{
 "metadata": {
  "name": "05.2_application_to_text_mining"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import pylab as pl\n",
      "import numpy as np\n",
      "\n",
      "# Some nice default configuration for plots\n",
      "pl.rcParams['figure.figsize'] = 10, 7.5\n",
      "pl.rcParams['axes.grid'] = True\n",
      "pl.gray()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 0
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Text Feature Extraction for Classification and Clustering"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Outline of this section:\n",
      "\n",
      "- Turn a corpus of text documents into **feature vectors** using a **Bag of Words** representation,\n",
      "- Train a simple text classifier on the feature vectors,\n",
      "- Wrap the vectorizer and the classifier with a **pipeline**,\n",
      "- Cross-validation and (manual) **model selection** on the pipeline.\n",
      "- Qualitative **model inspection**"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Text Classification in 20 lines of Python"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's start by implementing a canonical text classification example:\n",
      "\n",
      "- The 20 newsgroups dataset: around 18000 text posts from 20 newsgroups forums\n",
      "- Bag of Words features extraction with TF-IDF weighting\n",
      "- Naive Bayes classifier or Linear Support Vector Machine for the classifier itself"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_files\n",
      "from sklearn.feature_extraction.text import TfidfVectorizer\n",
      "from sklearn.naive_bayes import MultinomialNB\n",
      "\n",
      "# Load the text data\n",
      "categories = [\n",
      "    'alt.atheism',\n",
      "    'talk.religion.misc',\n",
      "    'comp.graphics',\n",
      "    'sci.space',\n",
      "]\n",
      "twenty_train_subset = load_files('datasets/20news-bydate-train/',\n",
      "    categories=categories, charset='latin-1')\n",
      "twenty_test_subset = load_files('datasets/20news-bydate-test/',\n",
      "    categories=categories, charset='latin-1')\n",
      "\n",
      "# Turn the text documents into vectors of word frequencies\n",
      "vectorizer = TfidfVectorizer(min_df=2)\n",
      "X_train = vectorizer.fit_transform(twenty_train_subset.data)\n",
      "y_train = twenty_train_subset.target\n",
      "\n",
      "# Fit a classifier on the training set\n",
      "classifier = MultinomialNB().fit(X_train, y_train)\n",
      "print(\"Training score: {0:.1f}%\".format(\n",
      "    classifier.score(X_train, y_train) * 100))\n",
      "\n",
      "# Evaluate the classifier on the testing set\n",
      "X_test = vectorizer.transform(twenty_test_subset.data)\n",
      "y_test = twenty_test_subset.target\n",
      "print(\"Testing score: {0:.1f}%\".format(\n",
      "    classifier.score(X_test, y_test) * 100))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training score: 95.1%\n",
        "Testing score: 85.1%"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is a workflow diagram summary of what happened previously:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image, display\n",
      "display(Image(filename='figures/supervised_scikit_learn.png'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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DL4ReDMuIyIy6FJeVlH3mcnrOxSuXcrPzLl/Nyb9SkFuYV5RfXHjtdklf6XjZYvl2BeIG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ICB4C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\nLKO9g4AAAAAASUVORK5CYII=\n",
       "text": [
        "<IPython.core.display.Image at 0x1102b3ad0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's now decompose what we just did to understand and customize each step:"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Loading the Dataset"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's explore the dataset loading utility without passing a list of categories: in this case we load the full 20 newsgroups dataset in memory. The source website for the 20 newsgroups already provides a date-based train / test split that is made available using the `subset` keyword argument: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls -l datasets/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "total 187176\r\n",
        "drwxr-xr-x  22 ogrisel  staff       748 Mar 18  2003 \u001b[34m20news-bydate-test\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  22 ogrisel  staff       748 Mar 18  2003 \u001b[34m20news-bydate-train\u001b[m\u001b[m/\r\n",
        "-rw-r--r--   1 ogrisel  staff  14464277 Jun 20 15:41 20news-bydate.tar.gz\r\n",
        "drwxr-xr-x   7 ogrisel  staff       238 Jun 24 16:04 \u001b[34mlfw_home\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x   4 ogrisel  staff       136 Jun 20 15:43 \u001b[34msentiment140\u001b[m\u001b[m/\r\n",
        "-rw-r--r--   1 ogrisel  staff  81363704 Jun 20 15:43 trainingandtestdata.zip\r\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls -lh datasets/20news-bydate-train"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "total 0\r\n",
        "drwxr-xr-x  482 ogrisel  staff    16K Mar 18  2003 \u001b[34malt.atheism\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  586 ogrisel  staff    19K Mar 18  2003 \u001b[34mcomp.graphics\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  593 ogrisel  staff    20K Mar 18  2003 \u001b[34mcomp.os.ms-windows.misc\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  592 ogrisel  staff    20K Mar 18  2003 \u001b[34mcomp.sys.ibm.pc.hardware\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  580 ogrisel  staff    19K Mar 18  2003 \u001b[34mcomp.sys.mac.hardware\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  595 ogrisel  staff    20K Mar 18  2003 \u001b[34mcomp.windows.x\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  587 ogrisel  staff    19K Mar 18  2003 \u001b[34mmisc.forsale\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  596 ogrisel  staff    20K Mar 18  2003 \u001b[34mrec.autos\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  600 ogrisel  staff    20K Mar 18  2003 \u001b[34mrec.motorcycles\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  599 ogrisel  staff    20K Mar 18  2003 \u001b[34mrec.sport.baseball\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  602 ogrisel  staff    20K Mar 18  2003 \u001b[34mrec.sport.hockey\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  597 ogrisel  staff    20K Mar 18  2003 \u001b[34msci.crypt\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  593 ogrisel  staff    20K Mar 18  2003 \u001b[34msci.electronics\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  596 ogrisel  staff    20K Mar 18  2003 \u001b[34msci.med\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  595 ogrisel  staff    20K Mar 18  2003 \u001b[34msci.space\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  601 ogrisel  staff    20K Mar 18  2003 \u001b[34msoc.religion.christian\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  548 ogrisel  staff    18K Mar 18  2003 \u001b[34mtalk.politics.guns\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  566 ogrisel  staff    19K Mar 18  2003 \u001b[34mtalk.politics.mideast\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  467 ogrisel  staff    16K Mar 18  2003 \u001b[34mtalk.politics.misc\u001b[m\u001b[m/\r\n",
