Notebook 5. example runs of spark based LDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#initial imports\n",
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import pyspark as ps\n",
    "from src.nltk_pipe import indexing_pipeline\n",
    "from pyspark.ml.clustering import LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_total = pd.read_pickle('data/reviews_unique.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spark_df = spark.createDataFrame(df_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+--------------------+\n",
      "|         attractions|             reviews|\n",
      "+--------------------+--------------------+\n",
      "|Sydney_Harbour-Sy...|Apart from the ma...|\n",
      "|Australian_War_Me...|This is a must vi...|\n",
      "+--------------------+--------------------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark_df.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_total_nltk, ls_nltk = indexing_pipeline(spark_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[attractions: string, reviews: string, bow: array<string>, vector_tf: vector, features: vector]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total_nltk.persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nltk_df = pd.read_pickle('data/df_total_nltk.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nltk_ls = pd.read_pickle('data/ls_nltk.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       " u'while',\n",
       " u'european',\n",
       " u'case',\n",
       " u'bath',\n",
       " u'profession',\n",
       " u'did',\n",
       " u'languag',\n",
       " u'cemeteri',\n",
       " u'tast',\n",
       " u'treasur',\n",
       " u'panama',\n",
       " u'giant',\n",
       " u'unbeliev',\n",
       " u'seren',\n",
       " u'japanes',\n",
       " u'model',\n",
       " u'limit',\n",
       " u'someon',\n",
       " u'thousand',\n",
       " u'seoul',\n",
       " u'toilet',\n",
       " u'numer',\n",
       " u'system',\n",
       " u'camp',\n",
       " u'worthwhil',\n",
       " u'respect',\n",
       " u'offic',\n",
       " u'wind',\n",
       " u'walkway',\n",
       " u'golden',\n",
       " u'onlin',\n",
       " u'whilst',\n",
       " u'beer',\n",
       " u'simpl',\n",
       " u'extens',\n",
       " u'wet',\n",
       " u'treat',\n",
       " u'actual',\n",
       " u'feet',\n",
       " u'board',\n",
       " u'lagoon',\n",
       " u'ton',\n",
       " u'coast',\n",
       " u'wood',\n",
       " u'american',\n",
       " u'cute',\n",
       " u'landmark',\n",
       " u'bottom',\n",
       " u'organ',\n",
       " u'nearbi',\n",
       " u'delici',\n",
       " u'chapel',\n",
       " u'mix',\n",
       " u'tunnel',\n",
       " u'similar',\n",
       " u'bigger',\n",
       " u'except',\n",
       " u'tripadvisor',\n",
       " u'ostrich',\n",
       " u'stall',\n",
       " u'august',\n",
       " u'tranquil',\n",
       " u'seal',\n",
       " u'egypt',\n",
       " u'cliff',\n",
       " u'costum',\n",
       " u'windi',\n",
       " u'russian',\n",
       " u'student',\n",
       " u'awar',\n",
       " u'shape',\n",
       " u'peak',\n",
       " u'height',\n",
       " u'stockholm',\n",
       " u'jewel',\n",
       " u'saw',\n",
       " u'signific',\n",
       " u'downtown',\n",
       " u'picturesqu',\n",
       " u'marin',\n",
       " u'wooden',\n",
       " u'cat',\n",
       " u'medina',\n",
       " u'lisbon',\n",
       " u'restor',\n",
       " u'east',\n",
       " u'arch',\n",
       " u'inspir',\n",
       " u'bat',\n",
       " u'..',\n",
       " u'danc',\n",
       " u'tropic',\n",
       " u'member',\n",
       " u'costa',\n",
       " u'tiger',\n",
       " u'crater',\n",
       " u'child',\n",
       " u'artwork',\n",
       " u'scale',\n",
       " u'mount',\n",
       " u'korean',\n",
       " u'valuabl',\n",
       " u'juli',\n",
       " u'chair',\n",
       " u'%',\n",
       " u'tabl',\n",
       " u'futur',\n",
       " u'poor',\n",
       " u'guard',\n",
       " u'era',\n",
       " u'giraff',\n",
       " u'penguin',\n",
       " u'budapest',\n",
       " u'helsinki',\n",
       " u'sort',\n",
       " u'emot',\n",
       " u'may',\n",
       " u'tank',\n",
       " u'istanbul',\n",
       " u'boy',\n",
       " u'vendor',\n",
       " u'india',\n",
       " u'ray',\n",
       " u'singl',\n",
       " u'passion',\n",
       " u'hop',\n",
       " u'egyptian',\n",
       " u'packag',\n",
       " u'combin',\n",
       " u'fit',\n",
       " u'background',\n",
       " u'hidden',\n",
       " u'touristi',\n",
       " u'clock',\n",
       " u'wine',\n",
       " u'/',\n",
       " u'holi',\n",
       " u'majest',\n",
       " u'paradis',\n",
       " u'multipl',\n",
       " u'mayan',\n",
       " u'general',\n",
       " u'hippo',\n",
       " u'fast',\n",
       " u'sydney',\n",
       " u'parliament',\n",
       " u'wrong',\n",
       " u'parti',\n",
       " u'god',\n",
       " u'owner',\n",
       " u'pm',\n",
       " u'fair',\n",
       " u'teenag',\n",
       " u'sit',\n",
       " u'inca',\n",
       " u'ceil',\n",
       " u'shrine',\n",
       " u'ballet',\n",
       " u'kong',\n",
       " u'sorri',\n",
       " u'thailand',\n",
       " u'km',\n",
       " u'decent',\n",
       " u'photographi',\n",
       " u'volunt',\n",
       " u'layout',\n",
       " u'standard',\n",
       " u'deep',\n",
       " u'islam',\n",
       " u'typic',\n",
       " u'oldest',\n",
       " u'pricey',\n",
       " u'chocol',\n",
       " u'field',\n",
       " u'info',\n",
       " u'perspect',\n",
       " u'older',\n",
       " u'mexico',\n",
       " u'vehicl',\n",
       " u'buse',\n",
       " u'us',\n",
       " u'just',\n",
       " u'bench',\n",
       " u'remark',\n",
       " u'crocodil',\n",
       " u'tile',\n",
       " u'basilica',\n",
       " u'come',\n",
       " u'get',\n",
       " u'creatur',\n",
       " u'wish',\n",
       " u'santa',\n",
       " u'crystal',\n",
       " u'conveni',\n",
       " u'men',\n",
       " u'strong',\n",
       " u'like',\n",
       " u'china',\n",
       " u'wear',\n",
       " u'women',\n",
       " u'dune',\n",
       " u'readi',\n",
       " u'indian',\n",
       " u'june',\n",
       " u'fortun',\n",
       " u'renov',\n",
       " u'spend',\n",
       " u'resid',\n",
       " u'intern',\n",
       " u'averag',\n",
       " u'suggest',\n",
       " u'cream',\n",
       " u'foreign',\n",
       " u'underground',\n",
       " u'thought',\n",
       " u'understand',\n",
       " u'nativ',\n",
       " u'rome',\n",
       " u'doubt',\n",
       " u'dream',\n",
       " u'charact',\n",
       " u'sanctuari',\n",
       " u'return',\n",
       " u'hong',\n",
       " u'matter',\n",
       " u'neat',\n",
       " u'japan',\n",
       " u'tri',\n",
       " u'jerusalem',\n",
       " u'note',\n",
       " u'watch',\n",
       " u'deal',\n",
       " u'unesco',\n",
       " u'marbl',\n",
       " u'april',\n",
       " u'elev',\n",
       " u'mosaic',\n",
       " u'suitabl',\n",
       " u'particular',\n",
       " u'tuk',\n",
       " u'athen',\n",
       " u'soldier',\n",
       " u'websit',\n",
       " u'manag',\n",
       " u'exterior',\n",
       " u'degre',\n",
       " u'romant',\n",
       " u'bank',\n",
       " u'authent',\n",
       " u'villa',\n",
       " u'monet',\n",
       " u'enorm',\n",
       " u'crazi',\n",
       " u'cusco',\n",
       " u'lift',\n",
       " u'cheaper',\n",
       " u'total',\n",
       " u'pace',\n",
       " u'tallinn',\n",
       " u'girl',\n",
       " u'jesus',\n",
       " u'had',\n",
       " u'process',\n",
       " u'bear',\n",
       " u'librari',\n",
       " u'bring',\n",
       " u'begin',\n",
       " u'current',\n",
       " u'christian',\n",
       " u'artefact',\n",
       " u'unusu',\n",
       " u'visibl',\n",
       " u'acoust',\n",
       " u'rica',\n",
       " u'birthday',\n",
       " u'bangkok',\n",
       " u'el',\n",
       " u'vacat',\n",
       " u'america',\n",
       " u'bali',\n",
       " u'longer',\n",
       " u'column',\n",
       " u'issu',\n",
       " u'indoor',\n",
       " u'machin',\n",
       " u'enthusiast',\n",
       " u'lower',\n",
       " u'mine',\n",
       " u'effect',\n",
       " u'bustl',\n",
       " u'row',\n",
       " u'septemb',\n",
       " u'earth',\n",
       " u'certain',\n",
       " u'aspect',\n",
       " u'eat',\n",
       " u'cheetah',\n",
       " u'cayman',\n",
       " u'gorg',\n",
       " u'quaint',\n",
       " u'divers',\n",
       " u'face',\n",
       " u'third',\n",
       " u'custom',\n",
       " u'bomb',\n",
       " u'sheer',\n",
       " u'tini',\n",
       " u'playground',\n",
       " u'challeng',\n",
       " u'coloni',\n",
       " u'masterpiec',\n",
       " u'militari',\n",
       " u'temperatur',\n",
       " u'oper',\n",
       " u'lifetim',\n",
       " u'preserv',\n",
       " u'larger',\n",
       " u'cuba',\n",
       " u'dont',\n",
       " u'extraordinari',\n",
       " u'ideal',\n",
       " u'cloud',\n",
       " u'picchu',\n",
       " u'greek',\n",
       " u'canyon',\n",
       " u'partner',\n",
       " ...]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk_ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spark_nltk_df = spark.createDataFrame(nltk_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[attractions: string, reviews: string, bow: array<string>, vector_tf: vector, features: vector]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark_nltk_df.persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = 'data/ls_nltk.pkl'\n",
    "with open(f, 'wb') as myfile:\n",
    "    pickle.dump(ls_nltk, myfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing spark LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample_df = df_total.sample(frac=0.01, replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spark_sample_df = spark.createDataFrame(sample_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample_nltk_df, sample_ls = indexing_pipeline(spark_sample_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lda = LDA(k=3, optimizer=\"em\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = lda.fit(sample_nltk_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "topics = model.describeTopics().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- TOPIC 0 -\n",
      "  - word 'waterfal': 0.107076734088\n",
      "  - word 'fall': 0.0225443648848\n",
      "  - word 'island': 0.0176960065402\n",
      "  - word 'wet': 0.0172834616148\n",
      "  - word 'fort': 0.0160332361729\n",
      "  - word 'el': 0.0139192917059\n",
      "  - word 'ferri': 0.0121036043839\n",
      "  - word 'waterproof': 0.0092313432661\n",
      "  - word 'fortress': 0.00919330767031\n",
      "  - word 'lighthous': 0.00745761548216\n",
      "- TOPIC 1 -\n",
      "  - word 'museum': 0.0263856076058\n",
      "  - word 'palac': 0.0224564929425\n",
      "  - word 'pragu': 0.018527278134\n",
      "  - word 'garden': 0.0174302952708\n",
      "  - word 'port': 0.0170167094791\n",
      "  - word 'collect': 0.0127449514114\n",
      "  - word 'mosaic': 0.0122871777836\n",
      "  - word 'histori': 0.0105498433234\n",
      "  - word 'josi': 0.00991788532738\n",
      "  - word 'concert': 0.0097557943973\n",
      "- TOPIC 2 -\n",
      "  - word 'bridg': 0.0394331900272\n",
      "  - word 'pragu': 0.0268651564562\n",
