LDaVis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/numpy/lib/utils.py:99: DeprecationWarning: `scipy.sparse.sparsetools` is deprecated!\n",
      "scipy.sparse.sparsetools is a private module for scipy.sparse, and should not be used.\n",
      "  warnings.warn(depdoc, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import unicodecsv\n",
    "import os\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim\n",
    "\n",
    "\n",
    "\n",
    "from nltk import RegexpTokenizer\n",
    "from lib.utils import *\n",
    "from itertools import chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/Hii/Projects/news_scraper/models/soya_month_lemma_1000_8_10_lda_english\n"
     ]
    }
   ],
   "source": [
    "class data:\n",
    "    def __init__(self, **kwargs):\n",
    "        self.__dict__.update(kwargs)\n",
    "args = data()\n",
    "args.filename = \"soya_month.csv\"\n",
    "args.stemmer = \"lemma\"\n",
    "args.dictionary = \"english\"\n",
    "args.num_iter = 1000\n",
    "args.num_top_words = 8\n",
    "args.num_topics = 10\n",
    "args.model = \"lda\"\n",
    "args.override = False\n",
    "args.input_field = \"contents\"\n",
    "\n",
    "\n",
    "dir = os.getcwd()\n",
    "model_dir = os.path.join(dir, 'models/')\n",
    "dataset_dir = os.path.join(dir, 'datasets/')\n",
    "dictionary_dir = os.path.join(dir, 'dictionaries/')\n",
    "executable_dir = os.path.join(dir, 'executables/')\n",
    "my_timeslices = [500,500,500,500,500, 346]\n",
    "\n",
    "model_filename = os.path.join(model_dir, get_model_with_arguments_filename(args))\n",
    "is_english_word = load_from_dictionary(args.dictionary)\n",
    "\n",
    "print model_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset_filepath = os.path.join(dataset_dir, args.filename)\n",
    "f = open(dataset_filepath)\n",
    "reader = unicodecsv.reader(f, encoding='utf-8')\n",
    "\n",
    "identifiers = reader.next()\n",
    "contents_idx = identifiers.index(\"contents\")\n",
    "title_idx = identifiers.index(\"title\")\n",
    "\n",
    "contents, titles = zip(*[(row[contents_idx], row[title_idx]) for row in reader])\n",
    "\n",
    "texts = preprocess(contents, args.stemmer, is_english_word)\n",
    "\n",
    "dictionary = corpora.Dictionary(texts)\n",
    "\n",
    "#remove extremes (similar to the min/max df step used when creating the tf-idf matrix)\n",
    "dictionary.filter_extremes(no_below=1, no_above=0.8)\n",
    "\n",
    "my_corpus = [dictionary.doc2bow(text) for text in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "try:\n",
    "  model = load_model(model_filename, args.model)\n",
    "  if args.override:\n",
    "    raise IOError(\"Override flag set\")\n",
    "except IOError:\n",
    "  print \"Generating model ...\"\n",
    "  model = generate_model(args.model, my_corpus, dictionary, args.num_topics, args.num_iter)\n",
    "  model.save(model_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0 : service mi time power mobile bank phone business\n",
      "Topic #1 : ad thing flight apple destination smart device book\n",
      "Topic #2 : rice cooker mi snapdragon usage variant heating brand\n",
      "Topic #3 : plan month edge free call speed subscription maxi\n",
      "Topic #4 : broadband plan payment quota apple customer sport watch\n",
      "Topic #5 : camera galaxy apple storage edge ram version priced\n",
      "Topic #6 : camera front galaxy processor android ram device flagship\n",
      "Topic #7 : front spec speaker ram back camera storage processor\n",
      "Topic #8 : plan month data maxi mobile postpaid network call\n",
      "Topic #9 : service text battery delivery android encryption apple feature\n",
      "\n",
      "0 :  This could be the best postpaid plan in town — RM80/month for unlimited everything (top topic: 4)\n",
      "1 :  iPhone SE and iPad Pro 9.7 official Malaysian pricing revealed (top topic: 5)\n",
      "2 :  Your WhatsApp conversations are now encrypted end to end (top topic: 9)\n",