        "drwxr-xr-x  379 ogrisel  staff    13K Mar 18  2003 \u001b[34mtalk.religion.misc\u001b[m\u001b[m/\r\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls -lh datasets/20news-bydate-train/alt.atheism/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "total 4480\r\n",
        "-rw-r--r--  1 ogrisel  staff    12K Mar 18  2003 49960\r\n",
        "-rw-r--r--  1 ogrisel  staff    31K Mar 18  2003 51060\r\n",
        "-rw-r--r--  1 ogrisel  staff   4.0K Mar 18  2003 51119\r\n",
        "-rw-r--r--  1 ogrisel  staff   1.6K Mar 18  2003 51120\r\n",
        "-rw-r--r--  1 ogrisel  staff   773B Mar 18  2003 51121\r\n",
        "-rw-r--r--  1 ogrisel  staff   4.8K Mar 18  2003 51122\r\n",
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        "-rw-r--r--  1 ogrisel  staff   1.4K Mar 18  2003 51124\r\n",
        "-rw-r--r--  1 ogrisel  staff   2.7K Mar 18  2003 51125\r\n",
        "-rw-r--r--  1 ogrisel  staff   427B Mar 18  2003 51126\r\n",
        "-rw-r--r--  1 ogrisel  staff   742B Mar 18  2003 51127\r\n",
        "-rw-r--r--  1 ogrisel  staff   650B Mar 18  2003 51128\r\n",
        "-rw-r--r--  1 ogrisel  staff   1.3K Mar 18  2003 51130\r\n",
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        "-rw-r--r--  1 ogrisel  staff   2.6K Mar 18  2003 51132\r\n",
        "-rw-r--r--  1 ogrisel  staff   1.5K Mar 18  2003 51133\r\n",
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        "-rw-r--r--  1 ogrisel  staff   2.1K Mar 18  2003 51136\r\n",
        "-rw-r--r--  1 ogrisel  staff   1.3K Mar 18  2003 51139\r\n",
        "-rw-r--r--  1 ogrisel  staff   409B Mar 18  2003 51140\r\n",
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        "-rw-r--r--  1 ogrisel  staff   9.0K Mar 18  2003 51142\r\n",
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        "-rw-r--r--  1 ogrisel  staff   631B Mar 18  2003 51146\r\n",
        "-rw-r--r--  1 ogrisel  staff   1.0K Mar 18  2003 51147\r\n",
        "-rw-r--r--  1 ogrisel  staff   1.8K Mar 18  2003 51148\r\n",
        "-rw-r--r--  1 ogrisel  staff   405B Mar 18  2003 51149\r\n",
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        "-rw-r--r--  1 ogrisel  staff   5.5K Mar 18  2003 51151\r\n",
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        "-rw-r--r--  1 ogrisel  staff   2.9K Mar 18  2003 51162\r\n",
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       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The `load_files` function can load text files from a 2 levels folder structure assuming folder names represent categories:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#print(load_files.__doc__)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_twenty_train = load_files('datasets/20news-bydate-train/',\n",
      "  charset='latin-1', random_state=42)\n",
      "all_twenty_test = load_files('datasets/20news-bydate-test/',\n",
      "    charset='latin-1', random_state=42)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_target_names = all_twenty_train.target_names\n",
      "all_target_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "['alt.atheism',\n",
        " 'comp.graphics',\n",
        " 'comp.os.ms-windows.misc',\n",
        " 'comp.sys.ibm.pc.hardware',\n",
        " 'comp.sys.mac.hardware',\n",
        " 'comp.windows.x',\n",
        " 'misc.forsale',\n",
        " 'rec.autos',\n",
        " 'rec.motorcycles',\n",
        " 'rec.sport.baseball',\n",
        " 'rec.sport.hockey',\n",
        " 'sci.crypt',\n",
        " 'sci.electronics',\n",
        " 'sci.med',\n",
        " 'sci.space',\n",
        " 'soc.religion.christian',\n",
        " 'talk.politics.guns',\n",
        " 'talk.politics.mideast',\n",
        " 'talk.politics.misc',\n",
        " 'talk.religion.misc']"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_twenty_train.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "array([12,  6,  9, ...,  9,  1, 12])"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_twenty_train.target.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "(11314,)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_twenty_test.target.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "(7532,)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(all_twenty_train.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "11314"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(all_twenty_train.data[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "unicode"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def display_sample(i, dataset):\n",
      "    print(\"Class name: \" + dataset.target_names[dataset.target[i]])\n",
      "    print(\"Text content:\\n\")\n",
      "    print(dataset.data[i])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "display_sample(0, all_twenty_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Class name: sci.electronics\n",
        "Text content:\n",
        "\n",
        "From: wtm@uhura.neoucom.edu (Bill Mayhew)\n",
        "Subject: Re: How to the disks copy protected.\n",
        "Organization: Northeastern Ohio Universities College of Medicine\n",
        "Lines: 23\n",
        "\n",
        "Write a good manual to go with the software.  The hassle of\n",
        "photocopying the manual is offset by simplicity of purchasing\n",
        "the package for only $15.  Also, consider offering an inexpensive\n",
        "but attractive perc for registered users.  For instance, a coffee\n",
        "mug.  You could produce and mail the incentive for a couple of\n",
        "dollars, so consider pricing the product at $17.95.\n",
        "\n",
        "You're lucky if only 20% of the instances of your program in use\n",
        "are non-licensed users.\n",
        "\n",
        "The best approach is to estimate your loss and accomodate that into\n",
        "your price structure.  Sure it hurts legitimate users, but too bad.\n",
        "Retailers have to charge off loss to shoplifters onto paying\n",
        "customers; the software industry is the same.\n",
        "\n",
        "Unless your product is exceptionally unique, using an ostensibly\n",
        "copy-proof disk will just send your customers to the competetion.\n",
        "\n",
        "\n",
        "-- \n",
        "Bill Mayhew      NEOUCOM Computer Services Department\n",
        "Rootstown, OH  44272-9995  USA    phone: 216-325-2511\n",
        "wtm@uhura.neoucom.edu (140.220.1.1)    146.580: N8WED\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "display_sample(1, all_twenty_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Class name: misc.forsale\n",
