import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
data_raw = pd.read_csv('train/train.tsv', delimiter = '\t', header = None,usecols=[0, 1], names = ['y', 'X'])

import string
import os

from sklearn.feature_extraction.text import TfidfVectorizer
from stop_words import get_stop_words


def preprocess(item):
    to_replace = '''~!@#$%^&*()_+-=[]{};\'":?/.>,<1234567890–”’'''
    for r in to_replace:
      item = item.replace(r, '')
    return item.lower()


stop_words = get_stop_words('polish') + ['aby', 'tych', 'tym', 'tyle', 'tymi', 'też']
vectorizer = TfidfVectorizer(stop_words=stop_words, preprocessor=preprocess)
tfs = vectorizer.fit_transform(data_raw.X)

from sklearn.naive_bayes import MultinomialNB
clf = MultinomialNB()

clf.fit(tfs,data_raw.y)

for dir in ['dev-0/', 'test-A/']:
  test_raw = pd.read_csv(dir+'in.tsv', delimiter = '\t', header = None,usecols=[0], names = ['X'])
  X_test = vectorizer.transform(test_raw.X)
  y_predicted = clf.predict(X_test)
  np.savetxt(dir+"out.tsv", y_predicted, delimiter="\t")