OkNLP-paper.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OkNLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from utils.categorize_demographics import *\n",
    "from utils.clean_up import clean_up\n",
    "from utils.distinctive_tokens import log_odds_ratio\n",
    "from utils.lexical_features import *\n",
    "from utils.nonnegative_matrix_factorization import nmf_labels, nmf_inspect\n",
    "from utils.plotting import lollipop_paper\n",
    "from utils.splits import *\n",
    "from utils.text_representation import feature_vectors\n",
    "\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/profiles.20120630.csv')\n",
    "\n",
    "essay_list = ['essay0', 'essay4']\n",
    "df_0, df_4 = clean_up(df, essay_list)\n",
    "\n",
    "df_0 = recategorize(df_0)\n",
    "df_4 = recategorize(df_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lexical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the universal tagset of Petrov, Das, & McDonald ([link](https://github.com/slavpetrov/universal-pos-tags)).\n",
    "\n",
    "```\n",
    "ADJ - adjectives\n",
    "ADP - adpositions (prepositions and postpositions)\n",
    "ADV - adverbs\n",
    "CONJ - conjunctions\n",
    "DET - determiners\n",
    "NOUN - nouns (common and proper)\n",
    "NUM - cardinal numbers\n",
    "PART - particles or other function words\n",
    "PRON - pronouns\n",
    "PUNCT - punctuation\n",
    "VERB - verbs (all tenses and modes)\n",
    "X - other: foreign words, typos, abbreviations\n",
    "```\n",
    "\n",
    "spaCy-specific information: https://spacy.io/docs#token-postags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pos = pos_df(df_0.essay0)\n",
    "pos_norm = pos_normalize(pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Essay Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Levels (in order): ['F' 'M']\n",
      "\n",
      "n_tokens\n",
      "[means] 'a': 149.58946578 'b': 138.72696526\n",
      "p-values:\n",
      "  [permutation]: 0.0\n",
      "  [classical]:   0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pos['n_tokens'] = pos.sum(axis=1)\n",
    "pos_by_split(df_0, pos, 'sex', ['n_tokens'], print_levels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Profanity and Slang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0600098191019\n",
      "profanity\n",
      "[means] 'a': 0.05815862 'b': 0.06125739\n",
      "p-values:\n",
      "  [permutation]: 0.1439\n",
      "  [classical]:   0.1390202\n",
      "\n"
     ]
    }
   ],
   "source": [
    "profane = load_words('data/profane.txt')\n",
    "profanity = pd.DataFrame(contains(profane, df_0.essay0),\n",
    "                         columns=['profanity'])\n",
    "print(profanity.profanity.sum() / profanity.shape[0])\n",
    "pos_by_split(df_0, profanity, 'sex', ['profanity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.559084557574\n",
      "slang\n",
      "[means] 'a': 0.5411097 'b': 0.57119828\n",
      "p-values:\n",
      "  [permutation]: 0.0\n",
      "  [classical]:   0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "slang = load_words('data/slang.txt')\n",
    "slang = pd.DataFrame(contains(slang, df_0.essay0),\n",
    "                     columns=['slang'])\n",
    "print(slang.slang.sum() / slang.shape[0])\n",
    "pos_by_split(df_0, slang, 'sex', ['slang'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjectives, Nouns, and Verbs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADJ\n",
      "[means] 'a': 0.10614445 'b': 0.10162557\n",
      "p-values:\n",
      "  [permutation]: 0.0\n",
      "  [classical]:   0.0\n",
      "\n",
      "NOUN\n",
      "[means] 'a': 0.18651103 'b': 0.18868403\n",
      "p-values:\n",
      "  [permutation]: 0.0\n",
      "  [classical]:   3.163e-05\n",
      "\n",
      "VERB\n",
      "[means] 'a': 0.18277245 'b': 0.18272425\n",
      "p-values:\n",
      "  [permutation]: 0.9122\n",
      "  [classical]:   0.90974631\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pos_by_split(df_0, pos_norm, 'sex', ['ADJ', 'NOUN', 'VERB'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = subset_df(df_0, 'sex', ['F'])\n",
    "m = subset_df(df_0, 'sex', ['M'])\n",
    "\n",
    "tagged_f = tag_corpus(f.essay0)\n",
    "tagged_m = tag_corpus(m.essay0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adjectives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my | happy | independent | favorite | sweet | silly | important | passionate | warm | amazing | beautiful | adventurous | creative | loyal | social\n",
      "\n",
      "that | nice | more | few | other | most | its | cool | interesting | your | easy | good | which | much | last\n"
     ]
    }
   ],
   "source": [
    "top_terms(tagged_f, tagged_m, 'ADJ', diff_prop, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "independent | sweet | my | sassy | silly | happy | warm | favorite | girly | fabulous\n",
      "\n",
      "nice | cool | its | that | few | interesting | martial | most | masculine | more\n"
     ]
    }
   ],
   "source": [
    "top_terms(tagged_f, tagged_m, 'ADJ', log_odds_ratio, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nouns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "girl | family | who | yoga | men | gal | heels | love | dancing | friends\n",
      "\n",
      "guy | computer | engineer | guitar | sports | software | women | video | technology | geek\n"
     ]
    }
   ],
   "source": [
    "top_terms(tagged_f, tagged_m, 'NOUN', log_odds_ratio, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verbs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "love | am | laugh | laughing | dancing | adore | loving | dance | appreciate | being\n",