        "Text content:\n",
        "\n",
        "From: andy@SAIL.Stanford.EDU (Andy Freeman)\n",
        "Subject: Re: Catalog of Hard-to-Find PC Enhancements (Repost)\n",
        "Organization: Computer Science Department,  Stanford University.\n",
        "Lines: 33\n",
        "\n",
        ">andy@SAIL.Stanford.EDU (Andy Freeman) writes:\n",
        ">> >In article <C5ELME.4z4@unix.portal.com> jdoll@shell.portal.com (Joe Doll) wr\n",
        ">> >>   \"The Catalog of Personal Computing Tools for Engineers and Scien-\n",
        ">> >>   tists\" lists hardware cards and application software packages for \n",
        ">> >>   PC/XT/AT/PS/2 class machines.  Focus is on engineering and scien-\n",
        ">> >>   tific applications of PCs, such as data acquisition/control, \n",
        ">> >>   design automation, and data analysis and presentation.  \n",
        ">> >\n",
        ">> >>   If you would like a free copy, reply with your (U. S. Postal) \n",
        ">> >>   mailing address.\n",
        ">> \n",
        ">> Don't bother - it never comes.  It's a cheap trick for building a\n",
        ">> mailing list to sell if my junk mail flow is any indication.\n",
        ">> \n",
        ">> -andy sent his address months ago\n",
        ">\n",
        ">Perhaps we can get Portal to nuke this weasal.  I never received a \n",
        ">catalog either.  If that person doesn't respond to a growing flame, then \n",
        ">we can assume that we'yall look forward to lotsa junk mail.\n",
        "\n",
        "I don't want him nuked, I want him to be honest.  The junk mail has\n",
        "been much more interesting than the promised catalog.  If I'd known\n",
        "what I was going to get, I wouldn't have hesitated.  I wouldn't be\n",
        "surprised if there were other folks who looked at the ad and said\n",
        "\"nope\" but who would be very interested in the junk mail that results.\n",
        "Similarly, there are people who wanted the advertised catalog who\n",
        "aren't happy with the junk they got instead.\n",
        "\n",
        "The folks buying the mailing lists would prefer an honest ad, and\n",
        "so would the people reading it.\n",
        "\n",
        "-andy\n",
        "--\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's compute the (uncompressed, in-memory) size of the training and test sets in MB assuming an 8 bit encoding (in this case, all chars can be encoded using the latin-1 charset)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def text_size(text, charset='iso-8859-1'):\n",
      "    return len(text.encode(charset)) * 8 * 1e-6\n",
      "\n",
      "train_size_mb = sum(text_size(text) for text in all_twenty_train.data) \n",
      "test_size_mb = sum(text_size(text) for text in all_twenty_test.data)\n",
      "\n",
      "print(\"Training set size: {0} MB\".format(int(train_size_mb)))\n",
      "print(\"Testing set size: {0} MB\".format(int(test_size_mb)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training set size: 176 MB\n",
        "Testing set size: 110 MB\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we only consider a small subset of the 4 categories selected from the initial example:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_subset_size_mb = sum(text_size(text) for text in twenty_train_subset.data) \n",
      "test_subset_size_mb = sum(text_size(text) for text in twenty_test_subset.data)\n",
      "\n",
      "print(\"Training set size: {0} MB\".format(int(train_subset_size_mb)))\n",
      "print(\"Testing set size: {0} MB\".format(int(test_subset_size_mb)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training set size: 31 MB\n",
        "Testing set size: 22 MB\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Extracting Text Features"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.feature_extraction.text import TfidfVectorizer\n",
      "\n",
      "TfidfVectorizer()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "TfidfVectorizer(analyzer='word', binary=False, charset='utf-8',\n",
        "        charset_error='strict', dtype=<type 'long'>, input='content',\n",
        "        lowercase=True, max_df=1.0, max_features=None, max_n=None,\n",
        "        min_df=2, min_n=None, ngram_range=(1, 1), norm='l2',\n",
        "        preprocessor=None, smooth_idf=True, stop_words=None,\n",
        "        strip_accents=None, sublinear_tf=False,\n",
        "        token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
        "        vocabulary=None)"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vectorizer = TfidfVectorizer(min_df=1)\n",
      "\n",
      "%time X_train = vectorizer.fit_transform(twenty_train_subset.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 1.87 s, sys: 0.08 s, total: 1.95 s\n",
        "Wall time: 1.91 s\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The results is not a `numpy.array` but instead a `scipy.sparse` matrix. This datastructure is quite similar to a 2D numpy array but it does not store the zeros."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<2034x34118 sparse matrix of type '<type 'numpy.float64'>'\n",
        "\twith 323433 stored elements in Compressed Sparse Row format>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "scipy.sparse matrices also have a shape attribute to access the dimensions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_samples, n_features = X_train.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This dataset has around 2000 samples (the rows of the data matrix):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_samples"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "2034"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is the same value as the number of strings in the original list of text documents:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(twenty_train_subset.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "2034"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The columns represent the individual token occurrences:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_features"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "34118"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This number is the size of the vocabulary of the model extracted during fit in a Python dictionary:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(vectorizer.vocabulary_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "dict"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(vectorizer.vocabulary_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "34118"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The keys of the `vocabulary_` attribute are also called feature names and can be accessed as a list of strings."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(vectorizer.get_feature_names())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "34118"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here are the first 10 elements (sorted in lexicographical order):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vectorizer.get_feature_names()[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "[u'00',\n",
