{
 "metadata": {
  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extract useful text"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os, json\n",
      "from boto.s3.connection import S3Connection\n",
      "import pandas as pd\n",
      "from IPython.parallel import Client\n",
      "\n",
      "cats =['advertising',\n",
      "      'analytics',\n",
      "      'automotive',\n",
      "      'biotech',\n",
      "      'cleantech',\n",
      "      'consulting',\n",
      "      'design',\n",
      "      'ecommerce',\n",
      "      'education',\n",
      "      'enterprise',\n",
      "      'fashion',\n",
      "      'finance',\n",
      "      'games_video',\n",
      "      'government',\n",
      "      'hardware',\n",
      "      'health',\n",
      "      'hospitality',\n",
      "      'legal',\n",
      "      'local',\n",
      "      'manufacturing',\n",
      "      'medical',\n",
      "      'messaging',\n",
      "      'mobile',\n",
      "      'music',\n",
      "      'nanotech',\n",
      "      'network_hosting',\n",
      "      'news',\n",
      "      'nonprofit',\n",
      "      'other',\n",
      "      'pets',\n",
      "      'photo_video',\n",
      "      'public_relations',\n",
      "      'real_estate',\n",
      "      'search',\n",
      "      'security',\n",
      "      'semiconductor',\n",
      "      'social',\n",
      "      'software',\n",
      "      'sports',\n",
      "      'transportation',\n",
      "      'travel',\n",
      "      'web']\n",
      "\n",
      "path='C:\\\\Users\\\\gkand_000\\\\data'\n",
      "c=Client()\n",
      "d=c.direct_view()\n",
      "\n",
      "with d.sync_imports():\n",
      "    import time, json, os\n",
      "    import pandas as pd\n",
      "    from boto.s3.connection import S3Connection\n",
      "    \n",
      "d.push(dict(path=path, cred=cred))    \n",
      "\n",
      "def build_output(companies_split):\n",
      "    #cred = pd.read_csv(''.join((path,'\\\\setup\\\\credentials.csv')))\n",
      "    s3conn=S3Connection(cred['Access Key Id'][0], cred['Secret Access Key'][0])\n",
      "    s3bucket = s3conn.get_bucket('kandlikards')\n",
      "    sections = ['name','overview','tag_list','description']\n",
      "    for company in companies_split:\n",
      "        try: \n",
      "            code = 'test' if not(company['category_code']) else company['category_code']\n",
      "        except KeyError:\n",
      "            code = 'test'\n",
      "        comppath=''.join((path,'\\\\cbproject\\\\',code,'\\\\',company['permalink'],'.json'))\n",
      "        if os.path.exists(comppath):\n",
      "            continue\n",
      "        try:\n",
      "            keyname='crunchbase/'+company['permalink']+'.json'\n",
      "        except KeyError:\n",
      "            continue\n",
      "        text = s3bucket.get_key(keyname).get_contents_as_string()\n",
      "        \n",
      "        try: \n",
      "            json_doc = json.loads(text, strict = False)\n",
      "            output = '\\n'.join(filter(None,[json_doc[key] for key in sections])).encode('utf-8')\n",
      "        except :\n",
      "            continue\n",
      "        with open(comppath, 'w') as outputfile:\n",
      "            outputfile.write(output)\n",
      "        time.sleep(0.10)\n",
      "\n",
      "\n",
      "def make_folders():\n",
      "    for cat in cats:\n",
      "        if not os.path.exists(''.join((path,'\\\\',cat))):\n",
      "            os.mkdir(''.join((path,'\\\\',cat)))\n",
      "            \n",
      "cred = pd.read_csv(''.join((path,'\\\\0setup\\\\credentials.csv')))\n",
      "s3conn=S3Connection(cred['Access Key Id'][0], cred['Secret Access Key'][0])\n",
      "s3bucket = s3conn.get_bucket('kandlikards')\n",
      "\n",
      "with open(path+'\\\\companies.json') as f:\n",
      "    companies_all=json.loads(f.read(), strict = False)\n",
      "\n",
      "companies_split=[]\n",
      "for i in range(8):\n",
      "    companies_split.append(companies_all[i::8])\n",
      "    \n",
      "make_folders()\n",
      "results = d.map_async(build_output, companies_split)\n",
      "        \n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "importing time on engine(s)\n",
        "importing json on engine(s)\n",
        "importing os on engine(s)\n",
        "importing pandas on engine(s)\n",
        "importing S3Connection from boto.s3.connection on engine(s)\n"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "\n",
      "while not results.ready():\n",
      "    print 'not just yet. %s' % (time.strftime(\"%d %b %Y %H:%M:%S\", time.gmtime()))\n",
      "    time.sleep(180)\n",
      "    \n",
      "valid=results.get()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "not just yet. 31 Mar 2014 18:42:33\n",
        "not just yet. 31 Mar 2014 18:45:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 18:48:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 18:51:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 18:54:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 18:57:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:00:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:03:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:06:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:09:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:12:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:15:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:18:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:21:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:24:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:27:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:30:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:33:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:36:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:39:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:42:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:45:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:48:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:51:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:54:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 