      "\n",
      "m | was | play | playing | laid | 'll | working | hit | moved | been\n"
     ]
    }
   ],
   "source": [
    "top_terms(tagged_f, tagged_m, 'VERB', log_odds_ratio, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semantic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specs = {'stop_words' : 'english', 'ngram_range' : (1, 3), 'min_df' : 0.005}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Essay 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Text Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts, tfidf, vocab = feature_vectors(df_0.essay0, specs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2058"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n",
      "Group 0:\n",
      "people | new people | new | meet | meet new | meet new people | meeting | meeting new | meeting new people | looking meet | looking meet new | love meeting | love meeting new | new friends | meet people | like meet | people 'm | love meet | friends | enjoy meeting | interesting | 'm looking meet | city | interesting people | meeting people | interested | want meet | interested meeting | 'm new | people love | experiences | just moved | online | dating | site | new experiences | people like | cool | outgoing | hang | new places | hoping | excited | make new | recently | make new friends | people n't | moved | people make | :)\n",
      "\n",
      "Group 1:\n",
      "san | francisco | san francisco | moved san | moved san francisco | city | living san | living san francisco | living | moved | just moved san | live san | live | diego | native | san diego | lived san | grew | lived san francisco | lived | originally | years | recently | year | raised san | home | york | currently | new york | born raised san | recently moved | new | los | university | offer | just moved | exploring | angeles | los angeles | working | chicago | santa | love living | hi | graduated | love city | explore | state | college | work\n",
      "\n",
      "Group 2:\n",
      "love | travel | love travel | outdoors | love laugh | love outdoors | love love | cook | laugh | dance | love cook | especially | love going | life love | love life | food | city | love good | people love | :) | love dance | world | love people | animals | favorite | nature | things love | dogs | sports | beach | great | love music | really love | love traveling | love sports | love meet | love food | heart | explore | big | 'd love | person love | love animals | friends love | love family | absolutely | love job | love city | adventure | hike\n",
      "\n",
      "Group 3:\n",
      "know | just | want | ask | want know | message | just ask | really | know just | :) | 'll | say | 'm just | n't know | nt | wanna | profile | just want | talk | write | getting | questions | u | send | getting know | know 'm | just moved | know want | feel | thing | lol | feel free | trying | need | like know | tell | meet | send message | really want | come | maybe | hey | n't want | guy | hit | shy | site | free | right | let know\n",
      "\n",
      "Group 4:\n",
      "'m | pretty | 'm pretty | 'm really | 'm looking | really | guy | 'm just | say 'm | 'll | think 'm | 'm good | 'm trying | nerd | life 'm | 'm working | bit | 'm bit | 'm going | laid | hi 'm | shy | sure | time 'm | 'm big | big | people 'm | girl | things 'm | 'm interested | 'm happy | fan | 'm kind | usually | 'd | 'm passionate | 'm currently | 'm usually | know 'm | 'm sure | friends 'm | geek | 'm originally | 'm new | 'm doing | 'm open | 'm laid | 'm little | sarcastic | right 'm\n",
      "\n",
      "Group 5:\n",
      "new | things | new things | trying | trying new | try | trying new things | try new | try new things | places | learning | new places | learning new | learning new things | exploring | learn | things like | restaurants | explore | exploring new | learn new | new restaurants | things love | things 'm | new york | york | love trying | doing | different | like try | things life | adventures | traveling | travel | 'm trying | explore new | experience | trying new restaurants | experiences | new experiences | looking new | love learning | finding | discovering | adventure | city | foods | things enjoy | willing | finding new\n",
      "\n",
      "Group 6:\n",
      "looking | 'm looking | guy | relationship | looking meet | meet | share | woman | nice | just looking | man | partner | guy looking | girl | looking new | single | forward | looking forward | friend | just | term | friends | long term | date | hang | new friends | looking fun | looking friends | special | guys | right | dating | long | wants | looking share | great | 'm looking meet | looking meet new | looking partner | sex | maybe | interests | friendship | smart | term relationship | long term relationship | women | nice guy | crime | chemistry\n",
      "\n",
      "Group 7:\n",
      "going | easy | easy going | 'm easy | 'm easy going | guy | going guy | easy going guy | pretty easy | pretty easy going | pretty | laid | love going | 'm going | person | like going | earth | 'm pretty | simple | enjoy going | friendly | drama | likes | going movies | movies | funny | try | concerts | loves | sports | 's going | hanging | gym | girl | playing | shows | staying | nice | beach | home | just | hang | 'm laid | :) | kind | enjoys | chill | positive | honest | relaxed\n",
      "\n",
      "Group 8:\n",
