import gensim as gensim
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from stop_words import get_stop_words
import os
import pandas as pd

def read_train_file(inDirectory):
    colnames = ['Y', 'X']
    df_train = pd.read_csv(inDirectory, sep="\t", names=colnames)
    return df_train

def read_evaluate_file(inDirectory):
    colnames = ['X']
    df_evaulate = pd.read_csv(inDirectory, sep="\n", names=colnames)
    return df_evaulate

def naivBayersWithTFIDFTrain(inDirectory):
    df_train = read_train_file(inDirectory)
    train_vectorized_corpus = vectorizer.fit_transform(df_train['X'])
    gnb.fit(train_vectorized_corpus, df_train['Y'])

def naivBayersWithTFIDFEvaluate(inDirectory, outDirectory):
    df_evaluate = read_evaluate_file(inDirectory)
    evaluate_vectorized_corpus = vectorizer.transform(df_evaluate['X'])
    evaluate = gnb.predict(evaluate_vectorized_corpus)
    with open(outDirectory, 'w') as file:
        for e in evaluate:
            file.write("%i\n" % e)

vectorizer = TfidfVectorizer(stop_words=get_stop_words('polish'))
gnb = MultinomialNB()

naivBayersWithTFIDFTrain("train/train.tsv")
naivBayersWithTFIDFEvaluate("dev-0/in.tsv", "dev-0/out.tsv")
naivBayersWithTFIDFEvaluate("test-A/in.tsv", "test-A/out.tsv")


os.system("./geval -t dev-0")