19:57:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:00:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:03:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:06:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:09:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:12:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:15:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:18:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:21:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:24:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:27:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:30:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:33:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:36:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:39:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:42:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:45:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:48:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:51:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:54:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 20:57:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 21:00:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 21:03:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 21:06:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 21:09:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 21:12:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 21:15:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 21:18:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 21:21:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 21:24:33"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "not just yet. 31 Mar 2014 21:27:33"
       ]
      },
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-89-6d610f450753>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mready\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m'not just yet. %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%d %b %Y %H:%M:%S\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgmtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m180\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mvalid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "After that is done, manually (randomly) assign docsto test/train folders"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Scikit Learn!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_files\n",
      "from sklearn.feature_extraction.text import TfidfVectorizer\n",
      "\n",
      "# Load the text data\n",
      "categories = [\n",
      "    'ecommerce',\n",
      "    'education',\n",
      "    'software',\n",
      "    'biotech',\n",
      "]\n",
      "path='C:\\\\Users\\\\gkand_000\\\\data\\\\cbproject'\n",
      "\n",
      "cb_train_small = load_files(''.join((path,'\\\\inittrain\\\\')),\n",
      "    categories=categories, encoding='utf-8')\n",
      "cb_test_small = load_files(''.join((path, '\\\\inittest\\\\')),\n",
      "    categories=categories, encoding='utf-8')\n",
      "\n",
      "\n",
      "'''\n",
      "I wanted to play around with the parameters a little more, but ran out of time.\n",
      "In particular, strip out any html tags. COnsidered BeautifulSoup.\n",
      "'''\n",
      "\n",
      "# Turn the text documents into vectors of word frequencies\n",
      "vectorizer = TfidfVectorizer(min_df=2)\n",
      "text_train = vectorizer.fit_transform(cb_train_small.data)\n",
      "cat_train = cb_train_small.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Use the Tf-Idf Vectorizer to transform data."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Run SVD analysis to see if reducing the data to 2 dimensions is informative."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "# Some nice default configuration for plots\n",
      "plt.rcParams['figure.figsize'] = 10, 7.5\n",
      "plt.rcParams['axes.grid'] = True\n",
      "plt.gray()\n",
      "\n",
      "from sklearn.feature_extraction.text import TfidfVectorizer\n",
      "\n",
      "vectorizer = TfidfVectorizer(min_df=1)\n",
      "\n",
      "cb_train_x_vector = vectorizer.fit_transform(cb_train_small.data)\n",
      "cb_train_y_vector = cat_train\n",
      "\n",
      "'''Figuring out the principal components'''\n",
      "'''Are the like records roughly grouped together?'''\n",
      "\n",
      "from sklearn.decomposition import TruncatedSVD\n",
      "\n",
      "cb_train_x_vector_pca = TruncatedSVD(n_components=2).fit_transform(cb_train_x_vector)\n",
      "\n",
      "from itertools import cycle\n",
      "\n",
      "colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n",
      "for i, c in zip(np.unique(cb_train_y_vector), cycle(colors)):\n",
      "    plt.scatter(cb_train_x_vector_pca[cb_train_y_vector == i, 0],\n",
      "               cb_train_x_vector_pca[cb_train_y_vector == i, 1],\n",
      "               c=c, label=cb_train_small.target_names[i], alpha=0.5)\n",
      "    \n",
      "_ = plt.legend(loc='best')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Fuztu/v5IIyI4c/48AGlpaXz87bfsAnZLpfzf99+TnJx8z47t4OBAREQE4eHh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LSrDt2MHnN2781DqzYDDIS/v3k7B5MyqDgfi5c9n7L/9C8759xOTk4Kqr487/Bn3rNf77uBZ4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\nZ88SbG8n6vcTv3gRr93O7MwMRbffj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CcmzGbYujV3n0g4F1RaXAwXumHVD6kEdG6AI0dQ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eD3Y7UjhMxuUip9OLfSwsFHN3dQmgebkNU2IZqaaEzHQYtW8KedqHTiqG6o3YrCuwWDSC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\nBYOyRj/4QdbFWloq+zM+no1nm5uTcagB+JWVci0el3VZtUpATK1zefq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XLnXiws9MXoBA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E5TvmQJ1zOQSKdJrVuHJR5nbmgIs8lELBQiU1gITiemeJy4Tpet37d1q1h0LBYBlIsXBRjUtAoV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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x17d2fb38>"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Recheck Dimensions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print cb_train_x_vector.shape\n",
      "print cb_train_y_vector.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(22238, 73593)\n",
        "(22238L,)\n"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print text_test.shape\n",
