paranormal-or-skeptic-ISI-public/run.ipynb

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  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "# coding: utf-8\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import  accuracy_score\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import lzma\n",
    "\n",
    "X_train = lzma.open(\"train/in.tsv.xz\", mode='rt', encoding='utf-8').readlines()\n",
    "y_train = open('train/expected.tsv').readlines()\n",
    "X_dev0 = lzma.open(\"dev-0/in.tsv.xz\", mode='rt', encoding='utf-8').readlines()\n",
    "y_expected_dev0 = open(\"dev-0/expected.tsv\", \"r\").readlines()\n",
    "X_test = lzma.open(\"test-A/in.tsv.xz\", mode='rt', encoding='utf-8').readlines()"
   ],
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    "pycharm": {
     "name": "#%%\n"
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  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "count_vect = CountVectorizer()\n",
    "X_train_counts = count_vect.fit_transform(X_train)\n",
    "X_dev0_counts = count_vect.transform(X_dev0)\n",
    "X_test_counts = count_vect.transform(X_test)"
   ],
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "clf = MultinomialNB().fit(X_train_counts, y_train)\n",
    "\n",
    "y_predicted_dev0_MNB = clf.predict(X_dev0_counts)\n",
    "y_predicted_test_MNB = clf.predict(X_test_counts)"
   ],
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  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy dev0: 0.8025417298937785\n"
     ]
    }
   ],
   "source": [
    "accuracy_dev0_MNB = accuracy_score(y_expected_dev0, y_predicted_dev0_MNB)\n",
    "print(f\"Accuracy dev0: {accuracy_dev0_MNB}\")\n"
   ],
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    "pycharm": {
     "name": "#%%\n"
    }
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  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "open(\"dev-0/out.tsv\", mode='w').writelines(y_predicted_dev0_MNB)\n",
    "open(\"test-A/out.tsv\", mode='w').writelines(y_predicted_test_MNB)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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