{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `pyLDAvis.lda_model`\n",
    "\n",
    "pyLDAvis now also supports LDA application from scikit-learn. Let's take a look into this in more detail. We will be using the 20 newsgroups dataset as provided by scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning) \n",
    "warnings.filterwarnings('ignore', category=FutureWarning) \n",
    "warnings.filterwarnings('ignore', category=UserWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pyLDAvis\n",
    "import pyLDAvis.lda_model\n",
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load 20 newsgroups dataset\n",
    "\n",
    "First, the 20 newsgroups dataset available in sklearn is loaded. As always, the headers, footers and quotes are removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11314\n"
     ]
    }
   ],
   "source": [
    "newsgroups = fetch_20newsgroups(remove=('headers', 'footers', 'quotes'))\n",
    "docs_raw = newsgroups.data\n",
    "print(len(docs_raw))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert to document-term matrix\n",
    "\n",
    "Next, the raw documents are converted into document-term matrix, possibly as raw counts or in TF-IDF form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11314, 9144)\n"
     ]
    }
   ],
   "source": [
    "tf_vectorizer = CountVectorizer(strip_accents = 'unicode',\n",
    "                                stop_words = 'english',\n",
    "                                lowercase = True,\n",
    "                                token_pattern = r'\\b[a-zA-Z]{3,}\\b',\n",
    "                                max_df = 0.5, \n",
    "                                min_df = 10)\n",
    "dtm_tf = tf_vectorizer.fit_transform(docs_raw)\n",
    "print(dtm_tf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11314, 9144)\n"
     ]
    }
   ],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(**tf_vectorizer.get_params())\n",
    "dtm_tfidf = tfidf_vectorizer.fit_transform(docs_raw)\n",
    "print(dtm_tfidf.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit Latent Dirichlet Allocation models\n",
    "\n",
    "Finally, the LDA models are fitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LatentDirichletAllocation(n_components=20, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LatentDirichletAllocation</label><div class=\"sk-toggleable__content\"><pre>LatentDirichletAllocation(n_components=20, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LatentDirichletAllocation(n_components=20, random_state=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for TF DTM\n",
    "lda_tf = LatentDirichletAllocation(n_components=20, random_state=0)\n",
    "lda_tf.fit(dtm_tf)\n",
    "# for TFIDF DTM\n",
    "lda_tfidf = LatentDirichletAllocation(n_components=20, random_state=0)\n",
    "lda_tfidf.fit(dtm_tfidf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the models with pyLDAvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el6967152541116165719472015\" style=\"background-color:white;\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el6967152541116165719472015_data = {\"mdsDat\": {\"x\": [0.08052462525384566, 0.1962070611030041, -0.10286874928787208, 0.0547880050861391, 0.12570669874313006, -0.12831796704937157, -0.012272487425201505, 0.09463620596027471, -0.12404412403277787, -0.03044504599741002, 0.14267203005376886, -0.061580786057522235, 0.06975041668681808, 0.11043584224246138, -0.13863814597193472, -0.17626157109516546, 0.11559494388285749, 0.038802119213739994, 0.07931669877471392, -0.33400577008349724], \"y\": [0.08496707855801469, 0.06218510966778203, -0.1704394934061325, -0.10302549338490963, 0.04546623028861709, -0.18621270652319483, -0.15262605750723132, 0.07191024954286523, 0.010609320099918175, -0.011851977203267871, 0.07166415758410381, -0.05521214040781447, -0.0602168694963306, -0.003364501233121089, -0.11087353780516104, -0.024068090320328632, 0.11058786487152643, 0.04473105495179635, 0.03401550402907574, 0.34175429769379284], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 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      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "6      0.080525  0.084967       1        1  10.698914\n",
