Chapter 7/Bag of Words.ipynb

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   "cell_type": "code",
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   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Feature Extraction with Bag-of-Words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"bag_of_words.svg\" width=\"80%\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = [\"Some say the world will end in fire,\",\n",
    "     \"Some say in ice.\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "vectorizer = CountVectorizer()\n",
    "vectorizer.fit(X)\n",
    "vectorizer.vocabulary_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_bag_of_words = vectorizer.transform(X)\n",
    "X_bag_of_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(X_bag_of_words.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(X)\n",
    "vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vectorizer.inverse_transform(X_bag_of_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tfidf Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "tfidf_vectorizer.fit(X)\n",
    "\n",
    "print(tfidf_vectorizer.get_feature_names())\n",
    "print(tfidf_vectorizer.transform(X).toarray())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bigrams and N-Grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "# look at sequences of tokens of minimum length 2 and maximum length 2\n",
    "bigram_vectorizer = CountVectorizer(ngram_range=(2, 2))\n",
    "bigram_vectorizer.fit(X)\n",
    "bigram_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bigram_vectorizer.transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gram_vectorizer = CountVectorizer(ngram_range=(1, 2))\n",
    "gram_vectorizer.fit(X)\n",
    "\n",
    "gram_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_1_2_gram = gram_vectorizer.transform(X)\n",
    "print(X_1_2_gram.shape)\n",
    "print(X_1_2_gram.toarray())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Character n-grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "char_vectorizer = CountVectorizer(ngram_range=(2, 3), analyzer=\"char\")\n",
    "char_vectorizer.fit(X)\n",
    "print(char_vectorizer.get_feature_names())"
   ]
  }
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