      "3 :  TM UniFi Advance subscription now starts from RM199/month (top topic: 3)\n",
      "4 :  11street is offering the iPhone SE from RM1,899 (top topic: 8)\n",
      "5 :  Xiaomi’s new Mi Pad is more than meets the eye (top topic: 3)\n",
      "6 :  Xiaomi Redmi Note 3 review: Best smartphone on a budget? (top topic: 9)\n",
      "7 :  More info on Malaysia’s newest 4G operator (top topic: 0)\n",
      "8 :  Vivo’s V3Max is a metal-bodied smartphone that packs a punch (top topic: 6)\n",
      "9 :  Huawei Mate 8 review: Bigger is better (top topic: 0)\n"
     ]
    }
   ],
   "source": [
    "show_topics(args.model, model, args.num_topics, args.num_top_words, titles, my_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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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",
       "6      19.512254        1       1  0.158265  0.106817\n",
       "8      15.977803        1       2 -0.221112  0.111180\n",
       "5      14.683048        1       3  0.105191  0.104562\n",
       "4      10.437065        1       4 -0.047707 -0.073616\n",
       "0       9.894987        1       5 -0.002840 -0.044538\n",
       "3       7.325275        1       6 -0.186754  0.049792\n",
       "9       6.830234        1       7  0.016885 -0.086965\n",
       "7       6.536736        1       8  0.126158  0.052742\n",
       "2       4.998317        1       9  0.047572 -0.043745\n",
       "1       3.804281        1      10  0.004343 -0.176229, topic_info=     Category        Freq          Term       Total  loglift  logprob\n",
       "term                                                                 \n",
       "1897  Default  164.000000          plan  164.000000  30.0000  30.0000\n",
       "1546  Default  148.000000         month  148.000000  29.0000  29.0000\n",
       "2601  Default   62.000000          data   62.000000  28.0000  28.0000\n",
       "357   Default   59.000000          maxi   59.000000  27.0000  27.0000\n",
       "2525  Default   69.000000         front   69.000000  26.0000  26.0000\n",
       "2106  Default   94.000000          edge   94.000000  25.0000  25.0000\n",
       "2785  Default   46.000000      postpaid   46.000000  24.0000  24.0000\n",
       "260   Default   40.000000          free   40.000000  23.0000  23.0000\n",
       "107   Default   59.000000          call   59.000000  22.0000  22.0000\n",
       "1011  Default  101.000000        camera  101.000000  21.0000  21.0000\n",
       "391   Default   18.000000          rice   18.000000  20.0000  20.0000\n",
       "338   Default   67.000000           ram   67.000000  19.0000  19.0000\n",
       "1140  Default   43.000000      offering   43.000000  18.0000  18.0000\n",
       "134   Default   43.000000            mi   43.000000  17.0000  17.0000\n",
       "1903  Default   65.000000        mobile   65.000000  16.0000  16.0000\n",
       "2399  Default   51.000000         apple   51.000000  15.0000  15.0000\n",
       "2179  Default   35.000000     broadband   35.000000  14.0000  14.0000\n",
       "1350  Default   59.000000         speed   59.000000  13.0000  13.0000\n",
       "1437  Default   56.000000     processor   56.000000  12.0000  12.0000\n",
       "301   Default   47.000000       service   47.000000  11.0000  11.0000\n",
       "20    Default   47.000000          spec   47.000000  10.0000  10.0000\n",
       "247   Default   38.000000       network   38.000000   9.0000   9.0000\n",
       "2882  Default   65.000000       storage   65.000000   8.0000   8.0000\n",
       "941   Default   32.000000    snapdragon   32.000000   7.0000   7.0000\n",
       "2856  Default   40.000000          inch   40.000000   6.0000   6.0000\n",
       "2436  Default   22.000000  subscription   22.000000   5.0000   5.0000\n",
       "2898  Default   52.000000       battery   52.000000   4.0000   4.0000\n",
       "691   Default   48.000000         offer   48.000000   3.0000   3.0000\n",
       "2141  Default   33.000000     unlimited   33.000000   2.0000   2.0000\n",