       "14     0.196207  0.062185       2        1  10.077951\n",
       "0     -0.102869 -0.170439       3        1   9.104501\n",
       "18     0.054788 -0.103025       4        1   7.036540\n",
       "13     0.125707  0.045466       5        1   6.281324\n",
       "8     -0.128318 -0.186213       6        1   5.411451\n",
       "19    -0.012272 -0.152626       7        1   5.387479\n",
       "12     0.094636  0.071910       8        1   5.377476\n",
       "9     -0.124044  0.010609       9        1   4.547461\n",
       "7     -0.030445 -0.011852      10        1   4.498512\n",
       "5      0.142672  0.071664      11        1   4.342297\n",
       "1     -0.061581 -0.055212      12        1   4.146615\n",
       "15     0.069750 -0.060217      13        1   4.078443\n",
       "2      0.110436 -0.003365      14        1   3.673889\n",
       "11    -0.138638 -0.110874      15        1   3.391038\n",
       "3     -0.176262 -0.024068      16        1   3.240042\n",
       "17     0.115595  0.110588      17        1   2.977799\n",
       "4      0.038802  0.044731      18        1   2.548118\n",
       "10     0.079317  0.034016      19        1   2.216235\n",
       "16    -0.334006  0.341754      20        1   0.963914, topic_info=            Term         Freq        Total Category  logprob  loglift\n",
       "5016         max  4601.000000  4601.000000  Default  30.0000  30.0000\n",
       "2653         edu  2438.000000  2438.000000  Default  29.0000  29.0000\n",
       "3523         god  1945.000000  1945.000000  Default  28.0000  28.0000\n",
       "4497         key  1211.000000  1211.000000  Default  27.0000  27.0000\n",
       "7677       space  1250.000000  1250.000000  Default  26.0000  26.0000\n",
       "...          ...          ...          ...      ...      ...      ...\n",
       "8805     virtual    15.110336   134.053142  Topic20  -6.2580   2.4591\n",
       "5140         mil    13.665991   113.450206  Topic20  -6.3585   2.5255\n",
       "6642     reality    15.259194   189.037514  Topic20  -6.2482   2.1252\n",
       "7221  scientific    15.126340   248.731356  Topic20  -6.2570   1.8420\n",
       "2749         end    13.682255   852.822371  Topic20  -6.3573   0.5095\n",
       "\n",
       "[1419 rows x 6 columns], token_table=      Topic      Freq          Term\n",
       "term                               \n",
       "20       11  0.929034     absolutes\n",
       "20       17  0.038710     absolutes\n",
       "37       12  0.032754  accelerators\n",
       "37       13  0.032754  accelerators\n",
       "37       14  0.884362  accelerators\n",
       "...     ...       ...           ...\n",
       "9113     19  0.022005         years\n",
       "9114     14  0.972649         yeast\n",
       "9132      9  0.041934       yzerman\n",
       "9132     18  0.922552       yzerman\n",
       "9134      6  0.949301        zenith\n",
       "\n",
       "[5975 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[7, 15, 1, 19, 14, 9, 20, 13, 10, 8, 6, 2, 16, 3, 12, 4, 18, 5, 11, 17])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.lda_model.prepare(lda_tf, dtm_tf, tf_vectorizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "\n",
       "<div id=\"ldavis_el6967152803997602760328908\" style=\"background-color:white;\"></div>\n",
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       "var ldavis_el6967152803997602760328908_data = {\"mdsDat\": {\"x\": [0.19557575517173603, 0.12052608038322256, 0.2994752968718688, 0.01713748581580471, 0.10404075598957413, 0.1394503083163099, -0.1207536880545361, -0.05273231134057452, -0.029779548560873646, -0.08740590703184996, -0.07485933816355891, -0.02562213464572624, -0.0640014861291666, -0.07299355844990726, -0.06839302990398265, -0.06432088208937829, -0.058345795506908986, -0.05462124470366695, -0.05363339144282401, -0.048743366525561715], \"y\": [0.043593329317624836, 0.13856802384131345, -0.13284966190001252, 0.18880672082044098, 0.1803716499434044, -0.14923302102700578, -0.015553921402083873, -0.030380650297065912, -0.07312977241643577, 