Pattern Result.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pymongo\n",
      "import pandas as pd\n",
      "import matplotlib.pylab as plt\n",
      "from pandasql import sqldf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import imp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imp.reload(pandasql)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "<module 'pandasql' from '/home/lq/.virtualenv/python3.3/lib/python3.3/site-packages/pandasql/__init__.py'>"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "db = pymongo.Connection(host='nosql01-dev.datayes.com',port=27017)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result = pd.DataFrame(list(db.report_sentiment.sentiment.find({'algVersion': 'Pattern_0.1'},\n",
      "                                                              {'_id':0,'fileURL':1,'posTermNum':1,'negTermNum':1})))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result.posTermNum.apply(lambda x: log10(x+1)).hist()1.2cjmnorv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f7346952ed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Kz52KCsBbb72F73znO1iyZEn0JO71euFyuVBWVoaPPvoIdrsdu3btQnJyMgBg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\nAIiIDIoFgIjIoFgAiIgMigWAiMig/h9JfdjBPFmB/wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7346952710>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result.negTermNum.apply(lambda x: log10(x+1)).hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f734689ced0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8PlRVVcHn88HpdKK+vv6Ry0MEzF2q0Z0BjsU0wzBmLSHqjWNhHb8KQpKFl4AMJP+kt1SX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\nWgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f73468bd7d0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(result.negTermNum, result.posTermNum,'.',alpha=0.3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f734676df10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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omsWL5XiHclZbUEAzcauVCr3OnKGxSkspXqHR0MojOdn7VhnKz90xbmGzyemx\nwlg5orbdwRhmoBlSbSP664sWrpLf/Q545ZWesYSZM3v69W/ckIvMhg0jxRYeTgo+Jkb238+aRQai\nqws4cYKU6KVLdO3evbK8bW103YIFpLyTkuSAY3U1KffKSppNWywUy7h1i9xZwkf9yCPkNklOpoCt\n1Ur9/y9fJgMwciTl7ick0IYxji0xlJ/Z8OHk1mpvB/bto9oFo5GU840bJK/YarKveeq9tdpwjDE8\n8wxVaz/5pOtCM/H5X71KgerBTl/jWAwzUAz6lYCr2by7zdIdi64ct1KsrycF7KiURMuHyEjnoGlJ\nCaVjiuIyg4EChUVFNINubiYf+7lzlEHy1VfkHrp2jTJxRo8mRQ303Jvgq6/oOquVFKZINxWz9oMH\n6b4dHcBPfuKcMx8X5+zamTED+PxzUqgzZ9J9bLaeTfSUn58Imre2kpurtJQC0VotfZYzZ9Ln4bhn\ncG8rqr602hCrEYBiIO5Wc4mJdG1ionMmlD/dRIF0OfHKhgk2g94IuPoSiWNnz5JSFW0VhAtG+Mnj\n40nxZmTQLFooJeEXLyoi90dDg5zr/847lLvf1ETjxsSQy+Tzz8lldPs2