      "  - word 'disney': 0.0242966701864\n",
      "  - word 'beach': 0.0174013199385\n",
      "  - word 'charl': 0.0151300901016\n",
      "  - word 'ride': 0.00961449603691\n",
      "  - word 'fort': 0.009322728664\n",
      "  - word 'restaur': 0.00931595996467\n",
      "  - word 'statu': 0.00863346571804\n",
      "  - word 'castl': 0.00810041286766\n"
     ]
    }
   ],
   "source": [
    "for topic in topics:\n",
    "    print(\"- TOPIC {} -\".format(topic[0]))\n",
    "    topickeys = topic[1]\n",
    "    topicvalues = topic[2]\n",
    "    for i in range(len(topickeys)):\n",
    "        print(\"  - word '{}': {}\".format(sample_ls[topickeys[i]],topicvalues[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = model.topicsMatrix().toArray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3096, 3)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3096"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sample_ls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'more', u'place', u'great', u'time', u'beauti']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_ls[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  55.3903943   101.9993294    73.07104798]\n",
      "[  28.35065615   78.60658886  114.93200096]\n",
      "[ 14.90557432  36.70174817  37.69430304]\n",
      "[ 0.  0.  0.]\n",
      "[ 46.62959495  69.87793315  48.27631839]\n",
      "[ 30.35290458  58.87363925  74.92513957]\n",
      "[  1.28108485e+00   8.88182421e+02   7.15788172e-01]\n",
      "[  46.77667469  233.95496797   16.71871124]\n",
      "[  10.92532999  355.12486675   73.73000913]\n",
      "[ 29.33871245  19.60558233  78.22584762]\n",
      "[  1.80417902e+03   4.85724223e-04   3.90784478e-04]\n",
      "[  1.12870234e+00   4.39175795e-02   9.94835406e+02]\n",
      "[ 10.003448     8.57858445  36.55825901]\n",
      "[ 17.85821472  30.58391453  58.49879414]\n",
      "[  7.2624596   13.55421994  31.24601426]\n",
      "[ 28.41723245  66.59187154  68.80997695]\n",
      "[ 24.9230352   96.26412584  36.45620057]\n",
      "[ 14.49034632  33.22215938  45.1101086 ]\n",
      "[  33.61210135  258.66728501    5.54419182]\n",
      "[  43.02419855    0.85529805  191.44443867]\n",
      "[ 21.90957221  76.86081951  34.00757368]\n",
      "[ 25.06243035  25.11428976  74.15026096]\n",
      "[   0.94635799  173.97324683   45.1571806 ]\n",
      "[  14.36511076  195.14998252    9.69864594]\n",
      "[   0.60112322   88.75185746  235.02655514]\n",
      "[  8.39776143e-03   5.86732058e+02   1.62700883e-01]\n",
      "[  1.90879956e-01   4.29015771e+02   4.03151658e-02]\n",
      "[  1.71633898e-03   6.23658284e+02   6.77764310e+02]\n",
      "[  9.12103797  50.84335048   9.04674931]\n",
      "[ 26.82048029  82.93148809  33.05990445]\n",
      "[  2.89307526e-03   7.55922038e+02   1.69990198e-01]\n",
      "[  3.01555088   8.33651788  18.44933528]\n",
      "[ 12.04960474  30.13361257  47.6897507 ]\n",
      "[  1.87872048e-01   3.01244338e+02   2.04360274e+02]\n",
      "[ 26.70419404  29.16008577  33.19609356]\n",
      "[  29.67852159    6.67413311  242.55813588]\n",
      "[  3.37379994e+01   6.09491525e-02   4.39007069e+02]\n",
      "[ 35.35739748  47.52603033  29.80406561]\n",
      "[ 26.6896132   51.11202942  66.61475775]\n",
      "[ 15.73984851  14.04641284  51.79824126]\n",
      "[ 10.76607187  51.50146053  18.99193235]\n",
      "[ 16.34371167  19.84229311  15.33528738]\n",
      "[  3.24518384e-02   2.55748954e+02   3.53652140e+01]\n",
      "[  9.10269855  12.89321749  86.0055628 ]\n",
      "[ 27.64743199  38.36174086  12.48747009]\n",
      "[   9.46245194   10.07776102  117.10877146]\n",
      "[ 10.20905693  46.22921248  19.94562746]\n",
      "[   5.19870515  157.29758434   39.54089816]\n",
      "[  4.96161164e+01   5.72810088e+02   1.99633318e-02]\n",
      "[  1.14314049e+00   1.96930551e-02   6.12965568e+02]\n",
      "[ 11.23164109  21.56128848  11.87992907]\n",
      "[  6.66660902  33.9165199   26.86222682]\n",
      "[ 19.39802916  33.57241729  14.31239036]\n",
      "[  2.03606363  86.27217859   3.62690092]\n",
      "[ 20.25282991  22.82199004  72.57337318]\n",
      "[ 48.95834409  49.2109648   40.93391925]\n",
      "[  0.98139516  68.80648736  16.12238474]\n",
      "[ 298.16714217    1.27078549    1.59947381]\n",
      "[ 379.85908485    1.25090479    1.02526368]\n",
      "[ 14.24787606  25.02938481  40.83127406]\n",
      "[ 20.60480953  26.44313665  29.93657903]\n",
      "[  7.741821    12.11514164  16.28169423]\n",
      "[  6.74820735  39.72722954  29.83965765]\n",
      "[ 13.31870073  29.70595282  31.05900548]\n",
      "[   0.91607114  166.04634974    0.58838372]\n",
      "[  4.90078798  30.87380682  81.21478681]\n",
      "[ 13.98290135   9.49498354  91.728122  ]\n",
      "[ 30.29697467  25.79740124  15.98099116]\n",
      "[ 25.65082653  24.56419118  64.27763929]\n",
      "[ 11.57773597  38.87473042  40.16738644]\n",
      "[  4.21292205e-02   3.24475869e+02   5.12614863e+00]\n",
      "[  2.70150454e+02   1.57315732e-02   2.35197318e+02]\n",
      "[ 18.98423093  17.4738437   12.29760422]\n",
      "[ 23.06198826  85.99975637  43.67511688]\n",
      "[  2.54023926  38.5599357    7.1679471 ]\n",
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      "[ 1.25276719  6.23582363  8.88115999]\n",
      "[  2.69402777e-03   7.01890543e+01   1.69111199e-03]\n",
      "[ 18.3709161   11.03283707   0.1410316 ]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[  1.79409641e+01   5.72743064e-03   4.58543927e+00]\n",
      "[ 2.52932772  3.89075556  3.96051677]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
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      "[  1.94701476e-02   3.77340398e+00   1.36584247e-03]\n",
      "[  1.73163997e-02   3.77571694e+00   1.20662966e-03]\n",
      "[ 0.10857025  1.79643095  1.88923877]\n",
      "[  3.46633141e-03   3.14818400e-03   3.78762545e+00]\n",
      "[  1.62927254e-03   1.89199263e+00   1.90061807e+00]\n",
      "[ 0.01162869  1.81023967  1.97237161]\n",
      "[  4.34118477e-03   6.49528151e-04   3.78924926e+00]\n",
      "[ 0.0067552   1.95103425  1.83645052]\n",
      "[ 1.80648167  0.01842255  1.96933575]\n",
      "[  1.90682612e+00   1.76015136e-03   1.88565370e+00]\n",
      "[  1.73163643e-02   3.77571698e+00   1.20662959e-03]\n",
      "[  1.19693859e-03   3.79155909e+00   1.48393946e-03]\n",
      "[ 1.47206222  2.28496732  0.03721043]\n",
      "[  1.28116185e-03   1.09564762e-03   3.79186316e+00]\n",
      "[  1.73163730e-02   3.77571697e+00   1.20662961e-03]\n",
      "[ 0.21833163  0.00567504  3.5702333 ]\n",
      "[  1.90682612e+00   1.76015136e-03   1.88565370e+00]\n",
      "[  2.67775713e-03   3.78991887e+00   1.64333849e-03]\n",
      "[ 2.50440799  1.21719213  0.07263985]\n",
      "[ 0.01262098  1.8793141   1.9023049 ]\n",
      "[ 0.00241873  1.87059388  1.92122736]\n",
      "[  1.92464976e+00   1.86815616e+00   1.43404206e-03]\n",
      "[ 0.3033862   0.00569834  3.48515542]\n",
      "[ 2.74749791  1.04250757  0.00423449]\n",
      "[  3.74919403e+00   3.29078822e-03   4.17551558e-02]\n",
      "[ 0.45455312  3.21438588  0.12530097]\n",
      "[ 1.73463247  1.84947816  0.21012934]\n",
      "[ 0.21819048  0.00567521  3.57037427]\n",
      "[  3.74918812e+00   3.29079684e-03   4.17610550e-02]\n",
      "[  1.94700859e-02   3.77340404e+00   1.36584235e-03]\n",
      "[ 0.03980773  1.86431303  1.89011921]\n",
      "[  1.67399226e-02   3.77590282e+00   1.59722617e-03]\n",
      "[  1.62927254e-03   1.89199263e+00   1.90061807e+00]\n",
      "[  3.79146836e+00   4.65620435e-04   2.30598516e-03]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.48878415  1.23059931  0.07485651]\n",
      "[  3.08745386e-03   3.78989859e+00   1.25392975e-03]\n",
      "[ 1.27340345  2.02306498  0.49777154]\n",
      "[  4.34118478e-03   6.49528151e-04   3.78924926e+00]\n",
      "[ 0.01260647  1.87931863  1.90231486]\n",
      "[ 0.23770352  0.00800638  3.54853006]\n",
      "[  1.67399228e-02   3.77590282e+00   1.59722617e-03]\n",
      "[  3.08745386e-03   3.78989859e+00   1.25392975e-03]\n",
      "[  2.67775713e-03   3.78991887e+00   1.64333849e-03]\n",
      "[ 0.00888201  1.88879553  1.89656243]\n",
      "[ 1.58096979  2.00133982  0.21193036]\n",
      "[ 0.01262037  1.87931428  1.90230531]\n",
      "[  6.71834555e-03   2.68458139e-03   3.78483704e+00]\n",
      "[  1.19693859e-03   3.79155909e+00   1.48393946e-03]\n",
      "[ 0.10431539  1.80002727  1.88989731]\n",
      "[ 1.67957791  2.10922792  0.00543414]\n",
      "[ 0.05035642  1.91912973  1.82475381]\n",
      "[ 0.11996511  1.78678278  1.88749208]\n",
      "[  3.78578940e+00   6.01143356e-03   2.43913387e-03]\n",
      "[  2.67775713e-03   3.78991887e+00   1.64333849e-03]\n",
      "[ 1.65353805  2.10342882  0.0372731 ]\n",
      "[  1.19693859e-03   3.79155909e+00   1.48393946e-03]\n",
      "[  4.34118479e-03   6.49528152e-04   3.78924926e+00]\n",
      "[ 0.31000107  0.00569081  3.47854809]\n",
      "[ 1.57335638  2.0018251   0.21905849]\n",
      "[  3.79146836e+00   4.65620435e-04   2.30598516e-03]\n",
      "[  3.08745386e-03   3.78989859e+00   1.25392975e-03]\n",
      "[  1.67399227e-02   3.77590282e+00   1.59722617e-03]\n",
      "[  1.73163952e-02   3.77571694e+00   1.20662965e-03]\n",
      "[  1.84431059e+00   1.26274615e-03   1.94866663e+00]\n",
      "[ 1.90930242  1.88273876  0.00219879]\n",
      "[  1.98466464e+00   1.80783525e+00   1.74008006e-03]\n",
      "[  3.74917214e+00   3.29082018e-03   4.17770139e-02]\n",
      "[ 1.66263147  2.12539453  0.00621398]\n",
      "[ 0.01263688  1.87930912  1.90229397]\n",
      "[ 1.57206108  2.21605259  0.0061263 ]\n",
      "[  2.77177802e+00   1.02081303e+00   1.64891820e-03]\n",
      "[ 0.00536668  1.89726939  1.89160391]\n",
      "[ 0.03980783  1.86431292  1.89011922]\n",
      "[  1.92464976e+00   1.86815616e+00   1.43404206e-03]\n",
      "[ 0.04222226  1.84212129  1.90989642]\n",
      "[  2.29809032e-02   3.76835570e+00   2.90336770e-03]\n",
      "[  1.94702750e-02   3.77340385e+00   1.36584272e-03]\n",
      "[  1.19693859e-03   3.79155909e+00   1.48393946e-03]\n",
      "[ 1.90898742  1.87995991  0.00529263]\n",
      "[  1.90682612e+00   1.76015136e-03   1.88565370e+00]\n",
      "[ 0.03980535  1.86431556  1.89011905]\n",
      "[ 0.217918    0.00567555  3.57064643]\n",
      "[  5.66363756e-03   3.78579527e+00   2.78106250e-03]\n",
      "[ 0.12861055  3.39160999  0.27401943]\n",
      "[  1.19693859e-03   3.79155909e+00   1.48393946e-03]\n",
      "[ 0.31851101  0.00568114  3.47004783]\n",
      "[  7.30373353e-03   3.78399114e+00   2.94509536e-03]\n",
      "[  1.73163924e-02   3.77571695e+00   1.20662964e-03]\n",
      "[  3.90738167e-03   1.19629501e-03   3.78913629e+00]\n",
      "[ 1.55374479  2.23359901  0.00689617]\n",
      "[ 1.82884686  0.00230256  1.96309055]\n",
      "[  2.67775713e-03   3.78991887e+00   1.64333849e-03]\n",
      "[ 0.03980457  1.8643164   1.890119  ]\n",
      "[ 2.51517837  1.20760877  0.07145283]\n",
      "[  1.90682612e+00   1.76015136e-03   1.88565370e+00]\n",
      "[ 0.04222089  1.84212265  1.90989643]\n",
      "[  1.90682612e+00   1.76015136e-03   1.88565370e+00]\n",
      "[ 0.00888768  1.88879564  1.89655665]\n",
      "[  1.90682612e+00   1.76015136e-03   1.88565370e+00]\n",
      "[ 0.01869711  1.81343003  1.96211283]\n",