      "m | nt | s | guy | u | ve | lol | chill | nice | old | pretty | alot | laid | kinda | n | really | say | hit | girl | years old | hi | wanna | hey | single | lets | :) | haha | type | shy | real | person | outgoing | good time | cool | hang | big | ca | 2 | school | just | good | sports | got | party | currently | cause | message | tattoos | student | smoke\n",
      "\n",
      "Group 9:\n",
      "like | 'd like | 'd | things | things like | really like | n't like | feel like | really | stuff | like think | feel | like people | think | people | lot | like going | watch | read | people like | games | like meet | movies | play | like travel | cook | eat | like things | like 'm | just like | like fun | like good | dance | like laugh | drink | like read | stay | like play | talk | bike | nice | life like | little | like cook | hang | time like | doing | sports | going | like stay\n",
      "\n",
      "Group 10:\n",
      "moved | sf | years | ago | coast | east | years ago | school | east coast | city | moved sf | just moved | grew | year | college | went | california | originally | recently | just | west | recently moved | living | west coast | nyc | job | months | grad | year ago | moved san | la | moved bay | grad school | spent | moved san francisco | went school | boston | months ago | 'm originally | york | new york | 2 | midwest | lived | came | couple | graduated | south | went college | transplant\n",
      "\n",
      "Group 11:\n",
      "n't | ca | ca n't | does | does n't | really | wo | wo n't | n't like | n't know | did | think | n't really | mean | 're | did n't | probably | say | seriously | n't think | mind | people | way | 'll | need | things | n't want | care | right | profile | n't mind | tell | believe | people n't | n't mean | long | thing | hate | actually | n't care | wait | does n't mean | n't seriously | try | n't believe | use | feel | read | stand | day\n",
      "\n",
      "Group 12:\n",
      "time | spend | spend time | good time | free | lot | free time | spending | spending time | lot time | good | spend lot | time friends | spend lot time | time time | time 'm | having | working | time like | time love | spent | school | having good | having good time | busy | spare | student | work time | spare time | doing | long time | long | spending time friends | time family | work | job | day | time enjoy | currently | great time | right | days | enjoy spending | enjoy spending time | spent time | thinking | taking | enjoying | quite | possible\n",
      "\n",
      "Group 13:\n",
      "good | humor | sense | sense humor | good sense | good sense humor | great | good food | food | great sense | great sense humor | good time | conversation | good conversation | sarcastic | 'm good | love good | dry | enjoy good | good company | company | good friends | good people | appreciate | listener | wine | good listener | like good | intelligent | times | guy | having good | pretty good | really good | man | bad | smart | honest | wit | witty | earth | having | friend | good friend | loyal | heart | important | nice | kind | life good\n",
      "\n",
      "Group 14:\n",
      "fun | loving | fun loving | 'm fun | having fun | having | outgoing | guy | girl | adventurous | like fun | looking fun | loves | caring | smart | likes | honest | fun 'm | love fun | spontaneous | fun love | earth | funny | loyal | intelligent | fun like | :) | energetic | lot fun | 's fun | witty | passionate | kind | hang | laid | sweet | ambitious | enjoys | friendly | adventure | creative | woman | compassionate | happy | athletic | romantic | chill | young | nice | active\n",
      "\n",
      "Group 15:\n",
      "'s | let | let 's | 're | think | 'll | way | self | right | 's just | thing | say | little | best | 'd | profile | think 's | great | day | summary | life 's | maybe | ok | better | long | friend | 's good | people 's | probably | 's 'm | said | favorite | man | self summary | yes | 's pretty | really | 's really | start | write | oh | just | tell | awesome | bit | know 's | makes | place | true | 's hard\n",
      "\n",
      "Group 16:\n",
      "bay | area | bay area | moved bay | moved bay area | native | area native | bay area native | grew | living | living bay | grew bay | grew bay area | 'm bay | lived | 'm bay area | east bay | area 'm | lived bay | lived bay area | raised bay area | raised bay | college | east | originally | exploring | recently | years | new | offer | explore | moved | home | born raised bay | transplant | place | live | berkeley | oakland | working | places | year | recently moved | enjoying | currently | moving | boston | work | la | went\n",
      "\n",
      "Group 17:\n",
      "work | hard | play | work hard | working | hard play | work hard play | play hard | hard play hard | hard working | progress | work progress | harder | job | try | week | live | love work | company | school | balance | day | work time | currently | industry | busy | weekends | 's hard | work 'm | gym | tech | lot | outside | 'm working | active | career | days | business | social | eat | sleep | travel | guitar | startup | doing | run | professional | healthy | ambitious | stay\n",
      "\n",
      "Group 18:\n",
      "born | raised | born raised | raised san | born raised san | raised bay | born raised bay | california | raised bay area | college | school | sf | went | years | family | lived | berkeley | oakland | currently | girl | southern | parents | graduated | went college | bay | northern | spent | came | south | went school | chinese | hawaii | la | sports | cal | old | northern california | american | giants | san | southern california | texas | studied | state | high school | educated | san diego | diego | uc | age\n",
