import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import time
import csv
import string
from sklearn.cross_validation import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

# Importing dataset
data = pd.read_csv("test.csv", quotechar='"', delimiter=',',quoting=csv.QUOTE_ALL, skipinitialspace=True,error_bad_lines=False)
df2 = data.set_index("name", drop = False)



df2['sentiment'] = df2['rating'].apply(lambda rating : +1 if rating > 3 else -1)


train, test = train_test_split(df2, test_size=0.2)


count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(train)
test_matrix = count_vect.transform(test)

clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)

clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is
the number of features.

y : array-like, shape = [n_samples]
Target values.