0.005969741182290474, -0.028866314317100867, -0.05317365845587747, -0.011279041070266078, -0.015141934774941622, -0.004103371097681732, -0.01897370363363889, -0.009242867444480909, -0.008681369352735033, -0.00806763753329644, 0.0013674596175483023], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 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\"kent\", \"kent\", \"kent\", \"kent\", \"key\", \"key\", \"key\", \"keys\", \"keys\", \"killed\", \"killed\", \"killing\", \"killing\", \"kingdom\", \"kingdom\", \"kingman\", \"knife\", \"know\", \"know\", \"know\", \"know\", \"know\", \"know\", \"know\", \"koresh\", \"koresh\", \"kurds\", \"larc\", \"larc\", \"laser\", \"laser\", \"laser\", \"laserjet\", \"launches\", \"law\", \"law\", \"law\", \"law\", \"lds\", \"leafs\", \"league\", \"league\", \"leagues\", \"leaks\", \"lebanese\", \"left\", \"left\", \"left\", \"leftover\", \"legislation\", \"lerc\", \"level\", \"level\", \"level\", \"level\", \"libertarians\", \"licensing\", \"life\", \"life\", \"life\", \"life\", \"like\", \"like\", \"like\", \"like\", \"like\", \"like\", \"like\", \"like\", \"like\", \"lineup\", \"lineup\", \"liquid\", \"liquid\", \"lisa\", \"listserv\", \"little\", \"little\", \"little\", \"little\", \"little\", \"little\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"looking\", \"looking\", \"looking\", \"looking\", \"looking\", \"loser\", \"loser\", \"lost\", \"lost\", \"lost\", \"lost\", \"lost\", \"lunar\", \"lutheran\", \"lyme\", \"mac\", \"mac\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mailing\", \"mailing\", \"mailing\", \"mailing\", \"mailing\", \"mailing\", \"maine\", \"maine\", \"make\", \"make\", \"make\", \"make\", \"make\", \"make\", \"manager\", \"manager\", \"manager\", \"manager\", \"manual\", \"manual\", \"manual\", \"manual\", \"manuals\", \"manuals\", \"map\", \"map\", \"map\", \"mapped\", \"marriage\", \"marriage\", \"mask\", \"mask\", \"mask\", \"math\", \"math\", \"matrix\", \"max\", \"max\", \"max\", \"mcrae\", \"meant\", \"meant\", \"meant\", \"medin\", \"mellon\", \"members\", \"members\", \"mercedes\", \"mercedes\", \"messenger\", \"mets\", \"metzger\", \"micron\", \"microsoft\", \"migraine\", \"migraine\", \"militia\", \"min\", \"min\", \"min\", \"minnesota\", \"miracles\", \"mississippi\", \"mlb\", \"moa\", \"modem\", \"modem\", \"modems\", \"mon\", \"mon\", \"mon\", \"mon\", \"mon\", \"mon\", \"money\", \"money\", \"money\", \"monitor\", \"monitor\", \"monitor\", \"monitors\", \"montana\", \"montreal\", \"moon\", \"moon\", \"moore\", \"moral\", \"moral\", \"moral\", \"morality\", \"morality\", \"morals\", \"morals\", \"mormon\", \"mormons\", \"mormons\", \"mother\", \"mother\", \"mother\", \"mother\", \"motherboards\", \"motherboards\", \"motorcycling\", \"mouse\", \"mouse\", \"moving\", \"msdos\", \"msdos\", \"msdos\", \"msg\", \"muslims\", \"mvp\", \"mvp\", \"mwm\", \"naive\", \"nasa\", \"nasa\", \"nasa\", \"nasa\", \"national\", \"national\", \"national\", \"nazis\", \"ncsa\", \"ncsl\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"neil\", \"netcom\", \"netcom\", \"netcom\", \"networking\", \"networking\", \"new\", \"new\", \"new\", \"new\", \"new\", \"new\", \"newest\", \"news\", \"news\", \"news\", \"news\", \"news\", \"nhl\", \"nist\", \"northeast\", \"norton\", \"notice\", \"nsa\", \"nth\", \"ntsc\", \"nyi\", \"nyr\", \"objective\", \"objective\", \"objective\", \"objective\", \"obo\", \"obo\", \"obsolete\", \"occupying\", \"octopus\", \"oem\", \"offended\", \"offer\", \"offer\", \"offer\", \"offer\", \"offers\", \"offers\", \"offers\", \"ohm\", \"ohm\", \"olwm\", \"openlook\", \"orbit\", \"orbit\", \"orbit\", \"orbital\", \"orbital\", \"orchid\", \"organizers\", \"orientation\", \"original\", \"original\", \"original\", \"original\", \"original\", \"original\", \"orthodox\", \"orthodox\", \"orthodox\", \"ott\", \"packard\", \"packard\", \"padres\", \"pairs\", \"pal\", \"palestine\", \"palestinean\", \"palestineans\", \"palestinian\", \"paperback\", \"paradise\", \"paradise\", \"paradox\", \"partition\", \"parts\", \"parts\", \"pass\", \"pds\", \"peace\", \"peace\", \"peace\", \"pens\", \"pentium\", \"pentium\", \"people\", \"people\", \"people\", \"people\", \"people\", \"people\", \"perry\", \"perry\", \"petri\", \"pex\", \"pgp\", \"pgp\", \"pharisees\", \"