notes_turns_and_reactions3.ipynb

{
 "metadata": {
  "name": "notes_turns_and_reactions3"
 }, 
 "nbformat": 2, 
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown", 
     "source": [
      "# Reactions per turn"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Focusing on generating evaluation results for our specific tasks."
     ]
    }, 
    {
     "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 9.87 s, sys: 0.56 s, total: 10.43 s", 
        "Wall time: 10.52 s"
       ]
      }
     ], 
     "prompt_number": 2
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "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": [], 
     "prompt_number": 3
    }, 
    {
     "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 4.73 s, sys: 0.34 s, total: 5.07 s", 
        "Wall time: 5.07 s"
       ]
      }
     ], 
     "prompt_number": 4
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "p2 = p[['UserID','party_1','political_views_2','candidate_choice_3','confidence_in_choice_4','likely_to_vote_5','candidate_preferred_29']]"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 5
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Simplify party membership into R/D/oth"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "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['candidate'] = p2.candidate_choice_3", 
      "#p2['candidate'] = p2.candidate_choice_3.apply(lambda a: {'obama':'democrat', ", 
      "#                                          'lean democrat':'democrat',", 
      "#                                          'lean republican':'republican',", 
      "#                                          'closest to republican party':'republican'}.get(a,'other'))"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 6
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "### Merge political questionnaire with reactions"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "%time r3 = r2.merge(p2[['UserID','party','candidate']])", 
      "print 'pre-merge:',len(r2),'post-merge:',len(r3)", 
      "#r3.head(2)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "CPU times: user 0.63 s, sys: 0.05 s, total: 0.67 s", 
        "Wall time: 0.67 s", 
        "pre-merge: 189015 post-merge: 189015"
       ]
      }
     ], 
     "prompt_number": 7
    }, 
    {
     "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]", 
      "#r4 = r3", 
      "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": 86
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## Group by turn"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Statements"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "st = r4.groupby(['statement']).first()[['Speaker_name','Transcript','turn',\"Sync'd start\",\"Sync'd end\"]]"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 87
    }, 
    {
     "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, ", 
      "                  'reactions_oba':r4[(r4.candidate=='obama')].groupby('turn').count().turn,", 
      "                  'reactions_rom':r4[(r4.candidate=='romney')].groupby('turn').count().turn,", 
      "                  '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,", 
      "                  'agree_oba':r4[(r4.candidate=='obama') & (r4.Reaction_what=='Agree')].groupby('turn').count().turn,", 
      "                  'agree_rom':r4[(r4.candidate=='romney') & (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,", 
      "                  'disagree_oba':r4[(r4.candidate=='obama') & (r4.Reaction_what=='Disagree')].groupby('turn').count().turn,", 
      "                  'disagree_rom':r4[(r4.candidate=='romney') & (r4.Reaction_what=='Disagree')].groupby('turn').count().turn,", 
      "                  'dodge_oba':r4[(r4.candidate=='obama') & (r4.Reaction_what=='Dodge')].groupby('turn').count().turn,", 
      "                  'dodge_rom':r4[(r4.candidate=='romney') & (r4.Reaction_what=='Dodge')].groupby('turn').count().turn,", 
      "                  'dodge':r4[r4.Reaction_what=='Dodge'].groupby('turn').count().turn,", 
      "                  'spin_oba':r4[(r4.candidate=='obama') & (r4.Reaction_what=='Spin')].groupby('turn').count().turn,", 
      "                  'spin_rom':r4[(r4.candidate=='romney') & (r4.Reaction_what=='Spin')].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['rps_oba'] = 1.0 * t.reactions_oba / t.dur", 
      "t['rps_rom'] = 1.0 * t.reactions_rom / t.dur", 
      "#t['sd_per_sec'] = 1.0 * (t.spin + t.dodge) / t.dur", 
      "t['sdps_oba'] = 1.0 * (t.spin_oba + t.dodge_oba) / t.dur", 
      "t['sdps_rom'] = 1.0 * (t.spin_rom + t.dodge_rom) / t.dur", 
      "t['a_to_d_dems'] = t.agree_dem / t.disagree_dem", 
      "t['a_to_d_reps'] = t.agree_rep / t.disagree_rep", 
      "t['a_to_d_oba'] = t.agree_oba / t.disagree_oba", 
      "t['a_to_d_rom'] = t.agree_rom / t.disagree_rom", 
      "", 
      "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']", 
      "del t['dur']"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 17
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "all_words = [w for word_list in t.words for w in word_list]", 
      "all_bigrams = nltk.bigrams(all_words)", 
      "ranked_unigrams = nltk.FreqDist(all_words).keys()", 
      "ranked_bigrams = nltk.FreqDist(all_bigrams).keys()", 
      "#MAX_FEATURES = 200 # avoid overfitting", 
      "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['bigrams'] = t.words.apply(lambda words: {\"%s_%s\"%(w1,w2):True for w1,w2 in nltk.bigrams(words) if ", 
      "                                            (w1,w2 in ranked_bigrams[:MAX_FEATURES]) and", 
      "                                            (not w1 in stopwords.words('english')) and", 
      "                                            (not w2 in stopwords.words('english'))", 
      "                                            })"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 33
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "### Filter"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "For now, we get rid of the really short turns."