        " u'000',\n",
        " u'0000',\n",
        " u'00000',\n",
        " u'000000',\n",
        " u'000005102000',\n",
        " u'000021',\n",
        " u'000062david42',\n",
        " u'0000vec',\n",
        " u'0001']"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's have a look at the features from the middle:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vectorizer.get_feature_names()[n_features / 2:n_features / 2 + 10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "[u'inadequate',\n",
        " u'inala',\n",
        " u'inalienable',\n",
        " u'inane',\n",
        " u'inanimate',\n",
        " u'inapplicable',\n",
        " u'inappropriate',\n",
        " u'inappropriately',\n",
        " u'inaudible',\n",
        " u'inbreeding']"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In adition to the text of the documents that has been vectorized one also has access to the label information:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_train = twenty_train_subset.target\n",
      "target_names = twenty_train_subset.target_names\n",
      "target_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "['alt.atheism', 'comp.graphics', 'sci.space', 'talk.religion.misc']"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_train.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "(2034,)"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_train"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "array([1, 2, 2, ..., 2, 1, 1])"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can shape that we have the same number of samples for the input data and the labels:\n",
      "X_train.shape[0] == y_train.shape[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we have extracted a vector representation of the data, it's a good idea to project the data on the first 2D of a Principal Component Analysis to get a feel of the data. Note that the `RandomizedPCA` class can accept `scipy.sparse` matrices as input (as an alternative to numpy arrays):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.decomposition import RandomizedPCA\n",
      "\n",
      "%time X_train_pca = RandomizedPCA(n_components=2).fit_transform(X_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 0.09 s, sys: 0.02 s, total: 0.10 s\n",
        "Wall time: 0.10 s\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/local/lib/python2.7/site-packages/scikits/__init__.py:1: UserWarning: Module argparse was already imported from /usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/argparse.pyc, but /usr/local/lib/python2.7/site-packages is being added to sys.path\n",
        "  __import__('pkg_resources').declare_namespace(__name__)\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from itertools import cycle\n",
      "\n",
      "colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n",
      "for i, c in zip(np.unique(y_train), cycle(colors)):\n",
      "    pl.scatter(X_train_pca[y_train == i, 0],\n",
      "               X_train_pca[y_train == i, 1],\n",
      "               c=c, label=target_names[i], alpha=0.5)\n",
      "    \n",
      "_ = pl.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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dCyHyNiwib8NiqHkLQkWIwksQBEEQBKGOiDFegiAIj6jCwkKys7OxsbF5JJ4M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C4G6vX0dbu5KzGRAcEgI/x+JEGgQSZQ4/Jy5GgpTpWS2xLiuSshgeLPP6esrIzcvXvpqhKJijKh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OkJOTw9e/9HUA6uvrOfXaa8Q0KtSLlqFWapGjUaRdh1m0aDZ2u53ly5fz5pv7+OMfDyEI+ciy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mzBx8eH1NTUm+psB79N6uvrSUlJxel0EhMT3RZYeyUOHkzk5EkXwsLuRRAESkoO8s03O25J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\n"
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can observe that there is a large overlap of the samples from different categories. This is to be expected as the PCA linear projection projects data from a 34118 dimensional space down to 2 dimensions: data that is linearly separable in 34118D is often no longer linearly separable in 2D.\n",
      "    \n",
      "Still we can notice an interesting pattern: the newsgroups on religion and atheism occupy the much the same region and computer graphics and space science / space overlap more together than they do with the religion or atheism newsgroups."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Training a Classifier on Text Features"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can now train a classifier, for instance a Multinomial Naive Bayesian classifier which is a fast baseline for text classification tasks:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.naive_bayes import MultinomialNB\n",
      "\n",
      "clf = MultinomialNB(alpha=0.1)\n",
      "clf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "MultinomialNB(alpha=0.1, class_prior=None, fit_prior=True)"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.fit(X_train, y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "MultinomialNB(alpha=0.1, class_prior=None, fit_prior=True)"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can now evaluate the classifier on the testing set. Let's first use the builtin score function, which is the rate of correct classification in the test set:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_test = vectorizer.transform(twenty_test_subset.data)\n",
      "y_test = twenty_test_subset.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_test.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "(1353, 34118)"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_test.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "(1353,)"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.score(X_test, y_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "0.89652623798965259"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also compute the score on the test set and observe that the model is both overfitting and underfitting a bit at the same time:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.score(X_train, y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "0.99262536873156337"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Introspecting the Behavior of the Text Vectorizer"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The text vectorizer has many parameters to customize it's behavior, in particular how it extracts tokens:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TfidfVectorizer()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "TfidfVectorizer(analyzer='word', binary=False, charset='utf-8',\n",
        "        charset_error='strict', dtype=<type 'long'>, input='content',\n",
