sport-text-classification-ball-ISI-public/.ipynb_checkpoints/run-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8bcddf9-596c-4493-bf2a-8e32255115ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da067d47-0543-48b3-bdf4-844061f827c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programy\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n",
      "\n",
      "\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\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": [
    "train = pd.read_csv('train/train.tsv', header=None, sep='\\t', error_bad_lines=False)\n",
    "train = train.head(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94390d90-898c-42df-8482-0e1b8a3ea706",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train[1]\n",
    "y_train = train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df870ce3-c258-4de0-bbda-f5d71a53163c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_dev = pd.read_csv('dev-0/in.tsv', header=None, sep='\\t', error_bad_lines=False)\n",
    "x_dev = x_dev[0]\n",
    "y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t', error_bad_lines=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce5621d9-655a-46d7-b235-8638daac733e",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bca4dc07-fdcd-4ae5-8f24-584a3cda3b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = vectorizer.fit_transform(x_train)\n",
    "x_dev = vectorizer.transform(x_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96840a5e-bfb9-4fae-a5f9-7acc0d7e4c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aed08803-9aef-43e2-8ee2-1d79458b49ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.fit(x_train.toarray(), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7461bb8d-3b3d-4164-9d47-a62b73dc0e36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9418561995597946\n"
     ]
    }
   ],
   "source": [
    "dev_predicted =  gnb.predict(x_dev.toarray())\n",
    "\n",
    "with open('dev-0/out.tsv', 'wt') as f:\n",
    "    for i in dev_predicted:\n",
    "        f.write(str(i)+'\\n')\n",
    "\n",
    "dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\\t')\n",
    "dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')\n",
    "print(accuracy_score(dev_out, dev_expected))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e18bdbe-6d06-42e3-b952-0d5e7bc60325",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('test-A/in.tsv', 'r', encoding = 'utf-8') as f:\n",
    "       x_test = f.readlines()\n",
    "        \n",
    "x_test = pd.Series(x_test)\n",
    "x_test = vectorizer.transform(x_test)\n",
    "\n",
    "test_predicted = gnb.predict(x_test.toarray())\n",
    "\n",
    "with open('test-A/out.tsv', 'wt') as f:\n",
    "    for i in test_predicted:\n",
    "        f.write(str(i)+'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9463e664-5f74-4a96-8959-03eb224715e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook run.ipynb to script\n",
      "[NbConvertApp] Writing 1502 bytes to run.py\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert --to script run.ipynb"
   ]
  }
 ],
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   "language": "python",
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