{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# pyLDAvis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "[`pyLDAvis`](https://github.com/bmabey/pyLDAvis) is a python libarary for interactive topic model visualization.\n",
    "It is a port of the fabulous [R package](https://github.com/cpsievert/LDAvis) by Carson Sievert and Kenny Shirley.  They did the hard work of crafting an effective visualization. `pyLDAvis` makes it easy to use the visualiziation from Python and, in particular, Jupyter notebooks. To learn more about the method behind the visualization I suggest reading the [original paper](http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf) explaining it.\n",
    "\n",
    "This notebook provides a quick overview of how to use `pyLDAvis`. Refer to the [documenation](https://pyldavis.readthedocs.org/en/latest/) for details.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## BYOM - Bring your own model\n",
    "\n",
    "`pyLDAvis` is agnostic to how your model was trained. To visualize it you need to provide the topic-term distributions, document-topic distributions, and basic information about the corpus which the model was trained on. The main function is the [`prepare`](https://pyldavis.readthedocs.org/en/latest/modules/API.html#pyLDAvis.prepare) function that will transform your data into the format needed for the visualization.\n",
    "\n",
    "Below we load a model trained in R and then visualize it. The model was trained on a corpus of 2000 movie reviews parsed by [Pang and Lee (ACL, 2004)](http://www.cs.cornell.edu/people/pabo/movie-review-data/), originally gathered from the IMDB archive of the rec.arts.movies.reviews newsgroup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic-Term shape: (20, 14567)\n",
      "Doc-Topic shape: (2000, 20)\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "\n",
    "def load_R_model(filename):\n",
    "    with open(filename, 'r') as j:\n",
    "        data_input = json.load(j)\n",
    "    data = {'topic_term_dists': data_input['phi'], \n",
    "            'doc_topic_dists': data_input['theta'],\n",
    "            'doc_lengths': data_input['doc.length'],\n",
    "            'vocab': data_input['vocab'],\n",
    "            'term_frequency': data_input['term.frequency']}\n",
    "    return data\n",
    "\n",
    "movies_model_data = load_R_model('data/movie_reviews_input.json')\n",
    "\n",
    "print('Topic-Term shape: %s' % str(np.array(movies_model_data['topic_term_dists']).shape))\n",
    "print('Doc-Topic shape: %s' % str(np.array(movies_model_data['doc_topic_dists']).shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now that we have the data loaded we use the `prepare` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyLDAvis\n",
    "movies_vis_data = pyLDAvis.prepare(**movies_model_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Once you have the visualization data prepared you can do a number of things with it. You can [save the vis](https://pyldavis.readthedocs.org/en/latest/modules/API.html#pyLDAvis.save_html) to an stand-alone HTML file, [serve it](https://pyldavis.readthedocs.org/en/latest/modules/API.html#pyLDAvis.show), or [display it](https://pyldavis.readthedocs.org/en/latest/modules/API.html#pyLDAvis.display) in the notebook. Let's go ahead and display it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el6760145161873765864712155\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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\"weller\", \"welles\", \"welles\", \"welles\", \"wen\", \"werewolf\", \"wes\", \"wes\", \"wes\", \"wes\", \"wes\", \"wes\", \"wes\", \"wes\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"whale\", \"whale\", \"wheres\", \"wheres\", \"whip\", \"whisperer\", \"whisperer\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"whodunit\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wild\", \"wild\", \"wild\", \"wild\", \"wild\", \"wild\", \"williams\", \"williams\", \"williams\", \"williams\", \"williams\", \"williamson\", \"williamson\", \"williamsons\", \"willis\", \"willis\", \"willis\", \"willis\", \"willis\", \"willis\", \"winkler\", \"winslet\", \"winslet\", \"wisecracks\", \"wiseguy\", \"witch\", \"witch\", \"witch\", \"witch\", \"witt\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"wonderland\", \"wong\", \"wong\", \"wong\", \"woo\", \"woo\", \"woo\", \"woo\", \"woods\", \"woods\", \"woods\", \"woods\", \"woods\", \"woody\", \"woody\", \"woody\", \"woody\", \"woody\", \"woodys\", \"work\", \"work\", \"work\", \"works\", \"works\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"worst\", \"worst\", \"worst\", \"worst\", \"wow\", \"wow\", \"wow\", \"wow\", \"wrestler\", \"wrestling\", \"wrestling\", \"writer\", \"writer\", \"writer\", \"writer\", \"writer\", \"writer\", \"writer\", \"yang\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"years\", \"years\", \"years\", \"years\", \"yeoh\", \"york\", \"york\", \"york\", \"york\", \"york\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"youre\", \"youre\", \"youre\", \"youre\", \"youre\", \"yuen\", \"yun\", \"zach\", \"zane\", \"zardoz\", \"zellweger\", \"zemeckis\", \"zeros\", \"zoolander\", \"zwick\"]}, \"mdsDat\": {\"Freq\": [53.76068106965404, 7.259355540713209, 5.255363765905493, 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0.07095073901597497, 0.1621459773241625, -0.052723429552872084, -0.26109374707176347, 0.09880276069863386, -0.10611533980731977, -0.13358488271014904, 0.17788516772845966, -0.16081575059452607, -0.19681622777074018, 0.16581269886834762, -0.08265893067047592, -0.024781636119500187, 0.046567113672727554, 0.09911756051614186, 0.10568345871447464, -0.010054292457069035, 0.06369117966712072]}};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el6760145161873765864712155\", ldavis_el6760145161873765864712155_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el6760145161873765864712155\", ldavis_el6760145161873765864712155_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el6760145161873765864712155\", ldavis_el6760145161873765864712155_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.display(movies_vis_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Pretty, huh?! Again, you should be thanking the original [LDAvis people](https://github.com/cpsievert/LDAvis) for that. You may thank me for the Jupyter integration though. :) Aside from being aesthetically pleasing this visualization more importantly represents a lot of information about the topic model that is hard to take in all at once with ad-hoc queries. To learn more about the visual elements and how they help you explore your model see [this documentation](http://cran.r-project.org/web/packages/LDAvis/vignettes/details.pdf) from the original R project and this presentation ([slides](https://speakerdeck.com/bmabey/visualizing-topic-models), [video](https://www.youtube.com/watch?v=IksL96ls4o0)).\n",
    "\n",
    "\n",
    "To see other models visualized check out [this notebook](http://nbviewer.ipython.org/github/bmabey/pyLDAvis/blob/master/notebooks/Movie%20Reviews,%20AP%20News,%20and%20Jeopardy.ipynb).\n",
    "\n",