        "        lowercase=True, max_df=1.0, max_features=None, max_n=None,\n",
        "        min_df=2, min_n=None, ngram_range=(1, 1), norm='l2',\n",
        "        preprocessor=None, smooth_idf=True, stop_words=None,\n",
        "        strip_accents=None, sublinear_tf=False,\n",
        "        token_pattern=u'(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
        "        vocabulary=None)"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(TfidfVectorizer.__doc__)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Convert a collection of raw documents to a matrix of TF-IDF features.\n",
        "\n",
        "    Equivalent to CountVectorizer followed by TfidfTransformer.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    input : string {'filename', 'file', 'content'}\n",
        "        If filename, the sequence passed as an argument to fit is\n",
        "        expected to be a list of filenames that need reading to fetch\n",
        "        the raw content to analyze.\n",
        "\n",
        "        If 'file', the sequence items must have 'read' method (file-like\n",
        "        object) it is called to fetch the bytes in memory.\n",
        "\n",
        "        Otherwise the input is expected to be the sequence strings or\n",
        "        bytes items are expected to be analyzed directly.\n",
        "\n",
        "    charset : string, 'utf-8' by default.\n",
        "        If bytes or files are given to analyze, this charset is used to\n",
        "        decode.\n",
        "\n",
        "    charset_error : {'strict', 'ignore', 'replace'}\n",
        "        Instruction on what to do if a byte sequence is given to analyze that\n",
        "        contains characters not of the given `charset`. By default, it is\n",
        "        'strict', meaning that a UnicodeDecodeError will be raised. Other\n",
        "        values are 'ignore' and 'replace'.\n",
        "\n",
        "    strip_accents : {'ascii', 'unicode', None}\n",
        "        Remove accents during the preprocessing step.\n",
        "        'ascii' is a fast method that only works on characters that have\n",
        "        an direct ASCII mapping.\n",
        "        'unicode' is a slightly slower method that works on any characters.\n",
        "        None (default) does nothing.\n",
        "\n",
        "    analyzer : string, {'word', 'char'} or callable\n",
        "        Whether the feature should be made of word or character n-grams.\n",
        "\n",
        "        If a callable is passed it is used to extract the sequence of features\n",
        "        out of the raw, unprocessed input.\n",
        "\n",
        "    preprocessor : callable or None (default)\n",
        "        Override the preprocessing (string transformation) stage while\n",
        "        preserving the tokenizing and n-grams generation steps.\n",
        "\n",
        "    tokenizer : callable or None (default)\n",
        "        Override the string tokenization step while preserving the\n",
        "        preprocessing and n-grams generation steps.\n",
        "\n",
        "\n",
        "    ngram_range : tuple (min_n, max_n)\n",
        "        The lower and upper boundary of the range of n-values for different\n",
        "        n-grams to be extracted. All values of n such that min_n <= n <= max_n\n",
        "        will be used.\n",
        "\n",
        "    stop_words : string {'english'}, list, or None (default)\n",
        "        If a string, it is passed to _check_stop_list and the appropriate stop\n",
        "        list is returned. 'english' is currently the only supported string\n",
        "        value.\n",
        "\n",
        "        If a list, that list is assumed to contain stop words, all of which\n",
        "        will be removed from the resulting tokens.\n",
        "\n",
        "        If None, no stop words will be used. max_df can be set to a value\n",
        "        in the range [0.7, 1.0) to automatically detect and filter stop\n",
        "        words based on intra corpus document frequency of terms.\n",
        "\n",
        "    lowercase : boolean, default True\n",
        "        Convert all characters to lowercase befor tokenizing.\n",
        "\n",
        "    token_pattern : string\n",
        "        Regular expression denoting what constitutes a \"token\", only used\n",
        "        if `tokenize == 'word'`. The default regexp select tokens of 2\n",
        "        or more letters characters (punctuation is completely ignored\n",
        "        and always treated as a token separator).\n",
        "\n",
        "    max_df : float in range [0.0, 1.0] or int, optional, 1.0 by default\n",
        "        When building the vocabulary ignore terms that have a term frequency\n",
        "        strictly higher than the given threshold (corpus specific stop words).\n",
        "        If float, the parameter represents a proportion of documents, integer\n",
        "        absolute counts.\n",
        "        This parameter is ignored if vocabulary is not None.\n",
        "\n",
        "    min_df : float in range [0.0, 1.0] or int, optional, 2 by default\n",
        "        When building the vocabulary ignore terms that have a term frequency\n",
        "        strictly lower than the given threshold.\n",
        "        This value is also called cut-off in the literature.\n",
        "        If float, the parameter represents a proportion of documents, integer\n",
        "        absolute counts.\n",
        "        This parameter is ignored if vocabulary is not None.\n",
        "\n",
        "    max_features : optional, None by default\n",
        "        If not None, build a vocabulary that only consider the top\n",
        "        max_features ordered by term frequency across the corpus.\n",
        "\n",
        "        This parameter is ignored if vocabulary is not None.\n",
        "\n",
        "    vocabulary : Mapping or iterable, optional\n",
        "        Either a Mapping (e.g., a dict) where keys are terms and values are\n",
        "        indices in the feature matrix, or an iterable over terms. If not\n",
        "        given, a vocabulary is determined from the input documents.\n",
        "\n",
        "    binary : boolean, False by default.\n",
        "        If True, all non zero counts are set to 1. This is useful for discrete\n",
        "        probabilistic models that model binary events rather than integer\n",
        "        counts.\n",
        "\n",
        "    dtype : type, optional\n",
        "        Type of the matrix returned by fit_transform() or transform().\n",
        "\n",
        "    norm : 'l1', 'l2' or None, optional\n",
        "        Norm used to normalize term vectors. None for no normalization.\n",
        "\n",
        "    use_idf : boolean, optional\n",