/S06bYpirPBweokdzQBZ\nOYqN3K9ckauXi4vldNP/+i+a1d93H7mVJk6kZxDPI3ofiWrd+fPpWTQaOZd/9mznYPjevRTA/uIL\nSpEFSA6xGc7ixWTQ7HZ597TGRmopLc4Virq/isyV287xGk/XR0VRfEWZmeNPZRpIxcyFX0ywGfRG\nwNWXSOkrF20fhBvn9GlSqkKhREbKs+78fHI1GAzy9W1t1A1T7PULyMVaWi2lZ547J7s1mpvJ/ZGd\nTYrz88+p42dcnBxPOHuWsoeam2WjVFcnjyuM2Nix1Idn61a6f0YGyaXRyMqqqoruI4LPOp1cDCYU\nrU7n3ERPdEytraVnXrFCLopLSqLxx40jwyOaxaWnkwwLFjgrxhs36POLiupZrAb0v1mdJ9zFffyp\nTAOpmIdqTQUzeBj0RsBVAzfxxZo5k2bzkybRTPjkSZppR0fTa9Qoar3w6aek9NraSJkmJ5NCfvhh\nyv7JyAD+93/lhmuSRDPvefPoZ1cXKV+rldw98fEUR5g1i8bUaMhYaLVUoXvrltwe+oEHyFV05Qq9\nP3EiyabVUmA4Joaqjj/6iNwkCQmk3BsaSHlHR1OsYMqUnoZQuQ/AggVUmWw2kyGcOJEMmAgUFxQA\nf/oTPQM7Ff7RAAAbdElEQVRAxxctojEqKsjQuWo4585FI/Cm0tadS8adAfGnMg2kYubCLybYDDoj\noFQOrqpXRTVsSQkpLDGrjo4mN8vw4TTD/cUvyJ9eU0NKu6mJDEZMDG2Gcu4c/R4TQ2OJ/vnDh1Pd\nwPjxlJETFUUKWpLIiEgSKdmYGDp33jyadXd10Yqiro7O/+orCkp/9RU9i0ZDBiIykuT4wx9IhsZG\n2vi9upqefdYs56wiq5XSWadPd+5CCvR0bYgVQFIS3Ssnh1Yjb71Fyv/qVTJiiYkkj1BSx4+732v4\niy9IjuvXac9lJd5U2irlVtYv9BZ38AVWzIyaGHTZQZ4yS8Qs+NQpen3+uRxI1WjIXZKRQT/PnCHl\n2thIyv/4cZrljholZ7GcPUvK7eOPSdEPGyb37X//fWoWV1pKwVeRgTNuHClRSaL20J9+Chw+TLP2\nEydIzqgokiM2Vk4pvXmTrlm0SO7OmZhIbimDgcaprSUlevUq/S2yiurrqd11VRXwwgv0GYisk0uX\n6PnE5xMZSUo/Lo7cTBcvytk216+TIYiKIqPo2EbCMVNKPL/IaJk4kWohUlJoPCXeVNq6qnRubh6Y\nzpmcocOomUG3EhCzSlFV6hgMFbNDkd8/ZoyzQvvGN8h3bbFQ0FXMLLu6SDlWVJBb5n/+hxTbBx/I\n/vTMTLqnUJQVFTSDNxrJ2Oh0dJ+CAsrCETPqlhaabX/8MSnA2FhSllOmkHskOlrOi4+KIsUnGsTF\nxpL7JzKSDIHNRgpdr6dn/va3SemmpJAB+PJLWiW8/DJ9Do2NJPONG+TPz8+X3UZigxrxWQnl/w//\n4LxlpMAxxiAC52KW7i5Y6/hv5liToNHQGJ6yb4RLRnRdVXZr9SecocOomUFnBEQMQPT5/+wzUj6d\nnbJCEW2Su7rI/bFvH82ya2roZ3s7vR8fTzPxMWNIMTU1kR++vJxmrsL9M2MGuWKamuh6m43kyMyk\n38PCyLVitdKrrU3u9R8RQauM27fpmqoqcgd1dlLfn2PH5DhCfT2dV11NBqazk2b7P/kJKez8fJI7\nI0Pe+H3+fPr7hRfIOFy/LvcEmjKFjEBiIrmnGhspBjBrllyVLD6rhx4iGdzVWTi6SFwZYkfD0Zs/\nX1QQe1K64lxRNS26tfrSk8iTXIEKBHPFMxNqhJwRKCjw/OUQMYDDh2kGDpBCdPQzi1nrJ5/QLPfi\nRZqZNzSQ0oqNJeXX2Ehf+k8+od/b2+WVQlUV/R4eTspco6GxmpvJXWMyyUVT7e0U3I2Joevi4mRD\n0N5Ox202MkqdnSSzzUbPMHw4rT5sNrrftGnk6hHbNKalyQq7spIMoMVChqeqSq6D2LkT2LaN5NNq\naYVSXU2fV12d7G4S7aYdex6JbJ++KiR3hrijo2cFsCsl3xel6yifq5WJ8ry+KFV3cgUyEMyrDibU\nCDkj0FvQsLSUlJ8IMra2kjHIzCSF8f77skIIC5P33x07lo7fukWuEL1ezvePiaFzb9+m98XGLZ2d\npMRtNlK8sbH03syZdK0kyasFsdOXVkv3SU6Wi7hGjCCXTE0NGYJhw+h+N2/S72VlNPPv6qLzWltJ\nyT7wALl8BKKC+PJles5Ro0iZ3HknfWbf/CZw6BClpkZEkOETLSNiY8mYlZZS3UFXl/O+wP1RfsIQ\nnztHsnR2Ogd8e1PyfVG6ji2shw/v/by+KFV3cgUyEMx1AUyoEXJGoLcvx8SJNGtPSqK/77pL3t3q\nuefILy5y7b/7XfLvp6bSLDUsjGbBH3xA1w4bRjNpi0XO7Bk3jhSkGEMEKEePJiOh1dJ7o0bRuSKD\nZtQoMhx33033GzuWgrJhYcDXv06z5VGjSCnfvg08+yywY4e8NaRGQ7/X15NR+vrXgR//2LnL6aef\nyjuQff3rZBQSE+W+ROHh1EJiwQI6dueddK4kkfLX68mF09FBqxpvfexCkQkXTVubcwUx4FnJ90Xp\n9