    "*ProTip:* To avoid tediously typing in `display` all the time use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the topics are projected to the 2D plane using [PCoA](https://en.wikipedia.org/wiki/PCoA) on a distance matrix created using the [Jensen-Shannon divergence](https://en.wikipedia.org/wiki/Jensen–Shannon_divergence) on the topic-term distributions. You can pass in a different multidimensional scaling function via the `mds` parameter. In addition to `pcoa`, other provided options are `tsne` and `mmds` which operate on the same JS-divergence distance matrix. Both `tsne` and `mmds` require that you have sklearn installed. Here is `tnse` in action:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el6760147158407601438245950\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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\"sequel\", \"sequences\", \"sequences\", \"sequences\", \"sequences\", \"sequences\", \"sequences\", \"series\", \"series\", \"series\", \"series\", \"series\", \"series\", \"series\", \"series\", \"set\", \"set\", \"set\", \"set\", \"sethe\", \"sewell\", \"sex\", \"sex\", \"sex\", \"sex\", \"sex\", \"sex\", \"sex\", \"sex\", \"shackleton\", \"shakespeare\", \"shakespeare\", \"shakespeare\", \"shakespeare\", \"shakespeares\", \"shandling\", \"shane\", \"shane\", \"shane\", \"shanghai\", \"shark\", \"shark\", \"shark\", \"shes\", \"shes\", \"shes\", \"shes\", \"shes\", \"ship\", \"ship\", \"ship\", \"ship\", \"ship\", \"shit\", \"shit\", \"shit\", \"shortcomings\", \"show\", \"show\", \"show\", \"show\", \"show\", \"showing\", \"shows\", \"shrek\", \"shyamalan\", \"sidney\", \"sidney\", \"sidney\", \"sidney\", \"sidneys\", \"siege\", \"siege\", \"sil\", \"silent\", \"silent\", \"silent\", \"silent\", \"silverstone\", \"similar\", \"simon\", \"simon\", \"simon\", \"simon\", \"simons\", \"sims\", \"singer\", \"singer\", \"singer\", \"sister\", \"sister\", \"sixth\", \"skarsg\", \"skarsgard\", \"skinhead\", \"skip\", \"skip\", \"skip\", \"skip\", \"skywalker\", \"skywalker\", \"slade\", \"slam\", \"slasher\", \"slasher\", \"slasher\", \"slave\", \"slave\", \"slave\", \"slave\", \"slavery\", \"slavery\", \"slayer\", \"sleepy\", \"sleepy\", \"sleepy\", \"sliding\", \"sliding\", \"smith\", \"smith\", \"smith\", \"smith\", \"smithee\", \"smiths\", \"snake\", \"snake\", \"snake\", \"snake\", \"snowman\", \"snuff\", \"snuff\", \"social\", \"social\", \"social\", \"social\", \"society\", \"society\", \"society\", \"society\", \"society\", \"society\", \"society\", \"society\", \"soldier\", \"soldier\", \"soldier\", \"soldier\", \"soldiers\", \"soldiers\", \"soldiers\", \"son\", \"son\", \"son\", \"son\", \"song\", \"song\", \"song\", \"song\", \"song\", \"song\", \"songs\", \"songs\", \"songs\", \"songs\", \"songs\", \"songs\", \"soul\", \"soul\", \"soviet\", \"space\", \"space\", \"space\", \"space\", \"spade\", \"spawn\", \"spawn\", \"special\", \"special\", \"special\", \"special\", \"special\", \"species\", \"species\", \"species\", \"sphere\", \"sphere\", \"spice\", \"spice\", \"spice\", \"spice\", \"spielberg\", \"spielberg\", \"spielberg\", \"spielberg\", \"spielberg\", \"spielbergs\", \"spielbergs\", \"spoof\", \"spoof\", \"spoof\", \"squad\", \"squad\", \"squid\", \"stab\", \"stab\", \"stalker\", \"star\", \"star\", \"star\", \"star\", \"star\", \"star\", \"star\", \"star\", \"star\", \"stargher\", \"starship\", \"starship\", \"states\", \"states\", \"states\", \"stellan\", \"stephane\", \"stephens\", \"stigmata\", \"stigmata\", \"stiles\", \"stiller\", \"stiller\", \"story\", \"story\", \"story\", \"story\", \"story\", \"story\", \"strawberry\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"studio\", \"studio\", \"studio\", \"studio\", \"stunts\", \"stunts\", \"stunts\", \"stunts\", \"stunts\", \"stupid\", \"stupid\", \"stupid\", \"stupid\", \"stupid\", \"stupid\", \"stupidest\", \"stupidity\", \"stupidity\", \"subtitles\", \"succession\", \"sucks\", \"sucks\", \"summer\", \"summer\", \"summer\", \"summer\", \"summer\", \"supergirl\", \"superheroes\", \"supernova\", \"supposed\", \"supposed\", \"supposed\", \"surprise\", \"susan\", \"susan\", \"susan\", \"susan\", \"suzanne\", \"suzie\", \"sweet\", \"sweet\", \"sweet\", \"sweet\", \"sweetback\", \"sweethearts\", \"swingers\", \"syd\", \"symbolic\", \"symbolism\", \"sympathy\", \"sympathy\", \"sympathy\", \"tale\", \"tale\", \"tale\", \"talent\", \"talented\", \"tanker\", \"taran\", \"tarantino\", \"tarantino\", \"tarantinos\", \"tarzan\", \"tarzan\", \"tatooine\", \"tatopoulos\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"teen\", \"teen\", \"teen\", \"teen\", \"teen\", \"television\", \"television\", \"television\", \"temper\", \"tenebrae\", \"terl\", \"terrorism\", \"terrorists\", \"terrorists\", \"terrorists\", \"thai\", \"theatrical\", \"theatrical\", \"theatrical\", \"thematic\", \"themes\", \"themes\", \"themes\", \"themes\", \"theron\", \"theyre\", \"theyre\", \"theyre\", \"theyre\", \"thing\", \"thing\", \"things\", \"things\", \"things\", \"things\", \"things\", \"thompsons\", \"thornton\", \"thornton\", \"thornton\", \"thriller\", \"thriller\", \"thriller\", \"thriller\", \"thriller\", \"thriller\", \"tibbs\", \"tidal\", \"time\", \"time\", \"time\", \"times\", \"tingle\", \"titanic\", \"titanic\", \"titanic\", \"titus\", \"toback\", \"tom\", \"tom\", \"tom\", \"tom\", \"tom\", \"tom\", \"tom\", \"tom\", \"tom\", \"tomb\", \"top\", \"top\", \"top\", \"tornadoes\", \"tough\", \"tough\", \"tough\", \"tough\", \"tough\", \"town\", \"town\", \"town\", \"town\", \"town\", \"toy\", \"toy\", \"toy\", \"toys\", \"toys\", \"toys\", \"trainspotting\", \"trainspotting\", \"transforms\", \"treasure\", \"trek\", \"trek\", \"trek\", \"tremors\", \"tribe\", \"tribe\", \"tribe\", \"troopers\", \"troopers\", \"troopers\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"truman\", \"truman\", \"truman\", \"trumans\", \"tsui\", \"tswsm\", \"tunney\", \"turkey\", \"turkey\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"twister\", \"twister\", \"twister\", \"unfunny\", \"unfunny\", \"unfunny\", \"unfunny\", \"united\", \"united\", \"united\", \"united\", \"united\", \"urban\", \"urban\", \"vader\", \"valek\", \"valentine\", \"valjean\", \"vampire\", \"vampires\", \"vampires\", \"van\", \"van\", \"van\", \"van\", \"van\", \"van\", \"van\", \"varsity\", \"vegas\", \"vegas\", \"vegas\", \"ventura\", \"verhoeven\", \"verhoeven\", \"version\", \"version\", \"version\", \"version\", \"video\", \"video\", \"video\", \"video\", \"video\", \"viewers\", \"views\", \"views\", \"vignettes\", \"vincent\", \"vincent\", \"vincent\", \"vincent\", \"vincent\", \"ving\", \"violence\", \"violence\", \"violence\", \"violence\", \"violence\", \"violence\", \"violin\", \"virtual\", \"virtual\", \"virus\", \"virus\", \"visual\", \"visual\", \"visual\", \"visual\", \"visual\", \"visual\", \"visually\", \"visually\", \"visuals\", \"visuals\", \"visuals\", \"visuals\", \"voice\", \"voice\", \"voice\", \"voice\", \"voice\", \"voiced\", \"voiced\", \"voiced\", \"voiced\", \"voiced\", \"volcano\", \"wan\", \"wan\", \"wan\", \"wan\", \"war\", \"war\", \"war\", \"warrior\", \"warrior\", \"warrior\", \"wars\", \"wars\", \"wars\", \"wars\", \"wasnt\", \"wasnt\", \"wasnt\", \"waste\", \"waste\", \"waste\", \"waste\", \"water\", \"water\", \"water\", \"water\", \"water\", \"waterworld\", \"wayans\", \"wayland\", \"wcw\", \"weathers\", \"webb\", \"webb\", \"wedding\", \"wedding\", \"wedding\", \"wedding\", \"wedding\", \"weddings\", \"weller\", \"welles\", \"welles\", \"welles\", \"wen\", \"werewolf\", \"wes\", \"wes\", \"wes\", \"wes\", \"wes\", \"wes\", \"wes\", \"wes\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"whale\", \"whale\", \"wheres\", \"wheres\", \"whip\", \"whisperer\", \"whisperer\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"whodunit\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wild\", \"wild\", \"wild\", \"wild\", \"wild\", \"wild\", \"williams\", \"williams\", \"williams\", \"williams\", \"williams\", \"williamson\", \"williamson\", \"williamsons\", \"willis\", \"willis\", \"willis\", \"willis\", \"willis\", \"willis\", \"winkler\", \"winslet\", \"winslet\", \"wisecracks\", \"wiseguy\", \"witch\", \"witch\", \"witch\", \"witch\", \"witt\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"wonderland\", \"wong\", \"wong\", \"wong\", \"woo\", \"woo\", \"woo\", \"woo\", \"woods\", \"woods\", \"woods\", \"woods\", \"woods\", \"woody\", \"woody\", \"woody\", \"woody\", \"woody\", \"woodys\", \"work\", \"work\", \"work\", \"works\", \"works\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"worst\", \"worst\", \"worst\", \"worst\", \"wow\", \"wow\", \"wow\", \"wow\", \"wrestler\", \"wrestling\", \"wrestling\", \"writer\", \"writer\", \"writer\", \"writer\", \"writer\", \"writer\", \"writer\", \"yang\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"years\", \"years\", \"years\", \"years\", \"yeoh\", \"york\", \"york\", \"york\", \"york\", \"york\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"youre\", \"youre\", \"youre\", \"youre\", \"youre\", \"yuen\", \"yun\", \"zach\", \"zane\", \"zardoz\", \"zellweger\", \"zemeckis\", \"zeros\", \"zoolander\", \"zwick\"]}, \"mdsDat\": {\"Freq\": [53.76068106965404, 