        "        Enable inverse-document-frequency reweighting.\n",
        "\n",
        "    smooth_idf : boolean, optional\n",
        "        Smooth idf weights by adding one to document frequencies, as if an\n",
        "        extra document was seen containing every term in the collection\n",
        "        exactly once. Prevents zero divisions.\n",
        "\n",
        "    sublinear_tf : boolean, optional\n",
        "        Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).\n",
        "\n",
        "    See also\n",
        "    --------\n",
        "    CountVectorizer\n",
        "        Tokenize the documents and count the occurrences of token and return\n",
        "        them as a sparse matrix\n",
        "\n",
        "    TfidfTransformer\n",
        "        Apply Term Frequency Inverse Document Frequency normalization to a\n",
        "        sparse matrix of occurrence counts.\n",
        "\n",
        "    \n"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The easiest way to introspect what the vectorizer is actually doing for a given test of parameters is call the `vectorizer.build_analyzer()` to get an instance of the text analyzer it uses to process the text:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "analyzer = TfidfVectorizer().build_analyzer()\n",
      "analyzer(\"I love scikit-learn: this is a cool Python lib!\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "[u'love', u'scikit', u'learn', u'this', u'is', u'cool', u'python', u'lib']"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can notice that all the tokens are lowercase, that the single letter word \"I\" was dropped, and that hyphenation is used. Let's change some of that default behavior:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "analyzer = TfidfVectorizer(\n",
      "    preprocessor=lambda text: text,  # disable lowercasing\n",
      "    token_pattern=ur'(?u)\\b[\\w-]+\\b', # treat hyphen as a letter\n",
      "                                      # do not exclude single letter tokens\n",
      ").build_analyzer()\n",
      "\n",
      "analyzer(\"I love scikit-learn: this is a cool Python lib!\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "[u'I',\n",
        " u'love',\n",
        " u'scikit-learn',\n",
        " u'this',\n",
        " u'is',\n",
        " u'a',\n",
        " u'cool',\n",
        " u'Python',\n",
        " u'lib']"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The analyzer name comes from the Lucene parlance: it wraps the sequential application of:\n",
      "\n",
      "- text preprocessing (processing the text documents as a whole, e.g. lowercasing)\n",
      "- text tokenization (splitting the document into a sequence of tokens)\n",
      "- token filtering and recombination (e.g. n-grams extraction, see later)\n",
      "\n",
      "The analyzer system of scikit-learn is much more basic than lucene's though."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Exercise**:\n",
      "\n",
      "- Write a pre-processor callable (e.g. a python function) to remove the headers of the text a newsgroup post.\n",
      "- Vectorize the data again and measure the impact on performance of removing the header info from the dataset.\n",
      "- Do you expect the performance of the model to improve or decrease? What is the score of a uniform random classifier on the same dataset?\n",
      "\n",
      "Hint: the `TfidfVectorizer` class can accept python functions to customize the `preprocessor`, `tokenizer` or `analyzer` stages of the vectorizer.\n",
      "    \n",
      "- type `TfidfVectorizer()` alone in a cell to see the default value of the parameters\n",
      "\n",
      "- type `TfidfVectorizer.__doc__` to print the constructor parameters doc or `?` suffix operator on a any Python class or method to read the docstring or even the `??` operator to read the source code."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Solution**:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# %load solutions/05B_strip_headers.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Assembling a Pipeline for Model Validation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The feature extraction class has many options to customize its behavior:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(TfidfVectorizer.__doc__)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Convert a collection of raw documents to a matrix of TF-IDF features.\n",
        "\n",
        "    Equivalent to CountVectorizer followed by TfidfTransformer.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    input : string {'filename', 'file', 'content'}\n",
        "        If filename, the sequence passed as an argument to fit is\n",
        "        expected to be a list of filenames that need reading to fetch\n",
        "        the raw content to analyze.\n",
        "\n",
        "        If 'file', the sequence items must have 'read' method (file-like\n",
        "        object) it is called to fetch the bytes in memory.\n",
        "\n",
        "        Otherwise the input is expected to be the sequence strings or\n",
        "        bytes items are expected to be analyzed directly.\n",
        "\n",
        "    charset : string, 'utf-8' by default.\n",
        "        If bytes or files are given to analyze, this charset is used to\n",
        "        decode.\n",
        "\n",
        "    charset_error : {'strict', 'ignore', 'replace'}\n",
        "        Instruction on what to do if a byte sequence is given to analyze that\n",
        "        contains characters not of the given `charset`. By default, it is\n",
        "        'strict', meaning that a UnicodeDecodeError will be raised. Other\n",
        "        values are 'ignore' and 'replace'.\n",
        "\n",
        "    strip_accents : {'ascii', 'unicode', None}\n",
        "        Remove accents during the preprocessing step.\n",
        "        'ascii' is a fast method that only works on characters that have\n",
        "        an direct ASCII mapping.\n",
        "        'unicode' is a slightly slower method that works on any characters.\n",
        "        None (default) does nothing.\n",
        "\n",
        "    analyzer : string, {'word', 'char'} or callable\n",
        "        Whether the feature should be made of word or character n-grams.\n",
        "\n",
        "        If a callable is passed it is used to extract the sequence of features\n",
        "        out of the raw, unprocessed input.\n",
        "\n",
        "    preprocessor : callable or None (default)\n",
        "        Override the preprocessing (string transformation) stage while\n",
        "        preserving the tokenizing and n-grams generation steps.\n",
        "\n",
        "    tokenizer : callable or None (default)\n",
        "        Override the string tokenization step while preserving the\n",
        "        preprocessing and n-grams generation steps.\n",