      "\n",
      "Group 19:\n",
      "open | minded | open minded | 'm open | creative | honest | relationship | adventurous | curious | passionate | intelligent | mind | kind | heart | strong | interested | independent | woman | nature | compassionate | loyal | self | playful | open mind | free | spontaneous | funny | caring | long | confident | earth | romantic | relationships | man | spirit | book | warm | spiritual | open new | smart | lover | seeking | sensual | generous | thoughtful | experiences | non | loving | affectionate | friendly\n",
      "\n",
      "Group 20:\n",
      "enjoy | friends | family | hiking | watching | friends family | outdoors | traveling | hanging | cooking | great | family friends | active | wine | movies | sports | reading | playing | activities | spending | home | going | running | camping | hanging friends | biking | beach | spending time | really enjoy | close | movie | city | dinner | exploring | dancing | important | outdoor | travel | enjoy going | enjoy life | skiing | staying | restaurants | watching movies | eating | taking | trips | swimming | enjoy outdoors | snowboarding\n",
      "\n",
      "Group 21:\n",
      "music | art | live | movies | live music | play | food | playing | games | love music | dancing | listening | books | guitar | shows | reading | listen | rock | video | creative | photography | dance | film | cooking | video games | design | watching | making | writing | nature | listening music | old | arts | science | artist | lover | favorite | coffee | sports | jazz | read | passion | interests | watch | beer | museums | history | kinds | nerd | hop\n",
      "\n",
      "Group 22:\n",
      "life | live | world | share | living | live life | believe | things life | want | fullest | day | man | best | happy | simple | enjoy life | important | experiences | partner | passionate | love life | enjoying | life fullest | experience | short | life 'm | life 's | woman | passion | adventure | great | positive | way | heart | life love | feel | moment | living life | life short | beautiful | special | family | things | life like | enjoying life | nature | healthy | better | journey | share life\n",
      "\n",
      "Group 23:\n",
      "person | people | make | laugh | think | funny | kind | pretty | say | try | things | happy | lot | make laugh | people laugh | believe | honest | smile | best | way | make people | really | little | better | tend | love laugh | make people laugh | making | friends | shy | positive | world | self | talk | bit | makes | important | smart | loyal | feel | consider | creative | care | usually | type | sarcastic | times | caring | interesting | thing\n",
      "\n",
      "Group 24:\n",
      "'ve | lived | years | 've lived | got | 've got | world | lot | traveled | year | bit | spent | past | 'd | places | travel | home | countries | different | worked | 5 | far | told | 've told | europe | 10 | 've traveled | seen | long | learned | 've spent | quite | years 've | old | couple | place | recently | 2 | 3 | feel | california | little | country | 4 | come | 6 | times | 'll | met | way\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "K = 25\n",
    "nmf_inspect(tfidf, vocab, k_vals=[K], n_words=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = ['meet & greet', 'the city', 'enthusiastic', 'straight talk', 'about me', 'novelty',\n",
    "          'seeking', 'carefree', 'casual', 'enjoy', 'transplant', 'nots', 'moments',\n",
    "          'personality', 'amusing', 'review', 'region', 'career-focused', 'locals',\n",
    "          'unconstrained', 'active', 'creative', 'carpe diem', 'cheerful', 'jet setter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_0['group'] = nmf_labels(tfidf, k=K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ol/SVpAQze0qtd7dnOec2xCovAAAA0JGYF2wzmy5psqSjO5hyraRTJT0o6QpJu0u6RNzR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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cbb1aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demog = 'sex'\n",
    "subset = subset_df(df_0, demog, ['F', 'M'])\n",
    "grouped = group_pct(subset, demog)\n",
    "lollipop_paper(grouped, demog, topic_labels=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Essay 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Text Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts, tfidf, vocab = feature_vectors(df_4.essay4, specs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2898"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n",
      "Group 0:\n",
      "like | music like | movies like | really | really like | stuff | food like | things | like music | books like | like movies | n't like | like food | movies | lot | stuff like | like lot | like read | kind | comedies | like good | music | good | shows like | things like | like books | think | like reading | make | different | like watch | especially | old | like kinds | feel | like eat | like types | just | people | bands | pretty | feel like | funny | probably | like cook | eat | ones | nt | lots | metal\n",
      "\n",
      "Group 1:\n",
      "men | mad | mad men | arrested | development | arrested development | bad | breaking | breaking bad | rock | 30 | 30 rock | tv | parks | sunny | wire | dexter | shows | office | curb | sunny philadelphia | 's sunny | philadelphia | rec | parks rec | community | curb enthusiasm | enthusiasm | recreation | parks recreation | 's sunny philadelphia | daily | seinfeld | tv shows | archer | shows arrested | weeds | shows arrested development | colbert | louie | lost | classic | sopranos | classic rock | walking | walking dead | blood | report | friday night | night lights\n",