7.259355540713209, 5.255363765905493, 3.465402133035367, 3.0626949145579516, 2.541895782039824, 2.491748724090263, 2.4204843551687834, 2.172923606155046, 2.081216588594323, 1.930721022281169, 1.8076545770028893, 1.7650105939585916, 1.6877694710511653, 1.6594044172118638, 1.480146641528301, 1.407835670527523, 1.3612117657796665, 1.3465670324697245, 1.0419123282748097], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"y\": [-52.3109963673211, -184.5424705507615, 1096.4755950098852, 177.22942564358388, -13.995971936992555, -157.60155372531375, 234.07578284352607, 28.192397056553464, -65.14573060521356, 118.38478180454088, 62.06845297139965, -121.31482576852034, -1065.2135302622203, 177.80404248710218, 61.70352871177283, -39.51247887822139, 108.13529912042941, -50.075217682115436, -132.95202934850647, -241.7600618084425], \"x\": [194.89320900097073, 193.99776613456208, -8.209169311032099, 43.069640220499174, 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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics           x            y\n",
       "topic                                                     \n",
       "18     53.760681        1       1  194.893209   -52.310996\n",
       "0       7.259356        1       2  193.997766  -184.542471\n",
       "10      5.255364        1       3   -8.209169  1096.475595\n",
       "16      3.465402        1       4   43.069640   177.229426\n",
       "13      3.062695        1       5  -54.455539   -13.995972\n",
       "14      2.541896        1       6   82.260655  -157.601554\n",
       "12      2.491749        1       7  -83.501828   234.075783\n",
       "9       2.420484        1       8 -157.662094    28.192397\n",
       "4       2.172924        1       9  364.968006   -65.145731\n",
       "6       2.081217        1      10  -92.370417   118.384782\n",
       "19      1.930721        1      11   31.341434    62.068453\n",
       "15      1.807655        1      12  -29.385923  -121.314826\n",
       "17      1.765011        1      13  -21.323004 -1065.213530\n",
       "8       1.687769        1      14  182.746291   177.804042\n",
       "2       1.659404        1      15  158.083441    61.703529\n",
       "7       1.480147        1      16   75.434268   -39.512479\n",
       "1       1.407836        1      17 -416.931713   108.135299\n",
       "11      1.361212        1      18 -239.154616   -50.075218\n",
       "3       1.346567        1      19 -143.069555  -132.952029\n",
       "5       1.041912        1      20  -18.067080  -241.760062, topic_info=     Category         Freq        Term        Total  loglift  logprob\n",
       "term                                                                 \n",
       "1     Default  5510.000000       movie  5510.000000  30.0000  30.0000\n",
       "0     Default  8913.000000        film  8913.000000  29.0000  29.0000\n",
       "2     Default  2399.000000        good  2399.000000  28.0000  28.0000\n",
       "7     Default  1922.000000   character  1922.000000  27.0000  27.0000\n",
       "4     Default  2130.000000       story  2130.000000  26.0000  26.0000\n",
       "13    Default  1386.000000         bad  1386.000000  25.0000  25.0000\n",
       "5     Default  2104.000000       films  2104.000000  24.0000  24.0000\n",
       "9     Default  1565.000000        life  1565.000000  23.0000  23.0000\n",
       "3     Default  2403.000000        time  2403.000000  22.0000  22.0000\n",
       "10    Default  1492.000000        plot  1492.000000  21.0000  21.0000\n",
       "6     Default  1943.000000  characters  1943.000000  20.0000  20.0000\n",
       "23    Default  1100.000000        love  1100.000000  19.0000  19.0000\n",
       "16    Default  1271.000000      scenes  1271.000000  18.0000  18.0000\n",
       "18    Default  1203.000000        dont  1203.000000  17.0000  17.0000\n",
       "20    Default  1150.000000      action  1150.000000  16.0000  16.0000\n",
       "22    Default  1145.000000       great  1145.000000  15.0000  15.0000\n",
       "14    Default  1388.000000       scene  1388.000000  14.0000  14.0000\n",
       "11    Default  1427.000000      movies  1427.000000  13.0000  13.0000\n",
       "21    Default  1143.000000         hes  1143.000000  12.0000  12.0000\n",
       "12    Default  1423.000000      people  1423.000000  11.0000  11.0000\n",
       "25    Default  1055.000000         big  1055.000000  10.0000  10.0000\n",
       "68    Default   617.000000        high   617.000000   9.0000   9.0000\n",
       "39    Default   826.000000       funny   826.000000   8.0000   8.0000\n",
       "38    Default   824.000000      comedy   824.000000   7.0000   7.0000\n",
       "53    Default   712.000000        show   712.000000   6.0000   6.0000\n",
       "37    Default   850.000000      things   850.000000   5.0000   5.0000\n",
       "45    Default   772.000000        john   772.000000   4.0000   4.0000\n",
       "27    Default  1055.000000        back  1055.000000   3.0000   3.0000\n",
       "15    Default  1322.000000         man  1322.000000   2.0000   2.0000\n",
       "41    Default   805.000000       thing   805.000000   1.0000   1.0000\n",
       "...       ...          ...         ...          ...      ...      ...\n",
       "3126  Topic20    29.668611    duchovny    33.961442   4.4290  -5.2574\n",
       "4424  Topic20    20.104262        glen    23.406589   4.4120  -5.6466\n",
       "2263  Topic20    35.407220        cole    47.489129   4.2705  -5.0806\n",
       "7040  Topic20    11.496348      briggs    12.848233   4.4529  -6.2055\n",
       "2790  Topic20    27.755741       poker    38.839650   4.2281  -5.3241\n",
       "1958  Topic20    36.363655         meg    55.388825   4.1433  -5.0539\n",
       "2990  Topic20    25.842871     malcolm    36.156522   4.2283  -5.3955\n",
       "1116  Topic20    50.710179       hanks    91.154084   3.9777  -4.7214\n",
       "4447  Topic20    18.191392      lemmon    23.397730   4.3124  -5.7466\n",
       "4182  Topic20    19.147827    thornton    25.440053   4.2800  -5.6953\n",
       "1381  Topic20    40.189395       ghost    77.013021   3.9137  -4.9539\n",
       "1110  Topic20    43.058700   christmas    91.486681   3.8105  -4.8849\n",
       "421   Topic20    60.274528        ryan   200.898632   3.3602  -4.5486\n",
       "2677  Topic20    22.973566       betty    40.305717   4.0020  -5.5132\n",
       "282   Topic20    56.448788         tom   254.741694   3.0572  -4.6142\n",
       "1769  Topic20    27.755741     beloved    60.893823   3.7784  -5.3241\n",