        "\n",
        "\n",
        "    ngram_range : tuple (min_n, max_n)\n",
        "        The lower and upper boundary of the range of n-values for different\n",
        "        n-grams to be extracted. All values of n such that min_n <= n <= max_n\n",
        "        will be used.\n",
        "\n",
        "    stop_words : string {'english'}, list, or None (default)\n",
        "        If a string, it is passed to _check_stop_list and the appropriate stop\n",
        "        list is returned. 'english' is currently the only supported string\n",
        "        value.\n",
        "\n",
        "        If a list, that list is assumed to contain stop words, all of which\n",
        "        will be removed from the resulting tokens.\n",
        "\n",
        "        If None, no stop words will be used. max_df can be set to a value\n",
        "        in the range [0.7, 1.0) to automatically detect and filter stop\n",
        "        words based on intra corpus document frequency of terms.\n",
        "\n",
        "    lowercase : boolean, default True\n",
        "        Convert all characters to lowercase befor tokenizing.\n",
        "\n",
        "    token_pattern : string\n",
        "        Regular expression denoting what constitutes a \"token\", only used\n",
        "        if `tokenize == 'word'`. The default regexp select tokens of 2\n",
        "        or more letters characters (punctuation is completely ignored\n",
        "        and always treated as a token separator).\n",
        "\n",
        "    max_df : float in range [0.0, 1.0] or int, optional, 1.0 by default\n",
        "        When building the vocabulary ignore terms that have a term frequency\n",
        "        strictly higher than the given threshold (corpus specific stop words).\n",
        "        If float, the parameter represents a proportion of documents, integer\n",
        "        absolute counts.\n",
        "        This parameter is ignored if vocabulary is not None.\n",
        "\n",
        "    min_df : float in range [0.0, 1.0] or int, optional, 2 by default\n",
        "        When building the vocabulary ignore terms that have a term frequency\n",
        "        strictly lower than the given threshold.\n",
        "        This value is also called cut-off in the literature.\n",
        "        If float, the parameter represents a proportion of documents, integer\n",
        "        absolute counts.\n",
        "        This parameter is ignored if vocabulary is not None.\n",
        "\n",
        "    max_features : optional, None by default\n",
        "        If not None, build a vocabulary that only consider the top\n",
        "        max_features ordered by term frequency across the corpus.\n",
        "\n",
        "        This parameter is ignored if vocabulary is not None.\n",
        "\n",
        "    vocabulary : Mapping or iterable, optional\n",
        "        Either a Mapping (e.g., a dict) where keys are terms and values are\n",
        "        indices in the feature matrix, or an iterable over terms. If not\n",
        "        given, a vocabulary is determined from the input documents.\n",
        "\n",
        "    binary : boolean, False by default.\n",
        "        If True, all non zero counts are set to 1. This is useful for discrete\n",
        "        probabilistic models that model binary events rather than integer\n",
        "        counts.\n",
        "\n",
        "    dtype : type, optional\n",
        "        Type of the matrix returned by fit_transform() or transform().\n",
        "\n",
        "    norm : 'l1', 'l2' or None, optional\n",
        "        Norm used to normalize term vectors. None for no normalization.\n",
        "\n",
        "    use_idf : boolean, optional\n",
        "        Enable inverse-document-frequency reweighting.\n",
        "\n",
        "    smooth_idf : boolean, optional\n",
        "        Smooth idf weights by adding one to document frequencies, as if an\n",
        "        extra document was seen containing every term in the collection\n",
        "        exactly once. Prevents zero divisions.\n",
        "\n",
        "    sublinear_tf : boolean, optional\n",
        "        Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).\n",
        "\n",
        "    See also\n",
        "    --------\n",
        "    CountVectorizer\n",
        "        Tokenize the documents and count the occurrences of token and return\n",
        "        them as a sparse matrix\n",
        "\n",
        "    TfidfTransformer\n",
        "        Apply Term Frequency Inverse Document Frequency normalization to a\n",
        "        sparse matrix of occurrence counts.\n",
        "\n",
        "    \n"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In order to evaluate the impact of the parameters of the feature extraction one can chain a configured feature extraction and linear classifier (as an alternative to the naive Bayes model:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import PassiveAggressiveClassifier\n",
      "from sklearn.pipeline import Pipeline\n",
      "\n",
      "pipeline = Pipeline((\n",
      "    ('vec', TfidfVectorizer(min_df=1, max_df=0.8, use_idf=True)),\n",
      "    ('clf', PassiveAggressiveClassifier(C=1)),\n",
      "))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Such a pipeline can then be used to evaluate the performance on the test set:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pipeline.fit(twenty_train_subset.data, twenty_train_subset.target)\n",
      "\n",
      "print(\"Train score:\")\n",
      "print(pipeline.score(twenty_train_subset.data, twenty_train_subset.target))\n",
      "print(\"Test score:\")\n",
      "print(pipeline.score(twenty_test_subset.data, twenty_test_subset.target))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Train score:\n",
        "1.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Test score:\n",
        "0.888396156689"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Introspecting Model Performance"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Displaying the Most Discriminative Features"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's collect info on the fitted components of the previously trained model:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vec_name, vec = pipeline.steps[0]\n",
      "clf_name, clf = pipeline.steps[1]\n",
      "\n",
      "feature_names = vec.get_feature_names()\n",
      "\n",
      "feature_weights = clf.coef_\n",
      "\n",
      "feature_weights.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "(4, 34109)"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By sorting the feature weights on the linear model and asking the vectorizer what their names is, one can get a clue on what the model did actually learn on the data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def display_important_features(feature_names, target_names, weights, n_top=30):\n",
