Notebook of TFIDF Logistic regression on IMDB sentiment dataset (provided by keras)

IMDB + TFIDF + LogReg.ipynb

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  {
   "cell_type": "code",
   "execution_count": 10,
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   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import classification_report\n",
    "from keras.datasets import imdb\n",
    "\n",
    "import json\n",
    "import numpy\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dictionaries\n",
    "\n",
    "Create an index -> word mapping for each word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "word_to_index = imdb.get_word_index()\n",
    "index_to_word = [None] * (max(word_to_index.values()) + 1)\n",
    "for w, i in word_to_index.items():\n",
    "    index_to_word[i] = w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Review reconstruction\n",
    "\n",
    "Translate the lists of word-indices to strings using the aforementioned dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = imdb.load_data()\n",
    "X_train = [\n",
    "    ' '.join(\n",
    "        index_to_word[i]\n",
    "        for i in X_train[i]\n",
    "        if i < len(index_to_word)\n",
    "    ) for i in range(X_train.shape[0])\n",
    "]\n",
    "\n",
    "X_test = [\n",
    "    ' '.join(\n",
    "        index_to_word[i]\n",
    "        for i in X_test[i]\n",
    "        if i < len(index_to_word)\n",
    "    ) for i in range(X_test.shape[0])\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TFIDF + logistic regression pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('tfidf', TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 2), norm='l2', preprocessor=None, smooth_idf=True,\n",
       " ...ty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Pipeline([\n",
    "    ('tfidf', TfidfVectorizer(ngram_range=(1,2))),\n",
    "    ('log', LogisticRegression())\n",
    "])\n",
    "\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.90      0.89     12280\n",
      "          1       0.90      0.88      0.89     12720\n",
      "\n",
      "avg / total       0.89      0.89      0.89     25000\n",
      "\n",
      "0.88904\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "print(classification_report(y_pred, y_test))\n",
    "print((y_pred == y_test).mean())"
   ]
  }
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