       "292   Topic20    48.797309       oscar   248.569485   2.9361  -4.7598\n",
       "87    Topic20    56.448788      series   536.882528   2.3117  -4.6142\n",
       "176   Topic20    46.884439         boy   346.585658   2.5637  -4.7998\n",
       "392   Topic20    33.494350  television   212.580256   2.7162  -5.1361\n",
       "84    Topic20    42.102265       sense   550.408489   1.9936  -4.9074\n",
       "1     Topic20    59.318093       movie  5510.877724   0.0325  -4.5646\n",
       "53    Topic20    38.276525        show   712.825681   1.6397  -5.0027\n",
       "298   Topic20    30.625046         dog   243.851815   2.4894  -5.2257\n",
       "555   Topic20    27.755741         bob   165.821255   2.7766  -5.3241\n",
       "719   Topic20    25.842871      willis   132.103315   2.9326  -5.3955\n",
       "813   Topic20    24.886436        mike   119.708520   2.9934  -5.4332\n",
       "1100  Topic20    23.930001         fbi    93.158293   3.2049  -5.4724\n",
       "305   Topic20    25.842871        fans   240.489458   2.3335  -5.3955\n",
       "25    Topic20    27.755741         big  1055.640041   0.9257  -5.3241\n",
       "\n",
       "[1508 rows x 6 columns], token_table=      Topic      Freq       Term\n",
       "term                            \n",
       "106       1  0.178144         10\n",
       "106       3  0.114195         10\n",
       "106       4  0.029691         10\n",
       "106      16  0.639490         10\n",
       "106      17  0.036542         10\n",
       "112       1  0.333592          2\n",
       "112       6  0.044636          2\n",
       "112       7  0.148002          2\n",
       "112      11  0.072826          2\n",
       "112      12  0.056382          2\n",
       "112      14  0.079874          2\n",
       "112      15  0.218479          2\n",
       "112      16  0.044636          2\n",
       "371       1  0.599728          3\n",
       "371       6  0.115332          3\n",
       "371      15  0.249118          3\n",
       "371      16  0.023066          3\n",
       "371      18  0.013840          3\n",
       "622       1  0.396382          5\n",
       "622       3  0.127648          5\n",
       "622      10  0.040310          5\n",
       "622      13  0.047028          5\n",
       "622      16  0.389663          5\n",
       "1973     13  1.002078         54\n",
       "6991      8  0.990051        571\n",
       "1502      1  0.526302          6\n",
       "1502     16  0.470901          6\n",
       "9013     19  0.991034        666\n",
       "860       1  0.098072          7\n",
       "860       6  0.044578          7\n",
       "...     ...       ...        ...\n",
       "31       11  0.001028      years\n",
       "7381     14  0.997839       yeoh\n",
       "286       1  0.523210       york\n",
       "286       4  0.051141       york\n",
       "286       5  0.094414       york\n",
       "286      12  0.184894       york\n",
       "286      13  0.149489       york\n",
       "51        1  0.481459      young\n",
       "51        2  0.225769      young\n",
       "51        4  0.110164      young\n",
       "51        9  0.091124      young\n",
       "51       10  0.001360      young\n",
       "51       16  0.023121      young\n",
       "51       18  0.063922      young\n",
       "51       20  0.002720      young\n",
       "188       1  0.438416      youre\n",
       "188       3  0.462604      youre\n",
       "188       7  0.063495      youre\n",
       "188      12  0.003024      youre\n",
       "188      13  0.033259      youre\n",
       "8379     14  0.992428       yuen\n",
       "7383     14  0.997839        yun\n",
       "9691     17  0.989742       zach\n",
       "4352     16  0.975430       zane\n",
       "9692     11  0.981566     zardoz\n",
       "5231     10  1.003559  zellweger\n",
       "7863      6  0.986707   zemeckis\n",
       "9007     18  0.994955      zeros\n",
       "8381     16  0.995945  zoolander\n",
       "7385      8  0.990051      zwick\n",
       "\n",
       "[3480 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'ylab': 'PC2', 'xlab': 'PC1'}, topic_order=[19, 1, 11, 17, 14, 15, 13, 10, 5, 7, 20, 16, 18, 9, 3, 8, 2, 12, 4, 6])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.prepare(mds='tsne', **movies_model_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is `mmds` in action:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el6760145088736164281836977\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el6760145088736164281836977_data = {\"topic.order\": [19, 1, 11, 17, 14, 15, 13, 10, 5, 7, 20, 16, 18, 9, 3, 8, 2, 12, 4, 6], \"lambda.step\": 0.01, \"tinfo\": {\"Freq\": [5510.0, 8913.0, 2399.0, 1922.0, 2130.0, 1386.0, 2104.0, 1565.0, 2403.0, 1492.0, 1943.0, 1100.0, 1271.0, 1203.0, 1150.0, 1145.0, 1388.0, 1427.0, 1143.0, 1423.0, 1055.0, 617.0, 826.0, 824.0, 712.0, 850.0, 772.0, 1055.0, 1322.0, 805.0, 990.7174234626916, 789.1858132841809, 724.336532781196, 586.6565218671628, 567.7005783355233, 431.01824865999737, 398.0947677892513, 385.12491168865495, 349.2083871023844, 282.36374412238484, 237.46808838954874, 231.48200095850385, 222.50286981193653, 209.53301371133952, 208.53533247283204, 207.53765123432458, 188.58170770268242, 186.58634522566743, 185.58866398715995, 184.59098274865278, 178.60489531760788, 176.6095328405929, 173.61648912507047, 171.6211266480555, 156.65590807044327, 153.6628643549211, 148.6744581623837, 146.67909568536874, 142.6883707313388, 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\"town\", \"toy\", \"toy\", \"toy\", \"toys\", \"toys\", \"toys\", \"trainspotting\", \"trainspotting\", \"transforms\", \"treasure\", \"trek\", \"trek\", \"trek\", \"tremors\", \"tribe\", \"tribe\", \"tribe\", \"troopers\", \"troopers\", \"troopers\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"truman\", \"truman\", \"truman\", \"trumans\", \"tsui\", \"tswsm\", \"tunney\", \"turkey\", \"turkey\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"tv\", \"twister\", \"twister\", \"twister\", \"unfunny\", \"unfunny\", \"unfunny\", \"unfunny\", \"united\", \"united\", \"united\", \"united\", \"united\", \"urban\", \"urban\", \"vader\", \"valek\", \"valentine\", \"valjean\", \"vampire\", \"vampires\", \"vampires\", \"van\", \"van\", \"van\", \"van\", \"van\", \"van\", \"van\", \"varsity\", \"vegas\", \"vegas\", \"vegas\", \"ventura\", \"verhoeven\", \"verhoeven\", \"version\", \"version\", \"version\", \"version\", \"video\", \"video\", \"video\", \"video\", \"video\", \"viewers\", \"views\", \"views\", \"vignettes\", \"vincent\", \"vincent\", \"vincent\", \"vincent\", \"vincent\", \"ving\", \"violence\", \"violence\", \"violence\", \"violence\", \"violence\", \"violence\", \"violin\", \"virtual\", \"virtual\", \"virus\", \"virus\", \"visual\", \"visual\", \"visual\", \"visual\", \"visual\", \"visual\", \"visually\", \"visually\", \"visuals\", \"visuals\", \"visuals\", \"visuals\", \"voice\", \"voice\", \"voice\", \"voice\", \"voice\", \"voiced\", \"voiced\", \"voiced\", \"voiced\", \"voiced\", \"volcano\", \"wan\", \"wan\", \"wan\", \"wan\", \"war\", \"war\", \"war\", \"warrior\", \"warrior\", \"warrior\", \"wars\", \"wars\", \"wars\", \"wars\", \"wasnt\", \"wasnt\", \"wasnt\", \"waste\", \"waste\", \"waste\", \"waste\", \"water\", \"water\", \"water\", \"water\", \"water\", \"waterworld\", \"wayans\", \"wayland\", \"wcw\", \"weathers\", \"webb\", \"webb\", \"wedding\", \"wedding\", \"wedding\", \"wedding\", \"wedding\", \"weddings\", \"weller\", \"welles\", \"welles\", \"welles\", \"wen\", \"werewolf\", \"wes\", \"wes\", \"wes\", \"wes\", \"wes\", \"wes\", \"wes\", \"wes\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