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  "name": "",
  "signature": "sha256:672c6fd4d08769e8c9724e7fb9ce750bc018076186ec2bfd9d5f2097fbedb52b"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Dealing with text data in sklearn\n",
      "\n",
      "This demo is partially based on [the scipy2013 sklearn presentations](http://nbviewer.ipython.org/github/glouppe/tutorial-sklearn-scipy2013/blob/master/rendered_notebooks/05.2_application_to_text_mining.ipynb)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Score function from slides\n",
      "from sklearn.cross_validation import KFold\n",
      "from sklearn.metrics import accuracy_score\n",
      "\n",
      "def score(clf, X, Y, folds=2, verbose=False, metric=accuracy_score):\n",
      "    predictions = np.zeros(len(Y))\n",
      "    for i, (train, test) in enumerate(KFold(len(X), n_folds=folds, shuffle=True)):\n",
      "        clf.fit(X[train], Y[train])\n",
      "        predictions[test] = clf.predict(X[test])\n",
      "        if verbose:\n",
      "            print(\"Fold {}: {}\".format(i + 1, accuracy_score(Y[test], predictions[test])))\n",
      "    if metric:\n",
      "        return metric(Y, predictions)\n",
      "    return Y, predictions"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import fetch_20newsgroups"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 20 Newsgroups dataset\n",
      "We will use a standard text dataset built into sklearn."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set to None to use all categories -- use 4 for speed purposes\n",
      "categories = [\n",
      "    'alt.atheism',\n",
      "    'talk.religion.misc',\n",
      "    'comp.graphics',\n",
      "    'sci.space',\n",
      "]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ng_train = fetch_20newsgroups(subset='train', categories=categories, remove=('headers', 'footers', 'quotes'))\n",
      "ng_test = fetch_20newsgroups(subset='test', categories=categories, remove=('headers', 'footers', 'quotes'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The 20ng dataset consists of posts from 20 topical forums.  The goal is to classify a post as coming from one of the forums."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ng_train.target_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "['alt.atheism', 'comp.graphics', 'sci.space', 'talk.religion.misc']"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ng_train.target.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "(2034,)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ng_train.data[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "u\"Hi,\\n\\nI've noticed that if you only save a model (with all your mapping planes\\npositioned carefully) to a .3DS file that when you reload it after restarting\\n3DS, they are given a default position and orientation.  But if you save\\nto a .PRJ file their positions/orientation are preserved.  Does anyone\\nknow why this information is not stored in the .3DS file?  Nothing is\\nexplicitly said in the manual about saving texture rules in the .PRJ file. \\nI'd like to be able to read the texture rule information, does anyone have \\nthe format for the .PRJ file?\\n\\nIs the .CEL file format available from somewhere?\\n\\nRych\""
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Converting text to feature vectors: Bag of Words\n",
      "Text is not in a form suitable for classification algorithms.  We have to convert each post to a vector of features.\n",
      "\n",
      "The simplest approach is <b>bag of words</b>.  In its simplest form, each dimension of the feature vector corresponds to a word (\"car\", \"body\", etc), and the value is the number of times that word appears in the post.\n",
      "\n",
      "It is more common to use <b>ngram based models</b>, in which each dimension corresponds to an ngram (a sequence of n words: \"I was wondering\", \"was wondering if\").  Values still correspond to the number of times the ngram appears\n",
      "\n",
      "sklearn has several feature extractors to make this easy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.feature_extraction.text import CountVectorizer\n",
      "\n",
      "# Learn ngrams of size 1,2,3\n",
      "# Use the 20000 most common items as the vocabulary\n",
      "counter = CountVectorizer(ngram_range=(1,3), max_features=20000)\n",
      "\n",
      "# Transformers also have a fit_transform method\n",
      "# This learns a transform then retursn the transform\n",