lVZKl1TnlaSoZCTHwoyMIwjIddF1G6XPH45hI9YbMAiKnJ1OsrlF0ViS5fSjP3UKXmGfeIEKb+K\nCsqmsVpJeVZXy64akbkTGUmK22aTq3EbGshQiEBoUhLN7mNiyPjExtKXfNo04C9/IcUrFFlREclc\nX0/KeOxYGufmTTo2ahTN1CWJjEtGBvC97zkrtJ/+lPz3w4fTeffdR6uN4mLKUGpro9n9E0/IfZEm\nTyb5mpqo5kGsACoqyPC4aj8NeHaziLEzMuT+Pv5WbH1t1yzOE32aRKfS3lJKGWYooapW0koF9Ktf\nye2eP/2UZtlGI/DLXwI/+xkp5/PnSTGImXlNjZzuOWYMKRCtlpT1/PnkburoID9/ZCQpd5G9YzaT\nwrXbSdnfvk2KPi6O3DFiRn/kCB27eJHGpeej+2r/vv6y28m909BAPy0WMmwREWQAtFrn1ssPP0wr\ngaYm4OmnyQ02YgQ9l91Oyn7lSpK3pYU+E9ESWhi22bNJJqXSVM7MPbV/7uu+DYJABEOVbbdFBpMr\nGTk4yww1fDECg65OQFkcJlIc29pIwbe2kl//V78ig1FaSrPtzz6jL359PSnj8HAaz2aTx9braYXQ\n1ETKvb1d7uWTlkazddGvR/S8/+ILUt63b9O4J0+Su0nk8tfVye4SkTLa2UnjbNlCqZ+ZmfTeM8+Q\nK2n7djIAyt3ApkyheoSwMODVV8knr9dTy4f77iMDEBYm9xNqa6NXUpIcuxDN6EQ6qmMTOMdceVd5\n+iKH3tN7rlDWdgwEyk6lrupHAikPwwwWQi4m4A7lBiy1tZTTDsh75Fos5CMvKqL3y8vp/bY2mvUK\nX//w4aQshZJoaaEZekMDKW2rlZSaaPcwZgy5gYxGGlPEC5qaaGUAkBEQ2zSGhdF4bW0kW0cHjTFs\nGK0iqqsp4KvTUafO8nKS32CgdNZz52i1c/06yQPQtbGxlO8vMpAiI6kNw65dNNbu3fReZCTw/e/T\nKmDECNcdP0VvIJEVdOAArZDC/j4tUObpOwbsPb3nikAEQ5VxBk++dw7OMozMoDECYvZWV0ezXLud\nUiwBUkYxMeT/Fi0j2tpoli+6aYqX3U6KTuzkJVYFOp3cz6e9XU7xFOPMmUPnijbL7e10H0A2CgcO\nyBuhxMTIFcHh4SR3RAQp2okTqYfQuHFkkCZPJqUlApuif09JCV1ns5HhEIVt4eHyBjZz5shN3UTQ\nPDGRDICrTWNEEz2xW5pQkFarXF1dXAzcf79znr6rvkSu3gN6ultc9XcaaDwFnzk4yzAyIW8EhEK5\ndIlmyteukdKtribXyKVLNPP9/HNSmDU1dH54uNzsrb2dlJskkXIVBkWjoRl1WJhsYACa4WsdPpmO\nDgrITpxI5yUk0AogLIzOEwHlF1+UC9daWsigdHXRPRsaaKVit5Mbpa6O7q3TkYI8f14O2p47R24u\nsWWkyES6epXunZREaaV33im7f86ede7hYzA4p8sqq6kBebc0gAxRURHdf/Nm+bgnhenuPWXaZkOD\nswuvP0HbgfDfc28ghpEJeSMgFIrBQIpz5EhSmjodKeuGBlnBOir/lhZSkCIYq9PJrZMBeq+jgxST\nmF2LvQOE4QDkFUR7Oyleq5W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       "text": [
        "<matplotlib.figure.Figure at 0x7f73468108d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result['ratio'] = (result.posTermNum+1e-3)/(result.negTermNum+1e-3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result.ratio.apply(log10).hist(bins=30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f7345474bd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f7345603b50>"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result.ratio.apply(lambda x: x>1).value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "True     4697\n",
        "False     220\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>negTermNum</th>\n",
        "      <th>posTermNum</th>\n",
        "      <th>ratio</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 4917.000000</td>\n",
        "      <td> 4917.000000</td>\n",
        "      <td>   4917.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>   11.254830</td>\n",