      "[ 1.90930242  1.88273876  0.00219879]\n",
      "[ 0.21443663  0.0063104   3.57349294]\n",
      "[ 0.01086806  1.95618184  1.82719007]\n",
      "[ 0.01265762  1.87930264  1.90227971]\n",
      "[ 1.74274943  2.04590174  0.0055888 ]\n",
      "[  1.92245703e+00   6.91550981e-04   1.87109139e+00]\n",
      "[  1.19693859e-03   3.79155909e+00   1.48393946e-03]\n",
      "[ 0.04224127  1.84210239  1.90989631]\n",
      "[  3.08745386e-03   3.78989859e+00   1.25392975e-03]\n",
      "[ 0.01259942  1.87932084  1.90231971]\n",
      "[  2.77124216e+00   1.02134930e+00   1.64851386e-03]\n",
      "[  1.67399228e-02   3.77590282e+00   1.59722617e-03]\n",
      "[  3.46633141e-03   3.14818400e-03   3.78762545e+00]\n",
      "[ 0.01263646  1.87930926  1.90229426]\n",
      "[  5.23007557e-03   3.78582685e+00   3.18304703e-03]\n",
      "[  1.28116185e-03   1.09564762e-03   3.79186316e+00]\n",
      "[  4.34118492e-03   6.49528152e-04   3.78924926e+00]\n",
      "[ 1.57629965  2.00163757  0.21630275]\n",
      "[ 1.82884688  0.00230256  1.96309053]\n",
      "[ 1.4266952   0.01826462  2.34928015]\n",
      "[  1.62927254e-03   1.89199263e+00   1.90061807e+00]\n",
      "[  1.92464976e+00   1.86815616e+00   1.43404206e-03]\n",
      "[ 0.09487291  1.8079962   1.89137086]\n",
      "[  1.67399227e-02   3.77590282e+00   1.59722617e-03]\n",
      "[ 0.03980958  1.86431105  1.89011934]\n",
      "[  1.67399236e-02   3.77590282e+00   1.59722618e-03]\n",
      "[ 0.00536668  1.89726939  1.89160391]\n",
      "[  5.66363756e-03   3.78579527e+00   2.78106250e-03]\n",
      "[  1.19693859e-03   3.79155909e+00   1.48393946e-03]\n",
      "[ 0.00627686  1.87581739  1.91214573]\n",
      "[  2.29812755e-02   3.76835533e+00   2.90336918e-03]\n",
      "[  3.88866883e-03   2.57885245e-03   3.78777245e+00]\n",
      "[ 1.68240706  1.94342172  0.1684112 ]\n",
      "[ 2.08919206  1.70029356  0.00475435]\n",
      "[ 0.05049484  1.91899414  1.82475099]\n",
      "[ 3.16147856  0.02864819  0.60411322]\n",
      "[  1.67399232e-02   3.77590282e+00   1.59722618e-03]\n",
      "[ 1.9408217   0.01638858  1.83702968]\n",
      "[  1.98466464e+00   1.80783525e+00   1.74008002e-03]\n",
      "[ 1.90930242  1.88273876  0.00219879]\n",
      "[  2.04150637e+00   9.09941973e-04   1.75182365e+00]\n",
      "[  3.08745386e-03   3.78989859e+00   1.25392975e-03]\n",
      "[ 0.3067012   0.00569457  3.4818442 ]\n",
      "[  1.96866119e+00   1.41641054e-03   1.82416237e+00]\n",
      "[ 1.90930242  1.88273876  0.00219879]\n",
      "[  2.67775713e-03   3.78991887e+00   1.64333849e-03]\n",
      "[ 3.77358239  0.01562646  0.00503112]\n",
      "[  1.19693859e-03   3.79155909e+00   1.48393946e-03]\n",
      "[ 0.00241873  1.87059388  1.92122736]\n",
      "[  1.19693859e-03   3.79155909e+00   1.48393946e-03]\n",
      "[ 0.00786439  1.87457639  1.91179919]\n",
      "[ 0.04970388  1.91976889  1.82476721]\n",
      "[ 0.03980521  1.86431571  1.89011904]\n",
      "[  1.92464976e+00   1.86815616e+00   1.43404206e-03]\n",
      "[ 3.54214628  0.07811284  0.17398085]\n",
      "[ 0.39643637  3.26875344  0.12905016]\n",
      "[ 0.02970322  0.01574712  3.74878963]\n",
      "[  1.67399226e-02   3.77590282e+00   1.59722617e-03]\n",
      "[ 1.49276749  2.26628968  0.0351828 ]\n",
      "[ 0.00627686  1.87581739  1.91214573]\n",
      "[ 0.00786468  1.8745765   1.91179878]\n",
      "[ 1.68079406  1.94360757  0.16983833]\n",
      "[  1.73163791e-02   3.77571696e+00   1.20662962e-03]\n",
      "[ 0.29562223  3.23289198  0.26572575]\n",
      "[ 0.00241873  1.87059388  1.92122736]\n",
      "[ 0.03980465  1.86431631  1.890119  ]\n",
      "[  1.19693859e-03   3.79155909e+00   1.48393946e-03]\n",
      "[ 0.00240155  1.90636178  1.88547663]\n",
      "[  5.66363756e-03   3.78579527e+00   2.78106250e-03]\n",
      "[  1.28116185e-03   1.09564762e-03   3.79186316e+00]\n",
      "[  1.67399227e-02   3.77590282e+00   1.59722617e-03]\n",
      "[ 0.03980658  1.86431426  1.89011913]\n",
      "[ 0.00887693  1.88879543  1.89656761]\n",
      "[ 0.03980617  1.86431469  1.89011911]\n"
     ]
    }
   ],
   "source": [
    "for i in m:\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transformed = model.transform(sample_nltk_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = transformed.select('topicDistribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.sql.dataframe.DataFrame"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topicDistribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.0115302494764, 0.0103710479144, 0.978098702...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.749023191888, 0.18992226399, 0.0610545441217]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.306404484301, 0.31001866227, 0.383576853429]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.989166343067, 0.00382556298841, 0.007008093...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.0144522292225, 0.971510984714, 0.0140367860...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[0.0122320523366, 0.00769150042494, 0.98007644...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[0.629540431277, 0.0828334456175, 0.287626123105]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[0.186149866002, 0.2129429922, 0.600907141799]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[0.931372291134, 0.0191126200627, 0.0495150888...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[0.00649468183311, 0.0206698656633, 0.97283545...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[0.0854851399965, 0.901487208056, 0.0130276519...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[0.550252450989, 0.214067435468, 0.235680113543]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[0.0063549299729, 0.984388902075, 0.0092561679...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[0.0373197607944, 0.0584702798796, 0.904209959...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[0.291818100534, 0.351356462672, 0.356825436794]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[0.648038266934, 0.274530286918, 0.0774314461474]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[0.0428753479715, 0.922397117884, 0.0347275341...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[0.0042731086246, 0.981422836479, 0.0143040548...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[0.285525130642, 0.366974424401, 0.347500444957]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    topicDistribution\n",
       "0   [0.0115302494764, 0.0103710479144, 0.978098702...\n",
       "1    [0.749023191888, 0.18992226399, 0.0610545441217]\n",
       "2     [0.306404484301, 0.31001866227, 0.383576853429]\n",
       "3   [0.989166343067, 0.00382556298841, 0.007008093...\n",
       "4   [0.0144522292225, 0.971510984714, 0.0140367860...\n",
       "5   [0.0122320523366, 0.00769150042494, 0.98007644...\n",
       "6   [0.629540431277, 0.0828334456175, 0.287626123105]\n",
       "7      [0.186149866002, 0.2129429922, 0.600907141799]\n",
       "8   [0.931372291134, 0.0191126200627, 0.0495150888...\n",
       "9   [0.00649468183311, 0.0206698656633, 0.97283545...\n",
       "10  [0.0854851399965, 0.901487208056, 0.0130276519...\n",
       "11   [0.550252450989, 0.214067435468, 0.235680113543]\n",
       "12  [0.0063549299729, 0.984388902075, 0.0092561679...\n",
       "13  [0.0373197607944, 0.0584702798796, 0.904209959...\n",
       "14   [0.291818100534, 0.351356462672, 0.356825436794]\n",
       "15  [0.648038266934, 0.274530286918, 0.0774314461474]\n",
       "16  [0.0428753479715, 0.922397117884, 0.0347275341...\n",
       "17  [0.0042731086246, 0.981422836479, 0.0143040548...\n",
       "18   [0.285525130642, 0.366974424401, 0.347500444957]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b['max'] = b.topicDistribution.apply(np.argmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "1     0\n",
       "2     2\n",
       "3     0\n",
       "4     1\n",
       "5     2\n",
       "6     0\n",
       "7     2\n",
       "8     0\n",
       "9     2\n",
       "10    1\n",
       "11    0\n",
       "12    1\n",
       "13    2\n",
       "14    2\n",
       "15    0\n",
       "16    1\n",
       "17    1\n",
       "18    1\n",
       "Name: max, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b['max']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from src.lda_pipe import save_to_s3, lda_model_score, print_topics, to_pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.sql.dataframe.DataFrame"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(spark_sample_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[attractions: string, reviews: string]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark_sample_df.persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[attractions: string, reviews: string, bow: array<string>, vector_tf: vector, features: vector]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sample_nltk_df, sample_ls\n",
    "sample_nltk_df.persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lda, model, topics, transformed = lda_model_score(sample_nltk_df, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- TOPIC 0 -\n",
      "  - word 'zoo': 0.0932548620658\n",
      "  - word 'rickshaw': 0.0693146603084\n",
      "  - word 'anim': 0.0433619749504\n",
      "  - word 'cathedr': 0.0385900403969\n",
      "  - word 'dear': 0.0250117964688\n",
      "  - word 'kyoto': 0.0211140409596\n",
      "  - word 'church': 0.0155085150807\n",
      "  - word 'lima': 0.0143645463891\n",
      "  - word 'thank': 0.0139644389732\n",
      "  - word 'bamboo': 0.0132319329591\n",
      "- TOPIC 1 -\n",
      "  - word 'templ': 0.0497711832453\n",
      "  - word 'miyajima': 0.0389182912482\n",
      "  - word 'island': 0.0341893726521\n",
      "  - word 'deer': 0.0267501927996\n",
      "  - word 'ferri': 0.0267026642301\n",
      "  - word 'shrine': 0.023790289556\n",
      "  - word 'jr': 0.0214090369797\n",
      "  - word 'hong': 0.0186842555288\n",
      "  - word 'kong': 0.0185061761436\n",
      "  - word 'garden': 0.0180525199746\n",
      "- TOPIC 2 -\n",
      "  - word 'beach': 0.083004488557\n",
      "  - word 'arch': 0.0413793829113\n",
      "  - word 'pragu': 0.0206591748725\n",
      "  - word 'concert': 0.0201020018016\n",
      "  - word 'municip': 0.0149893821865\n",
      "  - word 'hall': 0.0144158909909\n",
      "  - word 'art': 0.0119458405125\n",
      "  - word 'hike': 0.0115028756586\n",
      "  - word 'sand': 0.0112980000668\n",
      "  - word 'delic': 0.0109921256395\n",
      "- TOPIC 3 -\n",
      "  - word 'seal': 0.102403108273\n",
      "  - word 'hike': 0.0288341967756\n",
      "  - word 'museum': 0.0185882513414\n",
      "  - word 'london': 0.0154974832793\n",
      "  - word 'british': 0.0140539471435\n",
      "  - word 'canopi': 0.0134860577228\n",
      "  - word 'coloni': 0.0132253263241\n",
      "  - word 'exhibit': 0.010998132651\n",
      "  - word 'artifact': 0.00801114524116\n",
      "  - word 'collect': 0.00759855047604\n"
     ]
    }
   ],
   "source": [
    "print_topics(topics, sample_ls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attractions</th>\n",
       "      <th>reviews</th>\n",
       "      <th>bow</th>\n",
       "      <th>vector_tf</th>\n",
       "      <th>features</th>\n",
       "      <th>topicDistribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tokyo_Disneyland-Urayasu_Chiba_Prefecture_Kanto</td>\n",