      "print cat_test.shape\n",
      "\n",
      "print len(cb_test_small.data)\n",
      "print len(cb_test_small.target)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(11241, 47244)\n",
        "(11241L,)\n",
        "11241\n",
        "11241\n"
       ]
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.naive_bayes import MultinomialNB\n",
      "\n",
      "vectorizer = TfidfVectorizer(min_df=2)\n",
      "text_train = vectorizer.fit_transform(cb_train_small.data)\n",
      "cat_train = cb_train_small.target\n",
      "\n",
      "print cat_train.shape[0] == text_train.shape[0]\n",
      "\n",
      "# Fit a classifier on the training set\n",
      "classifier = MultinomialNB().fit(text_train, cat_train)\n",
      "print(\"Training score: {0:.1f}%\".format(\n",
      "    classifier.score(text_train, cat_train) * 100))\n",
      "\n",
      "# Evaluate the classifier on the testing set\n",
      "text_test = vectorizer.transform(cb_test_small.data)\n",
      "cat_test = cb_test_small.target\n",
      "\n",
      "text_test\n",
      "\n",
      "print(\"Testing score: {0:.1f}%\".format(\n",
      "    classifier.score(text_test, cat_test) * 100))\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n",
        "Training score: 81.4%"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Testing score: 75.0%"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Cross Validate - Results are not great\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import cross_val_score\n",
      "from scipy.stats import sem\n",
      "from sklearn.pipeline import Pipeline\n",
      "\n",
      "pipe = Pipeline((\n",
      "    ('vectorizer', TfidfVectorizer(max_df=0.8, use_idf=True, stop_words='english')),\n",
      "    ('classifier', MultinomialNB(alpha=1.0)),\n",
      "))\n",
      "\n",
      "scores = cross_val_score(pipe, cb_train_small.data, cb_train_small.target, cv = 4)\n",
      "\n",
      "print scores\n",
      "print \"\"\n",
      "print \"\\n Mean: \" + str(scores.mean())\n",
      "print \"\\n Standard Error: \" + str(sem(scores))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.72464029  0.71852518  0.7222522   0.72333153]\n",
        "\n",
        "\n",
        " Mean: 0.722187301427\n",
        "\n",
        " Standard Error: 0.00131471663303\n"
       ]
      }
     ],
     "prompt_number": 187
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "GridSearch to the Rescue."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.grid_search import GridSearchCV\n",
      "\n",
      "params = {\n",
      "    'vectorizer__max_df': [0.75, 0.8],\n",
      "    'vectorizer__use_idf': [True, False],\n",
      "    'vectorizer__ngram_range':[(1, 1), (1, 2)],\n",
      "    'classifier__alpha': [0.1, 1, 10, 100]\n",
      "}\n",
      "\n",
      "grid_search = GridSearchCV(pipe, params, verbose=2, refit=False)\n",
      "_ = grid_search.fit(cb_train_small.data, cb_train_small.target)\n",
      "\n",
      "\n",
      "print \"Best Score:  \" + str(grid_search.best_score_)\n",
      "print \"Best Params: \" + str(grid_search.best_params_)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Fitting 3 folds for each of 32 candidates, totalling 96 fits\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=0.1 -   2.1s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=0.1 -   2.0s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=0.1 -   2.0s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=0.1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=0.1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=0.1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=0.1 -   7.0s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=0.1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=0.1 -   7.1s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=0.1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=0.1 -   7.1s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=0.1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=0.1 -   6.7s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=0.1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=0.1 -   6.7s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=0.1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=0.1 -   6.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=0.1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=0.1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=0.1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=0.1 -   2.0s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=0.1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=0.1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=0.1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=0.1 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=0.1 -   7.0s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=0.1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=0.1 -   7.1s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=0.1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=0.1 -   7.1s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=0.1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=0.1 -   6.7s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=0.1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=0.1 -   6.8s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=0.1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=0.1 -   6.7s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=1 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=1 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=1 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=1 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=1 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=1 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=1 -   7.1s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=1 -   7.0s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=1 -   7.1s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=1 -   6.7s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=1 -   6.7s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=1 -   6.7s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=1 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=1 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=1 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=1 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=1 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[Parallel(n_jobs=1)]: Done   1 jobs       | elapsed:    2.1s\n",