       "2801  Default   16.000000            ad   16.000000   1.0000   1.0000\n",
       "...       ...         ...           ...         ...      ...      ...\n",
       "573   Topic10    1.389622        adding    2.845059   2.5525  -6.0455\n",
       "332   Topic10    1.389625       feeling    2.903861   2.5320  -6.0455\n",
       "360   Topic10    1.389622        object    2.903860   2.5320  -6.0455\n",
       "644   Topic10    1.389621          plug    2.903860   2.5320  -6.0455\n",
       "1880  Topic10    2.713073          trip    5.952709   2.4833  -5.3764\n",
       "272   Topic10    1.389623       created    3.005128   2.4978  -6.0455\n",
       "244   Topic10    1.389622      solution    3.005128   2.4978  -6.0455\n",
       "148   Topic10    3.374801         drive    8.107867   2.3925  -5.1582\n",
       "490   Topic10    2.713081      interest    6.991441   2.3224  -5.3764\n",
       "2746  Topic10    3.345883      computer    9.678374   2.2069  -5.1668\n",
       "2053  Topic10    3.985585         smart   14.519979   1.9762  -4.9918\n",
       "153   Topic10    6.703985         thing   35.068933   1.6144  -4.4718\n",
       "2440  Topic10    2.713074          ride    9.346327   2.0321  -5.3764\n",
       "919   Topic10    2.646077        making    9.614485   1.9789  -5.4014\n",
       "1662  Topic10    3.326271       company   18.572809   1.5492  -5.1727\n",
       "2399  Topic10    4.698258         apple   51.679916   0.8712  -4.8273\n",
       "816   Topic10    2.051353          team    6.240939   2.1564  -5.6560\n",
       "2968  Topic10    2.439941      location   10.173863   1.8412  -5.4825\n",
       "1665  Topic10    2.051353     basically    7.124352   2.0240  -5.6560\n",
       "1588  Topic10    2.711130        design   20.192797   1.2611  -5.3771\n",
       "1377  Topic10    2.713081         world   21.438117   1.2020  -5.3764\n",
       "2663  Topic10    3.591757        device   60.311721   0.4482  -5.0959\n",
       "2145  Topic10    2.051351           bit    8.825426   1.8099  -5.6560\n",
       "872   Topic10    2.051355          love   10.051506   1.6798  -5.6560\n",
       "158   Topic10    2.787006       feature   44.586176   0.4966  -5.3495\n",
       "1934  Topic10    2.575307        pretty   39.602263   0.5361  -5.4285\n",
       "1030  Topic10    2.273391          year   46.622873   0.2482  -5.5532\n",
       "1874  Topic10    2.230709        option   34.247828   0.5377  -5.5722\n",
       "2507  Topic10    2.099260            ll   35.918712   0.4294  -5.6329\n",
       "1957  Topic10    2.051352           set   25.491340   0.7492  -5.6560\n",
       "\n",
       "[680 rows x 6 columns], token_table=      Topic      Freq          Term\n",
       "term                               \n",
       "287      10  0.469951        accent\n",
       "953       3  0.174442     accessory\n",
       "953       5  0.523325     accessory\n",
       "953       9  0.174442     accessory\n",
       "735       3  0.781578         actor\n",
       "2801      4  0.184648            ad\n",
       "2801      5  0.307747            ad\n",
       "2801     10  0.492396            ad\n",
       "454       5  0.809293       adapter\n",
       "573       9  0.351487        adding\n",
       "573      10  0.351487        adding\n",
       "3070      4  0.809055  additionally\n",
       "1061      2  0.248770       advance\n",
       "1061      6  0.746309       advance\n",
       "2680      2  0.238268        affair\n",
       "2680      5  0.714803        affair\n",
       "1626     10  0.469951       airport\n",
       "2087      7  0.300325         alert\n",
       "2087      9  0.600649         alert\n",
       "2799      4  0.809056     allocated\n",
       "2110      5  0.809292    ambassador\n",
       "2858      1  0.657278       android\n",
       "2858      3  0.039835       android\n",
       "2858      4  0.019918       android\n",
       "2858      5  0.039835       android\n",
       "2858      6  0.019918       android\n",