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "MIN_WORDS = 30 # good results", 
      "#MIN_WORDS = 20 # ", 
      "#MIN_WORDS = 0 # this really affects the republican results strongly", 
      "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": 88
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## Crossvalidation code"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "def cv(df, num_folds = 10, classifier=nltk.NaiveBayesClassifier, maxent_params=None, print_features=False):", 
      "    df = df.copy()", 
      "    df = df.reindex(np.random.permutation(df.index))", 
      "    fold_size = len(df)/num_folds", 
      "    fold_starts = range(0, len(df)+fold_size, fold_size)", 
      "    folds = zip(fold_starts,fold_starts[1:])", 
      "    accs = []", 
      "    for (first,last) in folds:", 
      "        test_rows = df.index[first:last]", 
      "        tst = df.ix[test_rows]", 
      "        trn = df.drop(test_rows)", 
      "        if maxent_params == None:", 
      "            cl = classifier.train(zip(trn.features, trn.label))", 
      "        else:", 
      "            cl = classifier.train(zip(trn.features, trn.label), ", 
      "                                  algorithm=maxent_params['algorithm'], ", 
      "                                  max_iter=maxent_params['max_iter'],", 
      "                                  trace=maxent_params['trace'])", 
      "        accs.append(nltk.classify.accuracy(cl, zip(tst.features, tst.label)))", 
      "        #if print_features:", 
      "        #    cl", 
      "    #print accs", 
      "    return {'mean':mean(accs),'stdev':std(accs)}"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 35
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## N-Gram Type"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "t2['features'] = t2.bigrams"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 66
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## Task 1: Reactions per second"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "e1 = t2.copy()", 
      "e1['label'] = e1.rps_oba >= e1.rps_oba.quantile(.5)", 
      "print 'DT', cv(e1, classifier=nltk.classify.DecisionTreeClassifier)", 
      "print 'ME', cv(e1, classifier=nltk.classify.MaxentClassifier, ", 
      "               maxent_params={'algorithm':nltk.classify.MaxentClassifier.ALGORITHMS[0], ", 
      "                              'max_iter':25,", 
      "                              'trace':0})", 
      "print 'NB', cv(e1, classifier=nltk.NaiveBayesClassifier)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "DT "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.12453996981544782, 'mean': 0.39999999999999991}", 
        "ME "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.13997084244475305, 'mean': 0.59999999999999987}", 
        "NB "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.10690449676496977, 'mean': 0.59999999999999998}"
       ]
      }
     ], 
     "prompt_number": 67
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "e1 = t2.copy()", 
      "e1['label'] = e1.rps_rom >= e1.rps_rom.quantile(.5)", 
      "print 'DT', cv(e1, classifier=nltk.classify.DecisionTreeClassifier)", 
      "print 'ME', cv(e1, classifier=nltk.classify.MaxentClassifier, ", 
      "               maxent_params={'algorithm':nltk.classify.MaxentClassifier.ALGORITHMS[0], ", 
      "                              'max_iter':25,", 
      "                              'trace':0})", 
      "print 'NB', cv(e1, classifier=nltk.NaiveBayesClassifier)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "DT "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.16225452416572211, 'mean': 0.58571428571428563}", 
        "ME "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.18126539343499315, 'mean': 0.75714285714285712}", 
        "NB "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.15907898179514349, 'mean': 0.77142857142857135}"
       ]
      }
     ], 
     "prompt_number": 68
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "e1['label'] = e1.rps_rom >= e1.rps_rom.quantile(.5)", 
      "train_rows = random.sample(e1.index, len(e1)*9/10)", 
      "trn = e1.ix[train_rows]", 
      "tst = e1.drop(train_rows)", 
      "cl = nltk.NaiveBayesClassifier.train(zip(trn.features, trn.label))", 
      "nltk.classify.accuracy(cl, zip(tst.features, tst.label))", 
      "cl.show_most_informative_features(25)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Most Informative Features", 
        "               cut_taxes = True             True : False  =      3.6 : 1.0", 
        "                 get_rid = True             True : False  =      2.9 : 1.0", 
        "        trillion_dollars = True             True : False  =      2.9 : 1.0", 
        "          million_people = True             True : False  =      2.5 : 1.0", 
        "             health_care = True            False : True   =      2.5 : 1.0", 
        "        health_insurance = True            False : True   =      2.4 : 1.0", 
        "            running_mate = True            False : True   =      2.4 : 1.0", 
        "             get_america = True             True : False  =      2.3 : 1.0", 
        "        small_businesses = True             True : False  =      2.1 : 1.0", 
        "         governor_romney = True            False : True   =      2.1 : 1.0", 
        "             making_sure = True            False : True   =      1.9 : 1.0", 
        "              four_years = True             True : False  =      1.7 : 1.0", 