        "[Parallel(n_jobs=1)]: Done  41 jobs       | elapsed:  2.9min\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=1 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=1 -   1.9s"
       ]
      },
      {
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        "\n",
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       ]
      },
      {
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        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=1 "
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        "\n",
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        "\n",
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        "\n",
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      {
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        "\n",
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      {
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      {
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        "\n",
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      {
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        "\n",
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      {
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        "\n",
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      {
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        "\n",
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        "\n",
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        "\n",
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        "\n",
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        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.75, classifier__alpha=100 \n",
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        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=100 \n",
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        "\n",
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        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=100 "
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        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=100 -   6.9s"
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        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=100 "
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        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.75, classifier__alpha=100 "
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        "\n",
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        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=100 "
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        "\n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=100 -   2.0s"
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        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=100 \n",
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        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=100 \n",
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        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=100 \n",
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        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=100 \n",
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        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=100 \n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 1), vectorizer__max_df=0.8, classifier__alpha=100 -   1.9s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=100 \n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=100 -   7.2s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=100 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=100 -   7.1s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=100 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=True, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=100 -   8.4s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=100 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=100 -   8.1s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=100 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=100 -   8.5s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV] vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=100 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "[GridSearchCV]  vectorizer__use_idf=False, vectorizer__ngram_range=(1, 2), vectorizer__max_df=0.8, classifier__alpha=100 -   7.4s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Best Score:  0.837170608868"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Best Params: {'vectorizer__use_idf': False, 'vectorizer__ngram_range': (1, 1), 'vectorizer__max_df': 0.75, 'classifier__alpha': 0.1}\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[Parallel(n_jobs=1)]: Done  96 out of  96 | elapsed:  7.6min finished\n"
       ]
      }
     ],
     "prompt_number": 179
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Best Score:  0.837170608868\n",
      "    \n",
      "    compare to 0.722187301427\n",
      "    \n",
      "    \n",
      "Best Params: {'vectorizer__use_idf': False,\n",
      "              'vectorizer__ngram_range': (1, 1),\n",
      "              'vectorizer__max_df': 0.75,\n",
      "              'classifier__alpha': 0.1}\n",
      "\n",
      "[Parallel(n_jobs=1)]: Done  96 out of  96 | elapsed:  7.6min finished\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Fit a new model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''Performance'''\n",
      "pipe = Pipeline((\n",
      "    ('vectorizer', TfidfVectorizer(max_df=0.75, use_idf=False, stop_words='english')),\n",
      "    ('classifier', MultinomialNB(alpha=0.1)),\n",
      "))\n",
      "\n",
      "_ = pipe.fit(cb_train_small.data, cb_train_small.target)\n",
      "\n",
      "vectorizer_nm, vectorizer = pipe.steps[0]\n",
      "classifier_nm, classifier = pipe.steps[1]\n",
      "\n",
      "features = vectorizer.get_feature_names()\n",
      "targets = cb_train_small.target_names\n",
      "\n",
      "feature_weights = classifier.coef_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 190
