Research_LDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OCR and LDA on Lit Review Docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pdfminer as pdf\n",
    "import os\n",
    "import glob\n",
    "import pickle as pkl\n",
    "import pandas as pd\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.parsing.preprocessing import STOPWORDS\n",
    "import gensim\n",
    "import unicodedata\n",
    "import re, string\n",
    "from gensim import corpora, models\n",
    "import gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get a blob of the pdf filenames, and turn them into a list\n",
    "files = glob.glob('/Users/sararogis/Dropbox/FoodRecommender/MealRec_LitReview/*.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/sararogis/Dropbox/FoodRecommender/MealRec_LitReview/AutomatedRecHealthyPersMeals.pdf',\n",
       " '/Users/sararogis/Dropbox/FoodRecommender/MealRec_LitReview/Biologeek.pdf',\n",
       " '/Users/sararogis/Dropbox/FoodRecommender/MealRec_LitReview/BuonApetitoRestMealRec.pdf',\n",
       " '/Users/sararogis/Dropbox/FoodRecommender/MealRec_LitReview/ChallengesNutrRecSys.pdf',\n",
       " '/Users/sararogis/Dropbox/FoodRecommender/MealRec_LitReview/ClusteringEliminateSpam.pdf']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text_files = glob.glob('/Users/sararogis/Dropbox/FoodRecommender/MealRec_LitReview/LitRev_Text/*.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for x in files:\n",
    "    dest_loc = x.replace('.pdf','.txt').replace('MealRec_LitReview/','MealRec_LitReview/LitRev_Text/')\n",
    "    ocr_cmd = 'pdf2txt.py -o ' + dest_loc + ' -t text ' + x\n",
    "    os.system(ocr_cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "text_files[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Ok, now pull these text files in and lets do some lda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_texts = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for textpath in text_files:\n",
    "    # Set an open string to place document text into\n",
    "    all_doc_string = ''\n",
    "    \n",
    "    # Open the file and save contents to variable 'file_text'\n",
    "    file_text = open(textpath, 'rb')\n",
    "    \n",
    "    # Read the lines of the tester into a variable. \n",
    "    file_lines = file_text.readlines()\n",
    "    \n",
    "    # Iterate through the lines, and append them to the string to make one big text blob\n",
    "    for line in file_lines:\n",
    "        all_doc_string = all_doc_string + line\n",
    "        \n",
    "    # Append the new string to the list of all files\n",
    "    all_texts.append(all_doc_string)\n",
    "    \n",
    "    # String will reset next; close the file and flush the file buffer\n",
    "    file_text.flush()\n",
    "    file_text.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(text_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Hmmm. One off, but what the heck. Save it. \n",
    "pkl.dump(all_texts, open('all_texts_mealrec_litreview.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(all_texts,columns=['orig_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def preprocess_text(text):\n",
    "    from nltk.corpus import stopwords\n",
    "    from nltk.tokenize import LineTokenizer\n",
    "    from nltk.tokenize import WhitespaceTokenizer\n",
    "    from nltk.stem.porter import PorterStemmer\n",
    "    \n",
    "    # Create p_stemmer of class PorterStemmer\n",
    "    p_stemmer = PorterStemmer()\n",
    "    \n",
    "    sw = list(stopwords.words())\n",
    "    extra_stops = ['R', '', ' ', 'abstract', 'keywords', 'introduction', 'figure','morgan', 'harvey',\n",
    "                   'david','elsweiler','northumbria','university','newcastle','united','kingdom','university',\n",
    "                   'regensburg','germany', 'h', 'k', 'f', 'b', 'user', 'g', 'use']\n",
    "    for word in extra_stops:\n",
    "        sw.append(word)\n",
    "    \n",
    "    # Step 1 - Clean up unicode\n",
    "    clean_string = ''\n",
    "    doc = []\n",
    "    #for x in text:\n",
    "    #    if ord(x) <= 128:\n",
    "    #        clean_string += x\n",
    "    #clean_string = unicodedata.normalize('NFKD', clean_string.encode('utf-8', 'replace')).encode('ascii','replace')\n",
    "            \n",
    "    # Tokenize each line to get rid of the line carriages\n",