phi\", \"phigs\", \"phillies\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phones\", \"physician\", \"physician\", \"picks\", \"pirates\", \"pirates\", \"pit\", \"pitcher\", \"pitching\", \"pitt\", \"pittsburgh\", \"pittsburgh\", \"pixmap\", \"pixmap\", \"pkp\", \"plaintext\", \"plant\", \"plant\", \"plants\", \"plants\", \"play\", \"play\", \"play\", \"play\", \"played\", \"played\", \"player\", \"player\", \"player\", \"players\", \"players\", \"playoffs\", \"pmetzger\", \"political\", \"population\", \"popup\", \"popup\", \"port\", \"port\", \"port\", \"ported\", \"ported\", \"ports\", \"postage\", \"postage\", \"postscript\", \"postscript\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"powerpc\", \"powerpc\", \"preached\", \"president\", \"president\", \"pretty\", \"pretty\", \"pretty\", \"pretty\", \"pretty\", \"price\", \"price\", \"price\", \"price\", \"priest\", \"printer\", \"printer\", \"printers\", \"privacy\", \"privacy\", \"probably\", \"probably\", \"probably\", \"probably\", \"probably\", \"probably\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"profit\", \"profit\", \"profit\", \"program\", \"program\", \"program\", \"program\", \"program\", \"proportional\", \"proportional\", \"proposal\", \"proposal\", \"proposal\", \"proposal\", \"propulsion\", \"protestant\", \"proton\", \"proton\", \"providence\", \"pts\", \"pub\", \"pub\", \"public\", \"public\", \"public\", \"public\", \"public\", \"puck\", \"quack\", \"quack\", \"quadra\", \"quadra\", \"que\", \"que\", \"qur\", \"quran\", \"race\", \"radar\", \"radar\", \"radiator\", \"ram\", \"ram\", \"ram\", \"randy\", \"randy\", \"range\", \"rangers\", \"rape\", \"rape\", \"reactor\", \"reagan\", \"really\", \"really\", \"really\", \"really\", \"really\", \"really\", \"really\", \"rear\", \"rear\", \"recchi\", \"rectangle\", \"rectangle\", \"redesign\", \"refresh\", \"refresh\", \"refusal\", \"reject\", \"relay\", \"religion\", \"religion\", \"religion\", \"religious\", \"religious\", \"religious\", \"rental\", \"republicans\", \"resistant\", \"resolutions\", \"revelation\", \"revelation\", \"reverse\", \"ride\", \"riding\", \"riding\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"rights\", \"rights\", \"rights\", \"rights\", \"risc\", \"risc\", \"risc\", \"rll\", \"robert\", \"robert\", \"robert\", \"robert\", \"robert\", \"robert\", \"roberts\", \"rochester\", \"rochester\", \"romans\", \"royals\", \"royals\", \"rsa\", \"rubber\", \"rubber\", \"rubber\", \"runs\", \"runs\", \"runs\", \"sabbath\", \"sabbath\", \"sabbath\", \"said\", \"said\", \"said\", \"said\", \"said\", \"said\", \"said\", \"saint\", \"sale\", \"sale\", \"sale\", \"salvation\", \"salvation\", \"sam\", \"sam\", \"sam\", \"sam\", \"sam\", \"sandberg\", \"sanderson\", \"sandy\", \"sarcasm\", \"sarcasm\", \"satan\", \"satan\", \"satan\", \"savard\", \"say\", \"say\", \"say\", \"say\", \"say\", \"scheme\", \"scheme\", \"school\", \"school\", \"school\", \"school\", \"school\", \"science\", \"science\", \"science\", \"science\", \"science\", \"sciences\", \"scientific\", \"scorer\", \"scoring\", \"screen\", \"screen\", \"screen\", \"scsi\", \"scsi\", \"scsi\", \"sdio\", \"season\", \"season\", \"season\", \"secret\", \"secret\", \"secure\", \"secure\", \"security\", \"security\", \"security\", \"sell\", \"sell\", \"sell\", \"semiconductor\", \"semiconductor\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"serve\", \"server\", \"server\", \"server\", \"services\", \"services\", \"services\", \"sexual\", \"sexual\", \"shaft\", \"shaft\", \"shaft\", \"shameful\", \"shareware\", \"shareware\", \"shareware\", \"sharks\", \"shearson\", \"shift\", \"shipping\", \"shipping\", \"shipping\", \"shipping\", \"shock\", \"sig\", \"sig\", \"sig\", \"sig\", \"signal\", \"simmons\", \"simms\", \"simms\", \"sin\", \"sin\", \"sin\", \"skepticism\", \"skipjack\", \"soccer\", \"socialists\", \"societies\", \"software\", \"software\", \"software\", \"soldiers\", \"son\", \"son\", \"son\", \"soon\", \"soon\", \"soon\", \"soon\", \"soviet\", \"sox\", \"space\", \"space\", \"space\", \"space\", \"space\", \"space\", \"spacecraft\", \"spacecraft\", \"spelled\", \"spirit\", \"spirit\", \"springfield\", \"src\", \"ssd\", \"ssto\", \"stacker\", \"stage\", \"stage\", \"stage\", \"state\", \"state\", \"state\", \"state\", \"stats\", \"stats\", \"std\", \"steam\", \"steam\", \"stefan\", \"stephenson\", \"sticker\", \"stl\", \"stomach\", \"stomach\", \"string\", \"string\", \"stuff\", \"stuff\", \"stuff\", \"stuff\", \"stuff\", \"subdirectory\", \"subjective\", \"subscribe\", \"subscribe\", \"suck\", \"suck\", \"suck\", \"sue\", \"sue\", \"suit\", \"suit\", \"suit\", \"sunrise\", \"sunview\", \"surprise\", \"surprise\", \"surprise\", \"surrender\", \"surrender\", \"survey\", \"survivor\", \"survivor\", \"svga\", \"swapping\", \"swelling\", \"swelling\", \"sys\", \"talent\", \"tartar\", \"tasks\", \"teachings\", \"team\", \"team\", \"team\", \"teams\", \"teams\", \"ted\", \"ted\", \"ted\", \"tells\", \"tells\", \"tells\", \"tells\", \"temperature\", \"temperature\", \"temperatures\", \"tempest\", \"terminals\", \"terminals\", \"test\", \"test\", \"test\", \"test\", \"tgv\", \"thanks\", \"thanks\", \"thanks\", \"thanks\", \"thanks\", \"thanks\", \"thanks\", \"thanks\", \"thanks\", \"theory\", \"theory\", \"theory\", \"thereof\", \"thereof\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"things\", \"things\", \"things\", \"things\", \"things\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"thou\", \"thou\", \"thread\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"tire\", \"tires\", \"tires\", \"title\", \"title\", \"title\", \"title\", \"today\", \"today\", \"today\", \"today\", \"today\", \"todd\", \"todd\", \"token\", \"token\", \"toner\", \"toner\", \"tonight\", \"tonight\", \"tor\", \"toronto\", \"toronto\", \"toronto\", \"towers\", \"traded\", \"traded\", \"traded\", \"trades\", \"trades\", \"train\", \"train\", \"translate\", \"treatment\", \"treaty\", \"trillion\", \"true\", \"true\", \"true\", \"true\", \"true\", \"truetype\", \"truth\", \"truth\", \"truth\", \"turkish\", \"turkish\", \"turn\", \"turn\", \"turn\", \"tvtwm\", \"ucs\", \"udel\", \"ufl\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"ulf\", \"unity\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"unix\", \"urbana\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"used\", \"used\", \"used\", \"used\", \"used\", \"used\", \"using\", \"using\", \"using\", \"using\", \"uucp\", \"uucp\", \"uucp\", \"uunet\", \"uunet\", \"uunet\", \"uunet\", \"uunet\", \"values\", \"values\", \"values\", \"van\", \"van\", \"van\", \"vat\", \"vehicle\", \"venus\", \"versa\", \"verses\", \"verses\", \"version\", \"version\", \"version\", \"version\", \"version\", \"vesa\", \"vesselin\", \"vga\", \"vga\", \"video\", \"video\", \"video\", \"villages\", \"vinyl\", \"vinyl\", \"virgin\", \"virginia\", \"virginia\", \"virginia\", \"vlb\", \"vlb\", \"volunteer\", \"vram\", \"waco\", \"wagon\", \"wagon\", \"wallet\", \"want\", \"want\", \"want\", \"want\", \"want\", \"war\", \"war\", \"war\", \"war\", \"water\", \"water\", \"water\", \"watson\", \"wave\", \"wave\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"weiss\", \"widget\", \"widget\", \"widgets\", \"win\", \"win\", \"win\", \"win\", \"window\", \"window\", \"window\", \"window\", \"windows\", \"windows\", \"windows\", \"winfield\", \"winnipeg\", \"winnipeg\", \"wiretap\", \"women\", \"women\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"wuarchive\", \"wustl\", \"xcreatewindow\", \"xdm\", \"xerox\", \"xputimage\", \"xterm\", \"xterm\", \"xwindows\", \"xwindows\", \"yankees\", \"yankees\", \"yanks\", \"yeah\", \"yeah\", \"yeah\", \"yeah\", \"yeah\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yearly\", \"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"yount\", \"yup\", \"yup\", \"zip\", \"zip\", \"zip\", \"zip\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [16, 7, 13, 6, 11, 19, 5, 8, 12, 14, 10, 4, 15, 1, 2, 9, 18, 17, 20, 3]};\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_el6967152803997602760328908\", ldavis_el6967152803997602760328908_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://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el6967152803997602760328908\", ldavis_el6967152803997602760328908_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el6967152803997602760328908\", ldavis_el6967152803997602760328908_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "15     0.195576  0.043593       1        1  34.043427\n",
