{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from stop_words import get_stop_words\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(stop_words = get_stop_words('polish'))\n",
    "linear = LinearRegression()\n",
    "\n",
    "train = pd.read_csv('train/train.tsv', sep = \"\\t\", names = ['start_date', 'end_date', 'title', 'sort_title', 'data'])\n",
    "\n",
    "mean = (train['start_date'] + train['end_date']) / 2\n",
    "tv = vectorizer.fit_transform(train['data'])\n",
    "linear.fit(tv, mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getData(directory):\n",
    "    with open(directory, encoding=\"utf-8\") as file:\n",
    "          return file.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_dev = getData('dev-0/in.tsv')\n",
    "dataFrame_dev = pd.DataFrame(data = tmp_dev)\n",
    "evaluate_dev = linear.predict(vectorizer.transform(dataFrame_dev[0]))\n",
    "np.savetxt('dev-0/out.tsv', evaluate, fmt='%f', delimiter='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_test = getData('test-A/in.tsv')\n",
    "dataFrame_test = pd.DataFrame(data = tmp_test)\n",
    "evaluate_test = linear.predict(vectorizer.transform(dataFrame_test[0]))\n",
    "np.savetxt('test-A/out.tsv', evaluate, fmt='%f', delimiter='\\n')"
   ]
  }
 ],
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