       "      <td>We love Tokyo Disneyland. We had a 3-day pass ...</td>\n",
       "      <td>[tokyo, disneyland, 3-day, pass, time, round, ...</td>\n",
       "      <td>(635.0, 201.0, 145.0, 304.0, 33.0, 317.0, 0.0,...</td>\n",
       "      <td>(32.5712419361, 21.1774636472, 7.43752768619, ...</td>\n",
       "      <td>[0.00722041109051, 0.106651777385, 0.016021987...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bet_She_an_National_Park-Beit_She_an_Northern_...</td>\n",
       "      <td>Interesting piece of history close to Jordan ...</td>\n",
       "      <td>[piec, histori, close, jordan, border, hour, h...</td>\n",
       "      <td>(57.0, 22.0, 14.0, 15.0, 2.0, 11.0, 0.0, 11.0,...</td>\n",
       "      <td>(2.92371778009, 2.31793134447, 0.718106121426,...</td>\n",
       "      <td>[0.0420580089135, 0.0413411018979, 0.222497497...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Molhe_Barra_Sul-Balneario_Camboriu_State_of_Sa...</td>\n",
       "      <td>Nice view _Ù÷ great for walking..at day and n...</td>\n",
       "      <td>[nice, view, _u, great, walking..at, day, nigh...</td>\n",
       "      <td>(0.0, 0.0, 3.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.153879883163, 0.0, 0.0, 0.1053605...</td>\n",
       "      <td>[0.224604622584, 0.24496575215, 0.266211375942...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seljalandsfoss-South_Region</td>\n",
       "      <td>I have to echo what other reviewers have said ...</td>\n",
       "      <td>[other, review, anywher, truli, waterfal, comp...</td>\n",
       "      <td>(459.0, 96.0, 115.0, 89.0, 278.0, 101.0, 0.0, ...</td>\n",
       "      <td>(23.5436221239, 10.1146095032, 5.89872885457, ...</td>\n",
       "      <td>[0.00283150792066, 0.989770015213, 0.003448263...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The_Giant_s_House-Akaroa_Canterbury_Region_Sou...</td>\n",
       "      <td>Absolutely incredible. Her work is out of this...</td>\n",
       "      <td>[incred, work, world, make, sure, time, great,...</td>\n",
       "      <td>(370.0, 230.0, 86.0, 109.0, 152.0, 64.0, 2.0, ...</td>\n",
       "      <td>(18.9785189234, 24.2329186013, 4.41122331733, ...</td>\n",
       "      <td>[0.979210070855, 0.0054740134963, 0.0085136748...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>The_Walk_At_JBR-Dubai_Emirate_of_Dubai</td>\n",
       "      <td>As the weather in September is still hot I re...</td>\n",
       "      <td>[weather, septemb, hot, beach, even, mani, mor...</td>\n",
       "      <td>(296.0, 479.0, 207.0, 123.0, 75.0, 96.0, 0.0, ...</td>\n",
       "      <td>(15.1828151387, 50.4676870001, 10.6177119382, ...</td>\n",
       "      <td>[0.00530330268404, 0.0113053991565, 0.83967113...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>El_Morro-Havana_Ciudad_de_la_Habana_Province_Cuba</td>\n",
       "      <td>A great view from the cruise ship on the way ...</td>\n",
       "      <td>[great, view, cruis, ship, way, havana, great,...</td>\n",
       "      <td>(381.0, 206.0, 252.0, 118.0, 93.0, 108.0, 52.0...</td>\n",
       "      <td>(19.5427451617, 21.7042662255, 12.9259101857, ...</td>\n",
       "      <td>[0.00897887243706, 0.882010682535, 0.091321050...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Djura_Jaksic_Monument-Novi_Sad_Vojvodina</td>\n",
       "      <td>Dunavski park is in a city center and it's de...</td>\n",
       "      <td>[dunavski, park, citi, center, natur, relax, r...</td>\n",
       "      <td>(5.0, 1.0, 3.0, 1.0, 2.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.256466471938, 0.105360515658, 0.15387988316...</td>\n",
       "      <td>[0.168042981316, 0.142194944816, 0.16445670749...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Udo-Jeju_Jeju_Island</td>\n",
       "      <td>This is a perfect one-day trip from Jeju, I ca...</td>\n",
       "      <td>[perfect, one-day, trip, jeju, season, midweek...</td>\n",
       "      <td>(174.0, 65.0, 29.0, 37.0, 66.0, 64.0, 1.0, 41....</td>\n",
       "      <td>(8.92503322343, 6.84843351776, 1.48750553724, ...</td>\n",
       "      <td>[0.0115707033225, 0.0720802933086, 0.899410486...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Charles_Bridge-Prague_Bohemia</td>\n",
       "      <td>Wonderful views over to the castle area. Best ...</td>\n",
       "      <td>[wonder, view, castl, area, best, earli, day, ...</td>\n",
       "      <td>(282.0, 176.0, 184.0, 206.0, 282.0, 199.0, 7.0...</td>\n",
       "      <td>(14.4647090173, 18.5434507558, 9.43796616731, ...</td>\n",
       "      <td>[0.0127849394977, 0.00455967748244, 0.00524998...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Port_Arthur_Historic_Site-Port_Arthur_Tasmania</td>\n",
       "      <td>This is a must see for visitors to Hobart and ...</td>\n",
       "      <td>[visitor, hobart, tasmania, tour, hous, photo,...</td>\n",
       "      <td>(705.0, 287.0, 234.0, 284.0, 138.0, 429.0, 12....</td>\n",
       "      <td>(36.1617725432, 30.2384679938, 12.0026308867, ...</td>\n",
       "      <td>[0.00840188416221, 0.00743169458652, 0.9799431...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Kallio_Church-Helsinki_Uusimaa</td>\n",
       "      <td>This tall monolithic structure stands out lik...</td>\n",
       "      <td>[tall, monolith, structur, beacon, helsinki, d...</td>\n",
       "      <td>(17.0, 6.0, 3.0, 4.0, 9.0, 0.0, 0.0, 0.0, 1.0,...</td>\n",
       "      <td>(0.871986004588, 0.632163093947, 0.15387988316...</td>\n",
       "      <td>[0.203768366896, 0.57866901662, 0.116121723853...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Museo_Larco-Lima_Lima_Region</td>\n",
       "      <td>I think this place is one of the museums in Li...</td>\n",
       "      <td>[place, museum, lima, cours, big, reason, most...</td>\n",
       "      <td>(419.0, 116.0, 216.0, 132.0, 262.0, 53.0, 943....</td>\n",
       "      <td>(21.4918903484, 12.2218198163, 11.0793515877, ...</td>\n",
       "      <td>[0.0208341447386, 0.00405787117044, 0.97128442...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Parque_Forestal-Santiago_Santiago_Metropolitan...</td>\n",
       "      <td>Go to Emporio de la rosa to get an Ice cream (...</td>\n",
       "      <td>[emporio, de, rosa, ice, cream, top, world, he...</td>\n",
       "      <td>(69.0, 73.0, 48.0, 23.0, 38.0, 36.0, 24.0, 3.0...</td>\n",
       "      <td>(3.53923731274, 7.69131764302, 2.4620781306, 0...</td>\n",
       "      <td>[0.0300413603668, 0.0546191681367, 0.793213628...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>King_s_Gate-Freetown_Western_Area</td>\n",
       "      <td>It takes two mins to wander along and read th...</td>\n",
       "      <td>[min, plaqu, signific, gateway, old, gate, ent...</td>\n",
       "      <td>(3.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.153879883163, 0.0, 0.0, 0.0, 0.0, 0.1053605...</td>\n",
       "      <td>[0.211559851736, 0.262726365737, 0.26243450643...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Damara_Living_Museum-Damaraland_Otjozondjupa_R...</td>\n",
       "      <td>Nice place to get some insight into Damara cu...</td>\n",
       "      <td>[nice, place, insight, damara, cultur, visit, ...</td>\n",
       "      <td>(53.0, 9.0, 12.0, 14.0, 2.0, 7.0, 40.0, 15.0, ...</td>\n",
       "      <td>(2.71854460254, 0.94824464092, 0.615519532651,...</td>\n",
       "      <td>[0.0420463860855, 0.0506666237051, 0.838100618...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Varna_Archaeological_Museum-Varna_Varna_Province</td>\n",
       "      <td>What a wonderful place this is. So much inform...</td>\n",
       "      <td>[wonder, place, much, inform, much, histori, d...</td>\n",
       "      <td>(67.0, 24.0, 18.0, 28.0, 7.0, 12.0, 134.0, 5.0...</td>\n",
       "      <td>(3.43665072397, 2.52865237579, 0.923279298976,...</td>\n",
       "      <td>[0.0365116584026, 0.0189970796073, 0.912520520...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Lobkowicz_Palace-Prague_Bohemia</td>\n",
       "      <td>This palace has a very interesting history. Th...</td>\n",
       "      <td>[palac, interest, histori, famili, year, lost,...</td>\n",
       "      <td>(485.0, 107.0, 167.0, 138.0, 120.0, 54.0, 274....</td>\n",
       "      <td>(24.877247778, 11.2735751754, 8.56598016272, 0...</td>\n",
       "      <td>[0.980195489622, 0.00320858692792, 0.006847521...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Al_Hazm_Castle-Ar_Rustaq_Al_Batinah_Governorate</td>\n",
       "      <td>We have been traveling around UAE and Oman we ...</td>\n",
       "      <td>[uae, oman, al, hazm, castl, histori, amaz, ho...</td>\n",
       "      <td>(16.0, 8.0, 5.0, 3.0, 5.0, 5.0, 0.0, 8.0, 5.0,...</td>\n",
       "      <td>(0.820692710201, 0.842884125263, 0.25646647193...</td>\n",
       "      <td>[0.186421625377, 0.412613283126, 0.31460362591...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          attractions  \\\n",
       "0     Tokyo_Disneyland-Urayasu_Chiba_Prefecture_Kanto   \n",
       "1   Bet_She_an_National_Park-Beit_She_an_Northern_...   \n",
       "2   Molhe_Barra_Sul-Balneario_Camboriu_State_of_Sa...   \n",
       "3                         Seljalandsfoss-South_Region   \n",
       "4   The_Giant_s_House-Akaroa_Canterbury_Region_Sou...   \n",
       "5              The_Walk_At_JBR-Dubai_Emirate_of_Dubai   \n",
       "6   El_Morro-Havana_Ciudad_de_la_Habana_Province_Cuba   \n",
       "7            Djura_Jaksic_Monument-Novi_Sad_Vojvodina   \n",
       "8                                Udo-Jeju_Jeju_Island   \n",
       "9                       Charles_Bridge-Prague_Bohemia   \n",
       "10     Port_Arthur_Historic_Site-Port_Arthur_Tasmania   \n",
       "11                     Kallio_Church-Helsinki_Uusimaa   \n",
       "12                       Museo_Larco-Lima_Lima_Region   \n",
       "13  Parque_Forestal-Santiago_Santiago_Metropolitan...   \n",
       "14                  King_s_Gate-Freetown_Western_Area   \n",
       "15  Damara_Living_Museum-Damaraland_Otjozondjupa_R...   \n",
       "16   Varna_Archaeological_Museum-Varna_Varna_Province   \n",
       "17                    Lobkowicz_Palace-Prague_Bohemia   \n",
       "18    Al_Hazm_Castle-Ar_Rustaq_Al_Batinah_Governorate   \n",
       "\n",
       "                                              reviews  \\\n",
       "0   We love Tokyo Disneyland. We had a 3-day pass ...   \n",
       "1    Interesting piece of history close to Jordan ...   \n",
       "2   Nice view _Ù÷ great for walking..at day and n...   \n",
       "3   I have to echo what other reviewers have said ...   \n",
       "4   Absolutely incredible. Her work is out of this...   \n",
       "5    As the weather in September is still hot I re...   \n",
       "6    A great view from the cruise ship on the way ...   \n",
       "7    Dunavski park is in a city center and it's de...   \n",
       "8   This is a perfect one-day trip from Jeju, I ca...   \n",
       "9   Wonderful views over to the castle area. Best ...   \n",
       "10  This is a must see for visitors to Hobart and ...   \n",
       "11   This tall monolithic structure stands out lik...   \n",
       "12  I think this place is one of the museums in Li...   \n",
       "13  Go to Emporio de la rosa to get an Ice cream (...   \n",