       "6      0.120526  0.138568       2        1  20.066897\n",
       "12     0.299475 -0.132850       3        1  12.540161\n",
       "5      0.017137  0.188807       4        1   5.209774\n",
       "10     0.104041  0.180372       5        1   4.859943\n",
       "18     0.139450 -0.149233       6        1   3.561402\n",
       "4     -0.120754 -0.015554       7        1   2.472662\n",
       "7     -0.052732 -0.030381       8        1   2.044849\n",
       "11    -0.029780 -0.073130       9        1   1.982630\n",
       "13    -0.087406  0.005970      10        1   1.504950\n",
       "9     -0.074859 -0.028866      11        1   1.408547\n",
       "3     -0.025622 -0.053174      12        1   1.304860\n",
       "14    -0.064001 -0.011279      13        1   1.287910\n",
       "0     -0.072994 -0.015142      14        1   1.273234\n",
       "1     -0.068393 -0.004103      15        1   1.262125\n",
       "8     -0.064321 -0.018974      16        1   1.201762\n",
       "17    -0.058346 -0.009243      17        1   1.042810\n",
       "16    -0.054621 -0.008681      18        1   0.992643\n",
       "19    -0.053633 -0.008068      19        1   0.970807\n",
       "2     -0.048743  0.001367      20        1   0.968607, topic_info=        Term        Freq       Total Category  logprob  loglift\n",
       "4497     key   52.000000   52.000000  Default  30.0000  30.0000\n",
       "3523     god   82.000000   82.000000  Default  29.0000  29.0000\n",
       "2653     edu   90.000000   90.000000  Default  28.0000  28.0000\n",
       "8252  thanks  112.000000  112.000000  Default  27.0000  27.0000\n",
       "1322    chip   42.000000   42.000000  Default  26.0000  26.0000\n",
       "...      ...         ...         ...      ...      ...      ...\n",
       "8267  theory    1.719096   16.841984  Topic20  -5.7826   2.3550\n",
       "8906    wave    1.228214    8.026150  Topic20  -6.1188   2.7599\n",
       "1002   bring    1.138414   14.067291  Topic20  -6.1947   2.1229\n",
       "912     book    1.206459   37.538219  Topic20  -6.1367   1.1994\n",
       "913    books    1.079081   20.675158  Topic20  -6.2483   1.6842\n",
       "\n",
       "[1011 rows x 6 columns], token_table=      Topic      Freq          Term\n",
       "term                               \n",
       "13       10  0.696130       abraham\n",
       "24       19  0.444972     absurdity\n",
       "49        2  0.821916      accident\n",
       "51       14  0.746203  accidentally\n",
       "88        2  0.249232           acs\n",
       "...     ...       ...           ...\n",
       "9131     18  0.288959           yup\n",
       "9140      1  0.140220           zip\n",
       "9140      6  0.070110           zip\n",
       "9140      8  0.070110           zip\n",
       "9140     12  0.701101           zip\n",
       "\n",
       "[1994 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[16, 7, 13, 6, 11, 19, 5, 8, 12, 14, 10, 4, 15, 1, 2, 9, 18, 17, 20, 3])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.lda_model.prepare(lda_tfidf, dtm_tfidf, tfidf_vectorizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using different MDS functions\n",
    "\n",
    "With `sklearn` installed, other MDS functions, such as MMDS and TSNE can be used for plotting if the default PCoA is not satisfactory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el6967152804043528971017592\" style=\"background-color:white;\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el6967152804043528971017592_data = {\"mdsDat\": {\"x\": [-0.09994915100052613, -0.06128679718312059, -0.2048064724960201, -0.24892947099794582, 0.13094728749833126, -0.2864112681881935, -0.3579689991045058, -0.20055106904993758, 0.002477639681568635, 0.09239517623424369, 0.10806315669287032, 0.057300267372328656, -0.1386346303495708, -0.024170074878545847, -0.11198463717914178, 0.08258486392046706, 0.2441305124864404, 0.2919418744592428, 0.2723526054214855, 0.4524991866605297], \"y\": [-0.21327272636039651, -0.12038715502862785, 0.22099136739343617, -0.04958267693178617, 