        "    traditional_medicare = True            False : True   =      1.7 : 1.0", 
        "             wall_street = True            False : True   =      1.7 : 1.0", 
        "            balanced_way = True            False : True   =      1.7 : 1.0", 
        "                 go_back = True            False : True   =      1.7 : 1.0", 
        "               come_back = True            False : True   =      1.7 : 1.0", 
        "         unelected_board = True            False : True   =      1.7 : 1.0", 
        "         social_security = True            False : True   =      1.7 : 1.0", 
        "             romney_says = True            False : True   =      1.7 : 1.0", 
        "       training_programs = True            False : True   =      1.7 : 1.0", 
        "             whole_bunch = True            False : True   =      1.7 : 1.0", 
        "      federal_government = True            False : True   =      1.6 : 1.0", 
        "         american_energy = True             True : False  =      1.6 : 1.0"
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "       closing_loopholes = True             True : False  =      1.6 : 1.0"
       ]
      }
     ], 
     "prompt_number": 93
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## Task 2a: Majority agrees with speaker  THIS IS TASK 2"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Democrats"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "e2ad = t2.copy()", 
      "#e2ad['label'] = e2ad.agree_dem >= e2ad.disagree_dem", 
      "e2ad['label'] = e2ad.agree_oba >= e2ad.disagree_oba", 
      "print 'DT', cv(e2ad, classifier=nltk.classify.DecisionTreeClassifier)", 
      "print 'ME', cv(e2ad, classifier=nltk.classify.MaxentClassifier, ", 
      "               maxent_params={'algorithm':nltk.classify.MaxentClassifier.ALGORITHMS[0], ", 
      "                              'max_iter':25,", 
      "                              'trace':0})", 
      "print 'NB', cv(e2ad, classifier=nltk.NaiveBayesClassifier)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "DT "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.15386185163241442, 'mean': 0.74285714285714277}", 
        "ME "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.13997084244475305, 'mean': 0.82857142857142851}", 
        "NB "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.1743793659390529, 'mean': 0.84285714285714286}"
       ]
      }
     ], 
     "prompt_number": 39
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "e2ad['label'] = e2ad.agree_oba >= e2ad.disagree_oba", 
      "train_rows = random.sample(e2ad.index, len(e2ad)*9/10)", 
      "trn = e2ad.ix[train_rows]", 
      "tst = e2ad.drop(train_rows)", 
      "cl = nltk.NaiveBayesClassifier.train(zip(trn.features, trn.label))", 
      "nltk.classify.accuracy(cl, zip(tst.features, tst.label))", 
      "cl.show_most_informative_features(25)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Most Informative Features", 
        "                  romney = True             True : False  =     11.9 : 1.0", 
        "                governor = True             True : False  =      7.7 : 1.0", 
        "                  course = True            False : True   =      5.7 : 1.0", 
        "                  making = True             True : False  =      5.3 : 1.0", 
        "                 schools = True            False : True   =      4.8 : 1.0", 
        "                     rid = True            False : True   =      4.8 : 1.0", 
        "                    came = True            False : True   =      4.8 : 1.0", 
        "                  system = True             True : False  =      4.8 : 1.0", 
        "                    idea = True            False : True   =      4.5 : 1.0", 
        "                   place = True            False : True   =      4.5 : 1.0", 
        "                   comes = True             True : False  =      4.3 : 1.0", 
        "                question = True             True : False  =      4.3 : 1.0", 
        "                  answer = True            False : True   =      4.0 : 1.0", 
        "                    hire = True            False : True   =      4.0 : 1.0", 
        "                    rate = True            False : True   =      4.0 : 1.0", 
        "               important = True             True : False  =      3.8 : 1.0", 
        "                 problem = True             True : False  =      3.8 : 1.0", 
        "                approach = True             True : False  =      3.8 : 1.0", 
        "              republican = True             True : False  =      3.3 : 1.0", 
        "                    cuts = True             True : False  =      3.3 : 1.0", 
        "                families = True             True : False  =      3.3 : 1.0", 
        "                     get = True            False : True   =      3.2 : 1.0", 
        "                   first = True             True : False  =      3.2 : 1.0", 
        "                    ever = True            False : True   =      3.1 : 1.0", 
        "                 instead = True            False : True   =      3.1 : 1.0"
       ]
      }
     ], 
     "prompt_number": 95
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Republicans"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "e2ar = t2.copy()", 
      "#e2ar['label'] = e2ar.agree_rep >= e2ar.disagree_rep", 
      "e2ar['label'] = e2ar.agree_rom >= e2ar.disagree_rom", 
