notes_turns_and_reactions2.ipynb

{
 "metadata": {
  "name": "notes_turns_and_reactions2"
 }, 
 "nbformat": 2, 
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown", 
     "source": [
      "# Reactions per turn specific to politics of app user"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Continuing to other task than predicting total reactions in general."
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "import pandas as pd", 
      "import reactions", 
      "import nltk", 
      "import random", 
      "import matplotlib.pyplot as plt", 
      "from pandas.tools.plotting import scatter_matrix", 
      "from nltk.corpus import stopwords"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 1
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "%time r = reactions.link_reactions_to_transcript('data/reactions_oct3_4project.csv','corpora/oct3_coded_transcript_sync.csv')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "CPU times: user 8.96 s, sys: 0.56 s, total: 9.52 s", 
        "Wall time: 9.53 s"
       ]
      }
     ], 
     "prompt_number": 2
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "r2 = r.copy()", 
      "#del r2[\"Sync'd start\"]", 
      "#del r2[\"Sync'd end\"]", 
      "del r2[\"Time\"]", 
      "del r2[\"Speaker\"]", 
      "r2.head(2)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>Frame</th>", 
        "      <th>QuestionTopic</th>", 
        "      <th>Reaction_what</th>", 
        "      <th>Reaction_who</th>", 
        "      <th>Sync'd end</th>", 
        "      <th>Sync'd start</th>", 
        "      <th>Tone</th>", 
        "      <th>Topic</th>", 
        "      <th>Transcript</th>", 
        "      <th>UserID</th>", 
        "      <th>start</th>", 
        "      <th>statement</th>", 
        "      <th>turn</th>", 
        "      <th>Speaker_name</th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>0    </th>", 
        "      <td> 9</td>", 
        "      <td> 99</td>", 
        "      <td>    Agree</td>", 
        "      <td> Moderator</td>", 
        "      <td> 1:02:06</td>", 
        "      <td> 1:02:01</td>", 
        "      <td> 0</td>", 
        "      <td> 9</td>", 
        "      <td> Good evening from the Magness Arena at the Uni...</td>", 
        "      <td> ag1zfnJlYWN0bGFicy00ciwLEgRVc2VyIiJhX2YzNTQxZW...</td>", 
        "      <td>        01:02:01</td>", 
        "      <td> 0</td>", 
        "      <td> 1</td>", 
        "      <td> Moderator</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>56861</th>", 
        "      <td> 9</td>", 
        "      <td> 99</td>", 
        "      <td> Disagree</td>", 
        "      <td> Moderator</td>", 
        "      <td> 1:02:06</td>", 
        "      <td> 1:02:01</td>", 
        "      <td> 0</td>", 
        "      <td> 9</td>", 
        "      <td> Good evening from the Magness Arena at the Uni...</td>", 
        "      <td> ag1zfnJlYWN0bGFicy01ciwLEgRVc2VyIiJhX2U3YmFkZT...</td>", 
        "      <td> 01:02:01.401000</td>", 
        "      <td> 0</td>", 
        "      <td> 1</td>", 
        "      <td> Moderator</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 3, 
       "text": [
        "       Frame  QuestionTopic Reaction_what Reaction_who Sync'd end Sync'd start  Tone  \\", 
        "0          9             99         Agree    Moderator    1:02:06      1:02:01     0   ", 
        "56861      9             99      Disagree    Moderator    1:02:06      1:02:01     0   ", 
        "", 
        "       Topic                                         Transcript  \\", 
        "0          9  Good evening from the Magness Arena at the Uni...   ", 
        "56861      9  Good evening from the Magness Arena at the Uni...   ", 
        "", 
        "                                                  UserID            start  \\", 
        "0      ag1zfnJlYWN0bGFicy00ciwLEgRVc2VyIiJhX2YzNTQxZW...         01:02:01   ", 
        "56861  ag1zfnJlYWN0bGFicy01ciwLEgRVc2VyIiJhX2U3YmFkZT...  01:02:01.401000   ", 
        "", 
        "       statement  turn Speaker_name  ", 
        "0              0     1    Moderator  ", 
        "56861          0     1    Moderator  "
       ]
      }
     ], 
     "prompt_number": 3
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "r2"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 4, 
       "text": [
        "<class 'pandas.core.frame.DataFrame'>", 
        "Int64Index: 189015 entries, 0 to 191634", 
        "Data columns:", 
        "Frame            189015  non-null values", 
        "QuestionTopic    189015  non-null values", 
        "Reaction_what    189015  non-null values", 
        "Reaction_who     189015  non-null values", 
        "Sync'd end       189015  non-null values", 
        "Sync'd start     189015  non-null values", 
        "Tone             189015  non-null values", 
        "Topic            189015  non-null values", 
        "Transcript       189015  non-null values", 
        "UserID           189015  non-null values", 
        "start            189015  non-null values", 
        "statement        189015  non-null values", 
        "turn             189015  non-null values", 
        "Speaker_name     189015  non-null values", 
        "dtypes: float64(5), int64(1), object(8)"
       ]
      }
     ], 
     "prompt_number": 4
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## Political questionnaire data"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "%time p = reactions.split_reactions_file('data/reactions_oct3_4project.csv')['quest_political']"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "CPU times: user 5.14 s, sys: 0.36 s, total: 5.50 s", 
        "Wall time: 5.50 s"
       ]
      }
     ], 
     "prompt_number": 5
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "p2 = p[['UserID','party_1','political_views_2','candidate_choice_3','confidence_in_choice_4','likely_to_vote_5','candidate_preferred_29']]", 
      "p2.head(2)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>UserID</th>", 
        "      <th>party_1</th>", 
        "      <th>political_views_2</th>", 
        "      <th>candidate_choice_3</th>", 
        "      <th>confidence_in_choice_4</th>", 
        "      <th>likely_to_vote_5</th>", 
        "      <th>candidate_preferred_29</th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>0 </th>", 
        "      <td> ag1zfnJlYWN0bGFicy00ciwLEgRVc2VyIiJhX2E0Mjc1MD...</td>", 
        "      <td> closest to republican party</td>", 
        "      <td> 73</td>", 
        "      <td> romney</td>", 
        "      <td> 100</td>", 
        "      <td> 100</td>", 
        "      <td>NaN</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>62</th>", 
        "      <td> ag1zfnJlYWN0bGFicy00ciwLEgRVc2VyIiJhX2E0Mzk5OD...</td>", 
        "      <td> closest to democratic party</td>", 
        "      <td> 20</td>", 
        "      <td>  obama</td>", 
        "      <td> 100</td>", 
        "      <td> 100</td>", 
        "      <td>NaN</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 6, 
       "text": [
        "                                               UserID  \\", 
        "0   ag1zfnJlYWN0bGFicy00ciwLEgRVc2VyIiJhX2E0Mjc1MD...   ", 
        "62  ag1zfnJlYWN0bGFicy00ciwLEgRVc2VyIiJhX2E0Mzk5OD...   ", 
        "", 
        "                        party_1  political_views_2 candidate_choice_3  \\", 
        "0   closest to republican party                 73             romney   ", 
        "62  closest to democratic party                 20              obama   ", 
        "", 
        "    confidence_in_choice_4  likely_to_vote_5  candidate_preferred_29  ", 
        "0                      100               100                     NaN  ", 
        "62                     100               100                     NaN  "
       ]
      }
     ], 
     "prompt_number": 6
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "p2"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 7, 
       "text": [
        "<class 'pandas.core.frame.DataFrame'>", 
        "Int64Index: 3767 entries, 0 to 193268", 
        "Data columns:", 
        "UserID                    3767  non-null values", 
        "party_1                   3733  non-null values", 
        "political_views_2         3733  non-null values", 
        "candidate_choice_3        3733  non-null values", 
        "confidence_in_choice_4    3733  non-null values", 
        "likely_to_vote_5          3733  non-null values", 
        "candidate_preferred_29    2118  non-null values", 
        "dtypes: float64(4), object(3)"
       ]
      }
     ], 
     "prompt_number": 7
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "There are ~30 users for whom we don't have political preference info, and the the candidate_preferred_29 col was often left blank."
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "### Simplify party membership into R/D/oth"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Let's group the users into D/R/other."
