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

import pandas as pd

meh = TfidfVectorizer(stop_words = get_stop_words('polish'))
linReg = LinearRegression()
colnames = ['start_date', 'end_date', 'title', 'sort_title', 'data']
t = pd.read_csv('test-A/in.tsv', sep = "\t", names = colnames)
datFin = pd.read_csv('train/train.tsv', sep = "\t", names = colnames)
date = (datFin['start_date'] + datFin['end_date']) / 2
trn = meh.fit_transform(datFin['data'])
linReg.fit(trn, date)
r = meh.transform(t['data'])
x = linReg.predict(r)
with open('test-A/out.tsv', 'w') as file:
    for each in x:
        file.write("%f\n" % each)