       "2858      7  0.139423       android\n",
       "2858      8  0.059753       android\n",
       "2858      9  0.019918       android\n",
       "1714      4  0.907493       anytime\n",
       "...     ...       ...           ...\n",
       "2326      7  0.167627          wave\n",
       "2117      4  0.139179      wearable\n",
       "2117      5  0.278358      wearable\n",
       "2117      7  0.556717      wearable\n",
       "1356      2  0.812077        widest\n",
       "994       1  0.640465        window\n",
       "994       7  0.106744        window\n",
       "994       8  0.266860        window\n",
       "1377      3  0.233229         world\n",
       "1377      4  0.326521         world\n",
       "1377      6  0.046646         world\n",
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       "\n",
       "[1506 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[7, 9, 6, 5, 1, 4, 10, 8, 3, 2])"
      ]
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     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pyLDAvis.enable_notebook()\n",
    "pyLDAvis.gensim.prepare(model, my_corpus, dictionary)\n",
    "\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/IPython/core/formatters.py:98: DeprecationWarning: DisplayFormatter._formatters_default is deprecated: use @default decorator instead.\n",
      "  def _formatters_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/IPython/core/formatters.py:677: DeprecationWarning: PlainTextFormatter._deferred_printers_default is deprecated: use @default decorator instead.\n",
      "  def _deferred_printers_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/IPython/core/formatters.py:669: DeprecationWarning: PlainTextFormatter._singleton_printers_default is deprecated: use @default decorator instead.\n",
      "  def _singleton_printers_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/IPython/core/formatters.py:672: DeprecationWarning: PlainTextFormatter._type_printers_default is deprecated: use @default decorator instead.\n",
      "  def _type_printers_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/IPython/core/formatters.py:92: DeprecationWarning: DisplayFormatter._ipython_display_formatter_default is deprecated: use @default decorator instead.\n",
      "  def _ipython_display_formatter_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/IPython/core/formatters.py:669: DeprecationWarning: PlainTextFormatter._singleton_printers_default is deprecated: use @default decorator instead.\n",
      "  def _singleton_printers_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/IPython/core/formatters.py:672: DeprecationWarning: PlainTextFormatter._type_printers_default is deprecated: use @default decorator instead.\n",
      "  def _type_printers_default(self):\n",
      "/usr/local/lib/python2.7/site-packages/IPython/core/formatters.py:677: DeprecationWarning: PlainTextFormatter._deferred_printers_default is deprecated: use @default decorator instead.\n",
      "  def _deferred_printers_default(self):\n"
     ]
    },
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       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "7      30.199035        1       1  0.104082  0.184982\n",
       "4      15.571528        1       2 -0.223597  0.016330\n",
       "1      13.488578        1       3 -0.220154  0.050669\n",
       "9      10.510970        1       4  0.003450 -0.066944\n",
       "2       8.540344        1       5  0.084784  0.085647\n",
       "5       5.684242        1       6  0.051086 -0.094108\n",
       "0       5.374674        1       7  0.031918 -0.093433\n",
       "3       5.067513        1       8  0.069257  0.009196\n",
       "6       3.434300        1       9  0.043047 -0.065604\n",
       "8       2.128816        1      10  0.056126 -0.026736, topic_info=     Category        Freq          Term       Total  loglift  logprob\n",
       "term                                                                 \n",
       "1897  Default  168.000000          plan  168.000000  30.0000  30.0000\n",