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "p2.groupby('party_1').agg('count').UserID"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 8, 
       "text": [
        "party_1", 
        "closest to democratic party    1267", 
        "closest to republican party     479", 
        "independent                     598", 
        "lean democrat                   781", 
        "lean republican                 527", 
        "no answer                        81", 
        "Name: UserID"
       ]
      }
     ], 
     "prompt_number": 8
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "p2['party'] = p2.party_1.apply(lambda a: {'closest to democratic party':'democrat', ", 
      "                                          'lean democrat':'democrat',", 
      "                                          'lean republican':'republican',", 
      "                                          'closest to republican party':'republican'}.get(a,'other'))", 
      "p2.groupby('party').agg('count').UserID"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 9, 
       "text": [
        "party", 
        "democrat      2048", 
        "other          713", 
        "republican    1006", 
        "Name: UserID"
       ]
      }
     ], 
     "prompt_number": 9
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "### Merge political questionnaire with reactions"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "%time r3 = r2.merge(p2[['UserID','party']])", 
      "print 'pre-merge:',len(r2),'post-merge:',len(r3)", 
      "r3.head(2)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "CPU times: user 0.30 s, sys: 0.02 s, total: 0.32 s", 
        "Wall time: 0.32 s", 
        "pre-merge: 189015 post-merge: 189015"
       ]
      }, 
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>Frame</th>", 
        "      <th>QuestionTopic</th>", 
        "      <th>Reaction_what</th>", 
        "      <th>Reaction_who</th>", 
        "      <th>Sync'd end</th>", 
        "      <th>Sync'd start</th>", 
        "      <th>Tone</th>", 
        "      <th>Topic</th>", 
        "      <th>Transcript</th>", 
        "      <th>UserID</th>", 
        "      <th>start</th>", 
        "      <th>statement</th>", 
        "      <th>turn</th>", 
        "      <th>Speaker_name</th>", 
        "      <th>party</th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>0</th>", 
        "      <td> 9</td>", 
        "      <td> 99</td>", 
        "      <td> Agree</td>", 
        "      <td> Moderator</td>", 
        "      <td> 1:02:06</td>", 
        "      <td> 1:02:01</td>", 
        "      <td> 0</td>", 
        "      <td> 9</td>", 
        "      <td> Good evening from the Magness Arena at the Uni...</td>", 
        "      <td> ag1zfnJlYWN0bGFicy00ciwLEgRVc2VyIiJhX2YzNTQxZW...</td>", 
        "      <td>        01:02:01</td>", 
        "      <td>  0</td>", 
        "      <td> 1</td>", 
        "      <td> Moderator</td>", 
        "      <td> democrat</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>1</th>", 
        "      <td> 3</td>", 
        "      <td>  5</td>", 
        "      <td> Dodge</td>", 
        "      <td>     Obama</td>", 
        "      <td> 1:05:36</td>", 
        "      <td> 1:05:32</td>", 
        "      <td> 1</td>", 
        "      <td> 5</td>", 
        "      <td> Over the last 30 months, we've seen 5 million ...</td>", 
        "      <td> ag1zfnJlYWN0bGFicy00ciwLEgRVc2VyIiJhX2YzNTQxZW...</td>", 
        "      <td> 01:05:34.890000</td>", 
        "      <td> 30</td>", 
        "      <td> 2</td>", 
        "      <td>     Obama</td>", 
        "      <td> democrat</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 10, 
       "text": [
        "   Frame  QuestionTopic Reaction_what Reaction_who Sync'd end Sync'd start  Tone  \\", 
        "0      9             99         Agree    Moderator    1:02:06      1:02:01     0   ", 
        "1      3              5         Dodge        Obama    1:05:36      1:05:32     1   ", 
        "", 
        "   Topic                                         Transcript  \\", 
        "0      9  Good evening from the Magness Arena at the Uni...   ", 
        "1      5  Over the last 30 months, we've seen 5 million ...   ", 
        "", 
        "                                              UserID            start  statement  \\", 
        "0  ag1zfnJlYWN0bGFicy00ciwLEgRVc2VyIiJhX2YzNTQxZW...         01:02:01          0   ", 
        "1  ag1zfnJlYWN0bGFicy00ciwLEgRVc2VyIiJhX2YzNTQxZW...  01:05:34.890000         30   ", 
        "", 
        "   turn Speaker_name     party  ", 
        "0     1    Moderator  democrat  ", 
        "1     2        Obama  democrat  "
       ]
      }
     ], 
     "prompt_number": 10
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Limit to reactions to the speaker of the ***current turn***."
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "r4 = r3[r3.Reaction_who == r3.Speaker_name]", 
      "print 'before:',len(r3),'current-speaker-only:',len(r4), 'difference:',len(r4)-len(r3), 1.0*(len(r4)-len(r3))/len(r4),'percent'"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "before: 189015 current-speaker-only: 156622 difference: -32393 -0.206822796287 percent"
       ]
      }
     ], 
     "prompt_number": 11
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## Group by turn"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Statements"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "st = r4.groupby(['statement']).first()[['Speaker_name','Transcript','turn',\"Sync'd start\",\"Sync'd end\"]]", 
      "st.head(2)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>Speaker_name</th>", 
        "      <th>Transcript</th>", 
        "      <th>turn</th>", 
        "      <th>Sync'd start</th>", 
        "      <th>Sync'd end</th>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>statement</th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>0</th>", 
        "      <td> Moderator</td>", 
        "      <td> Good evening from the Magness Arena at the Uni...</td>", 
        "      <td> 1</td>", 
        "      <td> 1:02:01</td>", 
        "      <td> 1:02:06</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>1</th>", 
        "      <td> Moderator</td>", 
        "      <td>              I'm Jim Lehrer of the PBS NewsHour, </td>", 
        "      <td> 1</td>", 
        "      <td> 1:02:06</td>", 
        "      <td> 1:02:09</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 12, 
       "text": [
        "          Speaker_name                                         Transcript  turn  \\", 
        "statement                                                                         ", 
        "0            Moderator  Good evening from the Magness Arena at the Uni...     1   ", 
        "1            Moderator               I'm Jim Lehrer of the PBS NewsHour,      1   ", 
        "", 
        "          Sync'd start Sync'd end  ", 
        "statement                          ", 
        "0              1:02:01    1:02:06  ", 
        "1              1:02:06    1:02:09  "
       ]
      }
     ], 
     "prompt_number": 12
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Turns"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "t = pd.DataFrame({'speaker':st.groupby('turn').first().Speaker_name, ", 
      "                  'start':st.groupby('turn').first()[\"Sync'd start\"],", 
      "                  'end':st.groupby('turn').last()[\"Sync'd end\"],", 
      "                  'reactions':r4.groupby('turn').count().Speaker_name, ", 
      "                  'statements':st.groupby('turn').count().turn, ", 
      "                  'text':st.groupby('turn').apply(lambda x: ''.join(x.Transcript)),", 
      "                  'agree':r4[r4.Reaction_what=='Agree'].groupby('turn').count().turn,", 
      "                  'agree_dem':r4[(r4.party=='democrat') & (r4.Reaction_what=='Agree')].groupby('turn').count().turn,", 
      "                  'agree_rep':r4[(r4.party=='republican') & (r4.Reaction_what=='Agree')].groupby('turn').count().turn,", 
      "                  'disagree':r4[r4.Reaction_what=='Disagree'].groupby('turn').count().turn,", 
      "                  'disagree_dem':r4[(r4.party=='democrat') & (r4.Reaction_what=='Disagree')].groupby('turn').count().turn,", 
      "                  'disagree_rep':r4[(r4.party=='republican') & (r4.Reaction_what=='Disagree')].groupby('turn').count().turn,", 
      "                  'dodge':r4[r4.Reaction_what=='Dodge'].groupby('turn').count().turn,", 
      "                  'spin':r4[r4.Reaction_what=='Spin'].groupby('turn').count().turn,", 
      "                  })", 
      "tmpstart = pd.to_datetime(t.start)", 
      "tmpend = pd.to_datetime(t.end)", 
      "t['dur'] = (tmpend - tmpstart)", 
      "t.duration = 1.0 * t.dur / 1000000000.0", 
      "t['words'] = t.text.apply(lambda txt: [tok.lower() for tok in nltk.tokenize.word_tokenize(txt) if tok.isalpha()])", 
      "t['word_count'] = t.words.apply(lambda words: len(words))", 
      "t['r_per_st'] = 1.0 * t.reactions / t.statements", 
      "t['r_per_w'] = 1.0 * t.reactions / t.word_count", 
      "t['r_per_sec'] = 1.0 * t.reactions / t.dur", 
      "t['sd_per_sec'] = 1.0 * (t.spin + t.dodge) / t.dur", 
      "t['a_to_d_dems'] = t.agree_dem / t.disagree_dem", 
      "t['a_to_d_reps'] = t.agree_rep / t.disagree_rep", 
      "", 
      "del t['agree']", 
      "del t['agree_dem']", 
      "del t['agree_rep']", 
      "del t['disagree']", 
      "del t['disagree_dem']", 
      "del t['disagree_rep']", 
      "del t['dodge']", 
      "del t['spin']", 
      "del t['r_per_st']", 
      "del t['r_per_w']", 
      "del t['start']", 
      "del t['end']"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 13
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "ranked_unigrams = nltk.FreqDist([w for word_list in t.words for w in word_list]).keys()", 
      "MAX_FEATURES = 700 # avoid overfitting", 
      "t['unigrams'] = t.words.apply(lambda words: {w:True for w in words if w in ranked_unigrams[:MAX_FEATURES] and not w in stopwords.words('english')})", 
      "t['unigram_count'] = t.unigrams.apply(lambda unigrams: len(unigrams))"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 14
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "#t[['start','end','duration','text','speaker']].head()", 
      "t.head()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>reactions</th>", 
        "      <th>speaker</th>", 
        "      <th>statements</th>", 
        "      <th>text</th>", 
        "      <th>dur</th>", 
        "      <th>words</th>", 
        "      <th>word_count</th>", 
        "      <th>r_per_sec</th>", 
        "      <th>sd_per_sec</th>", 
        "      <th>a_to_d_dems</th>", 
        "      <th>a_to_d_reps</th>", 
        "      <th>unigrams</th>", 
        "      <th>unigram_count</th>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>turn</th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "      <th></th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>1</th>", 
        "      <td>  416</td>", 
        "      <td> Moderator</td>", 
        "      <td> 20</td>", 
        "      <td> Good evening from the Magness Arena at the Uni...</td>", 
        "      <td> 162000000000</td>", 
        "      <td> [good, evening, from, the, magness, arena, at,...</td>", 
        "      <td> 257</td>", 
        "      <td> 2.567901e-09</td>", 
        "      <td> 5.000000e-10</td>", 
        "      <td>  5.555556</td>", 
        "      <td>  2.454545</td>", 
        "      <td> {'governing': True, 'among': True, 'major': Tr...</td>", 
        "      <td>  57</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>2</th>", 