      "# applied to the training data\n",
      "train_counts = counter.fit_transform(ng_train.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# After the vectorizer is fit, we can convert\n",
      "# any posts to feature vectors using the vocabulary it learned\n",
      "test_counts = counter.transform(ng_test.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_counts.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "(1353, 20000)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# All nonzero terms are integers\n",
      "test_counts[2][test_counts[2].nonzero()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "matrix([[1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 2, 1, 1,\n",
        "         1, 1, 1, 1, 3, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2,\n",
        "         1, 1, 1, 3, 1, 2, 1, 1, 3, 1, 1, 1, 1, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1]])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Example vocabulary\n",
      "np.random.permutation(counter.get_feature_names())[:100]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([u'exist', u'fake', u'exists and', u'recognize that',\n",
        "       u'but there are', u'something', u'difference in', u'interaction',\n",
        "       u'polygon', u'the graphics', u'pushing', u'of pc',\n",
        "       u'know if anyone', u'patience', u'617', u'is never', u'of ways',\n",
        "       u'you mention', u'and source', u'in by', u'are available',\n",
        "       u'fractal', u'the believer', u'designers', u'the charge',\n",
        "       u'an application', u'with some', u'fear', u'nick', u'didn make',\n",
        "       u'by david', u'made', u'pretend', u'conference', u'so now',\n",
        "       u'question and', u'in any case', u'this the', u'press release',\n",
        "       u'in terms of', u'an enormous', u'proprietary', u'new version of',\n",
        "       u'spacecraft attitude', u'on something', u'office kjenks',\n",
        "       u'for over', u'll find', u'answers to', u'to face', u'would',\n",
        "       u'explosion', u'are on', u'typical', u'someone is', u'headline',\n",
        "       u'allow me to', u'of the word', u'figure', u'where can ftp',\n",
        "       u'to implement', u'from us', u'on what', u'terminal', u'war on',\n",
        "       u'will no', u'kind of like', u'more than one', u'venus',\n",
        "       u'sorry don', u'are distributed', u'the uk', u'far more',\n",
        "       u'that uses', u'exclusive', u'of users', u'kennedy space', u'alter',\n",
        "       u'battle', u'problem in', u'cost of', u'will this', u'best of',\n",
        "       u'planning to', u'the item', u'the committee', u'run on',\n",
        "       u'find it rather', u'nor', u'this leads', u'latter day', u'to save',\n",
        "       u'no real', u'heaven but', u'of the art', u'and tried to', u'mu',\n",
        "       u'in the field', u'to acknowledge', u'63'], \n",
        "      dtype='<U29')"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Converting text to feature vectors: TFIDF\n",
      "Simple word counts does not give any information on how identifying a word is. \"the\" may appear many times in a post, but it is so common that it provides very little information.\n",
      "\n",
      "TFIDF (term frequency--inverse document frequency) attempts to remedy this.  The score of a word in a document increases as the number of time it appears in the document increases, but it decreases if the word is very common across the corpus.  This means that words that appear in few posts will have a higher score, indicating more discriminative power."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.feature_extraction.text import TfidfVectorizer\n",
      "\n",
      "# Learn ngrams of size 1,2,3\n",
      "tfidf = TfidfVectorizer(ngram_range=(1,3), min_df=1, max_df=0.8, use_idf=True)\n",
      "\n",
      "train_tfidf = tfidf.fit_transform(ng_train.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_tfidf = tfidf.transform(ng_test.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# All nonzero terms are floats in tfidf\n",
      "test_tfidf[2][test_tfidf[2].nonzero()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "matrix([[ 0.13752008,  0.04263576,  0.09513934,  0.05312323,  0.11578155,\n",
        "          0.06981124,  0.0684628 ,  0.12162018,  0.07394826,  0.19182137,\n",
        "          0.06731552,  0.12549227,  0.0369442 ,  0.07350893,  0.09440082,\n",