       "1546  Default  151.000000         month  151.000000  29.0000  29.0000\n",
       "2106  Default   92.000000          edge   92.000000  28.0000  28.0000\n",
       "134   Default   42.000000            mi   42.000000  27.0000  27.0000\n",
       "2601  Default   60.000000          data   60.000000  26.0000  26.0000\n",
       "1350  Default   62.000000         speed   62.000000  25.0000  25.0000\n",
       "2399  Default   48.000000         apple   48.000000  24.0000  24.0000\n",
       "1011  Default  100.000000        camera  100.000000  23.0000  23.0000\n",
       "2525  Default   71.000000         front   71.000000  22.0000  22.0000\n",
       "2785  Default   46.000000      postpaid   46.000000  21.0000  21.0000\n",
       "357   Default   60.000000          maxi   60.000000  20.0000  20.0000\n",
       "2179  Default   37.000000     broadband   37.000000  19.0000  19.0000\n",
       "2500  Default   96.000000        galaxy   96.000000  18.0000  18.0000\n",
       "1140  Default   44.000000      offering   44.000000  17.0000  17.0000\n",
       "391   Default   18.000000          rice   18.000000  16.0000  16.0000\n",
       "2858  Default   48.000000       android   48.000000  15.0000  15.0000\n",
       "2141  Default   31.000000     unlimited   31.000000  14.0000  14.0000\n",
       "2856  Default   40.000000          inch   40.000000  13.0000  13.0000\n",
       "338   Default   72.000000           ram   72.000000  12.0000  12.0000\n",
       "1903  Default   65.000000        mobile   65.000000  11.0000  11.0000\n",
       "301   Default   47.000000       service   47.000000  10.0000  10.0000\n",
       "107   Default   59.000000          call   59.000000   9.0000   9.0000\n",
       "1437  Default   58.000000     processor   58.000000   8.0000   8.0000\n",
       "1415  Default   25.000000         power   25.000000   7.0000   7.0000\n",
       "20    Default   51.000000          spec   51.000000   6.0000   6.0000\n",
       "750   Default   19.000000        update   19.000000   5.0000   5.0000\n",
       "2882  Default   69.000000       storage   69.000000   4.0000   4.0000\n",
       "2801  Default   16.000000            ad   16.000000   3.0000   3.0000\n",
       "1337  Default   34.000000         model   34.000000   2.0000   2.0000\n",
       "2853  Default   20.000000       speaker   20.000000   1.0000   1.0000\n",
       "...       ...         ...           ...         ...      ...      ...\n",
       "2878  Topic10    1.918211         audio   11.019609   2.1013  -5.1426\n",
       "598   Topic10    1.101233        desire    4.275827   2.4931  -5.6975\n",
       "1558  Topic10    1.625635         sound    8.886320   2.1510  -5.3080\n",
       "2820  Topic10    0.995193      finished    3.661213   2.5470  -5.7988\n",
       "2853  Topic10    2.181602       speaker   20.140929   1.6269  -5.0139\n",
       "1337  Topic10    2.450793         model   34.183414   1.2143  -4.8975\n",
       "2230  Topic10    1.101232       forward    5.946100   2.1633  -5.6975\n",
       "3057  Topic10    1.101235          boom    6.274767   2.1095  -5.6975\n",
       "1880  Topic10    1.059463          trip    5.805944   2.1485  -5.7362\n",
       "2381  Topic10    1.101236       packing    6.416635   2.0871  -5.6975\n",
       "2856  Topic10    2.150028          inch   40.359044   0.9173  -5.0285\n",
       "2141  Topic10    1.925019     unlimited   31.732165   1.0472  -5.1390\n",
       "177   Topic10    0.974773     confirmed    5.271338   2.1618  -5.8195\n",
       "2668  Topic10    1.018695        stereo    5.992828   2.0776  -5.7754\n",
       "2706  Topic10    1.445152         local   15.468202   1.4790  -5.4257\n",
       "1377  Topic10    1.625633         world   21.810410   1.2531  -5.3080\n",
       "1149  Topic10    1.625632   performance   21.961118   1.2462  -5.3080\n",
       "2399  Topic10    2.150031         apple   48.319775   0.7372  -5.0285\n",
       "1705  Topic10    1.425898  connectivity   15.882203   1.4392  -5.4391\n",