        "      <td> 3976</td>", 
        "      <td>     Obama</td>", 
        "      <td> 22</td>", 
        "      <td> Well, thank you very much, Jim, for this oppor...</td>", 
        "      <td> 106000000000</td>", 
        "      <td> [well, thank, you, very, much, jim, for, this,...</td>", 
        "      <td> 278</td>", 
        "      <td> 3.750943e-08</td>", 
        "      <td> 9.311321e-09</td>", 
        "      <td> 39.346939</td>", 
        "      <td>  0.802048</td>", 
        "      <td> {'sector': True, 'two': True, 'reduce': True, ...</td>", 
        "      <td>  82</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>4</th>", 
        "      <td> 4392</td>", 
        "      <td>    Romney</td>", 
        "      <td> 33</td>", 
        "      <td> It's an honor to be here with youand I appreci...</td>", 
        "      <td> 127000000000</td>", 
        "      <td> [it, an, honor, to, be, here, with, youand, i,...</td>", 
        "      <td> 350</td>", 
        "      <td> 3.458268e-08</td>", 
        "      <td> 6.866142e-09</td>", 
        "      <td>  0.671460</td>", 
        "      <td> 51.296296</td>", 
        "      <td> {'two': True, 'right': True, 'particularly': T...</td>", 
        "      <td>  79</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>5</th>", 
        "      <td>   54</td>", 
        "      <td> Moderator</td>", 
        "      <td>  2</td>", 
        "      <td> Mr. President, please respond directly to what...</td>", 
        "      <td>  11000000000</td>", 
        "      <td> [president, please, respond, directly, to, wha...</td>", 
        "      <td>  22</td>", 
        "      <td> 4.909091e-09</td>", 
        "      <td> 1.181818e-09</td>", 
        "      <td>  6.666667</td>", 
        "      <td>  1.000000</td>", 
        "      <td> {'respond': True, 'said': True, 'please': True...</td>", 
        "      <td>   6</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>6</th>", 
        "      <td> 4635</td>", 
        "      <td>     Obama</td>", 
        "      <td> 23</td>", 
        "      <td> Well, let me talk specifically about what I th...</td>", 
        "      <td> 131000000000</td>", 
        "      <td> [well, let, me, talk, specifically, about, wha...</td>", 
        "      <td> 366</td>", 
        "      <td> 3.538168e-08</td>", 
        "      <td> 6.816794e-09</td>", 
        "      <td> 23.742574</td>", 
        "      <td>  1.564885</td>", 
        "      <td> {'colleges': True, 'code': True, 'particularly...</td>", 
        "      <td> 103</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 15, 
       "text": [
        "      reactions    speaker  statements  \\", 
        "turn                                     ", 
        "1           416  Moderator          20   ", 
        "2          3976      Obama          22   ", 
        "4          4392     Romney          33   ", 
        "5            54  Moderator           2   ", 
        "6          4635      Obama          23   ", 
        "", 
        "                                                   text           dur  \\", 
        "turn                                                                    ", 
        "1     Good evening from the Magness Arena at the Uni...  162000000000   ", 
        "2     Well, thank you very much, Jim, for this oppor...  106000000000   ", 
        "4     It's an honor to be here with youand I appreci...  127000000000   ", 
        "5     Mr. President, please respond directly to what...   11000000000   ", 
        "6     Well, let me talk specifically about what I th...  131000000000   ", 
        "", 
        "                                                  words  word_count     r_per_sec  \\", 
        "turn                                                                                ", 
        "1     [good, evening, from, the, magness, arena, at,...         257  2.567901e-09   ", 
        "2     [well, thank, you, very, much, jim, for, this,...         278  3.750943e-08   ", 
        "4     [it, an, honor, to, be, here, with, youand, i,...         350  3.458268e-08   ", 
        "5     [president, please, respond, directly, to, wha...          22  4.909091e-09   ", 
        "6     [well, let, me, talk, specifically, about, wha...         366  3.538168e-08   ", 
        "", 
        "        sd_per_sec  a_to_d_dems  a_to_d_reps  \\", 
        "turn                                           ", 
        "1     5.000000e-10     5.555556     2.454545   ", 
        "2     9.311321e-09    39.346939     0.802048   ", 
        "4     6.866142e-09     0.671460    51.296296   ", 
        "5     1.181818e-09     6.666667     1.000000   ", 
        "6     6.816794e-09    23.742574     1.564885   ", 
        "", 
        "                                               unigrams  unigram_count  ", 
        "turn                                                                    ", 
        "1     {'governing': True, 'among': True, 'major': Tr...             57  ", 
        "2     {'sector': True, 'two': True, 'reduce': True, ...             82  ", 
        "4     {'two': True, 'right': True, 'particularly': T...             79  ", 
        "5     {'respond': True, 'said': True, 'please': True...              6  ", 
        "6     {'colleges': True, 'code': True, 'particularly...            103  "
       ]
      }
     ], 
     "prompt_number": 15
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "### Filter"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "For now, we get rid of the really short turns, which would seem to likely have noise from adjacent turns and the small numbers of words make the math more sketchy."
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "MIN_WORDS = 30", 
      "t2 = t[t.word_count >= MIN_WORDS]", 
      "print len(t),'->',len(t2)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "181 -> 70"
       ]
      }
     ], 
     "prompt_number": 17
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## What to predict?"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "### Agree - to - disagree ratio"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "At least one person of each party agrees and disagrees for each turn."
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "t.describe()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>reactions</th>", 
        "      <th>statements</th>", 
        "      <th>dur</th>", 
        "      <th>word_count</th>", 
        "      <th>r_per_sec</th>", 
        "      <th>sd_per_sec</th>", 
        "      <th>a_to_d_dems</th>", 
        "      <th>a_to_d_reps</th>", 
        "      <th>unigram_count</th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>count</th>", 
        "      <td>  181.000000</td>", 
        "      <td> 181.000000</td>", 
        "      <td> 1.810000e+02</td>", 
        "      <td> 181.000000</td>", 
        "      <td> 1.810000e+02</td>", 
        "      <td> 1.230000e+02</td>", 
        "      <td> 120.000000</td>", 
        "      <td> 109.000000</td>", 
        "      <td> 181.000000</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>mean</th>", 
        "      <td>  865.314917</td>", 
        "      <td>   6.381215</td>", 
        "      <td> 2.957459e+10</td>", 
        "      <td>  81.044199</td>", 
        "      <td>          inf</td>", 
        "      <td> 5.401644e-09</td>", 
        "      <td>  13.267172</td>", 
        "      <td>  11.007680</td>", 
        "      <td>  21.486188</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>std</th>", 
        "      <td> 1530.045332</td>", 
        "      <td>   9.050689</td>", 
        "      <td> 1.585391e+08</td>", 
        "      <td> 123.345037</td>", 
        "      <td>          NaN</td>", 
        "      <td> 4.012107e-09</td>", 
        "      <td>  21.156246</td>", 
        "      <td>  16.133355</td>", 
        "      <td>  29.820315</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>min</th>", 
        "      <td>    1.000000</td>", 
        "      <td>   1.000000</td>", 
        "      <td> 0.000000e+00</td>", 
        "      <td>   1.000000</td>", 
        "      <td> 5.000000e-10</td>", 
        "      <td> 1.304348e-10</td>", 
        "      <td>   0.058824</td>", 
        "      <td>   0.111111</td>", 
        "      <td>   0.000000</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>25%</th>", 
        "      <td>   11.000000</td>", 
        "      <td>   1.000000</td>", 
        "      <td> 1.000000e+09</td>", 
        "      <td>   5.000000</td>", 
        "      <td> 8.000000e-09</td>", 
        "      <td> 2.000000e-09</td>", 
        "      <td>   0.453767</td>", 
        "      <td>   0.800000</td>", 
        "      <td>   2.000000</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>50%</th>", 
        "      <td>   50.000000</td>", 
        "      <td>   2.000000</td>", 
        "      <td> 4.000000e+09</td>", 
        "      <td>  15.000000</td>", 
        "      <td> 2.533333e-08</td>", 
        "      <td> 5.000000e-09</td>", 
        "      <td>   2.958333</td>", 
        "      <td>   3.680000</td>", 
        "      <td>   5.000000</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>75%</th>", 
        "      <td> 1071.000000</td>", 
        "      <td>   8.000000</td>", 
        "      <td> 3.800000e+10</td>", 
        "      <td>  95.000000</td>", 
        "      <td> 3.450000e-08</td>", 
        "      <td> 7.447183e-09</td>", 
        "      <td>  14.361979</td>", 
        "      <td>  15.500000</td>", 
        "      <td>  27.000000</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>max</th>", 
        "      <td> 7357.000000</td>", 
        "      <td>  54.000000</td>", 
        "      <td> 1.930000e+11</td>", 
        "      <td> 497.000000</td>", 
        "      <td>          inf</td>", 
        "      <td> 1.950000e-08</td>", 
        "      <td>  90.333333</td>", 
        "      <td> 100.500000</td>", 
        "      <td> 112.000000</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 17, 
       "text": [
        "         reactions  statements           dur  word_count     r_per_sec    sd_per_sec  \\", 
        "count   181.000000  181.000000  1.810000e+02  181.000000  1.810000e+02  1.230000e+02   ", 
        "mean    865.314917    6.381215  2.957459e+10   81.044199           inf  5.401644e-09   ", 
        "std    1530.045332    9.050689  1.585391e+08  123.345037           NaN  4.012107e-09   ", 
        "min       1.000000    1.000000  0.000000e+00    1.000000  5.000000e-10  1.304348e-10   ", 
        "25%      11.000000    1.000000  1.000000e+09    5.000000  8.000000e-09  2.000000e-09   ", 
        "50%      50.000000    2.000000  4.000000e+09   15.000000  2.533333e-08  5.000000e-09   ", 
        "75%    1071.000000    8.000000  3.800000e+10   95.000000  3.450000e-08  7.447183e-09   ", 
        "max    7357.000000   54.000000  1.930000e+11  497.000000           inf  1.950000e-08   ", 
        "", 
        "       a_to_d_dems  a_to_d_reps  unigram_count  ", 
        "count   120.000000   109.000000     181.000000  ", 
        "mean     13.267172    11.007680      21.486188  ", 
        "std      21.156246    16.133355      29.820315  ", 
        "min       0.058824     0.111111       0.000000  ", 
        "25%       0.453767     0.800000       2.000000  ", 
        "50%       2.958333     3.680000       5.000000  ", 
        "75%      14.361979    15.500000      27.000000  ", 
        "max      90.333333   100.500000     112.000000  "
       ]
      }
     ], 
     "prompt_number": 17
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Most of the time more people seem to be agreeing than disagreeing.  Republicans especially so.."