"whale\", \"whale\", \"wheres\", \"wheres\", \"whip\", \"whisperer\", \"whisperer\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"whodunit\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wild\", \"wild\", \"wild\", \"wild\", \"wild\", \"wild\", \"williams\", \"williams\", \"williams\", \"williams\", \"williams\", \"williamson\", \"williamson\", \"williamsons\", \"willis\", \"willis\", \"willis\", \"willis\", \"willis\", \"willis\", \"winkler\", \"winslet\", \"winslet\", \"wisecracks\", \"wiseguy\", \"witch\", \"witch\", \"witch\", \"witch\", \"witt\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"wonderland\", \"wong\", \"wong\", \"wong\", \"woo\", \"woo\", \"woo\", \"woo\", \"woods\", \"woods\", \"woods\", \"woods\", \"woods\", \"woody\", \"woody\", \"woody\", \"woody\", \"woody\", \"woodys\", \"work\", \"work\", \"work\", \"works\", \"works\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"worst\", \"worst\", \"worst\", \"worst\", \"wow\", \"wow\", \"wow\", \"wow\", \"wrestler\", \"wrestling\", \"wrestling\", \"writer\", \"writer\", \"writer\", \"writer\", \"writer\", \"writer\", \"writer\", \"yang\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"years\", \"years\", \"years\", \"years\", \"yeoh\", \"york\", \"york\", \"york\", \"york\", \"york\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"youre\", \"youre\", \"youre\", \"youre\", \"youre\", \"yuen\", \"yun\", \"zach\", \"zane\", \"zardoz\", \"zellweger\", \"zemeckis\", \"zeros\", \"zoolander\", \"zwick\"]}, \"mdsDat\": {\"Freq\": [53.76068106965404, 7.259355540713209, 5.255363765905493, 3.465402133035367, 3.0626949145579516, 2.541895782039824, 2.491748724090263, 2.4204843551687834, 2.172923606155046, 2.081216588594323, 1.930721022281169, 1.8076545770028893, 1.7650105939585916, 1.6877694710511653, 1.6594044172118638, 1.480146641528301, 1.407835670527523, 1.3612117657796665, 1.3465670324697245, 1.0419123282748097], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"y\": [-0.12331409934272078, 0.04899158662334216, -0.1830950658957201, -0.413945321931126, 0.39497964564793614, 0.17964185346369965, 0.1947999108257572, -0.3002053851327549, -0.4278227005819366, -0.3582012148404872, -0.2312743900714218, 0.34311208269143967, -0.08042031855573248, 0.10716875663166947, 0.3661866636867339, 0.4064458026484312, -0.13313016540061062, -0.10481553811730936, 0.08278083839268667, 0.23211705925812384], \"x\": [-0.18234042202022122, -0.4438307425641436, -0.07394201881147543, -0.14291516776394053, 0.09389532559252178, -0.1288295639302886, -0.3372370431935978, -0.3356483156936645, 0.035767960078985264, 0.27339554920797404, 0.1348805746264052, 0.22515198086756133, -0.37984169815719643, 0.09724257160495249, -0.24520352713157223, -0.07295503715819927, 0.2967875642676953, 0.43928615510566715, 0.38661769395317414, 0.35971816111936294]}};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el6760145088736164281836977\", ldavis_el6760145088736164281836977_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el6760145088736164281836977\", ldavis_el6760145088736164281836977_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el6760145088736164281836977\", ldavis_el6760145088736164281836977_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "18     53.760681        1       1 -0.182340 -0.123314\n",
       "0       7.259356        1       2 -0.443831  0.048992\n",
       "10      5.255364        1       3 -0.073942 -0.183095\n",
       "16      3.465402        1       4 -0.142915 -0.413945\n",
       "13      3.062695        1       5  0.093895  0.394980\n",
       "14      2.541896        1       6 -0.128830  0.179642\n",
       "12      2.491749        1       7 -0.337237  0.194800\n",
       "9       2.420484        1       8 -0.335648 -0.300205\n",
       "4       2.172924        1       9  0.035768 -0.427823\n",
       "6       2.081217        1      10  0.273396 -0.358201\n",
       "19      1.930721        1      11  0.134881 -0.231274\n",
       "15      1.807655        1      12  0.225152  0.343112\n",
       "17      1.765011        1      13 -0.379842 -0.080420\n",
       "8       1.687769        1      14  0.097243  0.107169\n",
       "2       1.659404        1      15 -0.245204  0.366187\n",
       "7       1.480147        1      16 -0.072955  0.406446\n",
       "1       1.407836        1      17  0.296788 -0.133130\n",
       "11      1.361212        1      18  0.439286 -0.104816\n",
       "3       1.346567        1      19  0.386618  0.082781\n",
       "5       1.041912        1      20  0.359718  0.232117, topic_info=     Category         Freq        Term        Total  loglift  logprob\n",
       "term                                                                 \n",
       "1     Default  5510.000000       movie  5510.000000  30.0000  30.0000\n",
       "0     Default  8913.000000        film  8913.000000  29.0000  29.0000\n",
       "2     Default  2399.000000        good  2399.000000  28.0000  28.0000\n",
       "7     Default  1922.000000   character  1922.000000  27.0000  27.0000\n",
       "4     Default  2130.000000       story  2130.000000  26.0000  26.0000\n",
       "13    Default  1386.000000         bad  1386.000000  25.0000  25.0000\n",
       "5     Default  2104.000000       films  2104.000000  24.0000  24.0000\n",
       "9     Default  1565.000000        life  1565.000000  23.0000  23.0000\n",
       "3     Default  2403.000000        time  2403.000000  22.0000  22.0000\n",
       "10    Default  1492.000000        plot  1492.000000  21.0000  21.0000\n",
       "6     Default  1943.000000  characters  1943.000000  20.0000  20.0000\n",
       "23    Default  1100.000000        love  1100.000000  19.0000  19.0000\n",
       "16    Default  1271.000000      scenes  1271.000000  18.0000  18.0000\n",
       "18    Default  1203.000000        dont  1203.000000  17.0000  17.0000\n",
       "20    Default  1150.000000      action  1150.000000  16.0000  16.0000\n",
       "22    Default  1145.000000       great  1145.000000  15.0000  15.0000\n",
       "14    Default  1388.000000       scene  1388.000000  14.0000  14.0000\n",
       "11    Default  1427.000000      movies  1427.000000  13.0000  13.0000\n",
       "21    Default  1143.000000         hes  1143.000000  12.0000  12.0000\n",
       "12    Default  1423.000000      people  1423.000000  11.0000  11.0000\n",
       "25    Default  1055.000000         big  1055.000000  10.0000  10.0000\n",
       "68    Default   617.000000        high   617.000000   9.0000   9.0000\n",
       "39    Default   826.000000       funny   826.000000   8.0000   8.0000\n",
       "38    Default   824.000000      comedy   824.000000   7.0000   7.0000\n",
       "53    Default   712.000000        show   712.000000   6.0000   6.0000\n",
       "37    Default   850.000000      things   850.000000   5.0000   5.0000\n",
       "45    Default   772.000000        john   772.000000   4.0000   4.0000\n",
       "27    Default  1055.000000        back  1055.000000   3.0000   3.0000\n",
       "15    Default  1322.000000         man  1322.000000   2.0000   2.0000\n",
       "41    Default   805.000000       thing   805.000000   1.0000   1.0000\n",
       "...       ...          ...         ...          ...      ...      ...\n",
       "3126  Topic20    29.668611    duchovny    33.961442   4.4290  -5.2574\n",
       "4424  Topic20    20.104262        glen    23.406589   4.4120  -5.6466\n",
       "2263  Topic20    35.407220        cole    47.489129   4.2705  -5.0806\n",
       "7040  Topic20    11.496348      briggs    12.848233   4.4529  -6.2055\n",
       "2790  Topic20    27.755741       poker    38.839650   4.2281  -5.3241\n",