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "feature_weights.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 191,
       "text": [
        "(4L, 73294L)"
       ]
      }
     ],
     "prompt_number": 191
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "What are the important distinguishing words for each category?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def display_important_features(features, targets, weights, n_top=15):\n",
      "    for i, target_name in enumerate(targets):\n",
      "        print \"\"\n",
      "        print '==================================='\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 (\"Most Important: \\n\")\n",
      "        print(\", \".join(\"{0}: {1:.4f}\".format(features[j].encode('utf-8', 'replace'), weights[i, j])\n",
      "                        for j in most_important))\n",
      "        print(\"...\")\n",
      "        \n",
      "        print (\"Least Important: \\n\")\n",
      "        least_important = sorted_features_indices[-n_top:]\n",
      "        print(\", \".join(\"{0}: {1:.4f}\".format(features[j].encode('utf-8', 'replace'), weights[i, j])\n",
      "                        for j in least_important))\n",
      "        print(\"\")\n",
      "        \n",
      "display_important_features(features, targets, feature_weights)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "===================================\n",
        "Class: biotech\n",
        "\n",
        "Most Important: \n",
        "\n",
        "company: -4.1491, based: -5.0161, development: -5.0201, products: -5.0750, technology: -5.4037, treatment: -5.4217, therapeutics: -5.5356, research: -5.5392, medical: -5.5583, drug: -5.5832, clinical: -5.6171, pharmaceuticals: -5.6357, founded: -5.6486, cancer: -5.6587, diseases: -5.6923\n",
        "...\n",
        "Least Important: \n",
        "\n",
        "oma: -12.2871, om: -12.2871, olympus: -12.2871, olympics: -12.2871, olympic: -12.2871, olympians: -12.2871, olympiads: -12.2871, olympiad: -12.2871, olymp: -12.2871, olx: -12.2871, olu\u00e5\u00ffturur: -12.2871, olur: -12.2871, olup: -12.2871, oltremare: -12.2871, 00: -12.2871\n",
        "\n",
        "\n",
        "===================================\n",
        "Class: ecommerce\n",
        "\n",
        "Most Important: \n",
        "\n",
        "online: -4.2340, com: -4.9231, products: -5.1555, ecommerce: -5.1792, commerce: -5.2778, company: -5.2939, web: -5.3134, 8217: -5.3399, shopping: -5.3467, social: -5.5032, business: -5.5494, services: -5.5840, design: -5.6834, website: -5.7058, service: -5.7117\n",
        "...\n",
        "Least Important: \n",
        "\n",
        "micropreneur: -12.8304, microplates: -12.8304, microplate: -12.8304, microphones: -12.8304, microphone: -12.8304, microphilanthropy: -12.8304, microphage: -12.8304, micropattern: -12.8304, microparticles: -12.8304, microparticle: -12.8304, micropact: -12.8304, microorganisms: -12.8304, micronutrients: -12.8304, microns: -12.8304, \ufb01rst: -12.8304\n",
        "\n",
        "\n",
        "===================================\n",
        "Class: education\n",
        "\n",
        "Most Important: \n",
        "\n",
        "education: -4.6225, learning: -4.9933, online: -5.0966, students: -5.1166, training: -5.2445, company: -5.4757, school: -5.5423, mobile: -5.6069, apps: -5.7941, 8217: -5.8101, applications: -5.9127, educational: -5.9134, based: -5.9322, platform: -5.9356, business: -5.9858\n",
        "...\n",
        "Least Important: \n",
        "\n",
        "navigable: -12.0903, naurex: -12.0903, navideck: -12.0903, navidad: -12.0903, naves: -12.0903, naver: -12.0903, naveen: -12.0903, navayuga: -12.0903, navarra: -12.0903, naval: -12.0903, nautilus: -12.0903, nautical: -12.0903, nausea: -12.0903, naurex\u00e2: -12.0903, \ufb01rst: -12.0903\n",
        "\n",
        "\n",
        "===================================\n",
        "Class: software\n",
        "\n",
        "Most Important: \n",
        "\n",
        "software: -3.9011, development: -4.5728, web: -4.6324, company: -4.7479, management: -4.7845, solutions: -4.8339, mobile: -4.9307, services: -5.0105, business: -5.0564, based: -5.1040, technology: -5.2855, data: -5.3205, online: -5.4612, 8217: -5.5069, design: -5.5137\n",
        "...\n",
        "Least Important: \n",
        "\n",
        "f\u00f6retag: -13.5012, f\u00f6retaget: -13.5012, f\u00f6retagsv\u00e4rdering: -13.5012, f\u00fchren: -13.5012, f\u00fchrungskr\u00e4fte: -13.5012, verhuur: -13.5012, g12e: -13.5012, g2: -13.5012, g28: -13.5012, quarries: -13.5012, g31p: -13.5012, quark\u00e2: -13.5012, g8: -13.5012, wishcast: -13.5012, intelligenti: -13.5012\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 192
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.metrics import classification_report\n",
      "\n",
      "predicted_vals = pipe.predict(cb_test_small.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 193
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Numbers!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(classification_report(cb_test_small.target, predicted_vals,\n",
      "                            target_names=cb_test_small.target_names))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "             precision    recall  f1-score   support\n",
        "\n",
        "    biotech       0.98      0.77      0.86      1829\n",
        "  ecommerce       0.93      0.71      0.81      3406\n",
        "  education       0.93      0.49      0.64      1003\n",
        "   software       0.73      0.97      0.83      5003\n",
        "\n",
        "avg / total       0.85      0.82      0.81     11241\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 194
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.metrics import confusion_matrix\n",
      "\n",
      "confusion_matrix(cb_test_small.target, predicted_vals)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 185,
       "text": [
        "array([[1404,   22,    3,  400],\n",
        "       [  15, 2431,   10,  950],\n",
        "       [   5,   54,  487,  457],\n",
        "       [  10,   98,   26, 4869]])"
       ]
      }
     ],
     "prompt_number": 185
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}