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "figsize(10,8)", 
      "", 
      "subplot(221)", 
      "log10(t.a_to_d_dems).hist()", 
      "xlabel('agree/disagree')", 
      "title('democrats')", 
      "", 
      "subplot(222)", 
      "log10(t.a_to_d_reps).hist()", 
      "xlabel('agree/disagree')", 
      "title('republicans')", 
      "", 
      "subplot(223)", 
      "log10(t2.a_to_d_dems).hist()", 
      "xlabel('agree/disagree')", 
      "title('democrats (long turns)')", 
      "", 
      "subplot(224)", 
      "log10(t2.a_to_d_reps).hist()", 
      "xlabel('agree/disagree')", 
      "title('republicans (long turns)')", 
      "", 
      "show()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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DhiEwMBDDhw9HSUmJhglrhvOXdRwjaxStAxiR7/myj0ctpIjIc4wfPx7btm0z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\nkU3/DzUFQRw5RPRpAAAAAElFTkSuQmCC\n"
      }
     ], 
     "prompt_number": 18
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "So it seems that taking out reactions from short turns reveals more polarization.  Perhaps this is because on short turns there is just more noise from adjacent turns?  Or that people become more and more energized in their responses as the speakers continue to talk?"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "PERC = .03", 
      "print '{:_^80}'.format('dems agree'.upper())", 
      "for v in t2[t2.a_to_d_dems > t2.a_to_d_dems.quantile(1-PERC)].text.values: print v+'\\n'", 
      "print '{:_^80}'.format('dems disagree'.upper())", 
      "for v in t2[t2.a_to_d_dems < t2.a_to_d_dems.quantile(PERC)].text.values: print v+'\\n'"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "___________________________________DEMS AGREE___________________________________", 
        "It means that -- Governor Romney talked about Medicaid and how we could send it back to the statesbut effectively this means a 30 percent cut in the primary program we help for seniors who are in nursing homes, for kids who are with disabilities --", 
        "", 
        "Well, four years ago when I was running for office I was traveling around and having those same conversations that Governor Romney talks about.And it wasn't just that small businesses were seeing costs skyrocket and they couldn't get affordable coverage even if they wanted to provide it to their employees; it wasn't just that this was the biggest driver of our federal deficit, our overall health care costs.But it was families who were worried about going bankrupt if they got sick -- millions of families, all across the country.If they had a pre-existing condition they might not be able to get coverage at all.If they did have coverage, insurance companies might impose an arbitrary limit.And so as a consequence, they're paying their premiums, somebody gets really sick, lo and behold they don't have enough money to pay the bills because the insurance companies say that they've hit the limit.So we did work on this alongside working on jobs, because this is part of making sure that middle-class families are secure in this country.And let me tell you exactly what \ufffdObamacare\ufffd did.Number one, if you've got health insurance it doesn't mean a government take over.You keep your own insurance, you keep your own doctor.But it does say insurance companies can't jerk you around.They can't impose arbitrary lifetime limits.They have to let you keep your kid on their insurance -- your insurance plan till you're 26 years old.And it also says that they're -- you're going to have to get rebates if insurance companies are spending more on administrative costs and profits than they are on actual care.Number two, if you don't have health insurance, we're essentially setting up a group plan that allows you to benefit from group rates that are typically 18 percent lower than if you're out there trying to get insurance on the individual market.Now, the last point I'd make before --", 
        "", 
        "Let me just point out, first of all, this board that we're talking about can't make decisions about what treatments are given. That's explicitly prohibited in the law.But let's go back to what Governor Romney indicated, that under his plan he would be able to cover people with pre-existing conditions.Well, actually, Governor, that isn't what your plan does. What your plan does is to duplicate what's already the law, which says if you are out of health insurance for three months then you can end up getting continuous coverage and an insurance company can't deny you if it's been under 90 days.But that's already the law. And that doesn't help the millions of people out there with pre-existing conditions. There's a reason why Governor Romney set up the plan that he did in Massachusetts. It wasn't a government takeover of health care. It was the largest expansion of private insurance. But what it does say is that insurers, you've got to take everybody. Now, that also means that you've got more customers.But when Governor Romney says that he'll replace it with something but can't detail how it will be in fact replacedand the reason he set up the system he did in Massachusetts is because there isn't a better way of dealing with the pre-existing conditions problem, it just reminds me of -- you know, he says that he's going to close deductions and loopholes for his tax plan.That's how it's going to be paid for. But we don't know the details. He says that he's going to replace Dodd-Frank, Wall Street reform. But we don't know exactly which ones. He won't tell us. He now says he's going to replace \ufffdObamacare\ufffd and assure that all the good things that are in it are going to be in there and you don't have to worry.And at some point, I think the American people have to ask themselves, is the reason that Governor Romney is keeping all these plans to replace secret because they're too good? Is it because that somehow middle-class families are going to benefit too much from them? No, the reason is because when we reform Wall Street, when we tackle the problem of pre-existing conditions, then, you know, these are tough problems, and we've got to make choices. And the choices we've made have been ones that ultimately are benefiting middle-class families all across the country.", 
        "", 
        "_________________________________DEMS DISAGREE__________________________________", 
        "Let me -- let me repeat -- let me repeat what I said -- (inaudible).I'm not in favor of a $5 trillion tax cut.That's not my plan.My plan is not to put in place any tax cut that will add to the deficit.That's point one.So you may keep referring to it as a $5 trillion tax cut, but that's not my plan.", 
        "", 
        "First of all, the Department of Energy has said the tax break for oil companies is $2.8 billion a year.And it's actually an accounting treatment, as you know, that's been in place for a hundred years. Now --", 
        "", 
        "And -- and in one year, you provided $90 billion in breaks to the green energy world.Now, I like green energy as well, but that's about 50 years' worth of what oil and gas receives, and you say Exxon and Mobil -- actually, this $2.8 billion goes largely to small companies, to drilling operators and so forth.But you know, if we get that tax rate from 35 percent down to 25 percent, why, that $2.8 billion is on the table, of course it's on the table.That's probably not going to survive, you get that rate down to 25 percent.But -- but don't forget, you put $90 billion -- like 50 years worth of breaks -- into solar and wind, to -- to Solyndra and Fisker and Tesla and Ener1.I mean, I -- I had a friend who said, you don't just pick the winners and losers; you pick the losers.All right? So -- so this is not -- this is not the kind of policy you want to have if you want to get America energy-secure.The second topic, which is you said you get a deduction for getting a plant overseas.Look, I've been in business for 25 years.I have no idea what you're talking about.I maybe need to get a new accountant."
       ]
      }
     ], 
     "prompt_number": 19
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "How large are the turns where dems either agree or disagree?"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "figsize(10,3)", 
      "subplot(121)", 
      "t2[t2.a_to_d_dems < t2.a_to_d_dems.quantile(.5)].word_count.hist()", 
      "title('dems disagree')", 
      "xlabel('words in turn')", 
      "subplot(122)", 
      "t2[t2.a_to_d_dems >= t2.a_to_d_dems.quantile(.5)].word_count.hist()", 
      "title('dems agree')", 
      "xlabel('words in turn')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 20, 
       "text": [
        "<matplotlib.text.Text at 0x940b590>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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Mnyt7nJzzaI/TmTNnMG7cOAwdOhTJycnYsGFDu9+IiIiISHZOJ0633XYbli9f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\nRERE5KL/B51U5pOO90qfAAAAAElFTkSuQmCC\n"
      }
     ], 
     "prompt_number": 20
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "The threshold for converting the ratio to a true/false label is ~1.7 dems agreeing to 1 dem disagreeing."
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "t2['label'] = t2.a_to_d_dems >= t2.a_to_d_dems.quantile(.5)", 
      "print t2.a_to_d_dems.quantile(.5)", 
      "t2.label.describe()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "2.73529411765"
       ]
      }, 
      {
       "output_type": "pyout", 
       "prompt_number": 21, 
       "text": [
        "count           70", 
        "mean           0.5", 
        "std      0.5036102", 
        "min          False", 
        "25%              0", 
        "50%            0.5", 
        "75%              1", 
        "max           True"
       ]
      }
     ], 
     "prompt_number": 21
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## Train and test experiment on dems"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "ex = t2"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 22
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "train_rows = random.sample(ex.index, len(ex)*9/10)", 
      "trn = ex.ix[train_rows]", 
      "tst = ex.drop(train_rows)", 
      "print len(trn)", 
      "print len(tst)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "63", 
        "7"
       ]
      }
     ], 
     "prompt_number": 23
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "%time cl = nltk.NaiveBayesClassifier.train(zip(trn.unigrams, trn.label))"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "CPU times: user 0.05 s, sys: 0.01 s, total: 0.06 s", 
        "Wall time: 0.06 s"
       ]
      }
     ], 
     "prompt_number": 24
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "nltk.classify.accuracy(cl, zip(tst.unigrams, tst.label))"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 25, 
       "text": [
        "1.0"
       ]
      }
     ], 
     "prompt_number": 25
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "cl.show_most_informative_features(10)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Most Informative Features", 
        "                governor = True             True : False  =      9.9 : 1.0", 
        "                   comes = True             True : False  =      5.5 : 1.0", 
        "                  system = True             True : False  =      5.5 : 1.0", 
        "                   means = True             True : False  =      4.8 : 1.0", 
        "                    cuts = True             True : False  =      4.8 : 1.0", 
        "                approach = True             True : False  =      4.8 : 1.0", 
        "                 problem = True             True : False  =      4.8 : 1.0", 
        "                     top = True             True : False  =      4.2 : 1.0", 
        "                    made = True             True : False  =      4.2 : 1.0", 
        "                governor = None            False : True   =      4.1 : 1.0"
       ]
      }
     ], 
     "prompt_number": 26
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Whoa! Dems really hate america.. haha"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## Train and test experiment on reps"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "The threshold we will use for republicans is higher than the threshold for democrats.  This appears to be because more republicans were agreeing with what was said during the debate over all compared to democrats."
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "t2['label2'] = t2.a_to_d_reps >= t2.a_to_d_reps.quantile(.5)", 
      "print t2.a_to_d_reps.quantile(.5)", 
      "t2.label2.describe()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "8.75"
       ]
      }, 
      {
       "output_type": "pyout", 
       "prompt_number": 27, 
       "text": [
        "count           70", 
        "mean           0.5", 
        "std      0.5036102", 
        "min          False", 
        "25%              0", 
        "50%            0.5", 
        "75%              1", 
        "max           True"
       ]
      }
     ], 
     "prompt_number": 27
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "ex = t2"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 28
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "train_rows2 = random.sample(ex.index, len(ex)*9/10)", 
      "trn2 = ex.ix[train_rows2]", 
      "tst2 = ex.drop(train_rows2)", 
      "print len(trn2)", 
      "print len(tst2)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "63", 
        "7"
       ]
      }
     ], 
     "prompt_number": 29
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "%time cl2 = nltk.NaiveBayesClassifier.train(zip(trn2.unigrams, trn2.label2))"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "CPU times: user 0.05 s, sys: 0.01 s, total: 0.06 s", 
        "Wall time: 0.05 s"
       ]
      }
     ], 
     "prompt_number": 30
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "nltk.classify.accuracy(cl2, zip(tst2.unigrams, tst2.label2))"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 31, 
       "text": [
        "0.8571428571428571"
       ]
      }
     ], 
     "prompt_number": 31
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "cl2.show_most_informative_features(10)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Most Informative Features", 
        "                governor = True            False : True   =     17.9 : 1.0", 
        "                  romney = True            False : True   =     16.5 : 1.0", 
        "                  system = True            False : True   =      6.2 : 1.0", 
        "                     top = True            False : True   =      4.8 : 1.0", 
        "                    cuts = True            False : True   =      4.8 : 1.0", 
        "                   comes = True            False : True   =      4.8 : 1.0", 
        "                governor = None             True : False  =      4.6 : 1.0", 
        "                   bring = True             True : False  =      4.6 : 1.0", 
        "                  making = True            False : True   =      4.2 : 1.0", 
        "                    even = True            False : True   =      4.0 : 1.0"
       ]
      }
     ], 
     "prompt_number": 32
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Really, these train/test sets are **so** small, that (1) we can't draw much information from them without cross validation and (2) we are very prone to overfitting."