      "\n",
      "Group 2:\n",
      "love | food love | love food | love music | music love | love movies | love love | movies love | cook | love good | eat | food | love cook | love read | love eat | good | books love | live | movies | especially | just | love reading | music | really love | love sushi | dance | love books | going | love going | great | love types | horror | actually | food love food | love actually | try | love watching | cooking | comedies | just love | love live | love kinds | romantic | pray | wine | make | old | life | eat pray | pray love\n",
      "\n",
      "Group 3:\n",
      "favorite | favorite food | favorite movies | favorite books | favorite music | favorite movie | favorite book | favorite shows | favorite tv | time favorite | time | favorite foods | music favorite | include | favorite tv shows | favorite band | foods | food favorite | movies favorite | favorite authors | band | probably | books favorite | authors | author | favorite things | tv shows | favorite bands | movies | say | bands | shows | artists | genre | listen | hard | far | moment | tv | current | really | r&b | recent | pick | genres | hands | artist | series | color | second\n",
      "\n",
      "Group 4:\n",
      "sci | fi | sci fi | action | comedy | horror | fantasy | movies | drama | comedies | fi fantasy | sci fi fantasy | romantic | rock | action movies | films | classic | adventure | thrillers | metal | flicks | romance | romantic comedies | mystery | horror movies | novels | stuff | series | anime | suspense | chick | good | classic rock | dramas | fan | punk | genres | books | chick flicks | documentaries | pop | foreign | historical | occasional | history | classics | comic | movies like | movie | movies comedies\n",
      "\n",
      "Group 5:\n",
      "hop | hip | hip hop | rock | r&b | jazz | reggae | rap | pop | country | music hip | music hip hop | classic | old | hip hop r&b | hop r&b | soul | alternative | classic rock | music | old school | school | classical | indie | metal | funk | listen | blues | house | punk | electronic | oldies | dance | dubstep | techno | trance | folk | 90 | 90 's | latin | indie rock | salsa | comedy | alternative rock | electro | electronica | 80 | 90s | 80 's | action\n",
      "\n",
      "Group 6:\n",
      "'m | fan | 've | reading | pretty | 'll | food 'm | right | 'm big | really | 'm reading | big | currently | 'm pretty | music 'm | just | open | huge | good | big fan | 'm really | picky | listening | right 'm | going | 'm currently | 'm open | time | books 'm | 'm huge | movies 'm | stuff | probably | 'm big fan | comes | 'm picky | sucker | 'd | try | 'm sucker | say | eat | sure | lately | favorites | 'm fan | currently reading | bit | usually | think\n",
      "\n",
      "Group 7:\n",
      "bang | big bang | bang theory | theory | big bang theory | big | mother | met | met mother | shows | fan | big fan | tv | big lebowski | lebowski | friends | tv shows | house | half | office | kiss | 'm big | community | criminal minds | walking dead | minds | walking | criminal | ncis | new girl | bones | scrubs | men | dead | 500 days summer | 500 days | days summer | big fish | psych | dexter | 500 | inception | man | seinfeld | try | knight | csi | sherlock | movies big | burn notice\n",
      "\n",
      "Group 8:\n",
      "italian | thai | mexican | food | indian | chinese | japanese | sushi | french | vietnamese | mexican food | korean | thai indian | food italian | mexican italian | food thai | food mexican | thai food | italian mexican | indian thai | italian food | food sushi | american | food indian | indian food | ethiopian | mexican thai | mediterranean | thai italian | greek | bbq | thai mexican | indian mexican | asian | sushi thai | pizza | italian thai | thai chinese | chinese food | eastern | thai japanese | foods | middle eastern | thai vietnamese | seafood | chinese japanese | indian italian | jazz | sushi mexican | middle\n",
      "\n",
      "Group 9:\n",
      "n't | ca | ca n't | watch | really | tv | n't watch | watch tv | know | n't really | does | n't like | does n't | think | eat | n't watch tv | just | n't know | did | say | n't read | wo | wo n't | n't think | want | n't eat | did n't | movies n't | people | make | food n't | wait | 'll | time | shows n't | n't tv | favorites | tv n't | listen | really watch | 're | care | music n't | just n't | probably | thing | netflix | 'd | anymore | seen\n",
      "\n",
      "Group 10:\n",
      "harry | potter | harry potter | series | potter series | harry potter series | games | hunger games | hunger | books harry | books harry potter | girl | books | twilight | dragon | girl dragon | tattoo | dragon tattoo | girl dragon tattoo | glee | met | pride | prejudice | pride prejudice | harry met | sally | met sally | harry met sally | disney | vampire | lady | kill | trilogy | girls | gaga | kill mockingbird | mockingbird | lady gaga | love actually | princess | movies | friends | dark | lotr | grey | pop | anatomy | met mother | mother | adele\n",
      "\n",
      "Group 11:\n",
      "books | movies | food | music | shows | music food | books movies | good | movies music | movies shows | country | yes | shows music | dance | action | lots | kind | horror | favorite books | food love | comedy | house | pop | classical | rap | tv | ones | pizza | good food | spicy | type | taste | healthy | funny | asian | art | mood | music country | food food | food good | :) | food yes | action movies | movies good | horror movies | country food | jazz | live | eclectic | alchemist\n",
