#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np
import pandas as pd
import sklearn
from sklearn.naive_bayes import GaussianNB
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_score


# In[2]:


train = pd.read_csv('train/train.tsv', header=None, sep='\t', error_bad_lines=False)
train = train.head(2000)


# In[3]:


x_train = train[1]
y_train = train[0]


# In[4]:


x_dev = pd.read_csv('dev-0/in.tsv', header=None, sep='\t', error_bad_lines=False)
x_dev = x_dev[0]
y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t', error_bad_lines=False)


# In[5]:


vectorizer = TfidfVectorizer()


# In[6]:


x_train = vectorizer.fit_transform(x_train)
x_dev = vectorizer.transform(x_dev)


# In[7]:


gnb = GaussianNB()


# In[8]:


gnb.fit(x_train.toarray(), y_train)


# In[9]:


dev_predicted =  gnb.predict(x_dev.toarray())

with open('dev-0/out.tsv', 'wt') as f:
    for i in dev_predicted:
        f.write(str(i)+'\n')

dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\t')
dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t')
print(accuracy_score(dev_out, dev_expected))


# In[10]:


with open('test-A/in.tsv', 'r', encoding = 'utf-8') as f:
       x_test = f.readlines()
        
x_test = pd.Series(x_test)
x_test = vectorizer.transform(x_test)

test_predicted = gnb.predict(x_test.toarray())

with open('test-A/out.tsv', 'wt') as f:
    for i in test_predicted:
        f.write(str(i)+'\n')