{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lzma\n",
    "import sys\n",
    "from io import StringIO\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import pandas as pd\n",
    "import numpy\n",
    "\n",
    "pathX = \"./train/in.tsv.xz\"\n",
    "# pathX = \"./train/in.tsv\"\n",
    "pathY = \"./train/expected.tsv\"\n",
    "nrows = 5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = lzma.open(pathX, mode='rt', encoding='utf-8').read()\n",
    "# stringIO = StringIO(data)\n",
    "# df = pd.read_csv(stringIO, sep=\"\\t\", header=None)\n",
    "df = pd.read_csv(pathX, sep='\\t', nrows=nrows, header=None)\n",
    "df = df.drop(df.columns[1], axis=1)\n",
    "topics = pd.read_csv(pathY, sep='\\t', nrows=nrows, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n",
      "5000\n"
     ]
    }
   ],
   "source": [
    "print(len(df.index))\n",
    "\n",
    "print(len(topics.index))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2823</th>\n",
       "      <td>Use her own logic against her.  Pharmaceutical...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      0\n",
       "2823  Use her own logic against her.  Pharmaceutical..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(lowercase=True, stop_words=['english'])\n",
    "X = vectorizer.fit_transform(df.to_numpy().ravel())\n",
    "# vectorizer.get_feature_names_out()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vectorizer.transform(\"Ala ma kotka\".lower().split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidfVector = vectorizer.transform(df[0])\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\python3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(tfidfVector.toarray(), topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "testXPath = \"./dev-0/in.tsv.xz\"\n",
    "testYPath = \"./dev-0/expected.tsv\"\n",
    "\n",
    "testX = pd.read_csv(testXPath, sep='\\t', nrows=nrows, header=None)\n",
    "\n",
    "testY = pd.read_csv(testYPath, sep='\\t', nrows=nrows, header=None)\n",
    "testXtfidfVector = vectorizer.transform(testX[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "testXPath = \"./dev-0/in.tsv.xz\"\n",
    "testYPath = \"./dev-0/out.tsv\"\n",
    "\n",
    "testX = pd.read_csv(testXPath, sep='\\t', header=None)\n",
    "\n",
    "# testY = pd.read_csv(testYPath, sep='\\t', nrows=nrows, header=None)\n",
    "testXtfidfVector = vectorizer.transform(testX[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "pred = gnb.predict(testXtfidfVector.toarray())\n",
    "print(pred)\n",
    "\n",
    "import csv\n",
    "with open(testYPath, 'w', newline='') as f_output:\n",
    "    tsv_output = csv.writer(f_output, delimiter='\\n')\n",
    "    tsv_output.writerow(pred)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
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  "language_info": {
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