      "\n",
      "Group 12:\n",
      "sunshine | mind | eternal | eternal sunshine | spotless | spotless mind | sunshine spotless | sunshine spotless mind | eternal sunshine spotless | miss sunshine | little miss sunshine | little miss | amelie | miss | little | garden state | state | garden | lost | life | beautiful | translation | lost translation | beauty | darko | donnie | donnie darko | 500 | days summer | summer | 500 days | 500 days summer | radiohead | american beauty | royal | beautiful mind | memento | tenenbaums | royal tenenbaums | requiem | american | requiem dream | dream | movies | days | sleep | city | adaptation | sky | things\n",
      "\n",
      "Group 13:\n",
      "'s | 80 | 80 's | let | good | let 's | 90 | 90 's | life | 've | just | 70 | world | 70 's | 's good | time | man | guide | great | know | 60 | day | think | ender | 's just | 's game | ender 's | 's guide | 60 's | 's music | ender 's game | stuff | story | night | people | hard | food 's | got | 're | hitchhiker | hitchhiker 's | grey | yes | little | oh | pan | hitchhiker 's guide | way | galaxy | king\n",
      "\n",
      "Group 14:\n",
      "read | lot | read lot | 've | time | watch | listen | recently | books | read books | just | n't read | books read | 've read | lately | reading | love read | like read | recently read | eat | time read | usually | watch lot | nt | loved | favorites | book read | just read | tend | used | work | enjoyed | listen lot | watched | liked | stuff | times | really | best | read book | school | listening | fun | 'll | finished | magazines | books 've | pretty | lot movies | movies\n",
      "\n",
      "Group 15:\n",
      "list | just | 'll | long | ask | way | goes | list goes | things | try | favorites | far | know | time | talk | say | 'll try | tell | short | think | 'll just | conversation | later | come | forever | good | right | oh | person | let | category | meet | 're | start | seriously | probably | 'll eat | food 'll | taste | make | want | question | eclectic | :) | just finished | tastes | feel | going | 'd | finished\n",
      "\n",
      "Group 16:\n",
      "david | black | john | tom | radiohead | bob | life | yeah | brothers | man | beatles | keys | james | dylan | black keys | bowie | dead | young | la | band | smith | new | white | music | bob dylan | michael | boys | johnny | david bowie | arcade | cash | neil | old | vonnegut | waits | stones | little | sedaris | bon | tom waits | paul | cat | velvet | ray | death | davis | pink | iver | bon iver | johnny cash\n",
      "\n",
      "Group 17:\n",
      "star | lord | wars | rings | lord rings | star wars | trek | star trek | trilogy | series | matrix | princess | rings trilogy | lord rings trilogy | dark | bride | indiana | indiana jones | firefly | princess bride | movies | original | jones | park | battlestar | galactica | 2 | dead | battlestar galactica | knight | avatar | doctor | man | dark knight | galaxy | lotr | v | guide | lost | batman | 1 | books | world | war | 3 | scott | x | guide galaxy | monty | python\n",
      "\n",
      "Group 18:\n",
      "family | modern | modern family | guy | family guy | shows | tv | office | park | south | south park | mother | met mother | met | tv shows | girl | glee | simpsons | dad | house | american dad | law | law order | dexter | american | new girl | 30 | parks | friends | order | daily | watch | 30 rock | anatomy | colbert | hangover | grey | report | true | channel | true blood | colbert report | grey 's | entourage | 's anatomy | housewives | community | grey 's anatomy | blood | dragon\n",
      "\n",
      "Group 19:\n",
      "club | fight | fight club | shawshank | redemption | shawshank redemption | pulp fiction | pulp | fear | red | hot | loathing | fear loathing | peppers | red hot | vegas | chili | las | las vegas | chili peppers | american | hot chili | red hot chili | hot chili peppers | loathing las | fear loathing las | loathing las vegas | movies | dead | rye | catcher | catcher rye | park | lebowski | big lebowski | old | kill | big | suspects | usual | day | usual suspects | dark | movies shawshank | movies shawshank redemption | knight | american beauty | office | 2 | hangover\n",
      "\n",
      "Group 20:\n",
      "kinds | kinds music | love kinds | kinds food | like kinds | music | kinds movies | music kinds | food | listen | different | country | foods | comedy | music food | music country | especially | action | rap | mood | lots | movies | music especially | watch | music love | open | food especially | metal | depends | music listen | long | depends mood | heavy metal | drama | movies like | rock | nt | love | comedies | heavy | kind | picky | try | usually | music really | reader | music like | pop | country music | biographies\n",
      "\n",
      "Group 21:\n",
      "book | movie | favorite book | favorite movie | food | music | good | fav | book read | reading | great | best | read book | just | saw | finished | called | currently | m | right | time | buff | life | day | pick | seen | pizza | loved | reader | currently reading | just finished | sushi | comic | story | type | week | current | recent | watched | person | kind | :) | series | novel | night | alchemist | huge | food sushi | nt | better\n",
      "\n",
      "Group 22:\n",