    "    lines = LineTokenizer().tokenize(text.lower())\n",
    "    \n",
    "    clean_lines = []\n",
    "    \n",
    "    for line in lines:\n",
    "        if line.startswith('e-mail') or line.startswith('doi') or line.startswith('For all other uses, contact') or line.find(' acm. isbn ') > 0:\n",
    "            pass\n",
    "        else:\n",
    "            line_str = ''\n",
    "            for char in line:\n",
    "                #if ord(char) <= 127:\n",
    "                if (char in string.ascii_letters) or char == ' ':\n",
    "                    line_str += char\n",
    "        \n",
    "        # Clean up other undesirable characters\n",
    "            if line_str != ' ' and line_str.rstrip().lstrip() not in sw:\n",
    "                clean_lines.append(line_str)\n",
    "    \n",
    "    # Tokenize the lines\n",
    "    for clean_line in clean_lines:\n",
    "        tokens = WhitespaceTokenizer().tokenize(clean_line)\n",
    "        stopped_tokens = [i for i in tokens if not i in sw]\n",
    "        stemmed_tokens = [p_stemmer.stem(i) for i in stopped_tokens]\n",
    "        [doc.append(i) for i in stemmed_tokens]\n",
    "        \n",
    "    \n",
    "    return doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.insert(df.shape[1], 'clean_text', df.orig_text.apply(preprocess_text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# turn our tokenized documents into a id <-> term dictionary\n",
    "dictionary = corpora.Dictionary(df.clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert tokenized documents into a document-term matrix\n",
    "corpus = [dictionary.doc2bow(text) for text in df.clean_text]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# generate LDA model\n",
    "ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=10, id2word = dictionary, passes=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ldamodel4 = gensim.models.ldamodel.LdaModel(corpus, num_topics=4, id2word = dictionary, passes=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ldamodel3 = gensim.models.ldamodel.LdaModel(corpus, num_topics=3, id2word = dictionary, passes=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, u'0.016*food + 0.013*algorithm + 0.012*cid + 0.009*r'), (1, u'0.000*food + 0.000*recip + 0.000*system + 0.000*recommend'), (2, u'0.028*recip + 0.023*menu + 0.015*recommend + 0.013*food'), (3, u'0.040*recip + 0.019*ingredi + 0.009*similar + 0.008*use'), (4, u'0.000*food + 0.000*recip + 0.000*use + 0.000*system'), (5, u'0.000*food + 0.000*recommend + 0.000*recip + 0.000*system'), (6, u'0.000*recip + 0.000*food + 0.000*system + 0.000*ingredi'), (7, u'0.048*food + 0.014*practic + 0.011*particip + 0.011*wast'), (8, u'0.017*system + 0.013*user + 0.012*product + 0.010*recip'), (9, u'0.024*recip + 0.022*recommend + 0.018*system + 0.016*user')]\n"
     ]
    }
   ],
   "source": [
    "print(ldamodel.print_topics(num_topics=10, num_words=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pyLDAvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "7      29.504711        1       1 -0.106822 -0.173555\n",
       "9      27.416633        1       2 -0.168737  0.027879\n",
       "3      13.685156        1       3 -0.090105  0.050671\n",
       "8      11.325331        1       4 -0.130220 -0.122096\n",
       "2       9.346031        1       5 -0.140154  0.090575\n",
       "0       8.708459        1       6 -0.094952  0.128187\n",
       "5       0.003420        1       7  0.181959 -0.000537\n",
       "4       0.003420        1       8  0.182305 -0.000579\n",
       "6       0.003420        1       9  0.183362 -0.000277\n",
       "1       0.003420        1      10  0.183365 -0.000266, topic_info=     Category         Freq       Term        Total  loglift  logprob\n",
       "term                                                                \n",
       "1248  Default  1565.000000      recip  1565.000000  30.0000  30.0000\n",
       "300   Default   238.000000       menu   238.000000  29.0000  29.0000\n",
       "3434  Default  1945.000000       food  1945.000000  28.0000  28.0000\n",
       "7283  Default   402.000000    practic   402.000000  27.0000  27.0000\n",
       "4185  Default   595.000000    ingredi   595.000000  26.0000  26.0000\n",
       "4460  Default   902.000000  recommend   902.000000  25.0000  25.0000\n",
       "216   Default   292.000000       wast   292.000000  24.0000  24.0000\n",