        "      <td>   53.522473</td>\n",
        "      <td>    745.367969</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>   12.843108</td>\n",
        "      <td>   59.151934</td>\n",
        "      <td>   4650.933941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>    0.000000</td>\n",
        "      <td>    0.000000</td>\n",
        "      <td>      0.000250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>    3.000000</td>\n",
        "      <td>   21.000000</td>\n",
        "      <td>      2.999500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>    7.000000</td>\n",
        "      <td>   32.000000</td>\n",
        "      <td>      5.153527</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>   14.000000</td>\n",
        "      <td>   62.000000</td>\n",
        "      <td>      8.998000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  159.000000</td>\n",
        "      <td>  996.000000</td>\n",
        "      <td> 128001.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "        negTermNum   posTermNum          ratio\n",
        "count  4917.000000  4917.000000    4917.000000\n",
        "mean     11.254830    53.522473     745.367969\n",
        "std      12.843108    59.151934    4650.933941\n",
        "min       0.000000     0.000000       0.000250\n",
        "25%       3.000000    21.000000       2.999500\n",
        "50%       7.000000    32.000000       5.153527\n",
        "75%      14.000000    62.000000       8.998000\n",
        "max     159.000000   996.000000  128001.000000\n",
        "\n",
        "[8 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(10,5))\n",
      "plt.subplot(1,3,1)\n",
      "plt.boxplot(result.posTermNum.apply(lambda x:log10(x+1)))\n",
      "plt.subplot(1,3,2)\n",
      "plt.boxplot(result.negTermNum.apply(lambda x:log10(x+1)))\n",
      "plt.subplot(1,3,3)\n",
      "plt.boxplot(result.ratio.apply(log10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "{'boxes': [<matplotlib.lines.Line2D at 0x7f734512c650>],\n",
        " 'caps': [<matplotlib.lines.Line2D at 0x7f7345128510>,\n",
        "  <matplotlib.lines.Line2D at 0x7f7345128d90>],\n",
        " 'fliers': [<matplotlib.lines.Line2D at 0x7f7345131790>,\n",
        "  <matplotlib.lines.Line2D at 0x7f7345131f90>],\n",
        " 'medians': [<matplotlib.lines.Line2D at 0x7f734512ced0>],\n",
        " 'whiskers': [<matplotlib.lines.Line2D at 0x7f7345122810>,\n",