       "1958  Topic20    36.363655         meg    55.388825   4.1433  -5.0539\n",
       "2990  Topic20    25.842871     malcolm    36.156522   4.2283  -5.3955\n",
       "1116  Topic20    50.710179       hanks    91.154084   3.9777  -4.7214\n",
       "4447  Topic20    18.191392      lemmon    23.397730   4.3124  -5.7466\n",
       "4182  Topic20    19.147827    thornton    25.440053   4.2800  -5.6953\n",
       "1381  Topic20    40.189395       ghost    77.013021   3.9137  -4.9539\n",
       "1110  Topic20    43.058700   christmas    91.486681   3.8105  -4.8849\n",
       "421   Topic20    60.274528        ryan   200.898632   3.3602  -4.5486\n",
       "2677  Topic20    22.973566       betty    40.305717   4.0020  -5.5132\n",
       "282   Topic20    56.448788         tom   254.741694   3.0572  -4.6142\n",
       "1769  Topic20    27.755741     beloved    60.893823   3.7784  -5.3241\n",
       "292   Topic20    48.797309       oscar   248.569485   2.9361  -4.7598\n",
       "87    Topic20    56.448788      series   536.882528   2.3117  -4.6142\n",
       "176   Topic20    46.884439         boy   346.585658   2.5637  -4.7998\n",
       "392   Topic20    33.494350  television   212.580256   2.7162  -5.1361\n",
       "84    Topic20    42.102265       sense   550.408489   1.9936  -4.9074\n",
       "1     Topic20    59.318093       movie  5510.877724   0.0325  -4.5646\n",
       "53    Topic20    38.276525        show   712.825681   1.6397  -5.0027\n",
       "298   Topic20    30.625046         dog   243.851815   2.4894  -5.2257\n",
       "555   Topic20    27.755741         bob   165.821255   2.7766  -5.3241\n",
       "719   Topic20    25.842871      willis   132.103315   2.9326  -5.3955\n",
       "813   Topic20    24.886436        mike   119.708520   2.9934  -5.4332\n",
       "1100  Topic20    23.930001         fbi    93.158293   3.2049  -5.4724\n",
       "305   Topic20    25.842871        fans   240.489458   2.3335  -5.3955\n",
       "25    Topic20    27.755741         big  1055.640041   0.9257  -5.3241\n",
       "\n",
       "[1508 rows x 6 columns], token_table=      Topic      Freq       Term\n",
       "term                            \n",
       "106       1  0.178144         10\n",
       "106       3  0.114195         10\n",
       "106       4  0.029691         10\n",
       "106      16  0.639490         10\n",
       "106      17  0.036542         10\n",
       "112       1  0.333592          2\n",
       "112       6  0.044636          2\n",
       "112       7  0.148002          2\n",
       "112      11  0.072826          2\n",
       "112      12  0.056382          2\n",
       "112      14  0.079874          2\n",
       "112      15  0.218479          2\n",
       "112      16  0.044636          2\n",
       "371       1  0.599728          3\n",
       "371       6  0.115332          3\n",
       "371      15  0.249118          3\n",
       "371      16  0.023066          3\n",
       "371      18  0.013840          3\n",
       "622       1  0.396382          5\n",
       "622       3  0.127648          5\n",
       "622      10  0.040310          5\n",
       "622      13  0.047028          5\n",
       "622      16  0.389663          5\n",
       "1973     13  1.002078         54\n",
       "6991      8  0.990051        571\n",
       "1502      1  0.526302          6\n",
       "1502     16  0.470901          6\n",
       "9013     19  0.991034        666\n",
       "860       1  0.098072          7\n",
       "860       6  0.044578          7\n",
       "...     ...       ...        ...\n",
       "31       11  0.001028      years\n",
       "7381     14  0.997839       yeoh\n",
       "286       1  0.523210       york\n",
       "286       4  0.051141       york\n",
       "286       5  0.094414       york\n",
       "286      12  0.184894       york\n",
       "286      13  0.149489       york\n",
       "51        1  0.481459      young\n",
       "51        2  0.225769      young\n",
       "51        4  0.110164      young\n",
       "51        9  0.091124      young\n",
       "51       10  0.001360      young\n",
       "51       16  0.023121      young\n",
       "51       18  0.063922      young\n",
       "51       20  0.002720      young\n",
       "188       1  0.438416      youre\n",
       "188       3  0.462604      youre\n",
       "188       7  0.063495      youre\n",
       "188      12  0.003024      youre\n",
       "188      13  0.033259      youre\n",
       "8379     14  0.992428       yuen\n",
       "7383     14  0.997839        yun\n",
       "9691     17  0.989742       zach\n",
       "4352     16  0.975430       zane\n",
       "9692     11  0.981566     zardoz\n",
       "5231     10  1.003559  zellweger\n",
       "7863      6  0.986707   zemeckis\n",
       "9007     18  0.994955      zeros\n",
       "8381     16  0.995945  zoolander\n",
       "7385      8  0.990051      zwick\n",
       "\n",
       "[3480 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'ylab': 'PC2', 'xlab': 'PC1'}, topic_order=[19, 1, 11, 17, 14, 15, 13, 10, 5, 7, 20, 16, 18, 9, 3, 8, 2, 12, 4, 6])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.prepare(mds='mmds', **movies_model_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Making the common case easy - Gensim and others!\n",
    "\n",
    "Built on top of the generic `prepare` function are helper functions for [gensim](https://radimrehurek.com/gensim/), [scikit-learn](http://scikit-learn.org/stable/), and [GraphLab Create](https://dato.com/products/create/). To demonstrate below, I am loading up a trained gensim model and corresponding dictionary and corpus (see [this notebook](http://nbviewer.ipython.org/github/bmabey/pyLDAvis/blob/master/notebooks/Gensim%20Newsgroup.ipynb) for how these were created):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "\n",
    "dictionary = gensim.corpora.Dictionary.load('newsgroups.dict')\n",
    "corpus = gensim.corpora.MmCorpus('newsgroups.mm')\n",
    "lda = gensim.models.ldamodel.LdaModel.load('newsgroups_50.model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "In the dark ages, in order to inspect our topics all we had was `show_topics` and friends:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0.029*pat + 0.014*resurrection + 0.010*threw + 0.010*black + 0.009*temple + 0.009*article + 0.009*aaron + 0.008*front + 0.008*weight + 0.008*back',\n",
       " '0.016*palestinians + 0.012*win + 0.011*soldiers + 0.011*japanese + 0.011*republic + 0.010*dale + 0.010*libertarian + 0.010*democratic + 0.010*trade + 0.009*cultural',\n",
       " '0.050*year + 0.016*percent + 0.013*young + 0.012*neutral + 0.012*media + 0.010*record + 0.010*last + 0.008*league + 0.008*playoffs + 0.008*boston',\n",
       " '0.032*posting + 0.031*host + 0.028*nntp + 0.025*article + 0.022*edu + 0.022*university + 0.021*western + 0.018*occupied + 0.018*case + 0.016*usa',\n",
       " '0.025*israeli + 0.020*file + 0.011*windows + 0.009*program + 0.009*use + 0.008*ftp + 0.008*available + 0.008*files + 0.008*version + 0.007*software',\n",
       " '0.025*coverage + 0.015*good + 0.014*mit + 0.012*morris + 0.012*cover + 0.010*tie + 0.010*new + 0.009*hallam + 0.009*rangers + 0.008*xlib',\n",
       " '0.022*government + 0.020*gun + 0.016*article + 0.016*people + 0.015*guns + 0.014*clipper + 0.013*crime + 0.012*drugs + 0.009*country + 0.008*bill',\n",
       " '0.075*turkey + 0.022*rochester + 0.021*cyprus + 0.018*planes + 0.016*libertarians + 0.011*josh + 0.010*personnel + 0.009*train + 0.009*randy + 0.009*weaver',\n",
       " '0.013*card + 0.008*use + 0.008*video + 0.007*msg + 0.007*get + 0.007*one + 0.007*problem + 0.006*apple + 0.006*computer + 0.006*com',\n",