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## Hyperparams grid search on dems"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Let's see if we can tune the max features hyper parameter (how many of the most frequent unigrams to use as features)."
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "gr = t2.copy()"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 18
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "len(ranked_unigrams)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 19, 
       "text": [
        "1752"
       ]
      }
     ], 
     "prompt_number": 19
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "t2['label'] = t2.a_to_d_dems >= t2.a_to_d_dems.quantile(.5)", 
      "p = []", 
      "trn_means = []", 
      "tst_means = []", 
      "#for max_feats in range(1,len(ranked_unigrams),100):", 
      "for max_feats in range(1,700,100):", 
      "    gr['unigrams'] = gr.words.apply(lambda words: {w:True for w in words if w in ranked_unigrams[:max_feats] and not w in stopwords.words('english')})", 
      "    trn_ac = []", 
      "    tst_ac = []", 
      "    print max_feats,", 
      "    for i in range(50):", 
      "        print i,", 
      "        train_rows = random.sample(gr.index, len(gr)*9/10)", 
      "        trn,tst = gr.ix[train_rows],gr.drop(train_rows)", 
      "        cl = nltk.NaiveBayesClassifier.train(zip(trn.unigrams, trn.label))", 
      "        trn_ac.append(nltk.classify.accuracy(cl, zip(trn.unigrams, trn.label)))", 
      "        tst_ac.append(nltk.classify.accuracy(cl, zip(tst.unigrams, tst.label)))", 
      "    p.append(max_feats)", 
      "    trn_means.append(mean(trn_ac))", 
      "    tst_means.append(mean(tst_ac))", 
      "    print ''"
     ], 
     "language": "python", 
     "outputs": [
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       "stream": "stdout", 
       "text": [
        "1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 "
       ]
      }, 
      {
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "35 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "36 37 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "38 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "39 40 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "41 42 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "43 44 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "45 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "46 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "47 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "48 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "49 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        ""
       ]
      }
     ], 
     "prompt_number": 24
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "figsize(6,5)", 
      "results = pd.DataFrame({'max_features':p, 'train':trn_means, 'test':tst_means})", 
      "results.plot(x='max_features')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 36, 
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x93d50b0>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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5U7byS/v2ExPlz7KPjwz0oUPlvy1aKF0xVcZkw760C0dVg87krVvlZCZzWVfD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\nW+Gx4jC/EpAAAAAASUVORK5CYII=\n"
      }
     ], 
     "prompt_number": 36
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "results"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>max_features</th>", 
        "      <th>test</th>", 
        "      <th>train</th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>0 </th>", 
        "      <td>    1</td>", 
        "      <td> 0.348571</td>", 
        "      <td> 0.516825</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>1 </th>", 
        "      <td>  101</td>", 
        "      <td> 0.725714</td>", 
        "      <td> 0.846667</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>2 </th>", 
        "      <td>  201</td>", 
        "      <td> 0.808571</td>", 
        "      <td> 0.964127</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>3 </th>", 
        "      <td>  301</td>", 
        "      <td> 0.785714</td>", 
        "      <td> 0.962540</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>4 </th>", 
        "      <td>  401</td>", 
        "      <td> 0.737143</td>", 
        "      <td> 0.961587</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>5 </th>", 
        "      <td>  501</td>", 
        "      <td> 0.737143</td>", 
        "      <td> 0.984762</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>6 </th>", 
        "      <td>  601</td>", 
        "      <td> 0.785714</td>", 
        "      <td> 0.990476</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>7 </th>", 
        "      <td>  701</td>", 
        "      <td> 0.820000</td>", 
        "      <td> 0.990476</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>8 </th>", 
        "      <td>  801</td>", 
        "      <td> 0.791429</td>", 
        "      <td> 0.990794</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>9 </th>", 
        "      <td>  901</td>", 
        "      <td> 0.811429</td>", 
        "      <td> 0.993016</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>10</th>", 
        "      <td> 1001</td>", 
        "      <td> 0.805714</td>", 
        "      <td> 0.993651</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>11</th>", 
        "      <td> 1101</td>", 
        "      <td> 0.794286</td>", 
        "      <td> 0.993333</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>12</th>", 
        "      <td> 1201</td>", 
        "      <td> 0.782857</td>", 
        "      <td> 0.992063</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>13</th>", 
        "      <td> 1301</td>", 
        "      <td> 0.751429</td>", 
        "      <td> 0.993651</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>14</th>", 
        "      <td> 1401</td>", 
        "      <td> 0.760000</td>", 
        "      <td> 1.000000</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>15</th>", 
        "      <td> 1501</td>", 
        "      <td> 0.848571</td>", 
        "      <td> 1.000000</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>16</th>", 
        "      <td> 1601</td>", 
        "      <td> 0.822857</td>", 
        "      <td> 1.000000</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>17</th>", 
        "      <td> 1701</td>", 
        "      <td> 0.811429</td>", 
        "      <td> 1.000000</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 37, 
       "text": [
        "    max_features      test     train", 
        "0              1  0.348571  0.516825", 
        "1            101  0.725714  0.846667", 
        "2            201  0.808571  0.964127", 
        "3            301  0.785714  0.962540", 
        "4            401  0.737143  0.961587", 
        "5            501  0.737143  0.984762", 
        "6            601  0.785714  0.990476", 
        "7            701  0.820000  0.990476", 
        "8            801  0.791429  0.990794", 
        "9            901  0.811429  0.993016", 
        "10          1001  0.805714  0.993651", 
        "11          1101  0.794286  0.993333", 
        "12          1201  0.782857  0.992063", 
        "13          1301  0.751429  0.993651", 
        "14          1401  0.760000  1.000000", 
        "15          1501  0.848571  1.000000", 
        "16          1601  0.822857  1.000000", 
        "17          1701  0.811429  1.000000"
       ]
      }
     ], 
     "prompt_number": 37
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "It looks like going past ~200 unigram features is not helpful, and by then we are overfitting on train."
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "How about narrowing down on a smaller number of max features, and seeing what is happening close to max_features == 0."