      "    for i, target_name in enumerate(target_names):\n",
      "        print(\"Class: \" + target_name)\n",
      "        print(\"\")\n",
      "        \n",
      "        sorted_features_indices = weights[i].argsort()[::-1]\n",
      "        \n",
      "        most_important = sorted_features_indices[:n_top]\n",
      "        print(\", \".join(\"{0}: {1:.4f}\".format(feature_names[j], weights[i, j])\n",
      "                        for j in most_important))\n",
      "        print(\"...\")\n",
      "        \n",
      "        least_important = sorted_features_indices[-n_top:]\n",
      "        print(\", \".join(\"{0}: {1:.4f}\".format(feature_names[j], weights[i, j])\n",
      "                        for j in least_important))\n",
      "        print(\"\")\n",
      "        \n",
      "display_important_features(feature_names, target_names, feature_weights)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Class: alt.atheism\n",
        "\n",
        "atheism: 2.8369, atheists: 2.7697, keith: 2.6781, cobb: 2.1986, islamic: 1.7952, okcforum: 1.6646, caltech: 1.5838, rice: 1.5769, bobby: 1.5187, peace: 1.5151, freedom: 1.4775, wingate: 1.4733, tammy: 1.4702, enviroleague: 1.4619, atheist: 1.4277, psilink: 1.3985, rushdie: 1.3846, tek: 1.3809, jaeger: 1.3783, osrhe: 1.3591, bible: 1.3543, wwc: 1.3375, mangoe: 1.3324, perry: 1.3082, religion: 1.2733, benedikt: 1.2581, liar: 1.2288, lunatic: 1.2110, free: 1.2060, charley: 1.2006\n",
        "...\n",
        "good: -0.8709, dm: -0.8764, 10: -0.8786, brian: -0.8900, objective: -0.8986, deal: -0.9098, thanks: -0.9174, order: -0.9174, image: -0.9258, scic: -0.9314, force: -0.9314, useful: -0.9377, com: -0.9414, weiss: -0.9428, interested: -0.9465, use: -0.9525, buffalo: -0.9580, fbi: -0.9660, 2000: -0.9810, they: -1.0051, muhammad: -1.0165, out: -1.0520, kevin: -1.0545, org: -1.0908, morality: -1.1773, mail: -1.1945, graphics: -1.5805, christian: -1.6466, hudson: -1.6503, space: -1.8655\n",
        "\n",
        "Class: comp.graphics\n",
        "\n",
        "graphics: 4.3650, image: 2.5319, tiff: 1.9232, file: 1.8831, animation: 1.8733, 3d: 1.7270, card: 1.7127, files: 1.6637, 42: 1.6542, 3do: 1.6326, points: 1.6154, code: 1.5795, computer: 1.5767, video: 1.5549, color: 1.5069, polygon: 1.5057, windows: 1.4597, comp: 1.4421, package: 1.3865, format: 1.3183, pc: 1.2518, email: 1.2262, cview: 1.2155, hi: 1.2004, 24: 1.1909, postscript: 1.1827, virtual: 1.1706, sphere: 1.1691, looking: 1.1613, images: 1.1561\n",
        "...\n",
        "astronomy: -0.9077, are: -0.9133, who: -0.9217, bill: -0.9354, atheism: -0.9397, org: -0.9404, christian: -0.9489, funding: -0.9494, that: -0.9597, by: -0.9654, solar: -0.9708, access: -0.9722, us: -0.9907, planets: -0.9992, cmu: -1.0507, moon: -1.0730, you: -1.0802, nasa: -1.0859, dgi: -1.1009, jennise: -1.1009, writes: -1.1152, was: -1.1369, beast: -1.1597, dc: -1.2858, he: -1.3806, orbit: -1.3853, edu: -1.4121, re: -1.4396, god: -1.6422, space: -3.5582\n",
        "\n",
        "Class: sci.space\n",
        "\n",
        "space: 5.7627, orbit: 2.3450, dc: 2.0973, nasa: 2.0815, moon: 1.9315, launch: 1.8711, sci: 1.7931, alaska: 1.7344, solar: 1.6946, henry: 1.6384, pat: 1.5734, ether: 1.5178, nick: 1.4982, planets: 1.4155, dietz: 1.3681, cmu: 1.3530, aurora: 1.3106, nicho: 1.2958, funding: 1.2768, lunar: 1.2757, astronomy: 1.2595, flight: 1.2418, rockets: 1.2048, jennise: 1.1963, dgi: 1.1963, shuttle: 1.1652, spacecraft: 1.1631, sky: 1.1593, digex: 1.1247, rochester: 1.1080\n",
        "...\n",
        "any: -0.8163, computer: -0.8183, gaspra: -0.8261, bible: -0.8342, video: -0.8485, religion: -0.8640, format: -0.8682, fbi: -0.8720, com: -0.8725, card: -0.8737, cc: -0.8828, code: -0.8875, 24: -0.8883, library: -0.8904, sgi: -0.9208, halat: -0.9531, 3d: -0.9607, ___: -0.9630, points: -1.0150, tiff: -1.0278, color: -1.0560, keith: -1.0664, koresh: -1.1302, file: -1.1529, files: -1.1679, image: -1.3169, christian: -1.3767, animation: -1.4241, god: -1.7873, graphics: -2.5640\n",
        "\n",
        "Class: talk.religion.misc\n",
        "\n",
        "christian: 3.0979, hudson: 1.8959, who: 1.8842, beast: 1.8652, fbi: 1.6698, mr: 1.6386, buffalo: 1.6148, 2000: 1.5694, abortion: 1.5172, church: 1.5061, koresh: 1.4853, weiss: 1.4829, morality: 1.4750, brian: 1.4736, order: 1.4545, frank: 1.4508, biblical: 1.4123, 666: 1.3742, thyagi: 1.3520, terrorist: 1.3306, christians: 1.3202, mormons: 1.2810, amdahl: 1.2641, blood: 1.2380, freenet: 1.2299, rosicrucian: 1.2122, mitre: 1.2032, christ: 1.1982, objective: 1.1635, love: 1.1519\n",
        "...\n",
        "file: -0.9489, saturn: -0.9516, university: -0.9569, on: -0.9592, ac: -0.9685, lunatic: -0.9820, for: -0.9882, orbit: -0.9893, some: -1.0031, anyone: -1.0355, uk: -1.0703, liar: -1.0715, ibm: -1.0965, wwc: -1.1029, thanks: -1.1200, freedom: -1.1455, nasa: -1.1951, free: -1.2008, thing: -1.2337, atheist: -1.2573, princeton: -1.2966, cobb: -1.3150, keith: -1.4660, caltech: -1.4869, graphics: -1.5331, edu: -1.5969, atheism: -1.7381, it: -1.7571, atheists: -1.9418, space: -2.2211\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Displaying the per-class Classification Reports"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.metrics import classification_report\n",
      "\n",
      "predicted = pipeline.predict(twenty_test_subset.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(classification_report(twenty_test_subset.target, predicted,\n",
      "                            target_names=target_names))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                    precision    recall  f1-score   support\n",
        "\n",
        "       alt.atheism       0.87      0.78      0.83       319\n",
        "     comp.graphics       0.93      0.96      0.95       389\n",
        "         sci.space       0.95      0.95      0.95       394\n",
        "talk.religion.misc       0.76      0.80      0.78       251\n",
        "\n",
        "       avg / total       0.89      0.89      0.89      1353\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Printing the Confusion Matrix"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The confusion matrix summarize which class where by having a look at off-diagonal entries: here we can see that articles about atheism have been wrongly classified as being about religion 57 times for instance: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.metrics import confusion_matrix\n",
      "\n",
      "confusion_matrix(twenty_test_subset.target, predicted)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "array([[250,   5,   7,  57],\n",
        "       [  2, 375,   6,   6],\n",
        "       [  2,  15, 375,   2],\n",
        "       [ 32,   9,   8, 202]])"
       ]
      }
     ],
     "prompt_number": 56
    }
   ],
   "metadata": {}
  }
 ]
}