       "1873  Default   895.000000     system   895.000000  23.0000  23.0000\n",
       "1621  Default   371.000000  algorithm   371.000000  22.0000  22.0000\n",
       "2435  Default   581.000000       rate   581.000000  21.0000  21.0000\n",
       "5238  Default   276.000000       shop   276.000000  20.0000  20.0000\n",
       "2845  Default   208.000000    product   208.000000  19.0000  19.0000\n",
       "3607  Default   415.000000   particip   415.000000  18.0000  18.0000\n",
       "1590  Default   183.000000        cid   183.000000  17.0000  17.0000\n",
       "4901  Default   414.000000     featur   414.000000  16.0000  16.0000\n",
       "4582  Default   824.000000       user   824.000000  15.0000  15.0000\n",
       "2738  Default   118.000000      graph   118.000000  14.0000  14.0000\n",
       "3804  Default   295.000000    similar   295.000000  13.0000  13.0000\n",
       "620   Default   355.000000     prefer   355.000000  12.0000  12.0000\n",
       "3531  Default   114.000000    groceri   114.000000  11.0000  11.0000\n",
       "7382  Default   212.000000          r   212.000000  10.0000  10.0000\n",
       "198   Default   110.000000      navig   110.000000   9.0000   9.0000\n",
       "3921  Default   134.000000      fridg   134.000000   8.0000   8.0000\n",
       "1162  Default   282.000000     social   282.000000   7.0000   7.0000\n",
       "101   Default   142.000000  household   142.000000   6.0000   6.0000\n",
       "4267  Default   157.000000       home   157.000000   5.0000   5.0000\n",
       "3642  Default    90.000000       imag    90.000000   4.0000   4.0000\n",
       "1343  Default   300.000000       plan   300.000000   3.0000   3.0000\n",
       "1385  Default   315.000000        set   315.000000   2.0000   2.0000\n",
       "304   Default    97.000000       busi    97.000000   1.0000   1.0000\n",
       "...       ...          ...        ...          ...      ...      ...\n",
       "3434  Topic10     0.000497       food  1945.074469  -4.8957  -8.7684\n",
       "1248  Topic10     0.000486      recip  1565.882060  -4.7017  -8.7912\n",
       "4460  Topic10     0.000454  recommend   902.943697  -4.2194  -8.8594\n",
       "4582  Topic10     0.000437       user   824.684741  -4.1673  -8.8980\n",
       "4901  Topic10     0.000422     featur   414.842097  -3.5155  -8.9333\n",
       "2435  Topic10     0.000424       rate   581.066894  -3.8464  -8.9272\n",
       "7291  Topic10     0.000423     inform   513.501629  -3.7264  -8.9308\n",
       "4185  Topic10     0.000420    ingredi   595.032866  -3.8801  -8.9372\n",
       "3804  Topic10     0.000409    similar   295.426228  -3.2075  -8.9648\n",
       "8308  Topic10     0.000415        use   661.088914  -3.9983  -8.9501\n",
       "765   Topic10     0.000410        one   367.100892  -3.4229  -8.9629\n",
       "3607  Topic10     0.000405   particip   415.519210  -3.5588  -8.9750\n",
       "2782  Topic10     0.000402      gener   266.748068  -3.1219  -8.9813\n",
       "7283  Topic10     0.000403    practic   402.610950  -3.5301  -8.9778\n",
       "7178  Topic10     0.000399       cook   374.983324  -3.4695  -8.9883\n",
       "5238  Topic10     0.000397       shop   276.517659  -3.1709  -8.9944\n",
       "5053  Topic10     0.000397    predict   276.849378  -3.1727  -8.9949\n",
       "2295  Topic10     0.000397       work   312.192835  -3.2919  -8.9939\n",
       "552   Topic10     0.000396       time   269.623542  -3.1472  -8.9959\n",
       "2845  Topic10     0.000394    product   208.803040  -2.8968  -9.0011\n",
       "7739  Topic10     0.000395     design   268.366068  -3.1468  -9.0002\n",
       "620   Topic10     0.000395     prefer   355.391136  -3.4254  -8.9979\n",
       "4549  Topic10     0.000395       meal   300.193105  -3.2584  -8.9997\n",
       "1302  Topic10     0.000392     differ   189.344738  -2.8048  -9.0070\n",
       "5578  Topic10     0.000392       valu   216.263405  -2.9375  -9.0067\n",
       "2389  Topic10     0.000390    network   171.025698  -2.7071  -9.0110\n",
       "1385  Topic10     0.000392        set   315.306823  -3.3135  -9.0057\n",
       "636   Topic10     0.000390      peopl   212.878156  -2.9278  -9.0128\n",
       "7890  Topic10     0.000390     health   232.898215  -3.0170  -9.0121\n",
       "4975  Topic10     0.000390       item   436.024846  -3.6429  -9.0109\n",
       "\n",