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "p = []", 
      "trn_means = []", 
      "tst_means = []", 
      "MAX = 500", 
      "for max_feats in range(1,500,10):", 
      "    gr['unigrams'] = gr.words.apply(lambda words: {w:True for w in words if w in ranked_unigrams[:max_feats] and not w in stopwords.words('english')})", 
      "    trn_ac = []", 
      "    tst_ac = []", 
      "    print max_feats,", 
      "    for i in range(50):", 
      "        print i,", 
      "        train_rows = random.sample(gr.index, len(gr)*9/10)", 
      "        trn,tst = gr.ix[train_rows],gr.drop(train_rows)", 
      "        cl = nltk.NaiveBayesClassifier.train(zip(trn.unigrams, trn.label))", 
      "        trn_ac.append(nltk.classify.accuracy(cl, zip(trn.unigrams, trn.label)))", 
      "        tst_ac.append(nltk.classify.accuracy(cl, zip(tst.unigrams, tst.label)))", 
      "    p.append(max_feats)", 
      "    trn_means.append(mean(trn_ac))", 
      "    tst_means.append(mean(tst_ac))", 
      "    print ''"
     ], 
     "language": "python", 
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       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "47 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "48 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "49 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": []
      }
     ], 
     "prompt_number": 50
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "figsize(6,5)", 
      "results = pd.DataFrame({'max_features':p, 'train':trn_means, 'test':tst_means})", 
      "results.plot(x='max_features')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 51, 
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x9542ef0>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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xcuRIAMCePXtw5MgRnDhxAgAwaNAg3Lt3D5cvX0ZaWhrmz58vP4ZAIChR1Or58+eIj49H\nTEwMxo0bh4kTJ+r0Z2CMISM3A69yX5X5epr1FO8ffB8/vvWjThc9JoToD82iUVFhqmbGjBlwdXWV\nPz927FgAQK1atTB//nz4+PhAJpPBzMysxH4AIJPJ8O2338LR0RFjx47FZ599hjt37qBVq8otVpCS\nmYL4p/F8MeNXRYsaJ71MgkAgQA1B+SUFRrcfjRFtR1Tq3IQQw2XwAV7wjXbKurIF2hmw9PX1LfH4\nhx9+wIkTJ3Dt2jXeY87IwIMHD9CsWbMy+zZs2BCOjo4AAHNzczg6OiIlJaVSAf5RxiN03NwRnd/s\nDFcHV7jWcUVP555wdXBFszrNYF/TXuNjE0KMm9IAHx0djZCQEOTn52PatGmYOnVqidfFYjEWLlyI\nU6dOwdraGrt27ULz5s211kBtBWZN1KhRo8wgq7l50SWLiYnBqlWrEBERgZYtW+LRo0do2bJliX10\nWXecMYaJxyZiuu90fNXrK52dhxBinJTm4KdPn47Q0FBERUVhw4YNSEtLK/H63r17IZVKER8fj1Wr\nVmHOnDk6a2xV69SpE65evVrh6ykpKahVqxbq16+PJ0+elMi/AzwA63Kq447rO/As6xnmdp+rs3MQ\nQoyXwgCfkZEBAOjVqxdcXFzg5+eHmJiYEtucPn0agwYNAgB07doV9+7d01FTq97HH3+MY8eOoW7d\nuggPDy/TG3/77bfRt29feHp6IjAwEKNGjSqxTelBVm325lMyUzAncg62D90OixoWWjsuIcR0KLzR\nKSoqClu3bsXevXsBAD/99BNSUlKwaNEi+Tbbt2/HmTNnsGnTJkRGRuLtt9/G/fv3y+SgjfFGJ31S\ndF0YYxiybwg6NeyEhcKFVdswQkiV0mstmlGjRuHff/9F79690apVK7i5ucHKqvyl2hYuXCj/XigU\nQigUVvb01dLuG7vxMOMhwkeG67sphBAtE4lEEIlEWjmWwh58RkYGhEIh4uLiAABTp05FQECAPCVT\nWlZWFnr06IH4+PiyJ6IevFoqui5Ps57C4ycPHH//ODo27KiHlhFCqpLOShXY2/MpdtHR0UhOTkZk\nZGSZaYIZGRnIy8tDdnY2li5dKr/Dk2gfYwyTfp+Ejzp+RMGdEKKU0hTNmjVrEBISAqlUimnTpsHR\n0RGhoaEAgJCQENy8eRNjx46FTCZD165d8dNPP+m80dXV/oT9+Cf9H+x7d5++m0IIMQJ6ryZZt25d\nvHz5siqaYFQcHBzw4sUL+ePnr5+jw6YOOBp0FN6NvPXYMkJIVTLqBT+KBzFSvpTMFAzbPwwTOk6g\n4E4IURkVGzNwFx9dhM/PPnjH/R0s7mP4i4cQQgyH3nvwpGLb4rbh86jPsX3odgxqWf7MJUIIqQgF\neAMkLZBi5smZiLgfgehx0Wjt2FrfTSKEGCFK0RiYtOw0+O/yx70X9xDzvxijCe6JicCZM/puBXDg\nACCR6LsVhBgGCvAG5In4CXy2+MCnkQ+OBh1FnZp19N0klaSnA/7+wKhRQHa2do6ZkwNcv67ePq9f\nA++/D5w+rZ02EGLsKMAbkJ+v/Qy/5n5Y1n8ZapiVv0iHocnLA4YPB4YNA7p1AzZv1s5x9+8HRqi5\nFsn584BUCvy32BYh1R4FeAMhYzKEXQ/DRx0/0ndTVMYY8MkngJ0dsHQp8PXXwIoVQG5u5Y99+jTw\nzz/Aw4fq7TN4MAV4QgpRgDcQ5x6eg42FjVGVIFi1CrhyBdi9G6hRA/DyAjp2BLZurdxxGQP+/BPo\n1AmIilJ9vz//BGbMAF69ApKSKtcGQkwBBXgDERYfhrEeY3W6ApQ2HT3KA/yRI0Dt2kXPf/01sGxZ\n5QY6793jQf7jj4HISNX2ycgAbt7kaSI/PyAiQvPzE2IqKMAbgKy8LBy6fQjvd3hf301RyV9/AePH\nAwcPAs7OJV/z8QHatgV27ND8+H/+CfTpAwwYAJw6Bchkyvc5exbw9QWsrICAAErTEAJQgDcIB28d\nRA/nHmhQu4G+m6LUs2fAkCHA+vU8oJZn/nyek5dKNTvH6dNA376AiwtQpw5/Q1Gm8E0B4D14kYgP\nABNSnRntjU6MMaNJZygTFh+Gyd6T9d0MufBwnlsvT0QEMHYs8N57Fe/frRvg6grs2gWMG6feuQvz\n78uW8cf9+/M8vKen4v1OnwY2bODfOzkBbm7AxYtA797qnZ8QU2K0Pfhh+4fh8O3D+m5GpSW/SsaN\n5zcwuOVgfTcFAJCcDEycCNja8tkxpb8mTQIWLFB+nK+/BpYsAfLz1Tv/zZtArVpA06b88YABygda\n09OB+/cB72J12Pz9KU1DiN7LBWuq5fqWEOeJcfOTm3CwdtDacZWRMRnin8bjVOIpRCVF4VXuK4xq\nOwpB7YLQ0Lah2sf79sy3SM1Oxfq31uugteobMwZo1gz45pvKHYcx3nueOBH44APV9/vxRyAurmgm\nzqtXQJMmQGoqULNm+fscPAhs2QIcP1703LlzwLRpwLVrmv8MhBgCna3oZMjEeWL4NvLF7MjZOj9X\n0sskhF4JxcgDI1F/RX2MDh+NBxkP8HGnj7G4z2L8/fxvtNnYBn47/fDL9V8glohVOq6MyeSzZwzB\nX3/xFMzMmZU/lkDAe/HffQcUFKi+3+nTRbl0gOfg27bl6ZaK/Pknz9kX16ULnyr59Kl67SbEpLAq\nou1T1fquFvs341/WZFUTdirxlFaPXUgmk7FVF1axesvrsQ8OfsDC4sLYo4xH5W6bnZfN9v+9nwXu\nCWT2S+1Z0K9B7HbqbYXHFyWJWLuN7ZhMJtNF89U2eDBjq1dr73gyGWNdujC2b59q2xcUMFa3LmP/\n/lvy+S+/ZOyLLyrer00bxmJjyz7/zjuM7dihensJMUSViZ1GGeALZAVMsFDACmQF7NidY6z52ubs\ndd5rrR2fMcYk+RI24fAE1mFTB5b8MlmtfVNfp7JlZ5exN1e+yRKeJ1S43djfxrIfzv9Q2aZqxdmz\njLm4MJabq93j/v47Y+3a8eCtTFwcYy1bln3+zz8Z8/Yuf5+nTxmrU4ex/Pyyr23ezFhQkFrNJcTg\nVCZ2GmWKJisvCzYWNjATmGFQy0HwaeSDBSIVRv5UlPo6Ff1/6Y/0nHScH38eLnVc1Nrf0cYRc3vM\nxfL+yzFg5wDcSr1VZpusvCwcumUYc98ZA+bOBb79ls8j16a33uLH/O035dsWTo8srWtX4PZtoLzF\nv/78E+jVi99JW5q/P79RSp0UESGmxCgDvFgihp2Vnfzx2oC12Hl9J648rmBunxr+fv43fH/2RQ/n\nHggfGY7alrWV71SBDzp8gOX9l6P/zv5lgnz4zXD0dOlpEHPfjx4FMjN5JUZtK8zFL1rE30gUKT6X\nvTgrK6B7d/66qvsA/CYsJycaaCXVl1EG+ExJJmytbOWPnWo54Qe/HzDhyARICzS8uwbAsbvH0HdH\nXyzqswhL+i2BmaDyl6eiIB92PQzjPNWcJK4DBQXAvHn8xqTyesHaMGQID+7HjlW8TX4+vxtVKCz/\n9YqmS5Y3wFqcKne1XrjAvwgxNUYZ4MV5Ytha2pZ47v3276ORbSMsif5erQJVhTZc3oCQYyE4EnRE\n62mTDzp8gGX9lqH/zv64nXYbSS+T8Pfzvw1i7vvOnXymyiAdrggoEABffaW4F3/tGp8OWb9++a/3\n71+2Ls2//wIvXwLt2lV87oAAxXVpbt7kb0DBwXTnKzE9xhngJeISPXiAzxVd67cJS06twYCg27hz\nR/XjiZJFWHx2MS6Mv4AujbtoubVcsEcwlvVbhn6/9MMC0QIEtQuCZQ1LnZxLVbm5/Kal5ct5ENal\nd97hC3KcPFn+6xXl3wu1bw+IxSWrRP75J59rb6bgt7hXLz798+XLsq+lpgKBgcDq1UCLFnwuPSGm\nxDgDfDk9eKkU+PxjF7R8sgCNPhmPFatVK0r+/PVzfHDwA+x4e4fag6nqKgzyu2/sxljPsTo9lyo2\nbgQ8PHh+W9fMzHgv/ttvy+/FK8qlA/wNqLBsQSFlbwoAvzmqe3detKw4iYS/6YwaxXvvy5YBixcD\nWVmq/0yEGDrjDPClevD5+fxuydxc4PKGT9DJrTF+yX4Pj58qzsfLmAzBh4LxoeeH8Gvup+tmA+BB\n/t/P/tV73feMDB7UliypunOOHAmkpZUdLM3L4zlwZXVjiufhGSt7U1RFSqdpGANCQng6aPFi/pyX\nF8//r16t8o9DiMEzzgCfVzSLpqAA+PBDfkt7eDhgXdMMB4J2oUGjPLwVOg4yVnGt2eXnliNbmo1v\nhJW8L19NmpQ0+PJLwNGRB8HJk4FNm4Do6PKnDqrip5/4NEJF+Wttq1GD/xzfflvy+cuXgZYtAQcl\nFSf69SsqH5yUxN8YWquwJnnhQGvhJ4fvvwdu3AB++aVkemfRImDNGp66IcQUGGeAl/AUjUwGTJjA\nb0f/7beiWiWWNSxx5P1w3Er5FyFHJpdbx+Hcw3NYG7MWe9/dC3Mzwy6qyRgfDD1wgE85bNmSD0p+\n/jkvyuXiwottqWP3buAjPawOOHo08OgRf3MqpGwmTKEmTfibXHx8UUpHlbGDli35m8vNm/z3ZP16\nvlBJrVolt2vRglfJrMpPNYToklEG+My8TNS2sEVICO/JHTkCWFuX3MaznTX6PT+CqISrmBs1t0SQ\nT8tOw+jw0dg2dBsa2zWu4tarLzYWsLHhKYT+/YHp0/mA4IULPNXy9tvA9u2qHy8hgff8e/TQWZMr\nZG7Op2UuWlT0nKqpFqAoTaNK/r2QQMB78StW8De1334DGjUqf9uvv+Y9+wcPVDu2utat420npCoY\nZYAXS8Q4E2mLW7eA338v2xMr9MUMO9TYexzH/zmOJWd5t0zGZBj721i81+49DHQbqPRct2/zj/RV\nU3OzfL/+CgwfXn5vVSDgqyvt3KnaykcAsG8fH1xUNPtEl4KD+YLaly4BOTn8DaxnT9X2LZwuqWxQ\ntrSAAL7K1KZNQOfOFW/XoAFfSFyVksjqysnhxx01qvybtgjROq0VTFBCm6ca+9tY1jjwZ3btmuLt\nZDJew2T7r49Zi3Ut2NpLa9mK8ytYl5+7sLz8PIX7/vMPY8HBjDk5MVa7NmNJSVprvlpkMsZcXXmd\nFkU6dOA1W1Q5XvPm5RfnqkqbNjE2cCBjp07xgmSqevWKMUtLxpo04T+LqiQSxkQi1bbNyGCsfn3G\nbtxQ/fiq+PVXxvr14/9PTk6MRUdr9/jENFUmdirtw0VHR8Pd3R1ubm5Yv75szfKcnBx8+OGH8PLy\nQu/evXH4sO4X4RBLxJC+tq2w515IIOClb7etbYio4Cj8cOEHfH/+e+x7dx8saliUu09yMs/rd+3K\nVwW6d48P7sXGav/nUEV8PP/Xw0PxdmPG8NSCMlev8n87dapcuypr3Djg+nVg5Ur1euL29rztffuq\nN3ff0lL11Z3s7Pj4xrx5qh9fFfv28Ry/UAjs2QO8+y7dQUt0TNk7gKenJztz5gxLTk5mrVq1Yqmp\nqSVe37RpE5s0aRJjjLHk5GTm6upabvlbFU6lMr+dfqye7x/sUfmVe0uQSnmVxEuXGEt8kciupFwp\nd7tHjxgLCeHlar/6irEXL4pe++47xmbN0k7b1TVvHmNz5ijf7vFjXlXxtZKimjNm8J/PEKxdyxjA\nWGSkevsdOKD73m9ODmPOzrzKpjZkZDBmZ8dYenrRcydO8J58TIx2zkFMU2Vip8IefEZGBgCgV69e\ncHFxgZ+fH2JiYkpsY29vD7FYDKlUihcvXsDGxkbna