      "print 'DT', cv(e2ar, classifier=nltk.classify.DecisionTreeClassifier)", 
      "print 'ME', cv(e2ar, classifier=nltk.classify.MaxentClassifier, ", 
      "               maxent_params={'algorithm':nltk.classify.MaxentClassifier.ALGORITHMS[0], ", 
      "                              'max_iter':25,", 
      "                              'trace':0})", 
      "print 'NB', cv(e2ar, classifier=nltk.NaiveBayesClassifier)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "DT "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.18126539343499315, 'mean': 0.75714285714285723}", 
        "ME "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.15971914124998499, 'mean': 0.7857142857142857}", 
        "NB "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.1743793659390529, 'mean': 0.44285714285714278}"
       ]
      }
     ], 
     "prompt_number": 43
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "e2ar['label'] = e2ar.agree_rom >= e2ar.disagree_rom", 
      "train_rows = random.sample(e2ar.index, len(e2ar)*9/10)", 
      "trn = e2ar.ix[train_rows]", 
      "tst = e2ar.drop(train_rows)", 
      "cl = nltk.NaiveBayesClassifier.train(zip(trn.features, trn.label))", 
      "nltk.classify.accuracy(cl, zip(tst.features, tst.label))", 
      "cl.show_most_informative_features(25)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "Most Informative Features", 
        "                    idea = True             True : False  =      6.1 : 1.0", 
        "                   folks = True            False : True   =      5.8 : 1.0", 
        "                  course = True             True : False  =      5.5 : 1.0", 
        "                    four = True             True : False  =      5.5 : 1.0", 
        "                  number = True             True : False  =      5.2 : 1.0", 
        "                    year = True             True : False  =      4.8 : 1.0", 
        "              difference = True            False : True   =      4.5 : 1.0", 
        "                   small = True             True : False  =      4.5 : 1.0", 
        "                  saying = True             True : False  =      4.2 : 1.0", 
        "               democrats = True             True : False  =      4.2 : 1.0", 
        "                  energy = True             True : False  =      4.2 : 1.0", 
        "                     job = True             True : False  =      4.2 : 1.0", 
        "                    best = True             True : False  =      4.2 : 1.0", 
        "                  worked = True            False : True   =      3.8 : 1.0", 
        "                american = True             True : False  =      3.7 : 1.0", 
        "               different = True             True : False  =      3.6 : 1.0", 
        "                    came = True             True : False  =      3.6 : 1.0", 
        "                    hire = True             True : False  =      3.6 : 1.0", 
        "                    seen = True             True : False  =      3.6 : 1.0", 
        "                 million = True             True : False  =      3.5 : 1.0", 
        "                  better = True             True : False  =      3.3 : 1.0", 
        "                spending = True             True : False  =      3.3 : 1.0", 
        "                 percent = True             True : False  =      3.3 : 1.0", 
        "                    look = True             True : False  =      3.2 : 1.0", 
        "                    work = True             True : False  =      3.1 : 1.0"
       ]
      }
     ], 
     "prompt_number": 94
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## Task 2b: Ratio agree-to-disagree above median"
     ]
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Democrats"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "e2bd = t2.copy()", 
      "#e2bd['label'] = e2bd.a_to_d_dems >= e2bd.a_to_d_dems.quantile(.5)", 
      "e2bd['label'] = e2bd.a_to_d_oba >= e2bd.a_to_d_oba.quantile(.5)", 
      "print 'DT', cv(e2bd, classifier=nltk.classify.DecisionTreeClassifier)", 
      "print 'ME', cv(e2bd, classifier=nltk.classify.MaxentClassifier, ", 
      "               maxent_params={'algorithm':nltk.classify.MaxentClassifier.ALGORITHMS[0], ", 
      "                              'max_iter':25,", 
      "                              'trace':0})", 
      "print 'NB', cv(e2bd, classifier=nltk.NaiveBayesClassifier)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "DT "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.14568627181693672, 'mean': 0.77142857142857146}", 
        "ME "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.18571428571428572, 'mean': 0.87142857142857155}", 
        "NB "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.11157499537009505, 'mean': 0.81428571428571428}"
       ]
      }
     ], 
     "prompt_number": 77
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Republicans"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "e2br = t2.copy()", 
      "#e2br['label'] = e2br.a_to_d_reps >= e2br.a_to_d_reps.quantile(.5)", 
      "e2br['label'] = e2br.a_to_d_rom >= e2br.a_to_d_rom.quantile(.5)", 
      "print 'DT', cv(e2br, classifier=nltk.classify.DecisionTreeClassifier)", 
      "print 'ME', cv(e2br, classifier=nltk.classify.MaxentClassifier, ", 
      "               maxent_params={'algorithm':nltk.classify.MaxentClassifier.ALGORITHMS[0], ", 
      "                              'max_iter':25,", 
      "                              'trace':0})", 
