from sklearn.preprocessing import LabelEncoder
dataset = pd.read_csv('LabelledData.txt',
delimiter=',,,',
quoting=3,
header=None,
engine='python')
y = dataset.iloc[:, 1].str.strip()
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)
# Cleaning the texts
corpus = []
cleaner = DataHandler(dataset.iloc[:, 0])
#print(cleaner.__dict__)
#corpus = cleaner.cleanLemmatizer()
corpus = cleaner.cleanStemmer()
# Creating the Bag of Words model
cv = CountVectorizer()
X = cv.fit_transform(corpus).toarray()
##############################################################
# Training the model
print("Training the model with train_set=80% & test_set=20%")
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.20,
random_state=0)