        "          0.10959236,  0.13752008,  0.1050397 ,  0.11346445,  0.13048426,\n",
        "          0.08293576,  0.11142063,  0.12162018,  0.13048426,  0.20996222,\n",
        "          0.08273834,  0.11578155,  0.10255655,  0.06195591,  0.13752008,\n",
        "          0.12162018,  0.04388796,  0.13752008,  0.12549227,  0.05227341,\n",
        "          0.05481063,  0.13752008,  0.04412567,  0.11346445,  0.07245694,\n",
        "          0.06691244,  0.13752008,  0.11346445,  0.11578155,  0.07486176,\n",
        "          0.11346445,  0.05274717,  0.11578155,  0.11346445,  0.1079385 ,\n",
        "          0.0761524 ,  0.13048426,  0.13752008,  0.08785384,  0.04679391,\n",
        "          0.13048426,  0.11845645,  0.07329337,  0.05373694,  0.13752008,\n",
        "          0.13048426,  0.06248572,  0.13752008,  0.13048426,  0.13048426,\n",
        "          0.1581784 ,  0.1486863 ,  0.08940883,  0.04248994,  0.12162018,\n",
        "          0.06430122,  0.03734435,  0.13752008,  0.092357  ,  0.08785384,\n",
        "          0.02855599,  0.10959236,  0.05769601,  0.08887487,  0.13048426,\n",
        "          0.08469011,  0.13752008,  0.17377913,  0.06073809,  0.04227349,\n",
        "          0.12549227,  0.08785384,  0.02416798,  0.05312323]])"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Visualizing posts\n",
      "We can use PCA to visualize the dataset in 2d"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def vis_proj(proj, targets=ng_train.target):\n",
      "    plt.scatter(proj[:, 0], proj[:, 1], c=targets)\n",
      "    plt.colorbar();plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.decomposition import TruncatedSVD\n",
      "# Truncated SVD is similar to PCA\n",
      "svd = TruncatedSVD(n_components=2)\n",
      "proj = svd.fit_transform(train_tfidf)\n",
      "vis_proj(proj)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7RX4ep+DP/IpDhw5RWFgIwEsvvUS3bt0ICgo6bUiajANA7949cLnWAYeAQhyO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\nxZjD4XBkAC/GHA6HIwP+B7QCnIAOBZViAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b7c5650>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "proj = svd.fit_transform(train_counts)\n",
      "vis_proj(proj)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZlryuskQpY4paanL7FZqnZZXsO5FSivXr17Ns2TJ+PHgQbVERQ4EOwI/ANw4O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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b7c5810>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Building a pipeline"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.pipeline import Pipeline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import svm\n",
      "pipeline = Pipeline([\n",
      "    ('tfidf', TfidfVectorizer(min_df=1, max_df=0.8, use_idf=True)),\n",
      "    ('clf', svm.LinearSVC())\n",
      "])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pipeline.fit(ng_train.data, ng_train.target)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "Pipeline(steps=[('tfidf', TfidfVectorizer(analyzer=u'word', binary=False, charset=None,\n",
        "        charset_error=None, decode_error=u'strict',\n",
        "        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
        "        lowercase=True, max_df=0.8, max_features=None, min_df=1,\n",
        "        ngram_range=(1, 1), nor...ling=1, loss='l2', multi_class='ovr', penalty='l2',\n",
        "     random_state=None, tol=0.0001, verbose=0))])"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pred = pipeline.predict(ng_test.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import metrics\n",
      "print metrics.classification_report(ng_test.target, pred,\n",
      "                                    target_names=ng_test.target_names)\n",
      "print metrics.confusion_matrix(ng_test.target, pred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                    precision    recall  f1-score   support\n",
        "\n",
        "       alt.atheism       0.69      0.62      0.65       319\n",
        "     comp.graphics       0.88      0.90      0.89       389\n",
        "         sci.space       0.79      0.89      0.83       394\n",
        "talk.religion.misc       0.68      0.61      0.64       251\n",
        "\n",
        "       avg / total       0.77      0.78      0.77      1353\n",
        "\n",
        "[[197  14  47  61]\n",
        " [  7 352  25   5]\n",
        " [ 18  21 349   6]\n",
        " [ 63  12  23 153]]\n"
       ]
      }
     ],
     "prompt_number": 24
    }
   ],
   "metadata": {}
  }
 ]
}