      "enjoy | new | types | trying | reading | trying new | things | foods | types music | good | films | try | music | live | really enjoy | new things | different | restaurants | really | cooking | jazz | types food | favorites | enjoy reading | especially | try new | classical | watching | foreign | rock | going | variety | world | open | love trying | love trying new | live music | great | new restaurants | enjoy good | trying new things | time | listening | currently | love types | music enjoy | movies enjoy | new foods | include | wide\n",
      "\n",
      "Group 23:\n",
      "game | thrones | game thrones | 's game | ender | ender 's | ender 's game | walking | walking dead | blood | dead | games | true blood | series | true | currently | hunger | hunger games | dexter | song ice | ice | shows | song | boardwalk | currently reading | empire | boardwalk empire | breaking | breaking bad | tv | firefly | reading | wire | books | bad | battlestar | hbo | archer | galactica | battlestar galactica | fringe | community | sherlock | californication | dune | lost | shameless | girl | sons | new\n",
      "\n",
      "Group 24:\n",
      "fiction | non | non fiction | science | science fiction | fiction books | pulp fiction | pulp | read non | read non fiction | historical | non fiction books | history | historical fiction | films | books | documentaries | biographies | classical | jazz | foreign | read fiction | tend | reading | novels | prefer | especially | fantasy | philosophy | rock | usually | world | indie | folk | good | lots | stuff | foreign films | political | politics | classic | generally | independent | art | blues | live | psychology | electronic | tv | occasional\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "K = 25\n",
    "nmf_inspect(tfidf, vocab, k_vals=[K], n_words=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = ['like', 'TV-hits', 'enthusiastic', 'favorite-0', 'genres-movies', 'genres-music',\n",
    "          'misc-0', 'TV-comedies-0', 'genres-food', 'nots', 'teen', 'everything',\n",
    "          'movies-drama-0', 'time periods', 'avid', 'miscellaneous', 'music-rock',\n",
    "          'movies-sci-fi', 'TV-comedies-1', 'movies-drama-1', 'kinds', 'favorite-1',\n",
    "          'novelty', 'TV-drama', 'genres-books']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_4['group'] = nmf_labels(tfidf, k=K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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AHxGwAQAAAB8RsAEAAAAfEbABAAAAHxGwAQAAAB8RsAEAAAAfEbABAAAAH4UM2GaWaGZX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\np0+fZo8VSY899pgvfVqr6dO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yhRdeqAEDBuj444/Xn/70J23evNm3MfLy8jz169u3b7PG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\nSdJBYtRAsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e57d8e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demog = 'sex'\n",
    "subset = subset_df(df_4, demog, ['M', 'F'])\n",
    "grouped = group_pct(subset, demog)\n",
    "lollipop_paper(grouped, demog, topic_labels=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Superordinate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mask = df_4['group'].isin([10, 12, 17, 19])\n",
    "movies = counts[np.array(mask), :]\n",
    "movies = counts_by_class(movies, df_4[mask], 'sex',\n",
    "                         one_vs_one=True, vals=['M', 'F'])\n",
    "log_odds = log_odds_ratio(movies, vocab, use_variance=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "star | star wars | wars | matrix | fight | park | fight club | man | dark knight | knight | pulp | godfather | trek | star trek | rings\n",
      "\n",
      "love | girl | harry | potter | pride | prejudice | harry potter | pride prejudice | bones | girls | anatomy | jane | hunger games | grey 's | 's anatomy\n"
     ]
    }
   ],
   "source": [
    "print_terms(log_odds, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7BftPwA9aOXYZ8Mds40iSJEnlrb0nOV4E/DMiXgJ+B8wANgK+CvQHdi1sPEmS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\n/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19bec2cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demog = 'drugs'\n",
    "subset = subset_df(df_4, demog, ['yes', 'no', 'unknown'])\n",
    "grouped = group_pct(subset, demog)\n",
    "lollipop_paper(grouped, demog, colors=['Black', 'LightGray', 'Red'], topic_labels=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def drug_labels(df):\n",
    "    labels = []\n",
    "    for i in df.index:\n",
    "        if df.drugs[i] in ('no', 'unknown'):\n",
    "            labels.append(0)\n",
    "        elif df.drugs[i] == 'yes':\n",
    "            labels.append(1)\n",
    "    return labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Essay 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# numerical drug usage labels\n",
    "df_4['labels'] = drug_labels(df_4)\n",
    "\n",
    "# split on drug usage status\n",
    "drugs_yes = subset_df(df_4, 'drugs', ['yes'])\n",
    "drugs_no = subset_df(df_4, 'drugs', ['no'])\n",
    "drugs_unknown = subset_df(df_4, 'drugs', ['unknown'])\n",
    "\n",
    "# balanced sample of known drug users\n",
    "drugs_known_sample = pd.concat([drugs_no.sample(drugs_yes.shape[0],\n",
    "                                                random_state=42),\n",
    "                                drugs_yes],\n",
    "                               ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_4 = {'vocabulary' : vocab}\n",
    "_, known, _ = feature_vectors(drugs_known_sample.essay4, pred_4)\n",
    "_, unknown, _ = feature_vectors(drugs_unknown.essay4, pred_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "known_train, known_test, y_train, y_test = train_test_split(known,\n",
    "                                                            drugs_known_sample['labels'],\n",
