{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('dev-0/in.tsv', 'r') as f:\n",
    "    dev_x = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_vocabulary = ['komputer', 'komputerze', 'aucie', 'auto', 'samochód', 'samochodzie', 'piwie', 'piwo', 'alkoholu', 'alkohol', 'żonie', 'żona', 'xboxie', 'xbox', 'co', 'e', 'XD', 'stary', 'staremu']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_vocabulary = ['zakupy', 'zakupach', 'mężem', 'mąż', 'nasze', 'my', 'dzieckiem', 'dziecko', 'domu', 'dom', 'mieszkaniu', 'mieszkanie', 'kocham', 'kocha', 'chłopakowai', 'chłopak', 'haha', 'boże', 'uh', 'uhh', \":)\", 'mama', 'mamie', 'włosy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(text):\n",
    "    score = 0\n",
    "\n",
    "    for word in m_vocabulary:\n",
    "        if word in text:\n",
    "            score += 1\n",
    "\n",
    "    for word in f_vocabulary:\n",
    "        if word in text:\n",
    "            score -= 1\n",
    "\n",
    "    if score == 0:\n",
    "        return random.randint(0, 1)\n",
    "    if score >0:\n",
    "        return 1\n",
    "    if score <0:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('dev-0/expected.tsv', 'r') as f:\n",
    "    dev_y = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [],
   "source": [
    "dev = zip(dev_x, dev_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct = 0\n",
    "incorrect = 0\n",
    "\n",
    "with open('dev-0/out.tsv', 'wt') as f:\n",
    "    for x, y in list(dev):\n",
    "        f.write(str(predict(x))+'\\n')\n",
    "        if predict(x) == int(y):\n",
    "            correct += 1\n",
    "        else:\n",
    "            incorrect += 1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5122792230182751"
      ]
     },
     "execution_count": 544,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correct/(correct+incorrect)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "8a24ca87d97ac268fc796e79e77f73ca37fd3e060a17758a6f2d8f8d4f13ae6a"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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