6WKJWLkie3KDKyWx9wc+Owz3lNs5tAMnd4s\n2XVljA9YennxW/bv3OEDgMWn7Hl786l8VY2xovy7Mg0b8kUuFFVtlMmA/fsVr6dalT76iOeju3VT\nb7/hw1XP2WuqZk2+qtXnn2tn/OXwYX5Xbd26Rc/5+/PB8cDAok9WhGiTwgAfGxuL1sUmGrdp0waX\nLl0qsU1QUBAKCgrg6OiIHj16YPfu3bppaTGZkkxIxLYqBXiAD0KePl3yNncASEkBBg7kc8JFIn7j\nj6Nj2f07d+bTEqu67GxCAr95S9GgYHHK0jTnzvEA07atdtpXWdbWPG1hY6PvlpQvOJhX2dy1q/LH\n2rcPCAqo7RIGAAAgAElEQVQq+/ygQcDmzfz3sDAdR4i2VHoexY8//ghzc3M8efIEp0+fxqBBgyBT\ndTqHhsR5YuRl2Va4Rmdptra8t7hmDX/MGJ8H7uXFe72XLikOeg4OwJtvArfKlnXXqfBwnqdV9QPR\n0KH8k8bjx+W/XlGQIeWrUYPPTpoxg8/60VR6On9zHTKk/NeHDgU2bOAzfc6f1/w8hJSm8A4fb29v\nzJ5dtOZpQkICAgICSmwTHR2NCRMmwMbGBr6+vnjzzTdx9+7dEj3/QgsXLpR/LxQKIayoNqwSYokY\nVrBVa5rf1Kn8rs1PPuGDZ3fu8EWaVR1s9PHhwbMq7/z89Vf+6UJVNja8vsqePcCsWSVfk0r58Up9\nACNKeHjwVM2oUXxdWE0WRAkP58G7toKlBYYP568PG8Y7IqNHa95mYtxEIhFEIpF2DqYsSV84yJqU\nlFTuIOtPP/3EJk+ezAoKCtj9+/dZixYttD5QUJxMJmPm35ozByf115b78EM+xW7OHD6Ipo61a/kg\nbFW5fZuxhg1VW+quuDNn+BJ5pce5jx9nzNdXe+2rTmQyxoYNY2z6dM32FwoZO3hQtW1v3OCTAhYu\nVG8aKDFdlYmdSu/RX7NmDUJCQiCVSjFt2jQ4OjoiNDQUABASEoL33nsPN2/eROfOneHk5IS1a9dq\n552nApICCQQQwMZS/a7U8uXAtGlARw3qfPn4AGFh6u+nqcL0jLo3I/XowSsixsfzFFShwil6RH0C\nAbB1K7+efftWnGopz+PH/P/irbdU275dO/4pa+hQ4O5dfl5VU5GElCb47x1C9ycSCMqtCaOu1Nep\naLnOHY7b0iqVF1VXbi5Qrx6vhqjq4G5ldOwIrFpV8QpHisyfz2unF1ZGzM3ls2wSEvhYAtHM+fM8\nBXb1KtBYxQoXa9bwAK9u5yAnhxfRS0nhM6OcnNRuboWkUj6m0KaN9o5JdKcysdPoShWI88SwMbet\n8pkXNWsC7u5VM9MhMZH/YWs6FTA4mOfhpf9VSz5+HPD0pOBeWd278zpAo0fzEtWq2LtXs4HtwhlG\nffrwiQCnTvE3lvK+VG1LoR9/BHx9qWpmdWDYZRTLkSnJhLWZ6lMktalwPnzXrro9T3g4LyCm6Rqp\nbm5A8+Z89aRBgzQPMqSszz/ndWQWLeKDr4okJvKpuf36aXYuMzNer75lS2Du3PLn46en89+Vwhli\nyuTk8KJrXbsC332n+n7EOBlfD14iRk2zqu/BA0UzaXRN1ZubFCmcEy8W88Uu3n1XO22r7szM+NTJ\nzZuVFwzbt4//P5pXshs1Zgxw5Ur5vffLl/k8/eRk1Y61dSu/r2LXLv5zqLofMU7GF+Dz+BRJffTg\nfXx0X5Pm4UNe213DGaRyI0fyRS5++YUPvNarp5XmEfCKk2Fh/FPRH39UvF1V3HdQvz4wZQofd1FG\nIuETDb7+Wr39iPEyvgAvEcOS6SfAt27NFxfRdBUlVRw8yGdpWJRfC01ldevy2ulz51J6Rhf8/Xnq\n65NP+E10mZklX09I4At9V8V6tzNm8HTcX38p3i4sjM/S8fbmj2fOVG0/YryML8DniWHB9JOiqVGD\nz265ckV359BGeqbQmDG8vMLQodo5HimpTx8eHAUCfkNU8ZRNVdbct7MDvvhCcfVLqRRYupT33tXZ\njxg34wvwEjEsZKoVGtMFXebhnzzhPT9NB+VKGzyYp5RsbZVvSzRjZ8fz8Rs28NlLn34KZGfz3n1V\n3nfw8cf8d+fs2fJf37mTL0lYurCbsv1MxaFDVV9LyhAYX4DPE6NGvn568IBuA/z583xqpCa3w5fH\nzKxqSytUZwMH8t58airQqhXv1VdlzX0rK76YeXnVL/Pz+TqzxXvvquxnKp494/cvKBovMVVGF+Az\nJZkwy9dPDh4oCvC6+GO4fp3PVyfGqW5dXsRu3Tq+zKOOq2aXMXo0nzV19GjJ5/fu5WvQVrTgSUX7\nmYrz5/mynhs26K8NBQX85rL/KrBXGaML8OI8MQRS/QX4Jk34v//+q/1jX7+ufOUmYviGDeNfVa1G\nDZ5nnzevKB1RUMDnu5fXey++35IlJfczJefO8QHluDhe/kHXHjwAjh3j5ceDg3mJC1tbnnr19q64\n2qsuGF+Al4gBif5SNAKB7hYAoQBPKmvgQP5JYudO/vjAAf5Y2bjOoEG8LHbhfqbk3Dm+WPv48XzR\ndV2RyYA5c3hq7scfebqub18+RvP8OZ8CPW4c/7949kx37SjO6O5kFeeJwST668EDRWkabd489PIl\nn37p6qq9Y5LqRyDgPcegIH4vxOLFwA8/KE8XCQR8jnxQEB8cNpUCZ1lZwM2bvFPm7MxnwS1apLh0\nsyZevwY++ID/Dd+5U/F9J198wWc09e3LFxnSZo2h8hhlD57l2ul1FSBd3PD0119A+/ZVM62OmLZu\n3fhYztChvKaNv796+61apdv2VaWYGP4z1awJuLjwSQzaXnTu8WO+HKO9PRAZqfymwvnz+aBv//68\n1IQuGV04EeeJUZCt3x68tzefC6/NfCWlZ4g2LVnCl6mcP1+9wd7164G1a3lgNAXnzvE7uQtNmcIH\nW7U1SSI+nheDGz6cr69raanaft9+y0tIDxjAP73ritEF+ExJpt4DfN26/FbvO3e0d0wK8ESb2rbl\nv1ODB6u3n7MzEBrKUzWvXummbVWpdIDv1w/Iy9POvP+jR3mAXrmSp17UeSMVCPiAuFDIP2HpanaN\n0QV4sUQM6Wv9DbIWUjYfPj6e145XFQV4om3t2mk2VfPtt/mg68SJle/pPnnCSx0Xlq6uSvn5/JNI\n8Zu7BAJg8uTKTZlkjFfh/PhjPltmxAjNjiMQ8DeHLl34ko6ly11og/EF+DwxpFn67cEDivPw27fz\nNI6qv0T5+XwgqH177bWPkMpYsYJPKdyyRfNj5OTwwDVlCl+LICSEp42qaipmfDzPu9etW/L5MWN4\nDR5Npivm5/M3iJ9/Bi5c4HX1K0Mg4Cmx/v3V6xCqyqgCfIGsADnSHEiyahlkD14m4x/VvvuOz2Q4\nc0a1Y925w29E0fbIPiGaqlkT2L8f+PJL4O+/NTvGrFn8rt6bN/nfiqsrf65RIx70z57V7d2zpdMz\nhezteQpq82b1jpeRwT/ZJCXx4O7iop12CgR8Zo8uZtAZVYDPystCLctayMk203sP3tOT1/DIzeWP\ns7P5tLRz5/iamhMm8F/qvDzlx6L0DDFErVrxKZajRvHfb3UcPMhXEtu8mQewZs14ZdNr13hgb9CA\nV+F86y3d3DQIVBzgAd4L37xZtb9PgAf1bt34YjpHj/IaRMbAqAK8OE8MOys75ORA7z14Gxv+B3D9\nOs8zCoV8SlpUFODoCNSpw1fiUWU6JQV4YqjGjOFzxz/9VPV9Hjzg+em9e/nfQWlubsBXXwE3bvBy\nyh078hustNmbZ4y/kVQU4Nu25X+/hw4pP9bFi7ydH3/Mb2Cq7AIuVcm4ArxEDFtLW2RnV83C18r4\n+PAa2126AIGBfHGN4oXCevdWLU1DNWiIoRIIgI0b+U05+/cr314q5emP2bOV56ctLHgJhYgIXrvn\n3Xf5HZ/acO8en7Lo7FzxNpM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AAIFAoNUG0iArIYRoxryyB/j000+xbNkyCAQCMMYUpmgW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\nhBBioijAE0KIifp/h+z79w0Kp18AAAAASUVORK5CYII=\n"
      }
     ], 
     "prompt_number": 51
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "results"
     ], 
     "language": "python", 
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">", 
        "<table border=\"1\" class=\"dataframe\">", 
        "  <thead>", 
        "    <tr style=\"text-align: right;\">", 
        "      <th></th>", 
        "      <th>max_features</th>", 
        "      <th>test</th>", 
        "      <th>train</th>", 
        "    </tr>", 
        "  </thead>", 
        "  <tbody>", 
        "    <tr>", 
        "      <th>0 </th>", 
        "      <td>   1</td>", 
        "      <td> 0.348571</td>", 
        "      <td> 0.516825</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>1 </th>", 
        "      <td>  11</td>", 
        "      <td> 0.348571</td>", 
        "      <td> 0.516825</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>2 </th>", 
        "      <td>  21</td>", 
        "      <td> 0.357143</td>", 
        "      <td> 0.526032</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>3 </th>", 
        "      <td>  31</td>", 
        "      <td> 0.814286</td>", 
        "      <td> 0.853016</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>4 </th>", 
        "      <td>  41</td>", 
        "      <td> 0.831429</td>", 
        "      <td> 0.826984</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>5 </th>", 
        "      <td>  51</td>", 
        "      <td> 0.794286</td>", 
        "      <td> 0.809206</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>6 </th>", 
        "      <td>  61</td>", 
        "      <td> 0.805714</td>", 
        "      <td> 0.831746</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>7 </th>", 
        "      <td>  71</td>", 
        "      <td> 0.814286</td>", 
        "      <td> 0.845714</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>8 </th>", 
        "      <td>  81</td>", 
        "      <td> 0.734286</td>", 
        "      <td> 0.850159</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>9 </th>", 
        "      <td>  91</td>", 
        "      <td> 0.748571</td>", 
        "      <td> 0.844127</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>10</th>", 
        "      <td> 101</td>", 
        "      <td> 0.680000</td>", 
        "      <td> 0.827302</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>11</th>", 
        "      <td> 111</td>", 
        "      <td> 0.771429</td>", 
        "      <td> 0.872063</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>12</th>", 
        "      <td> 121</td>", 
        "      <td> 0.822857</td>", 
        "      <td> 0.885714</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>13</th>", 
        "      <td> 131</td>", 
        "      <td> 0.820000</td>", 
        "      <td> 0.911111</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>14</th>", 
        "      <td> 141</td>", 
        "      <td> 0.774286</td>", 
        "      <td> 0.913333</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>15</th>", 
        "      <td> 151</td>", 
        "      <td> 0.822857</td>", 
        "      <td> 0.913333</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>16</th>", 
        "      <td> 161</td>", 
        "      <td> 0.854286</td>", 
        "      <td> 0.937143</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>17</th>", 
        "      <td> 171</td>", 
        "      <td> 0.854286</td>", 
        "      <td> 0.941905</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>18</th>", 
        "      <td> 181</td>", 
        "      <td> 0.874286</td>", 