      "print 'NB', cv(e2br, classifier=nltk.NaiveBayesClassifier)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "DT "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.24824658035241429, 'mean': 0.73232323232323226}", 
        "ME "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.19998979669922559, 'mean': 0.64646464646464641}", 
        "NB "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.15907086614165275, 'mean': 0.59595959595959602}"
       ]
      }
     ], 
     "prompt_number": 22
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## Task 3: Spins+dodges per second above median"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "e3 = t2.copy()", 
      "e3['label'] = e3.sdps_oba >= e3.sdps_oba.quantile(.5)", 
      "print 'DT', cv(e3, classifier=nltk.classify.DecisionTreeClassifier)", 
      "print 'ME', cv(e3, classifier=nltk.classify.MaxentClassifier, ", 
      "               maxent_params={'algorithm':nltk.classify.MaxentClassifier.ALGORITHMS[0], ", 
      "                              'max_iter':25,", 
      "                              'trace':0})", 
      "print 'NB', cv(e3, classifier=nltk.NaiveBayesClassifier)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "DT "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.15714285714285714, 'mean': 0.75714285714285712}", 
        "ME "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.15971914124998496, 'mean': 0.78571428571428559}", 
        "NB "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.13997084244475305, 'mean': 0.82857142857142851}"
       ]
      }
     ], 
     "prompt_number": 55
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "e3 = t2.copy()", 
      "e3['label'] = e3.sdps_rom >= e3.sdps_rom.quantile(.5)", 
      "print 'DT', cv(e3, classifier=nltk.classify.DecisionTreeClassifier)", 
      "print 'ME', cv(e3, classifier=nltk.classify.MaxentClassifier, ", 
      "               maxent_params={'algorithm':nltk.classify.MaxentClassifier.ALGORITHMS[0], ", 
      "                              'max_iter':25,", 
      "                              'trace':0})", 
      "print 'NB', cv(e3, classifier=nltk.NaiveBayesClassifier)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "DT "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.12453996981544781, 'mean': 0.68571428571428572}", 
        "ME "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.12453996981544782, 'mean': 0.59999999999999987}", 
        "NB "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "{'stdev': 0.1142857142857143, 'mean': 0.48571428571428565}"
       ]
      }
     ], 
     "prompt_number": 56
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## Hyperparameters"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "gr = t2.copy()", 
      "p = []", 
      "trn_means = []", 
      "tst_means = []", 
      "#MAX = 500", 
      "#gr['label'] = gr.rps_oba >= gr.rps_oba.quantile(.5) # Task 1 ~300", 
      "#gr['label'] = gr.agree_oba >= gr.disagree_oba # Task 2 ~700", 
      "gr['label'] = gr.agree_rom >= gr.disagree_rom # Task 2", 
      "#gr['label'] = gr.sdps_oba >= gr.sdps_oba.quantile(.5) # Task 3 ~500", 
      "#gr['label'] = gr.sdps_rom >= gr.sdps_rom.quantile(.5) # Task 3 ", 
      "", 
      "#gr['label'] = gr.a_to_d_dems >= gr.a_to_d_dems.quantile(.5)", 
      "", 
      "#for max_feats in range(1,700,100):", 
      "for max_feats in range(1,len(ranked_unigrams),100):", 
      "    gr['features'] = 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(0,50):", 
      "        print i,", 
      "        train_rows = random.sample(gr.index, len(gr)*9/10)", 
      "        #print train_rows", 
      "        trn,tst = gr.ix[train_rows],gr.drop(train_rows)", 
      "        cl = nltk.NaiveBayesClassifier.train(zip(trn.features, trn.label))", 
      "        trn_ac.append(nltk.classify.accuracy(cl, zip(trn.features, trn.label)))", 
      "        tst_ac.append(nltk.classify.accuracy(cl, zip(tst.features, tst.label)))", 
      "    p.append(max_feats)", 
      "    trn_means.append(mean(trn_ac))", 
      "    tst_means.append(mean(tst_ac))", 
      "    print ''"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 "
       ]
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        "15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 "
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 "
       ]
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "45 46 47 48 49 ", 
        "101"
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        " 0 1 2 3 "
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      {
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       "text": [
        "4 5 6 7 "
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        "8 9 10 11 "
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        "27 28 29 "
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       "text": [
        "30 31 32 "
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "33 34 35 36 "
       ]
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "37 38 39 40 "
       ]
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       "stream": "stdout", 
       "text": [
        "41 42 43 44 "
       ]
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "45 46 47 48 "
       ]
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       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "49 ", 
        "201"
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        " 0 1 "