    "                                                            test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(known_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.680029154519\n"
     ]
    }
   ],
   "source": [
    "yhat = model.predict(known_test)\n",
    "print(accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.552282893071\n"
     ]
    }
   ],
   "source": [
    "drugs_unknown['yhat'] = model.predict(unknown)\n",
    "print(drugs_unknown.yhat.sum() / drugs_unknown.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group\n",
       "20    0.243902\n",
       "7     0.245370\n",
       "18    0.295720\n",
       "8     0.312500\n",
       "10    0.313008\n",
       "22    0.352159\n",
       "2     0.388422\n",
       "3     0.436252\n",
       "11    0.461153\n",
       "17    0.482759\n",
       "21    0.500000\n",
       "24    0.501618\n",
       "14    0.506849\n",
       "4     0.517361\n",
       "13    0.577193\n",
       "23    0.585821\n",
       "0     0.593537\n",
       "6     0.598165\n",
       "5     0.621931\n",
       "9     0.626126\n",
       "1     0.631418\n",
       "15    0.649616\n",
       "12    0.660633\n",
       "19    0.661376\n",
       "16    0.840190\n",
       "Name: yhat, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugs_unknown.groupby('group')['yhat'].mean().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All Essays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Tell me something I don't know. Can I predict drug usage status based on text alone? How well? Then, find the terms that are most indicative of drug usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "essay_list = ['essay' + str(i) for i in range(10)]\n",
    "df_0, df_1, df_2, df_3, df_4, df_5, df_6, df_7, df_8, df_9 = clean_up(df,\n",
    "                                                                      essay_list,\n",
    "                                                                      min_words=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# combine all essays\n",
    "essays = df_0.essay0.str.cat([df_1.essay1, df_2.essay2, df_3.essay3,\n",
    "                              df_4.essay4, df_5.essay5, df_6.essay6,\n",
    "                              df_7.essay7, df_8.essay8, df_9.essay9], sep=' ')\n",
    "df_0['essays'] = essays\n",
    "\n",
    "df_0 = recategorize(df_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((57490, 34), (59946, 32))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only keep observations with more than five tokens\n",
    "all_essays = df_0[df_0.essays.str.split().str.len() > 5]\n",
    "all_essays.shape, df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# numerical drug usage labels\n",
    "all_essays['labels'] = drug_labels(all_essays)\n",
    "\n",
    "# split on drug usage status\n",
    "drugs_yes = subset_df(all_essays, 'drugs', ['yes'])\n",
    "drugs_no = subset_df(all_essays, 'drugs', ['no'])\n",
    "drugs_unknown = subset_df(all_essays, 'drugs', ['unknown'])\n",
    "\n",
    "# balanced sample of known drug users\n",
    "drugs_known_sample = pd.concat([drugs_no.sample(drugs_yes.shape[0],\n",
    "                                                random_state=42),\n",
    "                                drugs_yes],\n",
    "                               ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6835\n"
     ]
    }
   ],
   "source": [
    "# vocabulary for all essays\n",
    "_, _, all_vocab = feature_vectors(all_essays.essays, specs)\n",
    "print(len(all_vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_all = {'vocabulary' : all_vocab}\n",
    "_, known, _ = feature_vectors(drugs_known_sample.essays, pred_all)\n",
    "_, unknown, _ = feature_vectors(drugs_unknown.essays, pred_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "known_train, known_test, y_train, y_test = train_test_split(known,\n",
    "                                                            drugs_known_sample['labels'],\n",
    "                                                            test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(known_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.727417302799\n"
     ]
    }
   ],
   "source": [
    "yhat = model.predict(known_test)\n",
    "print(accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "drugs_unknown['yhat'] = model.predict(unknown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.524143472861\n"
     ]
    }
   ],
   "source": [
    "print(drugs_unknown.yhat.sum() / drugs_unknown.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most Predictive Terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "token_dict = {coeff : i for i, coeff in enumerate(model.coef_[0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coefficients = model.coef_[0]\n",
    "coefficients.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex (68.96), shit (45.51), music (20.95), weed (18.46), party (15.54), beer (14.18), dubstep (13.86), fuck (12.28), drinking (11.48), smoking (11.39), partying (10.59), chill (9.45), hair (8.84), park (8.09), fucking (7.93), dj (7.9), burning (7.78), electronic (7.05), drunk (6.67), ass (6.36), reggae (6.18), robbins (5.81), dude (5.74), smoke (5.68), cat (5.5), "
     ]
    }
   ],
   "source": [
    "for i in range(1, 26):\n",
    "    print(all_vocab[token_dict[coefficients[-i]]] + ' (' + \n",
    "          str(np.round(np.exp(coefficients[-i]), 2)) + ')', end=', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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