       "14   It takes two mins to wander along and read th...   \n",
       "15   Nice place to get some insight into Damara cu...   \n",
       "16  What a wonderful place this is. So much inform...   \n",
       "17  This palace has a very interesting history. Th...   \n",
       "18  We have been traveling around UAE and Oman we ...   \n",
       "\n",
       "                                                  bow  \\\n",
       "0   [tokyo, disneyland, 3-day, pass, time, round, ...   \n",
       "1   [piec, histori, close, jordan, border, hour, h...   \n",
       "2   [nice, view, _u, great, walking..at, day, nigh...   \n",
       "3   [other, review, anywher, truli, waterfal, comp...   \n",
       "4   [incred, work, world, make, sure, time, great,...   \n",
       "5   [weather, septemb, hot, beach, even, mani, mor...   \n",
       "6   [great, view, cruis, ship, way, havana, great,...   \n",
       "7   [dunavski, park, citi, center, natur, relax, r...   \n",
       "8   [perfect, one-day, trip, jeju, season, midweek...   \n",
       "9   [wonder, view, castl, area, best, earli, day, ...   \n",
       "10  [visitor, hobart, tasmania, tour, hous, photo,...   \n",
       "11  [tall, monolith, structur, beacon, helsinki, d...   \n",
       "12  [place, museum, lima, cours, big, reason, most...   \n",
       "13  [emporio, de, rosa, ice, cream, top, world, he...   \n",
       "14  [min, plaqu, signific, gateway, old, gate, ent...   \n",
       "15  [nice, place, insight, damara, cultur, visit, ...   \n",
       "16  [wonder, place, much, inform, much, histori, d...   \n",
       "17  [palac, interest, histori, famili, year, lost,...   \n",
       "18  [uae, oman, al, hazm, castl, histori, amaz, ho...   \n",
       "\n",
       "                                            vector_tf  \\\n",
       "0   (635.0, 201.0, 145.0, 304.0, 33.0, 317.0, 0.0,...   \n",
       "1   (57.0, 22.0, 14.0, 15.0, 2.0, 11.0, 0.0, 11.0,...   \n",
       "2   (0.0, 0.0, 3.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...   \n",
       "3   (459.0, 96.0, 115.0, 89.0, 278.0, 101.0, 0.0, ...   \n",
       "4   (370.0, 230.0, 86.0, 109.0, 152.0, 64.0, 2.0, ...   \n",
       "5   (296.0, 479.0, 207.0, 123.0, 75.0, 96.0, 0.0, ...   \n",
       "6   (381.0, 206.0, 252.0, 118.0, 93.0, 108.0, 52.0...   \n",
       "7   (5.0, 1.0, 3.0, 1.0, 2.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "8   (174.0, 65.0, 29.0, 37.0, 66.0, 64.0, 1.0, 41....   \n",
       "9   (282.0, 176.0, 184.0, 206.0, 282.0, 199.0, 7.0...   \n",
       "10  (705.0, 287.0, 234.0, 284.0, 138.0, 429.0, 12....   \n",
       "11  (17.0, 6.0, 3.0, 4.0, 9.0, 0.0, 0.0, 0.0, 1.0,...   \n",
       "12  (419.0, 116.0, 216.0, 132.0, 262.0, 53.0, 943....   \n",
       "13  (69.0, 73.0, 48.0, 23.0, 38.0, 36.0, 24.0, 3.0...   \n",
       "14  (3.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...   \n",
       "15  (53.0, 9.0, 12.0, 14.0, 2.0, 7.0, 40.0, 15.0, ...   \n",
       "16  (67.0, 24.0, 18.0, 28.0, 7.0, 12.0, 134.0, 5.0...   \n",
       "17  (485.0, 107.0, 167.0, 138.0, 120.0, 54.0, 274....   \n",
       "18  (16.0, 8.0, 5.0, 3.0, 5.0, 5.0, 0.0, 8.0, 5.0,...   \n",
       "\n",
       "                                             features  \\\n",
       "0   (32.5712419361, 21.1774636472, 7.43752768619, ...   \n",
       "1   (2.92371778009, 2.31793134447, 0.718106121426,...   \n",
       "2   (0.0, 0.0, 0.153879883163, 0.0, 0.0, 0.1053605...   \n",
       "3   (23.5436221239, 10.1146095032, 5.89872885457, ...   \n",
       "4   (18.9785189234, 24.2329186013, 4.41122331733, ...   \n",
       "5   (15.1828151387, 50.4676870001, 10.6177119382, ...   \n",
       "6   (19.5427451617, 21.7042662255, 12.9259101857, ...   \n",
       "7   (0.256466471938, 0.105360515658, 0.15387988316...   \n",
       "8   (8.92503322343, 6.84843351776, 1.48750553724, ...   \n",
       "9   (14.4647090173, 18.5434507558, 9.43796616731, ...   \n",
       "10  (36.1617725432, 30.2384679938, 12.0026308867, ...   \n",
       "11  (0.871986004588, 0.632163093947, 0.15387988316...   \n",
       "12  (21.4918903484, 12.2218198163, 11.0793515877, ...   \n",
       "13  (3.53923731274, 7.69131764302, 2.4620781306, 0...   \n",
       "14  (0.153879883163, 0.0, 0.0, 0.0, 0.0, 0.1053605...   \n",
       "15  (2.71854460254, 0.94824464092, 0.615519532651,...   \n",
       "16  (3.43665072397, 2.52865237579, 0.923279298976,...   \n",
       "17  (24.877247778, 11.2735751754, 8.56598016272, 0...   \n",
       "18  (0.820692710201, 0.842884125263, 0.25646647193...   \n",
       "\n",
       "                                    topicDistribution  \n",
       "0   [0.00722041109051, 0.106651777385, 0.016021987...  \n",
       "1   [0.0420580089135, 0.0413411018979, 0.222497497...  \n",
       "2   [0.224604622584, 0.24496575215, 0.266211375942...  \n",
       "3   [0.00283150792066, 0.989770015213, 0.003448263...  \n",
       "4   [0.979210070855, 0.0054740134963, 0.0085136748...  \n",
       "5   [0.00530330268404, 0.0113053991565, 0.83967113...  \n",
       "6   [0.00897887243706, 0.882010682535, 0.091321050...  \n",
       "7   [0.168042981316, 0.142194944816, 0.16445670749...  \n",
       "8   [0.0115707033225, 0.0720802933086, 0.899410486...  \n",
       "9   [0.0127849394977, 0.00455967748244, 0.00524998...  \n",
       "10  [0.00840188416221, 0.00743169458652, 0.9799431...  \n",
       "11  [0.203768366896, 0.57866901662, 0.116121723853...  \n",
       "12  [0.0208341447386, 0.00405787117044, 0.97128442...  \n",
       "13  [0.0300413603668, 0.0546191681367, 0.793213628...  \n",
       "14  [0.211559851736, 0.262726365737, 0.26243450643...  \n",
       "15  [0.0420463860855, 0.0506666237051, 0.838100618...  \n",
       "16  [0.0365116584026, 0.0189970796073, 0.912520520...  \n",
       "17  [0.980195489622, 0.00320858692792, 0.006847521...  \n",
       "18  [0.186421625377, 0.412613283126, 0.31460362591...  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_pickle(transformed, 'test.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## the entire dataframe!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from src.lda_pipe import save_to_s3, lda_model_score, print_topics, to_pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#nltk_df = pd.read_pickle('data/df_total_nltk.pkl')\n",
    "#nltk_ls = pd.read_pickle('data/ls_nltk.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spark_nltk_df = spark.createDataFrame(nltk_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "lda, model, topics, transformed = lda_model_score(spark_nltk_df, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- TOPIC 0 -\n",
      "  - word 'palac': 0.0391437436093\n",
      "  - word 'castl': 0.0347835681738\n",
      "  - word 'fort': 0.0181264415785\n",
      "  - word 'seoul': 0.0176783234263\n",
      "  - word 'copenhagen': 0.0137147294718\n",
      "  - word 'korean': 0.0124891402121\n",
      "  - word 'pragu': 0.0112232395745\n",
      "  - word 'korea': 0.009791533048\n",
      "  - word 'parliament': 0.00889395480336\n",
      "  - word 'garden': 0.00885978923237\n",
      "- TOPIC 1 -\n",
      "  - word 'dolphin': 0.0414207108434\n",
      "  - word 'aquarium': 0.0375584337744\n",
      "  - word 'shark': 0.0149105264004\n",
      "  - word 'dubai': 0.0134925454508\n",
      "  - word 'abu': 0.0128715646645\n",
      "  - word 'bucharest': 0.0111309079173\n",
      "  - word 'dhabi': 0.0110462810369\n",
      "  - word 'klcc': 0.0100862084927\n",
      "  - word 'potter': 0.00916688159209\n",
      "  - word 'harri': 0.00892475488562\n",
      "- TOPIC 2 -\n",
      "  - word 'glacier': 0.0384854759749\n",
      "  - word 'kyoto': 0.0152374337302\n",
      "  - word 'rickshaw': 0.0132583297236\n",
      "  - word 'london': 0.0126904986694\n",
      "  - word 'agora': 0.0118448010036\n",
      "  - word 'milford': 0.0111236976827\n",
      "  - word 'ebisuya': 0.0101567616223\n",
      "  - word 'gaudi': 0.0100336035029\n",
      "  - word 'bridg': 0.00940530866668\n",
      "  - word 'himeji': 0.00897889697922\n",
      "- TOPIC 3 -\n",
      "  - word 'dear': 0.0244662417239\n",
      "  - word 'slide': 0.0203116131626\n",
      "  - word 'theatr': 0.0196816875125\n",
      "  - word 'langkawi': 0.01461772007\n",
      "  - word 'ostrich': 0.013974668017\n",
      "  - word 'thank': 0.0126209198594\n",
      "  - word 'show': 0.0125372948072\n",
      "  - word 'canal': 0.0116185901571\n",
      "  - word 'feedback': 0.0112416340658\n",
      "  - word 'perform': 0.0109690887064\n",
      "- TOPIC 4 -\n",
      "  - word 'templ': 0.0365503700657\n",
      "  - word 'museum': 0.0195281487967\n",
      "  - word 'angkor': 0.0195230738927\n",
      "  - word 'wat': 0.0147520406224\n",
      "  - word 'cave': 0.0123882934578\n",
      "  - word 'jerusalem': 0.0114472142255\n",
      "  - word 'buddha': 0.0113513347034\n",
      "  - word 'bangkok': 0.0101103380313\n",
      "  - word 'neruda': 0.00939273714864\n",
      "  - word 'cambodia': 0.0091453605405\n",
      "- TOPIC 5 -\n",
      "  - word 'fjord': 0.0190642125868\n",
      "  - word 'beij': 0.0175530309601\n",
      "  - word 'cabl': 0.0167550992134\n",
      "  - word 'rio': 0.014925475262\n",
      "  - word 'taj': 0.0137896110446\n",
      "  - word 'moai': 0.0124736179313\n",
      "  - word 'mahal': 0.0113347618659\n",
      "  - word 'che': 0.00920288618808\n",
      "  - word 'catherin': 0.00873590269735\n",
      "  - word 'agra': 0.00858273151666\n",
      "- TOPIC 6 -\n",
      "  - word 'cusco': 0.0192617045196\n",
      "  - word 'inca': 0.0190411878683\n",
      "  - word 'volcano': 0.018143113505\n",
      "  - word 'costa': 0.0129613217098\n",
      "  - word 'bird': 0.0124513032899\n",
      "  - word 'rica': 0.0119113479045\n",
      "  - word 'san': 0.00959932867\n",
      "  - word 'crocodil': 0.00952720928317\n",
      "  - word 'auckland': 0.0082469268996\n",
      "  - word 'chocol': 0.00790649228203\n",
      "- TOPIC 7 -\n",
      "  - word 'zoo': 0.0761927331459\n",
      "  - word 'anim': 0.034126206484\n",
      "  - word 'opera': 0.0330872863933\n",
      "  - word 'berlin': 0.0149085454134\n",
      "  - word 'ballet': 0.00980187364572\n",
      "  - word 'enclosur': 0.00935483780175\n",
      "  - word 'sydney': 0.00869304357035\n",
      "  - word 'melbourn': 0.00806597924169\n",
      "  - word 'war': 0.00777240328301\n",
      "  - word 'wreck': 0.00765398817604\n",
      "- TOPIC 8 -\n",
      "  - word 'hi': 0.0148506297616\n",
      "  - word 'kidzania': 0.0135922399667\n",
      "  - word 'ride': 0.0117518290726\n",
      "  - word 'farm': 0.0116175830673\n",
      "  - word 'peppa': 0.0112887771204\n",
      "  - word 'feedback': 0.010943233805\n",
      "  - word 'park': 0.0109329870284\n",
      "  - word 'thank': 0.0107669720944\n",
      "  - word 'seal': 0.00942503240781\n",
      "  - word 'theme': 0.00871786837903\n",
      "- TOPIC 9 -\n",
      "  - word 'eleph': 0.0404602493499\n",
      "  - word 'fall': 0.0161604102752\n",
      "  - word 'monkey': 0.0159266641373\n",
      "  - word 'bali': 0.0139589965324\n",
      "  - word 'safari': 0.0113678411335\n",
      "  - word 'apartheid': 0.00972216945104\n",
      "  - word 'brazilian': 0.00959541248832\n",
      "  - word 'silk': 0.00850803321696\n",
      "  - word 'ephesus': 0.00816348582185\n",
      "  - word 'mandela': 0.00807195439418\n",