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       "output_type": "stream", 
       "stream": "stdout", 
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        "2 3 "
       ]
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "4 5 "
       ]
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "6 7 8 "
       ]
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       "output_type": "stream", 
       "stream": "stdout", 
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        "9 10 11 "
       ]
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       "output_type": "stream", 
       "stream": "stdout", 
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        "12 13 "
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       "stream": "stdout", 
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        "14 15 16 "
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        "17 18 19 "
       ]
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       "stream": "stdout", 
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        "24 25 "
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       "stream": "stdout", 
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        "26 27 "
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       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "28 29 30 "
       ]
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       "output_type": "stream", 
       "stream": "stdout", 
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        "31 32 "
       ]
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      {
       "output_type": "stream", 
       "stream": "stdout", 
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       "output_type": "stream", 
       "stream": "stdout", 
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        "36 37 38 "
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        "", 
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        " 0 1 "
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       "output_type": "stream", 
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       "stream": "stdout", 
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       "stream": "stdout", 
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       "stream": "stdout", 
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        "11 12 "
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       "stream": "stdout", 
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       "stream": "stdout", 
       "text": [
        "15 16 "
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       "stream": "stdout", 
       "text": [
        "17 18 "
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       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "19 "
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      {
       "output_type": "stream", 
       "stream": "stdout", 
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       "text": [
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       "stream": "stdout", 
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       "stream": "stdout", 
       "text": [
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       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
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       "stream": "stdout", 
       "text": [
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       "stream": "stdout", 
       "text": [
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       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "", 
        "401"
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    {
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      "results = pd.DataFrame({'max_features':p, 'train':trn_means, 'test':tst_means})", 
      "results.plot(x='max_features')", 
      "xlabel('Unigram Features')", 
      "title('Naive Bayes Accuracy on Task 1')", 
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7oZFZI3WXR0RlxLCnEh3++zAmHp4IB3MHDHEZgr6OfXnoAqJKinvQUhH3Mu5h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\nrkJggg==\n"
      }
     ], 
     "prompt_number": 48
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Task 2 obama"
     ]
    }, 
    {
     "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')", 
      "xlabel('Unigram Features')", 
      "title('Naive Bayes Accuracy on Task 2')", 
      "ylim(0,1)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 31, 
       "text": [
        "(0, 1)"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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ECeVLd+yYdupKSVGGHlpZiSxfXvZN5nffVcKxJEJDlZVDeccGP+uSkkT691dG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\n"
      }
     ], 
     "prompt_number": 31
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Task 2 Romney"
     ]
    }, 
    {
     "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')", 
      "xlabel('Unigram Features')", 
      "title('Naive Bayes Accuracy on Task 1')", 
      "ylim(0,1)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 90, 
       "text": [
        "(0, 1)"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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bjXHu4/CCzguqLouI6hiGvYo8GfK73Hcx5Imo2jDsaxhDnohUgWFfQxjyRKRK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\n"
      }
     ], 
     "prompt_number": 90
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "Task 3"
     ]
    }, 
    {
     "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')", 