0.032302174842401096, 0.17208869103471314, -0.0016635551758150222, -0.29168237818007836, -0.3745881527624914, -0.2550579902516327, -0.102475549712272, 0.19858112503973818, 0.04800342550311229, 0.006722328511730208, 0.34048036621107736, 0.3550014436596524, -0.23022755193826494, -0.11092188764525364, 0.06579298817877642, 0.30989571361198154], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 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       "\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_el6967152804043528971017592\", ldavis_el6967152804043528971017592_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://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el6967152804043528971017592\", ldavis_el6967152804043528971017592_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el6967152804043528971017592\", ldavis_el6967152804043528971017592_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "6     -0.099949 -0.213273       1        1  10.698914\n",
       "14    -0.061287 -0.120387       2        1  10.077951\n",
       "0     -0.204806  0.220991       3        1   9.104501\n",
       "18    -0.248929 -0.049583       4        1   7.036540\n",
       "13     0.130947  0.032302       5        1   6.281324\n",
       "8     -0.286411  0.172089       6        1   5.411451\n",
       "19    -0.357969 -0.001664       7        1   5.387479\n",
       "12    -0.200551 -0.291682       8        1   5.377476\n",
       "9      0.002478 -0.374588       9        1   4.547461\n",
       "7      0.092395 -0.255058      10        1   4.498512\n",
       "5      0.108063 -0.102476      11        1   4.342297\n",
       "1      0.057300  0.198581      12        1   4.146615\n",
       "15    -0.138635  0.048003      13        1   4.078443\n",
       "2     -0.024170  0.006722      14        1   3.673889\n",
       "11    -0.111985  0.340480      15        1   3.391038\n",
       "3      0.082585  0.355001      16        1   3.240042\n",
       "17     0.244131 -0.230228      17        1   2.977799\n",
       "4      0.291942 -0.110922      18        1   2.548118\n",
       "10     0.272353  0.065793      19        1   2.216235\n",
       "16     0.452499  0.309896      20        1   0.963914, topic_info=            Term         Freq        Total Category  logprob  loglift\n",
       "5016         max  4601.000000  4601.000000  Default  30.0000  30.0000\n",
       "2653         edu  2438.000000  2438.000000  Default  29.0000  29.0000\n",
       "3523         god  1945.000000  1945.000000  Default  28.0000  28.0000\n",
       "4497         key  1211.000000  1211.000000  Default  27.0000  27.0000\n",
       "7677       space  1250.000000  1250.000000  Default  26.0000  26.0000\n",
       "...          ...          ...          ...      ...      ...      ...\n",
       "8805     virtual    15.110336   134.053142  Topic20  -6.2580   2.4591\n",
       "5140         mil    13.665991   113.450206  Topic20  -6.3585   2.5255\n",
       "6642     reality    15.259194   189.037514  Topic20  -6.2482   2.1252\n",
       "7221  scientific    15.126340   248.731356  Topic20  -6.2570   1.8420\n",
       "2749         end    13.682255   852.822371  Topic20  -6.3573   0.5095\n",
       "\n",
       "[1419 rows x 6 columns], token_table=      Topic      Freq          Term\n",
       "term                               \n",
       "20       11  0.929034     absolutes\n",
       "20       17  0.038710     absolutes\n",
       "37       12  0.032754  accelerators\n",
       "37       13  0.032754  accelerators\n",
       "37       14  0.884362  accelerators\n",
       "...     ...       ...           ...\n",
       "9113     19  0.022005         years\n",
       "9114     14  0.972649         yeast\n",
       "9132      9  0.041934       yzerman\n",
       "9132     18  0.922552       yzerman\n",
       "9134      6  0.949301        zenith\n",
       "\n",
       "[5975 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[7, 15, 1, 19, 14, 9, 20, 13, 10, 8, 6, 2, 16, 3, 12, 4, 18, 5, 11, 17])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.lda_model.prepare(lda_tf, dtm_tf, tf_vectorizer, mds='mmds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "\n",