      "- TOPIC 10 -\n",
      "  - word 'beach': 0.0438720989207\n",
      "  - word 'island': 0.0208480681178\n",
      "  - word 'iceland': 0.0184204410859\n",
      "  - word 'wonderla': 0.0175913120083\n",
      "  - word 'waterfal': 0.0161915325493\n",
      "  - word 'pool': 0.0141416044421\n",
      "  - word 'sand': 0.012884384919\n",
      "  - word 'phi': 0.0117464007245\n",
      "  - word 'boat': 0.0109184954113\n",
      "  - word 'pyramid': 0.0105545592521\n",
      "- TOPIC 11 -\n",
      "  - word 'mosqu': 0.033893383432\n",
      "  - word 'medina': 0.019100957174\n",
      "  - word 'kathmandu': 0.0149788730003\n",
      "  - word 'istanbul': 0.0147344016459\n",
      "  - word 'marrakech': 0.013061680495\n",
      "  - word 'templ': 0.0114838984976\n",
      "  - word 'tallinn': 0.0109910730931\n",
      "  - word 'squar': 0.00965511033604\n",
      "  - word 'morocco': 0.00941139020548\n",
      "  - word 'sofia': 0.00877281249305\n",
      "- TOPIC 12 -\n",
      "  - word 'garden': 0.0427975168672\n",
      "  - word 'botan': 0.0163828496719\n",
      "  - word 'art': 0.0120940228507\n",
      "  - word 'vienna': 0.0115221411819\n",
      "  - word 'plant': 0.010122780123\n",
      "  - word 'monet': 0.00998618502467\n",
      "  - word 'museum': 0.00945536647521\n",
      "  - word 'pragu': 0.00907542671168\n",
      "  - word 'flower': 0.00849684570767\n",
      "  - word 'fountain': 0.00833356637311\n",
      "- TOPIC 13 -\n",
      "  - word 'monasteri': 0.0201684622684\n",
      "  - word 'stockholm': 0.0181573878383\n",
      "  - word 'butterfli': 0.0145784818234\n",
      "  - word 'penang': 0.0122109061019\n",
      "  - word 'hike': 0.0102553787678\n",
      "  - word 'abbey': 0.00981049489447\n",
      "  - word 'notr': 0.00897660096743\n",
      "  - word 'muscat': 0.0087725991546\n",
      "  - word 'picchu': 0.0087582745212\n",
      "  - word 'oman': 0.0087203774082\n",
      "- TOPIC 14 -\n",
      "  - word 'rock': 0.0146180145112\n",
      "  - word 'bat': 0.0137977089717\n",
      "  - word 'uluru': 0.0133631064995\n",
      "  - word 'legoland': 0.0128948159336\n",
      "  - word 'tokyo': 0.0124939509489\n",
      "  - word 'florenc': 0.0102089792396\n",
      "  - word 'belgrad': 0.0091679991132\n",
      "  - word 'reykjavik': 0.00867838450489\n",
      "  - word 'format': 0.00830527392938\n",
      "  - word 'prambanan': 0.0079706833494\n",
      "- TOPIC 15 -\n",
      "  - word 'dune': 0.0153863864873\n",
      "  - word 'moscow': 0.0136989306381\n",
      "  - word 'lima': 0.0126806866624\n",
      "  - word 'russian': 0.00866305414096\n",
      "  - word 'buddha': 0.00823959854703\n",
      "  - word 'iguana': 0.00822638748448\n",
      "  - word 'vancouv': 0.00749527104487\n",
      "  - word 'canyon': 0.00741866386957\n",
      "  - word 'belgrad': 0.00729018572986\n",
      "  - word 'gower': 0.00725051179583\n",
      "- TOPIC 16 -\n",
      "  - word 'templ': 0.0393273432588\n",
      "  - word 'church': 0.0392921322681\n",
      "  - word 'tomb': 0.015430211966\n",
      "  - word 'cathedr': 0.011348441702\n",
      "  - word 'egyptian': 0.00986118568403\n",
      "  - word 'budapest': 0.00981168001251\n",
      "  - word 'egypt': 0.00932838052954\n",
      "  - word 'danub': 0.00825713912323\n",
      "  - word 'basilica': 0.00818718721754\n",
      "  - word 'luxor': 0.00777056728058\n",
      "- TOPIC 17 -\n",
      "  - word 'acropoli': 0.0216974635903\n",
      "  - word 'ruin': 0.018167387371\n",
      "  - word 'cape': 0.0149715911441\n",
      "  - word 'mayan': 0.0130254489082\n",
      "  - word 'hippo': 0.0120477847718\n",
      "  - word 'sintra': 0.0114826937945\n",
      "  - word 'safari': 0.0100685683107\n",
      "  - word 'chateau': 0.00962118813393\n",
      "  - word 'athen': 0.00939150997137\n",
      "  - word 'mara': 0.00935908794824\n",
      "- TOPIC 18 -\n",
      "  - word 'dive': 0.0261100824638\n",
      "  - word 'havana': 0.024998782018\n",
      "  - word 'reef': 0.019419559256\n",
      "  - word 'snorkel': 0.0190927914341\n",
      "  - word 'cathedr': 0.0187768749331\n",
      "  - word 'coral': 0.0173235738731\n",
      "  - word 'fish': 0.0124963635403\n",
      "  - word 'shark': 0.0120854228752\n",
      "  - word 'cuba': 0.011562816402\n",
      "  - word 'cuban': 0.0109858489816\n",
      "- TOPIC 19 -\n",
      "  - word 'museum': 0.0212290293095\n",
      "  - word 'helsinki': 0.019590707957\n",
      "  - word 'lisbon': 0.018942974251\n",
      "  - word 'kong': 0.0140252351152\n",
      "  - word 'hong': 0.0137020247085\n",
      "  - word 'suomenlinna': 0.0114952172238\n",
      "  - word 'fou': 0.0114155940875\n",
      "  - word 'cheetah': 0.00980408454915\n",
      "  - word 'ipoh': 0.009463548623\n",
      "  - word 'mtr': 0.00945475938301\n"
     ]
    }
   ],
   "source": [
    "print_topics(topics, nltk_ls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = to_pickle(transformed, 'data/transformed_20.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "topics = model.describeTopics(maxTermsPerTopic=30).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- TOPIC 0 -\n",
      "  - word 'palac': 0.0391437436093\n",
      "  - word 'castl': 0.0347835681738\n",
      "  - word 'fort': 0.0181264415785\n",
      "  - word 'seoul': 0.0176783234263\n",
      "  - word 'copenhagen': 0.0137147294718\n",
      "  - word 'korean': 0.0124891402121\n",
      "  - word 'pragu': 0.0112232395745\n",
      "  - word 'korea': 0.009791533048\n",
      "  - word 'parliament': 0.00889395480336\n",
      "  - word 'garden': 0.00885978923237\n",
      "  - word 'edmonton': 0.00833871621775\n",
      "  - word 'sculptur': 0.0071207502416\n",
      "  - word 'museum': 0.00694328825027\n",
      "  - word 'statu': 0.00642846998394\n",
      "  - word 'tower': 0.00580757914838\n",
      "  - word 'audio': 0.00544559765411\n",
      "  - word 'histori': 0.0052701682644\n",
      "  - word 'royal': 0.00490155006673\n",
      "  - word 'danish': 0.00437242321843\n",
      "  - word 'build': 0.00393276059434\n",
      "  - word 'lobkowicz': 0.00350768540713\n",
      "  - word 'art': 0.00344623791097\n",
      "  - word 'klimt': 0.0033727854594\n",
      "  - word 'dmz': 0.00336788829287\n",
      "  - word 'fortress': 0.00332170691339\n",
      "  - word 'belveder': 0.00320798471762\n",
      "  - word 'turku': 0.00312348519642\n",
      "  - word 'architectur': 0.00310253643535\n",
      "  - word 'room': 0.00308269663941\n",
      "  - word 'tallinn': 0.00297321599719\n",
      "- TOPIC 1 -\n",
      "  - word 'dolphin': 0.0414207108434\n",
      "  - word 'aquarium': 0.0375584337744\n",
      "  - word 'shark': 0.0149105264004\n",
      "  - word 'dubai': 0.0134925454508\n",
      "  - word 'abu': 0.0128715646645\n",
      "  - word 'bucharest': 0.0111309079173\n",
      "  - word 'dhabi': 0.0110462810369\n",
      "  - word 'klcc': 0.0100862084927\n",
      "  - word 'potter': 0.00916688159209\n",
      "  - word 'harri': 0.00892475488562\n",
      "  - word 'munnar': 0.00864775743886\n",
      "  - word 'cayman': 0.00856937031149\n",
      "  - word 'fish': 0.00768942066869\n",
      "  - word 'tank': 0.0075749423762\n",
      "  - word 'romania': 0.00733164886517\n",
      "  - word 'stingray': 0.00713960672317\n",
      "  - word 'penguin': 0.0070910431899\n",
      "  - word 'tide': 0.00706511240286\n",
      "  - word 'sea': 0.00702885630889\n",
      "  - word 'whale': 0.00639998673231\n",
      "  - word 'marin': 0.00631548718576\n",
      "  - word 'islam': 0.00624639828916\n",
      "  - word 'mall': 0.00610165896255\n",
      "  - word 'kl': 0.00610075575651\n",
      "  - word 'emir': 0.00593489678772\n",
      "  - word 'ocean': 0.00550652705178\n",
      "  - word 'tea': 0.0053318954158\n",
      "  - word 'romanian': 0.00490588307556\n",
      "  - word 'aswan': 0.00486217993546\n",
      "  - word 'toronto': 0.0046266667555\n",
      "- TOPIC 2 -\n",
      "  - word 'glacier': 0.0384854759749\n",
      "  - word 'kyoto': 0.0152374337302\n",
      "  - word 'rickshaw': 0.0132583297236\n",
      "  - word 'london': 0.0126904986694\n",
      "  - word 'agora': 0.0118448010036\n",
      "  - word 'milford': 0.0111236976827\n",
      "  - word 'ebisuya': 0.0101567616223\n",
      "  - word 'gaudi': 0.0100336035029\n",
      "  - word 'bridg': 0.00940530866668\n",
      "  - word 'himeji': 0.00897889697922\n",
      "  - word 'turkey': 0.00790541047509\n",
      "  - word 'perito': 0.00788683452668\n",
      "  - word 'malmo': 0.00783653003085\n",
      "  - word 'iceberg': 0.00750373549892\n",
      "  - word 'arashiyama': 0.00745775128862\n",
      "  - word 'karnak': 0.00723969322173\n",
      "  - word 'blixen': 0.00695940253753\n",
      "  - word 'moreno': 0.00688586692199\n",
      "  - word 'ben': 0.00672049233331\n",
      "  - word 'tanzanit': 0.00654118365421\n",
      "  - word 'geyser': 0.00651728788912\n",
      "  - word 'maori': 0.00624105275961\n",
      "  - word 'rum': 0.00614609317283\n",
      "  - word 'poon': 0.00593197752135\n",
      "  - word 'pedrera': 0.00585350650937\n",
      "  - word 'karen': 0.00581465714329\n",
      "  - word 'templ': 0.00549975569306\n",
      "  - word 'barcelona': 0.00549910397372\n",
      "  - word 'albani': 0.00548838912942\n",
      "  - word 'ataturk': 0.00543789171187\n",
      "- TOPIC 3 -\n",
      "  - word 'dear': 0.0244662417239\n",
      "  - word 'slide': 0.0203116131626\n",
      "  - word 'theatr': 0.0196816875125\n",
      "  - word 'langkawi': 0.01461772007\n",
      "  - word 'ostrich': 0.013974668017\n",
      "  - word 'thank': 0.0126209198594\n",
      "  - word 'show': 0.0125372948072\n",
      "  - word 'canal': 0.0116185901571\n",
      "  - word 'feedback': 0.0112416340658\n",
      "  - word 'perform': 0.0109690887064\n",
      "  - word 'cemeteri': 0.00946388223862\n",
      "  - word 'regard': 0.00920410938461\n",
      "  - word 'waterpark': 0.00865753670589\n",
      "  - word 'theater': 0.00830400158862\n",
      "  - word 'panama': 0.00769817446098\n",
      "  - word 'constitut': 0.00692748451238\n",
      "  - word 'hello': 0.00686409721958\n",
      "  - word 'product': 0.00680739648201\n",
      "  - word 'lock': 0.00644966287152\n",
      "  - word 'music': 0.00562513468249\n",
      "  - word 'recoleta': 0.00534611845081\n",
      "  - word 'kokoda': 0.00533909322503\n",
      "  - word 'stratford': 0.0052357344486\n",
      "  - word 'actor': 0.00523398606663\n",
      "  - word 'stage': 0.00518772571918\n",
      "  - word 'costum': 0.00516855443181\n",
      "  - word 'seat': 0.00515229424608\n",
      "  - word 'cast': 0.00469364529897\n",
      "  - word 'mose': 0.00453624207669\n",
      "  - word 'aqua': 0.00446565733736\n",
      "- TOPIC 4 -\n",
      "  - word 'templ': 0.0365503700657\n",
      "  - word 'museum': 0.0195281487967\n",
      "  - word 'angkor': 0.0195230738927\n",
      "  - word 'wat': 0.0147520406224\n",
      "  - word 'cave': 0.0123882934578\n",
      "  - word 'jerusalem': 0.0114472142255\n",
      "  - word 'buddha': 0.0113513347034\n",
      "  - word 'bangkok': 0.0101103380313\n",
      "  - word 'neruda': 0.00939273714864\n",
      "  - word 'cambodia': 0.0091453605405\n",
      "  - word 'war': 0.00876045400869\n",
      "  - word 'exhibit': 0.00847449063149\n",
      "  - word 'maori': 0.00825990669715\n",