        "  <matplotlib.lines.Line2D at 0x7f7345122bd0>]}"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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YRQGDmFCPJmQAEkyoRxMyAAlpX9uuuLhY1dXVkqSCggJVVVXp/PnzSdu0tbWpoaFBklRb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\nAAAALKB5AgAAsOD/AVXv9OZmdhj/AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f73452f0210>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result.sort('ratio').head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>fileURL</th>\n",
        "      <th>negTermNum</th>\n",
        "      <th>posTermNum</th>\n",
        "      <th>ratio</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>862 </th>\n",
        "      <td> body/438709668947.txt</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1741</th>\n",
        "      <td> body/441206742358.txt</td>\n",
        "      <td> 18</td>\n",
        "      <td> 5</td>\n",
        "      <td> 0.277818</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>849 </th>\n",
        "      <td> body/438702933387.txt</td>\n",
        "      <td> 12</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0.333389</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>306 </th>\n",
        "      <td> body/447327065418.txt</td>\n",
        "      <td> 20</td>\n",
        "      <td> 7</td>\n",
        "      <td> 0.350032</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3621</th>\n",
        "      <td> body/444297242062.txt</td>\n",
        "      <td> 18</td>\n",
        "      <td> 7</td>\n",
        "      <td> 0.388923</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "                    fileURL  negTermNum  posTermNum     ratio\n",
        "862   body/438709668947.txt           4           0  0.000250\n",
        "1741  body/441206742358.txt          18           5  0.277818\n",
        "849   body/438702933387.txt          12           4  0.333389\n",
        "306   body/447327065418.txt          20           7  0.350032\n",
        "3621  body/444297242062.txt          18           7  0.388923\n",
        "\n",
        "[5 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result.sort('ratio').tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>fileURL</th>\n",
        "      <th>negTermNum</th>\n",
        "      <th>posTermNum</th>\n",
        "      <th>ratio</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2750</th>\n",
        "      <td> body/443023167464.txt</td>\n",
        "      <td> 0</td>\n",
        "      <td>  49</td>\n",
        "      <td>  49001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3903</th>\n",
        "      <td> body/445603314293.txt</td>\n",
        "      <td> 0</td>\n",
        "      <td>  57</td>\n",
        "      <td>  57001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2561</th>\n",
        "      <td> body/442916477229.txt</td>\n",
        "      <td> 0</td>\n",
