{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f7e1ae0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7582a8dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dev0\n",
    "dev0_data = pd.read_csv('dev-0/in.tsv', header=None, quoting=csv.QUOTE_NONE, sep='\\t')\n",
    "\n",
    "dev0_y = KMeans(n_clusters=50).fit_predict(TfidfVectorizer().fit_transform(dev0_data[0].values))\n",
    "\n",
    "#zapis wyników\n",
    "dev0_y.tofile('dev-0/out.tsv', sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3c75abc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TestA\n",
    "testA_data = pd.read_csv('test-A/in.tsv', header=None, quoting=csv.QUOTE_NONE, sep='\\t')\n",
    "\n",
    "testA_y = KMeans(n_clusters=50).fit_predict(TfidfVectorizer().fit_transform(testA_data[0].values))\n",
    "\n",
    "#zapis wyników\n",
    "testA_y.tofile('test-A/out.tsv', sep='\\n')"
   ]
  }
 ],
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