tf-idf

gistfile1.txt

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   "source": [
    "## TF-IDF\n",
    "\n",
    "Documentation: http://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction\n",
    "\n",
    "The examples assumes that we have two documents with each having a single sentence.\n",
    "\n",
    "*Doc1: This is a sample*\n",
    "\n",
    "*Doc2: This is another example*\n",
    "\n",
    "Column details of the matrix can be printed using *vocabulary_*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example calculated tf-idf using **TfidfTransformer**"
   ]
  },
  {
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   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.45329466,  0.45329466,  0.76749457,  0.        ,  0.        ],\n",
       "       [ 0.35959372,  0.35959372,  0.        ,  0.6088451 ,  0.6088451 ]])"
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "transformer = TfidfTransformer(smooth_idf=False)\n",
    "\n",
    "counts = [[1,1,1,0,0],\n",
    "          [1,1,0,1,1]]\n",
    "\n",
    "tfidf = transformer.fit_transform(counts)\n",
    "tfidf.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following example calculated tf-idf using **TfidfVectorizer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "[[ 0.          0.          0.50154891  0.70490949  0.50154891]\n",
      " [ 0.57615236  0.57615236  0.40993715  0.          0.40993715]]\n",
      "{u'this': 4, u'sample': 3, u'is': 2, u'example': 1, u'another': 0}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "sent=[\"This is a sample\", \"This is another example\"]\n",
    "tf = TfidfVectorizer(analyzer='word', ngram_range=(1,1), min_df = 0)\n",
    "tfidf_matrix =  tf.fit_transform(sent)\n",
    "print tfidf_matrix.toarray()\n",
    "print tf.vocabulary_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THe following example just prints the count, which is used to calculate tf-idf values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 1 1 1]\n",
      " [1 1 1 0 1]]\n",
      "{u'this': 4, u'sample': 3, u'is': 2, u'example': 1, u'another': 0}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer(min_df=0)\n",
    "corpus = [\n",
    "    'This is a sample',\n",
    "    'This is another example'\n",
    "]\n",
    "X = vectorizer.fit_transform(corpus)\n",
    "print X.toarray()\n",
    "print vectorizer.vocabulary_"
   ]
  }
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