        "      <td>  64</td>\n",
        "      <td>  64001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2256</th>\n",
        "      <td> body/442415308879.txt</td>\n",
        "      <td> 0</td>\n",
        "      <td>  75</td>\n",
        "      <td>  75001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3616</th>\n",
        "      <td> body/444238457753.txt</td>\n",
        "      <td> 0</td>\n",
        "      <td> 128</td>\n",
        "      <td> 128001</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "                    fileURL  negTermNum  posTermNum   ratio\n",
        "2750  body/443023167464.txt           0          49   49001\n",
        "3903  body/445603314293.txt           0          57   57001\n",
        "2561  body/442916477229.txt           0          64   64001\n",
        "2256  body/442415308879.txt           0          75   75001\n",
        "3616  body/444238457753.txt           0         128  128001\n",
        "\n",
        "[5 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_pickle('data.pickle')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tmp = pd.merge(data, result, left_on='Body_s3_url', right_on='fileURL')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tmp['Body_length']=tmp.Body.apply(len)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(10,6))\n",
      "plt.subplot(1,3,1)\n",
      "plt.plot(tmp.Body_length, np.log10(tmp.posTermNum+1),'.')\n",
      "plt.subplot(1,3,2)\n",
      "plt.plot(tmp.Body_length, np.log10(tmp.negTermNum+1),'.')\n",
      "plt.subplot(1,3,3)\n",
      "plt.plot(tmp.Body_length, np.log10(tmp.ratio),'.',alpha=0.2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f733ee3d750>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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l/jITznM/eAzh5sP69dT1qq3lHDbGnRrGCyGEyw2vEyeA8+ft97HUCtq8mcnD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\njIaV18vwYUUFz82SEuCGG+yG8mfPAi+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/n6FAk4Tu89FQMkLEZWU0hl591e7P6PXSA+zx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\nFwAAgCX/A9m3QOOlTidLAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f733eee2290>"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(np.log10(tmp.negTermNum+1), np.log10(tmp.ratio),'.