      "xlabel('Unigram Features')", 
      "title('Naive Bayes Accuracy on Task 3')", 
      "ylim(0,1)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 53, 
       "text": [
        "(0, 1)"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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xPkaPBr74QndDreLjlZEl8fG631POGLi6KgeHKh+1s3IlsGQJ8Oefypedh4fy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\nRK5CYII=\n"
      }
     ], 
     "prompt_number": 53
    }, 
    {
     "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> 0.351429</td>", 
        "      <td> 0.516508</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>1 </th>", 
        "      <td>  101</td>", 
        "      <td> 0.814286</td>", 
        "      <td> 0.909524</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>2 </th>", 
        "      <td>  201</td>", 
        "      <td> 0.754286</td>", 
        "      <td> 0.931429</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>3 </th>", 
        "      <td>  301</td>", 
        "      <td> 0.845714</td>", 
        "      <td> 0.975238</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>4 </th>", 
        "      <td>  401</td>", 
        "      <td> 0.828571</td>", 
        "      <td> 0.975873</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>5 </th>", 
        "      <td>  501</td>", 
        "      <td> 0.840000</td>", 
        "      <td> 0.984444</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>6 </th>", 
        "      <td>  601</td>", 
        "      <td> 0.800000</td>", 
        "      <td> 0.989524</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>7 </th>", 
        "      <td>  701</td>", 
        "      <td> 0.834286</td>", 
        "      <td> 0.985714</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>8 </th>", 
        "      <td>  801</td>", 
        "      <td> 0.831429</td>", 
        "      <td> 0.993333</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>9 </th>", 
        "      <td>  901</td>", 
        "      <td> 0.820000</td>", 
        "      <td> 0.992698</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>10</th>", 
        "      <td> 1001</td>", 
        "      <td> 0.825714</td>", 
        "      <td> 0.996508</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>11</th>", 
        "      <td> 1101</td>", 
        "      <td> 0.825714</td>", 
        "      <td> 0.998095</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>12</th>", 
        "      <td> 1201</td>", 
        "      <td> 0.842857</td>", 
        "      <td> 0.998730</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>13</th>", 
        "      <td> 1301</td>", 
        "      <td> 0.860000</td>", 
        "      <td> 0.998095</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>14</th>", 
        "      <td> 1401</td>", 
        "      <td> 0.805714</td>", 
        "      <td> 0.997778</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>15</th>", 
        "      <td> 1501</td>", 
        "      <td> 0.825714</td>", 
        "      <td> 0.998095</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>16</th>", 
        "      <td> 1601</td>", 
        "      <td> 0.857143</td>", 
        "      <td> 0.998095</td>", 
        "    </tr>", 
        "    <tr>", 
        "      <th>17</th>", 
        "      <td> 1701</td>", 
        "      <td> 0.825714</td>", 
        "      <td> 0.998095</td>", 
        "    </tr>", 
        "  </tbody>", 
        "</table>", 
        "</div>"
       ], 
       "output_type": "pyout", 
       "prompt_number": 54, 
       "text": [
        "    max_features      test     train", 
        "0              1  0.351429  0.516508", 
        "1            101  0.814286  0.909524", 
        "2            201  0.754286  0.931429", 
        "3            301  0.845714  0.975238", 
        "4            401  0.828571  0.975873", 
        "5            501  0.840000  0.984444", 
        "6            601  0.800000  0.989524", 
        "7            701  0.834286  0.985714", 
        "8            801  0.831429  0.993333", 
        "9            901  0.820000  0.992698", 
        "10          1001  0.825714  0.996508", 
        "11          1101  0.825714  0.998095", 
        "12          1201  0.842857  0.998730", 
        "13          1301  0.860000  0.998095", 
        "14          1401  0.805714  0.997778", 
        "15          1501  0.825714  0.998095", 
        "16          1601  0.857143  0.998095", 
        "17          1701  0.825714  0.998095"
       ]
      }
     ], 
     "prompt_number": 54
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "### Smaller range"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "gr = t2.copy()", 
      "p = []", 
      "trn_means = []", 
      "tst_means = []", 
      "MAX = 500", 
      "gr['label'] = gr.rps_oba >= gr.rps_oba.quantile(.5)", 
      "for max_feats in range(1,200,10):", 
      "    gr['features'] = 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(0,50):", 
      "        print i,", 
      "        train_rows = random.sample(gr.index, len(gr)*9/10)", 
      "        #print train_rows", 
      "        trn,tst = gr.ix[train_rows],gr.drop(train_rows)", 
      "        cl = nltk.NaiveBayesClassifier.train(zip(trn.features, trn.label))", 
      "        trn_ac.append(nltk.classify.accuracy(cl, zip(trn.features, trn.label)))", 
      "        tst_ac.append(nltk.classify.accuracy(cl, zip(tst.features, tst.label)))", 
      "    p.append(max_feats)", 