        "      <td> 0.947302</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>19</th>", 
        "      <td> 191</td>", 
        "      <td> 0.840000</td>", 
        "      <td> 0.949206</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>20</th>", 
        "      <td> 201</td>", 
        "      <td> 0.805714</td>", 
        "      <td> 0.966032</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>21</th>", 
        "      <td> 211</td>", 
        "      <td> 0.871429</td>", 
        "      <td> 0.970794</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>22</th>", 
        "      <td> 221</td>", 
        "      <td> 0.808571</td>", 
        "      <td> 0.961587</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>23</th>", 
        "      <td> 231</td>", 
        "      <td> 0.874286</td>", 
        "      <td> 0.955873</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>24</th>", 
        "      <td> 241</td>", 
        "      <td> 0.814286</td>", 
        "      <td> 0.948889</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>25</th>", 
        "      <td> 251</td>", 
        "      <td> 0.834286</td>", 
        "      <td> 0.956508</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>26</th>", 
        "      <td> 261</td>", 
        "      <td> 0.802857</td>", 
        "      <td> 0.954921</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>27</th>", 
        "      <td> 271</td>", 
        "      <td> 0.780000</td>", 
        "      <td> 0.956190</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>28</th>", 
        "      <td> 281</td>", 
        "      <td> 0.768571</td>", 
        "      <td> 0.959683</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>29</th>", 
        "      <td> 291</td>", 
        "      <td> 0.817143</td>", 
        "      <td> 0.959048</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>30</th>", 
        "      <td> 301</td>", 
        "      <td> 0.800000</td>", 
        "      <td> 0.968254</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>31</th>", 
        "      <td> 311</td>", 
        "      <td> 0.785714</td>", 
        "      <td> 0.959048</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>32</th>", 
        "      <td> 321</td>", 
        "      <td> 0.785714</td>", 
        "      <td> 0.958095</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>33</th>", 
        "      <td> 331</td>", 
        "      <td> 0.754286</td>", 
        "      <td> 0.959365</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>34</th>", 
        "      <td> 341</td>", 
        "      <td> 0.788571</td>", 
        "      <td> 0.955873</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>35</th>", 
        "      <td> 351</td>", 
        "      <td> 0.757143</td>", 
        "      <td> 0.967619</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>36</th>", 
        "      <td> 361</td>", 
        "      <td> 0.734286</td>", 
        "      <td> 0.969206</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>37</th>", 
        "      <td> 371</td>", 
        "      <td> 0.725714</td>", 
        "      <td> 0.971429</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>38</th>", 
        "      <td> 381</td>", 
        "      <td> 0.748571</td>", 
        "      <td> 0.968889</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>39</th>", 
        "      <td> 391</td>", 
        "      <td> 0.742857</td>", 
        "      <td> 0.966667</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>40</th>", 
        "      <td> 401</td>", 
        "      <td> 0.731429</td>", 
        "      <td> 0.960952</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>41</th>", 
        "      <td> 411</td>", 
        "      <td> 0.731429</td>", 
        "      <td> 0.950159</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>42</th>", 
        "      <td> 421</td>", 
        "      <td> 0.700000</td>", 
        "      <td> 0.962857</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>43</th>", 
        "      <td> 431</td>", 
        "      <td> 0.771429</td>", 
        "      <td> 0.967302</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>44</th>", 
        "      <td> 441</td>", 
        "      <td> 0.731429</td>", 
        "      <td> 0.960317</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>45</th>", 
        "      <td> 451</td>", 
        "      <td> 0.734286</td>", 
        "      <td> 0.963810</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>46</th>", 
        "      <td> 461</td>", 
        "      <td> 0.748571</td>", 
        "      <td> 0.965714</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>47</th>", 
        "      <td> 471</td>", 
        "      <td> 0.762857</td>", 
        "      <td> 0.977778</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>48</th>", 
        "      <td> 481</td>", 
        "      <td> 0.765714</td>", 
        "      <td> 0.978413</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>49</th>", 
        "      <td> 491</td>", 
        "      <td> 0.748571</td>", 
        "      <td> 0.985397</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 52, 
       "text": [
        "    max_features      test     train", 
        "0              1  0.348571  0.516825", 
        "1             11  0.348571  0.516825", 
        "2             21  0.357143  0.526032", 
        "3             31  0.814286  0.853016", 
        "4             41  0.831429  0.826984", 
        "5             51  0.794286  0.809206", 
        "6             61  0.805714  0.831746", 
        "7             71  0.814286  0.845714", 
        "8             81  0.734286  0.850159", 
        "9             91  0.748571  0.844127", 
        "10           101  0.680000  0.827302", 
        "11           111  0.771429  0.872063", 
        "12           121  0.822857  0.885714", 
        "13           131  0.820000  0.911111", 
        "14           141  0.774286  0.913333", 
        "15           151  0.822857  0.913333", 
        "16           161  0.854286  0.937143", 
        "17           171  0.854286  0.941905", 
        "18           181  0.874286  0.947302", 
        "19           191  0.840000  0.949206", 
        "20           201  0.805714  0.966032", 
        "21           211  0.871429  0.970794", 
        "22           221  0.808571  0.961587", 
        "23           231  0.874286  0.955873", 
        "24           241  0.814286  0.948889", 
        "25           251  0.834286  0.956508", 
        "26           261  0.802857  0.954921", 
        "27           271  0.780000  0.956190", 
        "28           281  0.768571  0.959683", 
        "29           291  0.817143  0.959048", 
        "30           301  0.800000  0.968254", 
        "31           311  0.785714  0.959048", 
        "32           321  0.785714  0.958095", 
        "33           331  0.754286  0.959365", 
        "34           341  0.788571  0.955873", 
        "35           351  0.757143  0.967619", 
        "36           361  0.734286  0.969206", 
        "37           371  0.725714  0.971429", 
        "38           381  0.748571  0.968889", 
        "39           391  0.742857  0.966667", 
        "40           401  0.731429  0.960952", 
        "41           411  0.731429  0.950159", 
        "42           421  0.700000  0.962857", 
        "43           431  0.771429  0.967302", 
        "44           441  0.731429  0.960317", 
        "45           451  0.734286  0.963810", 
        "46           461  0.748571  0.965714", 
        "47           471  0.762857  0.977778", 
        "48           481  0.765714  0.978413", 
        "49           491  0.748571  0.985397"
       ]
      }
     ], 
     "prompt_number": 52
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [], 
     "language": "python", 
     "outputs": []
    }
   ]
  }
 ]
}