       " \"0.017*would + 0.013*don't + 0.010*one + 0.010*think + 0.010*like + 0.009*people + 0.008*it's + 0.008*make + 0.007*much + 0.007*get\"]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.show_topics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Thankfully, in addition to these *still helpful functions*, we can get a feel for all of the topics with this one-liner:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "46     12.859783        1       1  0.261719  0.065079\n",
       "30      7.062133        1       2  0.223414  0.039362\n",
       "6       6.564059        1       3  0.225668  0.119312\n",
       "2       6.226808        1       4  0.115293 -0.064645\n",
       "16      5.064616        1       5  0.188052  0.168255\n",
       "48      4.565666        1       6 -0.201218  0.009103\n",
       "39      4.340880        1       7  0.209195 -0.125400\n",
       "28      3.029494        1       8  0.147132 -0.174707\n",
       "47      2.920813        1       9  0.140357  0.135432\n",
       "31      2.589622        1      10  0.137396 -0.073357\n",
       "49      2.314591        1      11  0.016563 -0.005124\n",
       "0       2.271693        1      12  0.131888  0.039923\n",
       "7       2.048925        1      13  0.018617  0.186202\n",
       "41      1.972638        1      14  0.121525 -0.023405\n",
       "32      1.929108        1      15  0.051571 -0.233736\n",
       "40      1.808129        1      16 -0.087086  0.018227\n",
       "5       1.752667        1      17  0.084853  0.081672\n",
       "20      1.717381        1      18  0.021080  0.032786\n",
       "38      1.623305        1      19 -0.069950  0.098264\n",
       "18      1.575303        1      20  0.001086 -0.118720\n",
       "9       1.374415        1      21 -0.050045  0.164712\n",
       "11      1.315563        1      22 -0.065465 -0.129421\n",
       "35      1.293510        1      23  0.068778  0.117125\n",
       "43      1.240653        1      24 -0.035228  0.136413\n",
       "45      1.179862        1      25  0.088970 -0.024780\n",
       "13      1.162513        1      26 -0.081018  0.100031\n",
       "34      1.155500        1      27  0.091613 -0.187025\n",
       "37      1.129053        1      28 -0.013147 -0.021178\n",
       "21      1.101742        1      29  0.007740  0.071922\n",
       "44      1.024845        1      30 -0.072332  0.065149\n",
       "17      0.978471        1      31 -0.076000  0.054092\n",
       "12      0.947424        1      32  0.043191 -0.165050\n",
       "19      0.909543        1      33 -0.116769 -0.046389\n",
       "15      0.900763        1      34 -0.028614  0.069812\n",
       "26      0.838892        1      35 -0.062925  0.058828\n",
       "27      0.833690        1      36  0.027215 -0.060409\n",
       "4       0.818916        1      37 -0.058755  0.048751\n",
       "24      0.818263        1      38  0.040752 -0.120218\n",
       "23      0.747757        1      39 -0.138143 -0.005278\n",
       "33      0.735501        1      40 -0.077180 -0.030381\n",
       "10      0.681656        1      41 -0.040527 -0.035513\n",
       "3       0.610708        1      42 -0.067085 -0.089631\n",
       "36      0.609130        1      43 -0.114182 -0.037627\n",
       "22      0.601347        1      44 -0.068577 -0.091707\n",
       "25      0.574820        1      45 -0.127056 -0.007569\n",
       "8       0.574561        1      46 -0.131165  0.003532\n",
       "42      0.573461        1      47 -0.138972  0.022201\n",
       "1       0.507192        1      48 -0.165909 -0.053731\n",
       "29      0.455008        1      49 -0.188856  0.014065\n",
       "14      0.067627        1      50 -0.187466  0.004755, topic_info=      Category          Freq                   Term         Total  loglift  \\\n",
       "term                                                                         \n",
       "5875   Default  60496.000000                    'ax  60496.000000  30.0000   \n",
       "16683  Default   4804.000000                    max   4804.000000  29.0000   \n",
       "3177   Default   5512.000000                posting   5512.000000  28.0000   \n",
       "1536   Default   4920.000000                   host   4920.000000  27.0000   \n",
       "3674   Default   4770.000000                   nntp   4770.000000  26.0000   \n",
       "9041   Default   6250.000000                 people   6250.000000  25.0000   \n",
       "3963   Default   3403.000000                    edu   3403.000000  24.0000   \n",
       "7538   Default   5601.000000             university   5601.000000  23.0000   \n",
       "464    Default   7529.000000                article   7529.000000  22.0000   \n",
       "7314   Default   2475.000000                israeli   2475.000000  21.0000   \n",
       "20248  Default   2014.000000                  space   2014.000000  20.0000   \n",
       "8437   Default   1677.000000                    key   1677.000000  19.0000   \n",
       "12620  Default   2325.000000                    god   2325.000000  18.0000   \n",
       "15175  Default   3996.000000                    new   3996.000000  17.0000   \n",
       "21119  Default   1961.000000             government   1961.000000  16.0000   \n",
       "15769  Default   5202.000000                   know   5202.000000  15.0000   \n",
       "8475   Default   2125.000000                   file   2125.000000  14.0000   \n",
       "738    Default   1768.000000                   year   1768.000000  13.0000   \n",
       "2154   Default   1276.000000                turkish   1276.000000  12.0000   \n",
       "20210  Default   4117.000000                    use   4117.000000  11.0000   \n",
       "19159  Default   1133.000000                 israel   1133.000000  10.0000   \n",
       "3813   Default   8931.000000                  would   8931.000000   9.0000   \n",
       "4673   Default   1438.000000                  drive   1438.000000   8.0000   \n",
       "16539  Default   1187.000000                    car   1187.000000   7.0000   \n",
       "18812  Default   1044.000000                   jews   1044.000000   6.0000   \n",
       "6829   Default   4493.000000                  think   4493.000000   5.0000   \n",
       "2512   Default   2169.000000                  going   2169.000000   4.0000   \n",
       "9566   Default   3399.000000                   good   3399.000000   3.0000   \n",
       "163    Default   2475.000000                  reply   2475.000000   2.0000   \n",
       "10720  Default   1103.000000                   team   1103.000000   1.0000   \n",
       "...        ...           ...                    ...           ...      ...   \n",
       "11970  Topic50     48.911257            rosicrucian     49.844711   7.2800   \n",
       "3077   Topic50     39.106224             ceremonial     40.048948   7.2751   \n",
       "15991  Topic50    146.406376            homosexuals    150.246393   7.2730   \n",
       "15898  Topic50     27.517641                    oto     28.451080   7.2656   \n",
       "13229  Topic50     25.989524                  amorc     26.922964   7.2636   \n",
       "2791   Topic50     21.405175                 thyagi     22.338616   7.2562   \n",
       "18389  Topic50     20.405425               benedikt     21.338864   7.2542   \n",
       "11286  Topic50     19.877060               templars     20.810500   7.2530   \n",
       "5269   Topic50     18.348944                   ordo     19.282384   7.2493   \n",
       "9991   Topic50     18.348942                 crucis     19.282385   7.2493   \n",
       "20629  Topic50     16.820828                morgoth     17.754268   7.2449   \n",