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f733ea24890>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1zJgxAw0NDTh8+HBQsy446dZFZ2cnysvL0dTUhKamJtx///15WMrsu+222zB+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\nYagTEWmEoU5EpBGGOhGRRhjqREQa+X8NSpne8V6aIgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f733ecbd210>"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data.Body[data.Body_s3_url=='body/447327065418.txt'].iloc[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u8bc1\u5238\u7814\u7a76\u62a5\u544a\u3010\u4ea4\u6613\u7b56\u7565\u70b9\u8bc4\u3011\u6280\u672f\u8d70\u52bf\u5206\u6790\uff1a\u8be5\u80a1\u4e3a2\u670824\u65e5\u516c\u53f8\u5ba2\u6237\u4ea4\u6613\u91cf\u6700\u5927\u7684\u80a1\u7968\u4e4b\u4e00\uff0c\u623f\u5730\u4ea7\u677f\u5757\u53d7\u5229\u7a7a\u6253\u51fb\u5927\u8dcc\uff0c\u8be5\u80a1\u8df3\u7a7a\u4e0b\u8dcc\u518d\u521b\u65b0\u4f4e\uff0c\u4ece\u8054\u8baf\u7684\u5206\u6790\u7cfb\u7edf\u770b\uff0c\u8be5\u80a1\u535a\u5f08\u7ebf\u6709\u518d\u6b21\u53d1\u6563\u5411\u4e0b\u7684\u503e\u5411\uff0c\u663e\u793a\u8fd1\u65e5\u8be5\u80a1\u7684\u51cf\u6301\u538b\u529b\u52a0\u5927\uff0c\u76ee\u524d\u8d44\u91d1\u6d41\u51fa\u901f\u5ea6\u548c\u89c4\u6a21\u8d85\u51fa\u4e86\u524d\u671f\uff0c\u673a\u6784\u7ebf\u8d70\u52bf\u770b\uff0c\u8be5\u80a1\u957f\u7ebf\u8d44\u91d1\u4f9d\u7136\u5904\u4e8e\u6301\u7eed\u51cf\u6301\u72b6\u6001\uff0c\u76ee\u524d\u8fd8\u6ca1\u6709\u6539\u53d8\u7684\u8ff9\u8c61\uff0c\u8be5\u80a1\u867d\u7136\u4e0d\u6392\u9664\u8fd1\u671f\u4f1a\u51fa\u73b0\u53cd\u62bd\uff0c\u4f46\u5728\u6574\u4f53\u683c\u5c40\u8d8b\u5411\u7a7a\u5934\u7684\u60c5\u51b5\u4e0b\uff0c\u63d0\u5347\u98ce\u9669\u610f\u8bc6\u662f\u5fc5\u8981\u548c\u5fc5\u9700\u7684\u3002\uff08\u6ce8\u610f\uff1a\u8bf7\u53c2\u8003\u62a5\u544a\u6700\u540e\u7684\u70ed\u80a1\u7efc\u5408\u8bc4\u7ea7\uff09\u516c\u53f8\u73af\u5883\u5206\u6790\uff1a1\uff0c\u8fd1\u671f\u6709\u5173\u5546\u4e1a\u94f6\u884c\u505c\u6b62\u623f\u8d37\u7684\u4f20\u95fb\u5bf9\u623f\u5730\u4ea7\u677f\u5757\u7684\u4f30\u503c\u4ea7\u751f\u8d1f\u9762\u5f71\u54cd\uff0c\u4f46\u6709\u5173\u4f20\u95fb\u8fd8\u9700\u8981\u8bc1\u5b9e\u30022\u3001\u516c\u53f8\u8d22\u52a1\u72b6\u51b5\u9884\u6d4b\u5e02\u573a\u7cfb\u7edf\u73af\u5883\u5b9a\u4f4d\uff1a\uff082\u670824\u65e5\uff09\u5468\u4e00\u5e02\u573a\u51fa\u73b0\u4e86\u8f83\u5927\u5e45\u5ea6\u7684\u4e0b\u8dcc\uff0c\u6caa\u6307\u6700\u5927\u8dcc\u5e45\u63a5\u8fd140\u70b9\uff0c\u7a7a\u95f4\u4e0a\u4e0b\u63a2\u4e8630\u65e5\u5747\u7ebf\uff0c\u5176\u540e\u51fa\u73b0\u4e86\u4e00\u5b9a\u5e45\u5ea6\u53cd\u5f39\uff0c\u4f46\u53cd\u5f39\u529b\u5ea6\u5341\u5206\u6709\u9650\uff0c\u5468\u4e00\u5e02\u573a\u7684\u8c03\u6574\uff0c\u80cc\u540e\u9886\u8dcc\u7684\u677f\u5757\u662f\u623f\u5730\u4ea7\u677f\u5757\uff0c\u5468\u672b\u6709\u5173\u623f\u5730\u4ea7\u4e00\u4e9b\u4e0d\u5229\u4fe1\u606f\uff0c\u6253\u51fb\u4e86\u6295\u8d44\u8005\u4fe1\u5fc3\uff0c\u8be5\u677f\u5757\u5f00\u76d8\u5373\u5927\u8dcc\uff0c\u623f\u5730\u4ea7\u7684\u4e0b\u8dcc\u5e26\u52a8\u6743\u91cd\u677f\u5757\u6574\u4f53\u51fa\u73b0\u4e86\u8c03\u6574\uff0c\u6743\