{
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   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "b'Skipping line 25706: expected 2 fields, saw 3\\nSkipping line 58881: expected 2 fields, saw 3\\nSkipping line 73761: expected 2 fields, saw 3\\n'\n",
      "b'Skipping line 1983: expected 1 fields, saw 2\\nSkipping line 5199: expected 1 fields, saw 2\\n'\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "import pandas as pd\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "r_in = './train/train.tsv'\n",
    "\n",
    "r_ind_ev = './dev-0/in.tsv'\n",
    "tsv_read = pd.read_table(r_in, error_bad_lines=False, sep='\\t', header=None)\n",
    "tsv_read_dev = pd.read_table(r_ind_ev, error_bad_lines=False, sep='\\t', header=None)\n",
    "\n",
    "y_train = tsv_read[0].values\n",
    "X_train = tsv_read[1].values\n",
    "X_dev = tsv_read_dev[0].values\n",
    "\n",
    "vectorizer = TfidfVectorizer()\n",
    "counts = vectorizer.fit_transform(X_train)\n",
    "\n",
    "\n",
    "classifier = MultinomialNB()\n",
    "classifier.fit(counts, y_train)\n",
    "\n",
    "counts2 = vectorizer.transform(X_dev)\n",
    "predictions = classifier.predict(counts2)\n",
    "\n",
    "predictions.tofile(\"./dev-0/out.tsv\", sep='\\n')\n",
    "\n",
    "tsv_read_test_in =  pd.read_table('./test-A/in.tsv', error_bad_lines=False, header= None)\n",
    "X_test= tsv_read_test_in[0].values\n",
    "\n",
    "counts3 = vectorizer.transform(X_test)\n",
    "predictions_test_A = classifier.predict(counts3)\n",
    "predictions_test_A.tofile('./test-A/out.tsv', sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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