      "  - word 'reap': 0.00777623953691\n",
      "  - word 'siem': 0.00762699471137\n",
      "  - word 'tuk': 0.00740987525732\n",
      "  - word 'jesus': 0.00668051150515\n",
      "  - word 'israel': 0.00642765451071\n",
      "  - word 'audio': 0.00634111988589\n",
      "  - word 'histori': 0.00577533966882\n",
      "  - word 'model': 0.00565207795408\n",
      "  - word 'thailand': 0.00511299930528\n",
      "  - word 'ayutthaya': 0.00500761097296\n",
      "  - word 'khmer': 0.00485022449312\n",
      "  - word 'ruin': 0.00469724844993\n",
      "  - word 'mcg': 0.00449321635563\n",
      "  - word 'holocaust': 0.00411687246648\n",
      "  - word 'dunedin': 0.00404470815192\n",
      "  - word 'bayon': 0.00391162307374\n",
      "  - word 'display': 0.00387641256243\n",
      "- TOPIC 5 -\n",
      "  - word 'fjord': 0.0190642125868\n",
      "  - word 'beij': 0.0175530309601\n",
      "  - word 'cabl': 0.0167550992134\n",
      "  - word 'rio': 0.014925475262\n",
      "  - word 'taj': 0.0137896110446\n",
      "  - word 'moai': 0.0124736179313\n",
      "  - word 'mahal': 0.0113347618659\n",
      "  - word 'che': 0.00920288618808\n",
      "  - word 'catherin': 0.00873590269735\n",
      "  - word 'agra': 0.00858273151666\n",
      "  - word 'orm': 0.00856574678466\n",
      "  - word 'geirang': 0.00748882354387\n",
      "  - word 'china': 0.00684850898669\n",
      "  - word 'batik': 0.0067328931583\n",
      "  - word 'lu': 0.00667321939153\n",
      "  - word 'duomo': 0.00661412108256\n",
      "  - word 'fort': 0.00620859118123\n",
      "  - word 'mountain': 0.00613938018958\n",
      "  - word 'forbidden': 0.00568851423344\n",
      "  - word 'monument': 0.00511466672382\n",
      "  - word 'orchid': 0.0050455284023\n",
      "  - word 'chines': 0.0049419058598\n",
      "  - word 'mutianyu': 0.00489516588352\n",
      "  - word 'treati': 0.00486504999888\n",
      "  - word 'norway': 0.00480764958084\n",
      "  - word 'arch': 0.00478480564764\n",
      "  - word 'altun': 0.00450617059067\n",
      "  - word 'car': 0.00439344323593\n",
      "  - word 'delhi': 0.00437386536433\n",
      "  - word 'wall': 0.00437095038863\n",
      "- TOPIC 6 -\n",
      "  - word 'cusco': 0.0192617045196\n",
      "  - word 'inca': 0.0190411878683\n",
      "  - word 'volcano': 0.018143113505\n",
      "  - word 'costa': 0.0129613217098\n",
      "  - word 'bird': 0.0124513032899\n",
      "  - word 'rica': 0.0119113479045\n",
      "  - word 'san': 0.00959932867\n",
      "  - word 'crocodil': 0.00952720928317\n",
      "  - word 'auckland': 0.0082469268996\n",
      "  - word 'chocol': 0.00790649228203\n",
      "  - word 'peru': 0.0077180552951\n",
      "  - word 'monkey': 0.00769404522872\n",
      "  - word 'machu': 0.00728454692036\n",
      "  - word 'jose': 0.00718268486619\n",
      "  - word 'shaw': 0.00696882592722\n",
      "  - word 'panda': 0.00678235241806\n",
      "  - word 'bridg': 0.00644142530553\n",
      "  - word 'granada': 0.00629676340027\n",
      "  - word 'anim': 0.00621573865776\n",
      "  - word 'crater': 0.00604283774069\n",
      "  - word 'picchu': 0.00586001397201\n",
      "  - word 'spanish': 0.00578604591953\n",
      "  - word 'balboa': 0.00578251078762\n",
      "  - word 'incan': 0.00542851536377\n",
      "  - word 'macaw': 0.0050474552069\n",
      "  - word 'parqu': 0.00472729340829\n",
      "  - word 'nicaragua': 0.00459642855954\n",
      "  - word 'toucan': 0.00458656053391\n",
      "  - word 'croc': 0.00432226534465\n",
      "  - word 'diego': 0.00401305697789\n",
      "- TOPIC 7 -\n",
      "  - word 'zoo': 0.0761927331459\n",
      "  - word 'anim': 0.034126206484\n",
      "  - word 'opera': 0.0330872863933\n",
      "  - word 'berlin': 0.0149085454134\n",
      "  - word 'ballet': 0.00980187364572\n",
      "  - word 'enclosur': 0.00935483780175\n",
      "  - word 'sydney': 0.00869304357035\n",
      "  - word 'melbourn': 0.00806597924169\n",
      "  - word 'war': 0.00777240328301\n",
      "  - word 'wreck': 0.00765398817604\n",
      "  - word 'tiger': 0.00728884886861\n",
      "  - word 'owl': 0.00709284771651\n",
      "  - word 'harbour': 0.00617682873437\n",
      "  - word 'giraff': 0.00557637504185\n",
      "  - word 'memori': 0.00525267764447\n",
      "  - word 'perform': 0.00471275157122\n",
      "  - word 'penguin': 0.00465907813608\n",
      "  - word 'exhibit': 0.00458016654294\n",
      "  - word 'australian': 0.00449801139603\n",
      "  - word 'lion': 0.00430755231305\n",
      "  - word 'lanta': 0.00427215931225\n",
      "  - word 'orchestra': 0.00425687870242\n",
      "  - word 'mexico': 0.00403271551734\n",
      "  - word 'churchil': 0.00395519555524\n",
      "  - word 'brandenburg': 0.00379498342271\n",
      "  - word 'submarin': 0.00376705379745\n",
      "  - word 'museum': 0.00370986691846\n",
      "  - word 'concert': 0.00363044636281\n",
      "  - word 'cat': 0.00334544182848\n",
      "  - word 'bear': 0.00331074927044\n",
      "- TOPIC 8 -\n",
      "  - word 'hi': 0.0148506297616\n",
      "  - word 'kidzania': 0.0135922399667\n",
      "  - word 'ride': 0.0117518290726\n",
      "  - word 'farm': 0.0116175830673\n",
      "  - word 'peppa': 0.0112887771204\n",
      "  - word 'feedback': 0.010943233805\n",
      "  - word 'park': 0.0109329870284\n",
      "  - word 'thank': 0.0107669720944\n",
      "  - word 'seal': 0.00942503240781\n",
      "  - word 'theme': 0.00871786837903\n",
      "  - word 'huski': 0.00825528352802\n",
      "  - word 'oslo': 0.00801813372896\n",
      "  - word 'zip': 0.00779183946512\n",
      "  - word 'kid': 0.00769204300152\n",
      "  - word 'proyecto': 0.00736227882079\n",
      "  - word 'turtl': 0.0071713827543\n",
      "  - word 'coaster': 0.00708650130917\n",
      "  - word 'amus': 0.006975359799\n",
      "  - word 'mumbai': 0.00694222704157\n",
      "  - word 'oliv': 0.00658129402517\n",
      "  - word 'kai': 0.00656822785847\n",
      "  - word 'fun': 0.00604762401714\n",
      "  - word 'pig': 0.00597284748053\n",
      "  - word 'dear': 0.00596243911792\n",
      "  - word 'vike': 0.00575958537139\n",
      "  - word 'asi': 0.0056446995116\n",
      "  - word 'regard': 0.00555342893457\n",
      "  - word 'pepper': 0.00546788519377\n",
      "  - word 'roller': 0.00489038039843\n",
      "  - word 'anim': 0.00472559925642\n",
      "- TOPIC 9 -\n",
      "  - word 'eleph': 0.0404602493499\n",
      "  - word 'fall': 0.0161604102752\n",
      "  - word 'monkey': 0.0159266641373\n",
      "  - word 'bali': 0.0139589965324\n",
      "  - word 'safari': 0.0113678411335\n",
      "  - word 'apartheid': 0.00972216945104\n",
      "  - word 'brazilian': 0.00959541248832\n",
      "  - word 'silk': 0.00850803321696\n",
      "  - word 'ephesus': 0.00816348582185\n",
      "  - word 'mandela': 0.00807195439418\n",
      "  - word 'iguazu': 0.00781539789771\n",
      "  - word 'argentinian': 0.0077398843171\n",
      "  - word 'niagara': 0.00755370267595\n",
      "  - word 'jaipur': 0.0073688699024\n",
      "  - word 'pattaya': 0.00727067243775\n",
      "  - word 'jakarta': 0.00724153367853\n",
      "  - word 'landmin': 0.00680331682458\n",
      "  - word 'brazil': 0.00678956108911\n",
      "  - word 'rice': 0.00675975678099\n",
      "  - word 'sloth': 0.00671347496092\n",
      "  - word 'mahmya': 0.00670026910111\n",
      "  - word 'balines': 0.00603505295572\n",
      "  - word 'argentina': 0.00586307304077\n",
      "  - word 'anim': 0.0057060025479\n",
      "  - word 'ubud': 0.00527511419244\n",
      "  - word 'alcatraz': 0.00523665581754\n",
      "  - word 'thailand': 0.00516030101014\n",
      "  - word 'jim': 0.00506686665869\n",
      "  - word 'rahul': 0.00485773793921\n",
      "  - word 'forest': 0.00478723831647\n",
      "- TOPIC 10 -\n",
      "  - word 'beach': 0.0438720989207\n",
      "  - word 'island': 0.0208480681178\n",
      "  - word 'iceland': 0.0184204410859\n",
      "  - word 'wonderla': 0.0175913120083\n",
      "  - word 'waterfal': 0.0161915325493\n",
      "  - word 'pool': 0.0141416044421\n",
      "  - word 'sand': 0.012884384919\n",
      "  - word 'phi': 0.0117464007245\n",
      "  - word 'boat': 0.0109184954113\n",
      "  - word 'pyramid': 0.0105545592521\n",
      "  - word 'water': 0.0087291538935\n",
      "  - word 'wave': 0.00866334643225\n",
      "  - word 'lake': 0.00739253283445\n",
      "  - word 'lagoon': 0.00693027276616\n",
      "  - word 'thermal': 0.00634655900792\n",
      "  - word 'swim': 0.00581670399771\n",
      "  - word 'spring': 0.00581034013876\n",
      "  - word 'perth': 0.00553094515801\n",
      "  - word 'geotherm': 0.00514386792785\n",
      "  - word 'ferri': 0.00494310330663\n",
      "  - word 'cave': 0.00480888263876\n",
      "  - word 'ignacio': 0.00436682854019\n",
      "  - word 'crystal': 0.00416237736652\n",
      "  - word 'bath': 0.0036586430086\n",
      "  - word 'park': 0.00358075934295\n",
      "  - word 'geyser': 0.00349335697411\n",
      "  - word 'mangrov': 0.00341329570157\n",
      "  - word 'blue': 0.0034062671892\n",
      "  - word 'roatan': 0.00337345216413\n",
      "  - word 'fall': 0.00336606361836\n",
      "- TOPIC 11 -\n",
      "  - word 'mosqu': 0.033893383432\n",
      "  - word 'medina': 0.019100957174\n",
      "  - word 'kathmandu': 0.0149788730003\n",
      "  - word 'istanbul': 0.0147344016459\n",
      "  - word 'marrakech': 0.013061680495\n",
      "  - word 'templ': 0.0114838984976\n",
      "  - word 'tallinn': 0.0109910730931\n",
      "  - word 'squar': 0.00965511033604\n",
      "  - word 'morocco': 0.00941139020548\n",
      "  - word 'sofia': 0.00877281249305\n",
      "  - word 'nepal': 0.00861792601212\n",
      "  - word 'hiroshima': 0.00846184573276\n",
      "  - word 'durbar': 0.0082860637228\n",
      "  - word 'bomb': 0.00795672420916\n",
      "  - word 'stupa': 0.0077364174741\n",
      "  - word 'annapurna': 0.00735784144194\n",
      "  - word 'ski': 0.00724256009942\n",
      "  - word 'earthquak': 0.00714500195708\n",
      "  - word 'atom': 0.00711233258818\n",
      "  - word 'pokhara': 0.00694913945473\n",
      "  - word 'hagia': 0.00594711044513\n",
      "  - word 'porto': 0.00489884757711\n",
      "  - word 'sophia': 0.00486835719149\n",
      "  - word 'dome': 0.00481044644423\n",
      "  - word 'vinal': 0.00474172634725\n",
      "  - word 'bhaktapur': 0.00464383921414\n",
      "  - word 'muslim': 0.00458373508514\n",
      "  - word 'marrakesh': 0.00448052651631\n",
      "  - word 'toompea': 0.00443191013806\n",
      "  - word 'hermitag': 0.00432090299476\n",
      "- TOPIC 12 -\n",
      "  - word 'garden': 0.0427975168672\n",
      "  - word 'botan': 0.0163828496719\n",
      "  - word 'art': 0.0120940228507\n",
      "  - word 'vienna': 0.0115221411819\n",
      "  - word 'plant': 0.010122780123\n",
      "  - word 'monet': 0.00998618502467\n",
      "  - word 'museum': 0.00945536647521\n",
      "  - word 'pragu': 0.00907542671168\n",
      "  - word 'flower': 0.00849684570767\n",
      "  - word 'fountain': 0.00833356637311\n",
      "  - word 'dubai': 0.00791422791227\n",
      "  - word 'vallarta': 0.00687446261302\n",
      "  - word 'panama': 0.006860777641\n",
      "  - word 'collect': 0.00667215282148\n",
      "  - word 'paint': 0.00624531440647\n",