      "    trn_means.append(mean(trn_ac))", 
      "    tst_means.append(mean(tst_ac))", 
      "    print ''"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "1 0 1 2 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "3 4 5 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "6 7 8 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "9 10 11 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "12 13 14 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "15 16 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "17 18 19 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "20 21 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "22 23 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "24 25 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "26 27 28 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "29 30 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "31 32 33 "
       ]
      }, 
      {
       "output_type": "stream", 
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        " 20 21 22 "
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       "text": [
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       "stream": "stdout", 
       "text": [
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       "stream": "stdout", 
       "text": [
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       "stream": "stdout", 
       "text": [
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      }, 
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       "stream": "stdout", 
       "text": [
        "37 38 "
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      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "39 40 "
       ]
      }, 
      {
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       "stream": "stdout", 
       "text": [
        "41 42 "
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       "stream": "stdout", 
       "text": [
        "43 44 45 "
       ]
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       "stream": "stdout", 
       "text": [
        "46 47 48 "
       ]
      }, 
      {
       "output_type": "stream", 
       "stream": "stdout", 
       "text": [
        "49 "
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      }
     ], 
     "prompt_number": 18
    }, 
    {
     "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": 19, 
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x8b171f0>"
       ]
      }, 
      {
       "output_type": "display_data", 
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+PnD/PnD5sq1Lwph2HPgZM6MrV8R/OfAze8aBnzEzunIFkMmArCxbl4Qx7Tjw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\nSVkVKzlZDNi6fFk0GDPrKyoS8ywVFoq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61NqRz2lpaUhJSUFwcLDDXg+FQoGuXbuiZcuW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\n"
      }
     ], 
     "prompt_number": 19
    }, 
    {
     "cell_type": "markdown", 
     "source": [
      "## More interpretation"
     ]
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "figsize(10,8)", 
      "", 
      "BINS=20", 
      "", 
      "subplot(221)", 
      "#log10(t.a_to_d_dems).hist()", 
      "log10(t.a_to_d_oba).hist(bins=BINS)", 
      "xlabel('log(agree/disagree)')", 
      "title('Reactions of Obama Supporters')", 
      "", 
      "subplot(222)", 
      "#log10(t.a_to_d_reps).hist(bins=30)", 
      "log10(t.a_to_d_rom).hist(bins=BINS)", 
      "xlabel('log(agree/disagree)')", 
      "title('Reactions of Romney Supporters')", 
      "", 
      "subplot(223)", 
      "#log10(t2.a_to_d_dems).hist()", 
      "log10(t2.a_to_d_oba).hist(bins=BINS)", 
      "xlabel('log(agree/disagree)')", 
      "title('Reactions of Obama Supporters (All Turns)')", 
      "", 
      "subplot(224)", 
      "#log10(t2.a_to_d_reps).hist()", 
      "log10(t2.a_to_d_rom).hist(bins=BINS)", 
      "xlabel('log(agree/disagree)')", 
      "title('Reactions of Romney Supporters (All Turns)')", 
      "", 
      "show()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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DvGMtKR1Ai2jtI1oeQLxMouWxlMGB1Lhx49C0aVMEBwfLr+Xk5CAmJgb+/v4Y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\nAElFTkSuQmCC\n"
      }
     ], 
     "prompt_number": 80
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "figsize(5,5)", 
      "", 
      "BINS=20", 
      "", 
      "#log10(t.a_to_d_dems).hist()", 
      "log10(t.a_to_d_oba).hist(bins=BINS)", 
      "xlabel('log(agree/disagree)')", 
      "title('Reactions of Obama Supporters')", 
      "", 
      "", 
      "show()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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FCg0NleeMiYmh06dP0+3bt8loNFJWVhZduXKFTCYTTZ48mYiIZs2a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\nY0wX/h+n9ZlsqSNBLAAAAABJRU5ErkJggg==\n"
      }
     ], 
     "prompt_number": 84
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "figsize(5,5)", 
      "", 
      "BINS=20", 
      "", 
      "#log10(t.a_to_d_reps).hist(bins=30)", 
      "log10(t.a_to_d_rom).hist(bins=BINS)", 
      "xlabel('log(agree/disagree)')", 
      "title('Reactions of Romney Supporters')", 
      "", 
      "", 
      "show()"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "display_data", 
       "png": 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      }
     ], 
     "prompt_number": 85
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [], 
     "language": "python", 
     "outputs": []
    }
   ]
  }
 ]
}