u91cd\u677f\u5757\u7684\u4e0b\u8dcc\u5bf9\u6307\u6570\u4ea7\u751f\u8f83\u5927\u7684\u6740\u4f24\u529b\uff0c\u901a\u8fc7\u4eca\u5929\u5e02\u573a\u70e6\u4eba\u8c03\u6574\uff0c\u77ed\u5468\u671f\u8d70\u52bf\u4e0a\u5e02\u573a\u5df2\u7ecf\u5904\u4e8e\u8d85\u5356\u7684\u72b6\u6001\uff0c\u5e02\u573a\u5728\u5468\u4e8c\u51fa\u73b0\u53cd\u5f39\u7684\u53ef\u80fd\u5927\u5927\u589e\u52a0\u3002\u3010\u673a\u6784\u6210\u672c\u5206\u6790\u3011\u6210\u672c\u5206\u6790\u8d44\u6599\u6765\u6e90\uff1a\u548c\u8baf\u7b79\u7801\u53d8\u52a8\u3010\u6295\u8d44\u8005\u6301\u4ed3\u5206\u6790\u3011\u3010\u8054\u8baf\u70ed\u80a1\u7efc\u5408\u8bc4\u7ea7\u3011\u7efc\u5408\u8bc4\u7ea7\u53c2\u6570\u7528\u6cd5\u8bf4\u660e\uff1a\u6d3b\u8dc3\u5ea6\uff1a\u725b\u718a\u5206\u754c\u4e3a50\uff0c\u5927\u4e8e50\u4e3a\u4f18\uff0c\u6570\u503c\u8d8a\u5927\u8d8a\u6709\u5229\u4e8e\u83b7\u5229\u3002\u6ce8\u610f\uff0c\u6d3b\u8dc3\u5ea6\u9ad8\u4e0d\u7b49\u4e8e\u9a6c\u4e0a\u4f1a\u4e0a\u6da8\uff0c\u6d3b\u8dc3\u5ea6\u9ad8\u6307\u5982\u679c\u4e0a\u6da8\uff0c\u6da8\u901f\u4f1a\u5f88\u5feb\uff0c\u5bf9\u5feb\u901f\u83b7\u5229\u6709\u76ca\u3002\u6da8\u8dcc\u6bd4\uff1a\u6bd4\u503c\u5927\u4e8e1\u770b\u591a\uff0c\u6570\u503c\u8d8a\u5927\u8d8a\u6709\u5229\uff0c\u6bd4\u503c\u5c0f\u4e8e1\u770b\u7a7a\uff0c\u6570\u503c\u8d8a\u5c0f\u8d8a\u4e0d\u5229\u3002\u4ea4\u6613\u7cfb\u6570\uff1a\u725b\u718a\u5206\u754c\u4e3a50\uff0c\u5927\u4e8e50\u53ef\u4ee5\u4ea4\u6613\uff0c\u6570\u503c\u8d8a\u5927\u53ef\u64cd\u4f5c\u6027\u8d8a\u5f3a\u3002\u5c0f\u4e8e50\u4e0d\u4ea4\u6613\u3002\u4e2a\u80a1\u4ed3\u4f4d\uff1a\u6307\u70b9\u8bc4\u4e2a\u7968\u7684\u4ed3\u4f4d\u5360\u603b\u4ed3\u4f4d\u7684\u6bd4\u4f8b\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b\u4e2a\u80a1\u4ed3\u4f4d\u4e0d\u8d85\u8fc7\u603b\u4ed3\u4f4d\u768430%\u3002\u6ce2\u52a8\u533a\u95f4\uff1a\u662f\u6307\u70b9\u8bc4\u4e2a\u80a1\u8fd1\u671f\u80a1\u4ef7\u6ce2\u52a8\u7684\u53ef\u80fd\u4e0a\u9650\u548c\u53ef\u80fd\u4e0b\u9650\uff0c\u57fa\u672c\u7528\u6cd5\u662f\u80a1\u4ef7\u6da8\u81f3\u4e0a\u9650\u6b62\u76c8\uff0c\u8dcc\u7834\u4e0b\u9650\u6b62\u635f\u3002\u3010\u98ce\u9669\u63d0\u793a\u3011\u8bc1\u5238\u5e02\u573a\u4e2d\uff0c\u5728\u4efb\u4f55\u5e02\u573a\u80cc\u666f\u4e0b\u90fd\u8981\u8003\u8651\u5230\u98ce\u9669\u56e0\u7d20\u7684\u5b58\u5728\uff0c\u5236\u5b9a\u98ce\u9669\u63a7\u5236\u63aa\u65bd\u662f\u5fc5\u8981\u548c\u5fc5\u9700\u7684\u3002\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data.Body[data.Body_s3_url=='body/444238457753.txt'].iloc[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        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       ]
      }
     ],
     "prompt_number": 38
    }
   ],
   "metadata": {}
  }
 ]
}