sentiment'] == 0) | (data['Sentiment'] == 4)]
pos_neg['Binary'] = pos_neg.apply(lambda x: 0
if x['Sentiment'] == 0 else 1,
axis=1)
model.vectorizer_fit(pos_neg.loc[:, 'Phrase'])
print('Vectorizer fit complete')
X = model.vectorizer_transform(pos_neg.loc[:, 'Phrase'])
print('Vectorizer transform complete')
y = pos_neg.loc[:, 'Binary']
X_train, X_test, y_train, y_test = train_test_split(X, y)
model.train(X_train, y_train)
print('Model training complete')
model.pickle_clf()
model.pickle_vectorizer()
model.plot_roc(X_test, y_test, size_x=12, size_y=12)