       "11373  Topic50     16.820828               nagasiva     17.754268   7.2449   \n",
       "8687   Topic50     13.764596                 templi     14.698035   7.2333   \n",
       "18645  Topic50     13.764596                  rosae     14.698036   7.2333   \n",
       "6137   Topic50     13.632427               remedies     14.565866   7.2327   \n",
       "13179  Topic50     12.236480               orientis     13.169919   7.2254   \n",
       "8991   Topic50     11.711084                rosenau     12.644523   7.2222   \n",
       "10082  Topic50     11.245278                loosing     12.178717   7.2192   \n",
       "17431  Topic50     10.982791                    sda     11.916231   7.2173   \n",
       "14024  Topic50     10.832839                 noonan     11.766279   7.2163   \n",
       "14419  Topic50      8.629593                  bowen      9.563033   7.1962   \n",
       "13441  Topic50      8.369213            prostitutes      9.302652   7.1932   \n",
       "11327  Topic50      6.856544           braunschweig      7.789984   7.1713   \n",
       "897    Topic50      6.747142                fenholt      7.680581   7.1693   \n",
       "19479  Topic50      4.501945  rrrrrrrrrrrrrrrabbits      5.435384   7.1105   \n",
       "19154  Topic50      3.276990               pretoria      4.210429   7.0483   \n",
       "13309  Topic50      3.126529              trondheim      4.059969   7.0377   \n",
       "7810   Topic50     18.477673               lecointe     26.166869   6.9510   \n",
       "10561  Topic50      5.528356             witnessing     16.182349   6.2249   \n",
       "3913   Topic50      7.398738                 kinsey     43.255767   5.5331   \n",
       "\n",
       "       logprob  \n",
       "term            \n",
       "5875   30.0000  \n",
       "16683  29.0000  \n",
       "3177   28.0000  \n",
       "1536   27.0000  \n",
       "3674   26.0000  \n",
       "9041   25.0000  \n",
       "3963   24.0000  \n",
       "7538   23.0000  \n",
       "464    22.0000  \n",
       "7314   21.0000  \n",
       "20248  20.0000  \n",
       "8437   19.0000  \n",
       "12620  18.0000  \n",
       "15175  17.0000  \n",
       "21119  16.0000  \n",
       "15769  15.0000  \n",
       "8475   14.0000  \n",
       "738    13.0000  \n",
       "2154   12.0000  \n",
       "20210  11.0000  \n",
       "19159  10.0000  \n",
       "3813    9.0000  \n",
       "4673    8.0000  \n",
       "16539   7.0000  \n",
       "18812   6.0000  \n",
       "6829    5.0000  \n",
       "2512    4.0000  \n",
       "9566    3.0000  \n",
       "163     2.0000  \n",
       "10720   1.0000  \n",
       "...        ...  \n",
       "11970  -3.1019  \n",
       "3077   -3.3256  \n",
       "15991  -2.0055  \n",
       "15898  -3.6771  \n",
       "13229  -3.7342  \n",
       "2791   -3.9283  \n",
       "18389  -3.9761  \n",
       "11286  -4.0024  \n",
       "5269   -4.0824  \n",
       "9991   -4.0824  \n",
       "20629  -4.1693  \n",
       "11373  -4.1693  \n",
       "8687   -4.3698  \n",
       "18645  -4.3698  \n",
       "6137   -4.3795  \n",
       "13179  -4.4875  \n",
       "8991   -4.5314  \n",
       "10082  -4.5720  \n",
       "17431  -4.5956  \n",
       "14024  -4.6093  \n",
       "14419  -4.8367  \n",
       "13441  -4.8674  \n",
       "11327  -5.0667  \n",
       "897    -5.0828  \n",
       "19479  -5.4874  \n",
       "19154  -5.8050  \n",
       "13309  -5.8520  \n",
       "7810   -4.0754  \n",
       "10561  -5.2820  \n",
       "3913   -4.9906  \n",
       "\n",
       "[3024 rows x 6 columns], token_table=       Topic      Freq       Term\n",
       "term                             \n",
       "18830     44  0.983286       #and\n",
       "4437      28  0.982924    #define\n",
       "294       11  0.007134   #email's\n",
       "294       32  0.988098   #email's\n",
       "2645      28  0.990593   #include\n",
       "2596      27  0.815997       #the\n",
       "2596      44  0.163199       #the\n",
       "6412       6  0.988523        #tq\n",
       "12622      6  0.977689        'as\n",
       "12622     24  0.008216        'as\n",
       "20151      6  0.990659       'as'\n",
       "2091       6  0.990834        'au\n",
       "20721      6  0.991996        'aw\n",
       "5875       6  0.999990        'ax\n",
       "6417      19  0.992413        'in\n",
       "2266      47  0.984434        'we\n",
       "1528      46  0.973374        a's\n",
       "11705     38  0.995502      aaron\n",
       "2279      36  0.028717        abc\n",
       "2279      37  0.966821        abc\n",
       "10774      5  0.984847    abolish\n",
       "11581      1  0.017020   abortion\n",
       "11581      3  0.017020   abortion\n",
       "11581      9  0.011347   abortion\n",
       "11581     12  0.022694   abortion\n",
       "11581     29  0.924768   abortion\n",
       "5167      38  0.983401        abs\n",
       "8269       9  0.990338  absolutes\n",
       "18827     48  0.990722        abu\n",
       "5606      35  0.972590     abuses\n",
       "...      ...       ...        ...\n",
       "5970       9  0.004634     you're\n",
       "5970      10  0.004634     you're\n",
       "5970      12  0.029657     you're\n",
       "5970      17  0.006488     you're\n",
       "5970      23  0.009268     you're\n",
       "5970      25  0.007414     you're\n",
       "5970      27  0.038925     you're\n",
       "5970      32  0.003707     you're\n",
       "5970      34  0.000927     you're\n",
       "5970      39  0.008341     you're\n",
       "5970      44  0.002780     you're\n",
       "7762       1  0.131449      young\n",
       "7762       2  0.182662      young\n",
       "7762       3  0.058042      young\n",
       "7762       5  0.049507      young\n",
       "7762      19  0.081942      young\n",
       "7762      24  0.022193      young\n",
       "7762      30  0.375567      young\n",
       "7762      32  0.001707      young\n",
       "7762      35  0.047799      young\n",
       "7762      36  0.006828      young\n",
       "7762      42  0.003414      young\n",
       "7762      48  0.037557      young\n",
       "1325      24  0.986813    zionism\n",
       "16478     21  0.989475    zionist\n",
       "18764     33  0.968994    zisfein\n",
       "18859      8  0.019855       zone\n",
       "18859     11  0.029783       zone\n",
       "18859     34  0.948095       zone\n",
       "2061       8  0.995830    zoology\n",
       "\n",
       "[10284 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'ylab': 'PC2', 'xlab': 'PC1'}, topic_order=[47, 31, 7, 3, 17, 49, 40, 29, 48, 32, 50, 1, 8, 42, 33, 41, 6, 21, 39, 19, 10, 12, 36, 44, 46, 14, 35, 38, 22, 45, 18, 13, 20, 16, 27, 28, 5, 25, 24, 34, 11, 4, 37, 23, 26, 9, 43, 2, 30, 15])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis.gensim_models as gensimvis\n",
    "\n",
    "pyLDAvis.gensimvis.prepare(lda, corpus, dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## sklearn\n",
    "\n",
    "For examples on how to use \n",
    "scikit-learn's topic models with pyLDAvis see [this notebook](http://nbviewer.jupyter.org/github/bmabey/pyLDAvis/blob/master/notebooks/LDA%20model.ipynb). \n",
    "\n",
    "\n",
    "## GraphLab\n",
    "\n",
    "For GraphLab integration check out [this notebook](http://nbviewer.ipython.org/github/bmabey/pyLDAvis/blob/master/notebooks/GraphLab.ipynb#topic=7&lambda=0.41&term=).\n",
    "\n",
    "\n",
    "## Go forth and visualize!\n",
    "\n",
    "What are you waiting for? Go ahead and `pip install pyldavis`."
   ]
  }
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