       "1476  Topic10    1.625636        faster   23.141662   1.1939  -5.3080\n",
       "1445  Topic10    1.421889         image   15.869990   1.4372  -5.4420\n",
       "100   Topic10    1.466079          unit   19.833870   1.2448  -5.4114\n",
       "2011  Topic10    1.510100        simply   28.122407   0.9252  -5.3818\n",
       "2525  Topic10    1.760255         front   71.332433   0.1477  -5.2285\n",
       "675   Topic10    1.527482       support   39.439754   0.5985  -5.3703\n",
       "2897  Topic10    1.505799           day   44.818466   0.4563  -5.3846\n",
       "1011  Topic10    1.625633        camera  100.485499  -0.2745  -5.3080\n",
       "2137  Topic10    1.101237         march   29.971517   0.5458  -5.6975\n",
       "1349  Topic10    1.101237         touch    9.276903   1.7185  -5.6975\n",
       "2588  Topic10    1.101236      powerful   11.115135   1.5377  -5.6975\n",
       "\n",
       "[695 rows x 6 columns], token_table=      Topic      Freq        Term\n",
       "term                             \n",
       "2200     10  0.772566      abroad\n",
       "2163      2  0.272821  absolutely\n",
       "2163      7  0.272821  absolutely\n",
       "2163      9  0.272821  absolutely\n",
       "287       9  0.488183      accent\n",
       "2366      1  0.243655      access\n",
       "2366      2  0.406092      access\n",
       "2366      3  0.081218      access\n",
       "2366      7  0.243655      access\n",
       "2366     10  0.081218      access\n",
       "2651      9  0.488180    accuracy\n",
       "2542      7  0.895083    activate\n",
       "735       5  0.835134       actor\n",
       "2801      1  0.301213          ad\n",
       "2801      2  0.120485          ad\n",
       "2801      3  0.120485          ad\n",
       "2801      9  0.481941          ad\n",
       "2899      2  0.313912         add\n",
       "2899      3  0.104637         add\n",
       "2899      7  0.418549         add\n",
       "2899      8  0.104637         add\n",
       "1061      2  0.689735     advance\n",
       "1061      3  0.229912     advance\n",
       "1626      6  0.885317     airport\n",
       "2110      9  0.488179  ambassador\n",
       "2858      1  0.613125     android\n",
       "2858      4  0.020438     android\n",
       "2858      5  0.061313     android\n",
       "2858      7  0.204375     android\n",
       "2858      8  0.061313     android\n",
       "...     ...       ...         ...\n",
       "695       1  0.081352    wireless\n",
       "695       2  0.732170    wireless\n",
       "695       3  0.162705    wireless\n",
       "1782      9  0.709622    wondered\n",
       "133       1  0.134385        work\n",
       "133       2  0.268770        work\n",
       "133       3  0.089590        work\n",
       "133       4  0.089590        work\n",
       "133       5  0.089590        work\n",
       "133       6  0.089590        work\n",
       "133       7  0.179180        work\n",
       "884       5  0.632688     working\n",
       "884       8  0.210896     working\n",
       "1377      1  0.458497       world\n",
       "1377      4  0.091699       world\n",
       "1377      5  0.183399       world\n",
       "1377      7  0.045850       world\n",
       "1377      8  0.091699       world\n",
       "1377      9  0.045850       world\n",
       "1377     10  0.091699       world\n",
       "1030      1  0.173131        year\n",
       "1030      2  0.173131        year\n",
       "1030      3  0.216414        year\n",
       "1030      4  0.151490        year\n",
       "1030      5  0.108207        year\n",
       "1030      6  0.086566        year\n",
       "1030      8  0.043283        year\n",
       "1030      9  0.021641        year\n",
       "570      10  0.772566         yep\n",
       "9         3  0.798230      yonder\n",
       "\n",
       "[1429 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[8, 5, 2, 10, 3, 6, 1, 4, 7, 9])"
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