       "<div id=\"ldavis_el6967152804040165824413097\" style=\"background-color:white;\"></div>\n",
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       "\n",
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\"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"years\", \"yeast\", \"yzerman\", \"yzerman\", \"zenith\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [7, 15, 1, 19, 14, 9, 20, 13, 10, 8, 6, 2, 16, 3, 12, 4, 18, 5, 11, 17]};\n",
       "\n",
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       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
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      "text/plain": [
       "PreparedData(topic_coordinates=               x           y  topics  cluster       Freq\n",
       "topic                                                   \n",
       "6     -14.853498  -45.800789       1        1  10.698914\n",
       "14    -96.739922   30.589661       2        1  10.077951\n",
       "0      15.219661   60.205353       3        1   9.104501\n",
       "18     -8.863257   22.755390       4        1   7.036540\n",
       "13    -37.461292  -11.603455       5        1   6.281324\n",
       "8      -7.041614  -89.441154       6        1   5.411451\n",
       "19     86.750648   11.611804       7        1   5.387479\n",
       "12     38.504055   97.494286       8        1   5.377476\n",
       "9     -52.937565   25.837904       9        1   4.547461\n",
       "7       8.071974  -12.579908      10        1   4.498512\n",
       "5     -27.465855   60.084621      11        1   4.342297\n",
       "1      35.104191   24.467779      12        1   4.146615\n",
       "15    -68.970993   76.778412      13        1   4.078443\n",
       "2      78.914879  -46.868855      14        1   3.673889\n",
       "11     68.260010   56.726215      15        1   3.391038\n",
       "3     -58.801720  -60.212524      16        1   3.240042\n",
       "17     30.836576  -58.077229      17        1   2.977799\n",
       "4     -83.038345  -17.733229      18        1   2.548118\n",
       "10    -15.602712  103.489136      19        1   2.216235\n",
       "16     48.532097  -14.421998      20        1   0.963914, topic_info=            Term         Freq        Total Category  logprob  loglift\n",
       "5016         max  4601.000000  4601.000000  Default  30.0000  30.0000\n",
       "2653         edu  2438.000000  2438.000000  Default  29.0000  29.0000\n",
       "3523         god  1945.000000  1945.000000  Default  28.0000  28.0000\n",
       "4497         key  1211.000000  1211.000000  Default  27.0000  27.0000\n",
       "7677       space  1250.000000  1250.000000  Default  26.0000  26.0000\n",
       "...          ...          ...          ...      ...      ...      ...\n",
       "8805     virtual    15.110336   134.053142  Topic20  -6.2580   2.4591\n",
       "5140         mil    13.665991   113.450206  Topic20  -6.3585   2.5255\n",
       "6642     reality    15.259194   189.037514  Topic20  -6.2482   2.1252\n",
       "7221  scientific    15.126340   248.731356  Topic20  -6.2570   1.8420\n",
       "2749         end    13.682255   852.822371  Topic20  -6.3573   0.5095\n",
       "\n",
       "[1419 rows x 6 columns], token_table=      Topic      Freq          Term\n",
       "term                               \n",
       "20       11  0.929034     absolutes\n",
       "20       17  0.038710     absolutes\n",
       "37       12  0.032754  accelerators\n",
       "37       13  0.032754  accelerators\n",
       "37       14  0.884362  accelerators\n",
       "...     ...       ...           ...\n",
       "9113     19  0.022005         years\n",
       "9114     14  0.972649         yeast\n",
       "9132      9  0.041934       yzerman\n",
       "9132     18  0.922552       yzerman\n",
       "9134      6  0.949301        zenith\n",
       "\n",
       "[5975 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[7, 15, 1, 19, 14, 9, 20, 13, 10, 8, 6, 2, 16, 3, 12, 4, 18, 5, 11, 17])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.lda_model.prepare(lda_tf, dtm_tf, tf_vectorizer, mds='tsne')"
   ]
  }
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