{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "stworzyć klasyfikator bazujący na naiwnym bayessie (może być gotowa biblioteka), może też korzystać z gotowych implementacji tfidf\n",
    "\n",
    "stworzyć predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n",
    "\n",
    "wynik accuracy sprawdzony za pomocą narzędzia geval (patrz poprzednie zadanie) powinien wynosić conajmniej 0.67\n",
    "\n",
    "proszę umieścić predykcję oraz skrypty generujące (w postaci tekstowej a nie jupyter) w repo, zadanie oddajemy w gonito, termin 10.05, 40 punktów\n",
    "\n",
    "Output label is the probability of a paranormal subreddit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_1208\\74507919.py:1: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  data = pd.read_csv('train/train.tsv', sep='\\t', header=None, error_bad_lines=False)\n",
      "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"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('train/train.tsv', sep='\\t', header=None, error_bad_lines=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[1]\n",
    "\n",
    "with open('dev-0/in.tsv', 'r', encoding='utf8') as f:\n",
    "    Xdev = f.readlines()\n",
    "Xdev = pd.Series(Xdev)\n",
    "\n",
    "with open('test-A/in.tsv', 'r', encoding='utf8') as f:\n",
    "    Xtest = f.readlines()\n",
    "Xtest = pd.Series(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[0].astype('string')\n",
    "\n",
    "ydev = pd.read_csv('dev-0/expected.tsv', sep='\\t', header=None)\n",
    "ydev = ydev.squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
       "                ('multinomialnb', MultinomialNB())])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_dev0 = model.predict(Xdev)\n",
    "predictions_dev0 = pd.Series(predictions_dev0)\n",
    "predictions_dev0 = predictions_dev0.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('dev-0/out.tsv', 'wt') as f:\n",
    "    for pred in predictions_dev0:\n",
    "        f.write(str(pred)+'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_testA = model.predict(Xtest)\n",
    "predictions_testA = pd.Series(predictions_testA)\n",
    "predictions_testA = predictions_testA.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('test-A/out.tsv', 'wt') as f:\n",
    "    for pred in predictions_testA:\n",
    "        f.write(str(pred)+'\\n')"
   ]
  }
 ],
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