      "  - word 'galleri': 0.00620883232353\n",
      "  - word 'alhambra': 0.00615789208095\n",
      "  - word 'casino': 0.00611939051415\n",
      "  - word 'salzburg': 0.00591014142927\n",
      "  - word 'impressionist': 0.00590808590123\n",
      "  - word 'picasso': 0.00585643664617\n",
      "  - word 'exhibit': 0.00569317818851\n",
      "  - word 'clock': 0.0052142785989\n",
      "  - word 'burj': 0.00469766609514\n",
      "  - word 'artist': 0.00434781570748\n",
      "  - word 'greenhous': 0.00406183171485\n",
      "  - word 'tree': 0.00388806571816\n",
      "  - word 'ramoji': 0.0037534332648\n",
      "  - word 'mall': 0.00374621382437\n",
      "  - word 'albertina': 0.00367597269973\n",
      "- TOPIC 13 -\n",
      "  - word 'monasteri': 0.0201684622684\n",
      "  - word 'stockholm': 0.0181573878383\n",
      "  - word 'butterfli': 0.0145784818234\n",
      "  - word 'penang': 0.0122109061019\n",
      "  - word 'hike': 0.0102553787678\n",
      "  - word 'abbey': 0.00981049489447\n",
      "  - word 'notr': 0.00897660096743\n",
      "  - word 'muscat': 0.0087725991546\n",
      "  - word 'picchu': 0.0087582745212\n",
      "  - word 'oman': 0.0087203774082\n",
      "  - word 'ruin': 0.00852809495486\n",
      "  - word 'cathedr': 0.00830915676037\n",
      "  - word 'dame': 0.00827250407692\n",
      "  - word 'pari': 0.00811126157502\n",
      "  - word 'sevill': 0.00794419696144\n",
      "  - word 'waimangu': 0.00693039414769\n",
      "  - word 'mansion': 0.00687827519061\n",
      "  - word 'mont': 0.00683889918764\n",
      "  - word 'delphi': 0.00650979042575\n",
      "  - word 'wadi': 0.00614090064578\n",
      "  - word 'salt': 0.00570037596809\n",
      "  - word 'meteora': 0.0056501016805\n",
      "  - word 'ile': 0.00564123728062\n",
      "  - word 'plovdiv': 0.00562977439571\n",
      "  - word 'machu': 0.00527613938286\n",
      "  - word 'melk': 0.00526139085917\n",
      "  - word 'vasa': 0.00522258731483\n",
      "  - word 'spice': 0.00510591167437\n",
      "  - word 'omani': 0.00504735236981\n",
      "  - word 'oaxaca': 0.00497737518104\n",
      "- TOPIC 14 -\n",
      "  - word 'rock': 0.0146180145112\n",
      "  - word 'bat': 0.0137977089717\n",
      "  - word 'uluru': 0.0133631064995\n",
      "  - word 'legoland': 0.0128948159336\n",
      "  - word 'tokyo': 0.0124939509489\n",
      "  - word 'florenc': 0.0102089792396\n",
      "  - word 'belgrad': 0.0091679991132\n",
      "  - word 'reykjavik': 0.00867838450489\n",
      "  - word 'format': 0.00830527392938\n",
      "  - word 'prambanan': 0.0079706833494\n",
      "  - word 'kata': 0.00747553206183\n",
      "  - word 'disney': 0.00723195709902\n",
      "  - word 'surf': 0.00714469826376\n",
      "  - word 'disneyland': 0.0070144799451\n",
      "  - word 'duomo': 0.00654054276472\n",
      "  - word 'lego': 0.0064839888013\n",
      "  - word 'mongolia': 0.00632033672585\n",
      "  - word 'borobudur': 0.00619596171931\n",
      "  - word 'tjuta': 0.00616305444721\n",
      "  - word 'bondi': 0.00579280642577\n",
      "  - word 'gorg': 0.0057643308868\n",
      "  - word 'hike': 0.00561645305331\n",
      "  - word 'grotto': 0.00561623037332\n",
      "  - word 'cave': 0.00525264487618\n",
      "  - word 'fortress': 0.00523181548682\n",
      "  - word 'sunris': 0.00513041376243\n",
      "  - word 'nami': 0.0050106615237\n",
      "  - word 'sunset': 0.00486123434149\n",
      "  - word 'climb': 0.00448860551971\n",
      "  - word 'eilat': 0.00445799732639\n",
      "- TOPIC 15 -\n",
      "  - word 'dune': 0.0153863864873\n",
      "  - word 'moscow': 0.0136989306381\n",
      "  - word 'lima': 0.0126806866624\n",
      "  - word 'russian': 0.00866305414096\n",
      "  - word 'buddha': 0.00823959854703\n",
      "  - word 'iguana': 0.00822638748448\n",
      "  - word 'vancouv': 0.00749527104487\n",
      "  - word 'canyon': 0.00741866386957\n",
      "  - word 'belgrad': 0.00729018572986\n",
      "  - word 'gower': 0.00725051179583\n",
      "  - word 'desert': 0.00707967462004\n",
      "  - word 'fountain': 0.00634743290674\n",
      "  - word 'concert': 0.00620419667956\n",
      "  - word 'park': 0.00566006287136\n",
      "  - word 'prado': 0.0055864833373\n",
      "  - word 'bryce': 0.00547447019632\n",
      "  - word 'miraflor': 0.0054543055052\n",
      "  - word 'street': 0.00544125309335\n",
      "  - word 'madrid': 0.00515741119502\n",
      "  - word 'samara': 0.00506361692853\n",
      "  - word 'bolshoi': 0.00473326640333\n",
      "  - word 'totem': 0.00471760392264\n",
      "  - word 'palau': 0.00468270057504\n",
      "  - word 'bike': 0.00458858072041\n",
      "  - word 'squar': 0.00457033809595\n",
      "  - word 'nunneri': 0.00456823992564\n",
      "  - word 'stanley': 0.0044070734283\n",
      "  - word 'flamenco': 0.00439466078739\n",
      "  - word 'nairobi': 0.00438245849949\n",
      "  - word 'kremlin': 0.00423983330192\n",
      "- TOPIC 16 -\n",
      "  - word 'templ': 0.0393273432588\n",
      "  - word 'church': 0.0392921322681\n",
      "  - word 'tomb': 0.015430211966\n",
      "  - word 'cathedr': 0.011348441702\n",
      "  - word 'egyptian': 0.00986118568403\n",
      "  - word 'budapest': 0.00981168001251\n",
      "  - word 'egypt': 0.00932838052954\n",
      "  - word 'danub': 0.00825713912323\n",
      "  - word 'basilica': 0.00818718721754\n",
      "  - word 'luxor': 0.00777056728058\n",
      "  - word 'museum': 0.00704827851173\n",
      "  - word 'cairo': 0.00667901310198\n",
      "  - word 'chapel': 0.00634325566288\n",
      "  - word 'architectur': 0.00618066138461\n",
      "  - word 'edinburgh': 0.0059657799256\n",
      "  - word 'statu': 0.00585994112501\n",
      "  - word 'delhi': 0.00546913388286\n",
      "  - word 'holi': 0.00533716892234\n",
      "  - word 'histori': 0.00519325871678\n",
      "  - word 'religi': 0.00495968911196\n",
      "  - word 'sikh': 0.00470685107716\n",
      "  - word 'mummi': 0.00466098130489\n",
      "  - word 'gurudwara': 0.00441853170676\n",
      "  - word 'crypt': 0.00430269804081\n",
      "  - word 'arthur': 0.00426739757609\n",
      "  - word 'prison': 0.00407724908992\n",
      "  - word 'kargil': 0.00396621673543\n",
      "  - word 'gothic': 0.00385389631641\n",
      "  - word 'christian': 0.00374464715537\n",
      "  - word 'lucknow': 0.00367827802843\n",
      "- TOPIC 17 -\n",
      "  - word 'acropoli': 0.0216974635903\n",
      "  - word 'ruin': 0.018167387371\n",
      "  - word 'cape': 0.0149715911441\n",
      "  - word 'mayan': 0.0130254489082\n",
      "  - word 'hippo': 0.0120477847718\n",
      "  - word 'sintra': 0.0114826937945\n",
      "  - word 'safari': 0.0100685683107\n",
      "  - word 'chateau': 0.00962118813393\n",
      "  - word 'athen': 0.00939150997137\n",
      "  - word 'mara': 0.00935908794824\n",
      "  - word 'lion': 0.00851478072232\n",
      "  - word 'bergen': 0.00831557537795\n",
      "  - word 'serengeti': 0.0078771205001\n",
      "  - word 'parthenon': 0.00761740297522\n",
      "  - word 'crater': 0.00742566903436\n",
      "  - word 'rhino': 0.00735102945093\n",
      "  - word 'kilimanjaro': 0.00725813437674\n",
      "  - word 'itza': 0.00697421210962\n",
      "  - word 'chichen': 0.0068727530132\n",
      "  - word 'mount': 0.00684972103025\n",
      "  - word 'stavang': 0.00673802251847\n",
      "  - word 'cabl': 0.00637852165367\n",
      "  - word 'zebra': 0.00624021123572\n",
      "  - word 'mountain': 0.00587193899124\n",
      "  - word 'nessebar': 0.00582156051142\n",
      "  - word 'wildebeest': 0.00566404273734\n",
      "  - word 'anim': 0.00562168364396\n",
      "  - word 'gorg': 0.00542982632716\n",
      "  - word 'fliuyen': 0.00542452473369\n",
      "  - word 'tanzania': 0.00502392595174\n",
      "- TOPIC 18 -\n",
      "  - word 'dive': 0.0261100824638\n",
      "  - word 'havana': 0.024998782018\n",
      "  - word 'reef': 0.019419559256\n",
      "  - word 'snorkel': 0.0190927914341\n",
      "  - word 'cathedr': 0.0187768749331\n",
      "  - word 'coral': 0.0173235738731\n",
      "  - word 'fish': 0.0124963635403\n",
      "  - word 'shark': 0.0120854228752\n",
      "  - word 'cuba': 0.011562816402\n",
      "  - word 'cuban': 0.0109858489816\n",
      "  - word 'church': 0.0103865254714\n",
      "  - word 'turtl': 0.00950998422096\n",
      "  - word 'santiago': 0.00943247117454\n",
      "  - word 'villa': 0.00859481071671\n",
      "  - word 'akaroa': 0.00839101436145\n",
      "  - word 'plaza': 0.00837238915647\n",
      "  - word 'rome': 0.00822196137741\n",
      "  - word 'diver': 0.00808343679722\n",
      "  - word 'boat': 0.00799421373899\n",
      "  - word 'hemingway': 0.00744648043515\n",
      "  - word 'island': 0.00733407847745\n",
      "  - word 'burano': 0.00727616685392\n",
      "  - word 'venic': 0.00709884953972\n",
      "  - word 'ray': 0.00700257306393\n",
      "  - word 'squar': 0.00649313810619\n",
      "  - word 'roman': 0.0061144959503\n",
      "  - word 'mosaic': 0.00608033518598\n",
      "  - word 'tivoli': 0.00579314947927\n",
      "  - word 'sea': 0.00546854760355\n",
      "  - word 'cozumel': 0.00536205589277\n",
      "- TOPIC 19 -\n",
      "  - word 'museum': 0.0212290293095\n",
      "  - word 'helsinki': 0.019590707957\n",
      "  - word 'lisbon': 0.018942974251\n",
      "  - word 'kong': 0.0140252351152\n",
      "  - word 'hong': 0.0137020247085\n",
      "  - word 'suomenlinna': 0.0114952172238\n",
      "  - word 'fou': 0.0114155940875\n",
      "  - word 'cheetah': 0.00980408454915\n",
      "  - word 'ipoh': 0.009463548623\n",
      "  - word 'mtr': 0.00945475938301\n",
      "  - word 'acrobat': 0.00867685999746\n",
      "  - word 'ferri': 0.00811552068493\n",
      "  - word 'hk': 0.00744960659147\n",
      "  - word 'lemur': 0.00725319786872\n",
      "  - word 'du': 0.00702212713234\n",
      "  - word 'sphinx': 0.00641686329654\n",
      "  - word 'finnish': 0.00639079687871\n",
      "  - word 'show': 0.00623733233866\n",
      "  - word 'collect': 0.0060877298375\n",
      "  - word 'shrine': 0.0059462291258\n",
      "  - word 'chin': 0.00586590131621\n",
      "  - word 'artifact': 0.00580213983862\n",
      "  - word 'exhibit': 0.00563800324585\n",
      "  - word 'island': 0.00525816483761\n",
      "  - word 'metro': 0.00525571564475\n",
      "  - word 'han': 0.00511694469055\n",
      "  - word 'hakka': 0.00509874051723\n",
      "  - word 'knosso': 0.00509719597547\n",
      "  - word 'station': 0.00503488546682\n",
      "  - word 'tram': 0.00500901441541\n"
     ]
    }
   ],
   "source": [
    "print_topics(topics, nltk_ls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"lda_20.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "type object 'LDA' has no attribute 'DistributedLDAModel'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-59-ca9b23a991f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLDA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDistributedLDAModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'lda_20.model'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: type object 'LDA' has no attribute 'DistributedLDAModel'"
     ]
    }
   ],
   "source": [
    "model2 = LDA.DistributedLDAModel.load('lda_20.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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