       "[787 rows x 6 columns], token_table=      Topic      Freq         Term\n",
       "term                              \n",
       "6866      6  0.688533        abowd\n",
       "3007      6  0.688674      abraham\n",
       "7294      1  0.136595  acceleromet\n",
       "7294      4  0.887865  acceleromet\n",
       "1021      1  0.386258          acm\n",
       "1021      2  0.335243          acm\n",
       "1021      3  0.054659          acm\n",
       "1021      4  0.109318          acm\n",
       "1021      5  0.065591          acm\n",
       "1021      6  0.047371          acm\n",
       "5183      2  0.996518     acquisit\n",
       "835       1  0.261871        activ\n",
       "835       2  0.411511        activ\n",
       "835       3  0.043645        activ\n",
       "835       4  0.255636        activ\n",
       "835       5  0.024940        activ\n",
       "6433      6  0.688616         agat\n",
       "7307      3  0.217493          ahn\n",
       "7307      4  0.739475          ahn\n",
       "7307      6  0.043499          ahn\n",
       "6285      6  0.688665      ahyoung\n",
       "7007      1  0.031176         aisl\n",
       "7007      4  0.935284         aisl\n",
       "7007      6  0.031176         aisl\n",
       "1071      5  0.867041           aj\n",
       "5530      6  0.688442      alberto\n",
       "1621      2  0.502817    algorithm\n",
       "1621      3  0.045711    algorithm\n",
       "1621      4  0.013444    algorithm\n",
       "1621      5  0.155954    algorithm\n",
       "...     ...       ...          ...\n",
       "7107      4  0.971178     wireless\n",
       "8475      6  0.688685   withreturn\n",
       "12        5  0.680205          wji\n",
       "7595      6  0.688667     woontack\n",
       "2295      1  0.333127         work\n",
       "2295      2  0.355549         work\n",
       "2295      3  0.140939         work\n",
       "2295      4  0.044844         work\n",
       "2295      5  0.076876         work\n",
       "2295      6  0.044844         work\n",
       "8774      6  0.688642         wpnc\n",
       "6666      6  0.998178       wrrcid\n",
       "3600      1  0.069357            x\n",
       "3600      2  0.219630            x\n",
       "3600      3  0.242749            x\n",
       "3600      5  0.069357            x\n",
       "3600      6  0.393023            x\n",
       "2944      6  0.847053           xr\n",
       "2942      3  0.051816           xx\n",
       "2942      6  0.932690           xx\n",
       "8261      6  0.688649         ycid\n",
       "7631      6  0.688653         ycui\n",
       "6606      5  0.876908         yelp\n",
       "6606      6  0.125273         yelp\n",
       "3432      3  0.071640           yi\n",
       "3432      6  0.931326           yi\n",
       "5505      6  0.688656      ylongqi\n",
       "3442      2  0.959197           ys\n",
       "3445      6  0.847022           yt\n",
       "5756      6  0.688657           za\n",
       "\n",
       "[1245 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[8, 10, 4, 9, 3, 1, 6, 5, 7, 2])"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis.gensim\n",
    "\n",
    "pyLDAvis.gensim.prepare(ldamodel, corpus, dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       "\n",
       "<div id=\"ldavis_el993746608887842069210274\"></div>\n",
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       "var ldavis_el993746608887842069210274_data = {\"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 3, 1], \"token.table\": {\"Topic\": [2, 1, 2, 3, 1, 2, 3, 2, 1, 2, 3, 1, 2, 3, 2, 3, 1, 1, 2, 3, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 2, 3, 1, 2, 3, 3, 2, 3, 2, 3, 2, 1, 2, 3, 3, 1, 3, 3, 1, 3, 2, 1, 3, 1, 2, 3, 2, 3, 2, 1, 2, 3, 1, 2, 3, 1, 2, 3, 3, 2, 3, 1, 2, 3, 2, 3, 3, 1, 2, 3, 1, 2, 3, 2, 3, 2, 1, 2, 3, 1, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 1, 2, 3, 2, 3, 1, 2, 3, 2, 1, 2, 3, 1, 3, 1, 2, 3, 2, 1, 1, 3, 1, 1, 1, 1, 2, 3, 2, 1, 2, 3, 3, 1, 3, 1, 2, 3, 2, 1, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 1, 1, 2, 1, 3, 1, 2, 3, 1, 2, 3, 3, 1, 2, 3, 1, 1, 3, 1, 2, 1, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 2, 1, 1, 2, 3, 1, 1, 2, 3, 2, 3, 3, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 2, 3, 1, 2, 3, 2, 1, 2, 3, 1, 3, 1, 2, 3, 3, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 3, 1, 2, 3, 2, 3, 1, 1, 2, 3, 1, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 1, 3, 3, 1, 2, 3, 2, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "1      37.912012        1       1  0.136382  0.005009\n",
       "2      32.402903        1       2 -0.059993 -0.064994\n",
       "0      29.685085        1       3 -0.076389  0.059986, topic_info=     Category         Freq       Term        Total  loglift  logprob\n",
       "term                                                                \n",
       "1248  Default  1498.000000      recip  1498.000000  30.0000  30.0000\n",
       "4185  Default   572.000000    ingredi   572.000000  29.0000  29.0000\n",
       "4460  Default   869.000000  recommend   869.000000  28.0000  28.0000\n",
       "3434  Default  1875.000000       food  1875.000000  27.0000  27.0000\n",
       "7283  Default   384.000000    practic   384.000000  26.0000  26.0000\n",
       "2435  Default   560.000000       rate   560.000000  25.0000  25.0000\n",
       "216   Default   278.000000       wast   278.000000  24.0000  24.0000\n",
       "5238  Default   266.000000       shop   266.000000  23.0000  23.0000\n",
       "3607  Default   397.000000   particip   397.000000  22.0000  22.0000\n",
       "1621  Default   365.000000  algorithm   365.000000  21.0000  21.0000\n",
       "300   Default   232.000000       menu   232.000000  20.0000  20.0000\n",
       "1873  Default   869.000000     system   869.000000  19.0000  19.0000\n",
       "5053  Default   267.000000    predict   267.000000  18.0000  18.0000\n",
       "4901  Default   403.000000     featur   403.000000  17.0000  17.0000\n",
       "101   Default   135.000000  household   135.000000  16.0000  16.0000\n",
       "3921  Default   128.000000      fridg   128.000000  15.0000  15.0000\n",
       "4267  Default   150.000000       home   150.000000  14.0000  14.0000\n",
       "620   Default   344.000000     prefer   344.000000  13.0000  13.0000\n",
       "2389  Default   163.000000    network   163.000000  12.0000  12.0000\n",
       "5986  Default    95.000000    patient    95.000000  11.0000  11.0000\n",
       "2845  Default   203.000000    product   203.000000  10.0000  10.0000\n",
       "3531  Default   111.000000    groceri   111.000000   9.0000   9.0000\n",
       "1590  Default   182.000000        cid   182.000000   8.0000   8.0000\n",
       "4582  Default   797.000000       user   797.000000   7.0000   7.0000\n",
       "3804  Default   288.000000    similar   288.000000   6.0000   6.0000\n",
       "3464  Default   104.000000    sustain   104.000000   5.0000   5.0000\n",
       "1173  Default   221.000000     select   221.000000   4.0000   4.0000\n",
       "1198  Default   140.000000      learn   140.000000   3.0000   3.0000\n",
       "7244  Default   191.000000      share   191.000000   2.0000   2.0000\n",
       "2797  Default   136.000000      mobil   136.000000   1.0000   1.0000\n",
       "...       ...          ...        ...          ...      ...      ...\n",
       "1590   Topic3   116.335648        cid   182.626755   0.7636  -5.4747\n",
       "453    Topic3    98.113598     measur   156.348428   0.7486  -5.6451\n",
       "4566   Topic3    77.992371    content   119.021315   0.7918  -5.8746\n",
       "2738   Topic3    76.394208      graph   117.468683   0.7843  -5.8953\n",
       "5231   Topic3    47.387690       pair    65.724372   0.8874  -6.3728\n",
       "5744   Topic3    41.331526  construct    55.671856   0.9167  -6.5096\n",
       "3804   Topic3   154.295558    similar   288.688616   0.5880  -5.1923\n",
       "1385   Topic3   149.667329        set   307.295368   0.4951  -5.2228\n",
       "4460   Topic3   303.947331  recommend   869.948741   0.1629  -4.5143\n",
       "5603   Topic3   106.617930        two   219.554988   0.4922  -5.5619\n",
       "7291   Topic3   186.738116     inform   494.933113   0.2398  -5.0015\n",
       "6183   Topic3    73.382947      onlin   135.766404   0.5993  -5.9355\n",
       "8308   Topic3   201.452628        use   639.866665   0.0588  -4.9256\n",
       "673    Topic3    98.696667       data   239.673706   0.3273  -5.6391\n",
       "1621   Topic3   126.615187  algorithm   365.635648   0.1540  -5.3900\n",
       "4582   Topic3   199.076254       user   797.078290  -0.1727  -4.9375\n",
       "7178   Topic3   121.607153       cook   359.243557   0.1313  -5.4304\n",
       "6946   Topic3    81.399526   approach   182.828855   0.4053  -5.8318\n",
       "3475   Topic3    98.825026       base   266.463716   0.2226  -5.6378\n",
       "765    Topic3   114.680630        one   355.089897   0.0843  -5.4890\n",
       "4901   Topic3   118.036182     featur   403.016392  -0.0135  -5.4602\n",
       "1369   Topic3    86.353819     factor   216.440362   0.2957  -5.7727\n",
       "2435   Topic3   129.170771       rate   560.993042  -0.2540  -5.3700\n",
       "620    Topic3   105.263072     prefer   344.756553   0.0281  -5.5747\n",
       "4975   Topic3   110.321902       item   422.067334  -0.1272  -5.5278\n",
       "3434   Topic3   177.327729       food  1875.919168  -1.1443  -5.0532\n",
       "2295   Topic3    95.667249       work   299.585219   0.0730  -5.6703\n",
       "1873   Topic3   116.053483     system   869.814765  -0.7997  -5.4771\n",
       "6169   Topic3    83.960101        tag   204.951830   0.3221  -5.8008\n",
       "2782   Topic3    82.808005      gener   256.777964   0.0828  -5.8147\n",
       "\n",
       "[260 rows x 6 columns], token_table=      Topic      Freq           Term\n",
       "term                                \n",
       "6905      2  0.972988        absolut\n",
       "1897      1  0.186796       accuraci\n",
       "1897      2  0.648870       accuraci\n",
       "1897      3  0.157302       accuraci\n",
       "1021      1  0.471956            acm\n",
       "1021      2  0.377565            acm\n",
       "1021      3  0.151026            acm\n",
       "5183      2  0.956473       acquisit\n",
       "1163      1  0.811355         action\n",
       "1163      2  0.030426         action\n",
       "1163      3  0.152129         action\n",
       "835       1  0.289709          activ\n",
       "835       2  0.585857          activ\n",
       "835       3  0.122322          activ\n",
       "522       2  0.918927          adopt\n",
       "522       3  0.051052          adopt\n",
       "7007      1  0.995696           aisl\n",
       "1621      1  0.010940      algorithm\n",
       "1621      2  0.642716      algorithm\n",
       "1621      3  0.347340      algorithm\n",
       "2667      3  0.919761  allrecipescom\n",
       "4817      1  0.634265         applic\n",
       "4817      2  0.255599         applic\n",
       "4817      3  0.108866         applic\n",
       "6946      1  0.229723       approach\n",
       "6946      2  0.328176       approach\n",
       "6946      3  0.443037       approach\n",
       "967       1  0.846143         around\n",
       "967       2  0.072787         around\n",
       "967       3  0.081885         around\n",
       "...     ...       ...            ...\n",
       "552       1  0.515030           time\n",
       "552       2  0.215236           time\n",
       "552       3  0.269046           time\n",
       "6158      1  0.053032          train\n",
       "6158      2  0.777810          train\n",
       "6158      3  0.159097          train\n",
       "5603      1  0.250507            two\n",
       "5603      2  0.264171            two\n",
       "5603      3  0.487349            two\n",
       "4498      1  0.056519          updat\n",
       "4498      2  0.876042          updat\n",
       "4498      3  0.084778          updat\n",
       "8308      1  0.332882            use\n",
       "8308      2  0.353199            use\n",
       "8308      3  0.314128            use\n",
       "4582      1  0.228334           user\n",
       "4582      2  0.521906           user\n",
       "4582      3  0.249662           user\n",
       "1219      3  0.936934        variant\n",
       "7229      1  0.936945         visibl\n",
       "7229      2  0.038243         visibl\n",
       "7229      3  0.019121         visibl\n",
       "2116      2  0.980662            vsi\n",
       "216       1  0.997217           wast\n",
       "2295      1  0.360498           work\n",
       "2295      2  0.320443           work\n",
       "2295      3  0.320443           work\n",
       "6666      3  0.974974         wrrcid\n",
       "2949      3  0.919292             xt\n",
       "6606      2  0.950295           yelp\n",
       "\n",
       "[410 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 3, 1])"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "pyLDAvis.gensim.prepare(ldamodel3, corpus, dictionary)"
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