visualize NLP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from gensim.models import Word2Vec\n",
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "start_data_dir = '.'\n",
    "data_directory = os.path.join(start_data_dir, 'data',\n",
    "                              'yelp_dataset')\n",
    "\n",
    "intermediate_directory = os.path.join(data_directory, 'intermediate')\n",
    "\n",
    "tsne_filepath = os.path.join(intermediate_directory,\n",
    "                             u'tsne_model')\n",
    "\n",
    "tsne_vectors_filepath = os.path.join(intermediate_directory,\n",
    "                                     u'tsne_vectors.npy')\n",
    "\n",
    "\n",
    "word2vec_filepath = os.path.join(intermediate_directory, 'word2vec_model_all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 training epochs so far.\n"
     ]
    }
   ],
   "source": [
    "food2vec = Word2Vec.load(word2vec_filepath)\n",
    "food2vec.init_sims()\n",
    "\n",
    "print(u'{} training epochs so far.'.format(food2vec.train_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/ipykernel_launcher.py:18: DeprecationWarning: Call to deprecated `syn0norm` (Attribute will be removed in 4.0.0, use self.wv.vectors_norm instead).\n"
     ]
    },
    {
     "data": {
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       "  </thead>\n",
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       "      <th>-PRON-</th>\n",
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       "      <td>0.056881</td>\n",
       "      <td>0.251399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037885</td>\n",
       "      <td>0.228471</td>\n",
       "      <td>0.006402</td>\n",
       "      <td>0.044171</td>\n",
       "      <td>-0.117742</td>\n",
       "      <td>-0.040299</td>\n",
       "      <td>-0.077579</td>\n",
       "      <td>0.083768</td>\n",
       "      <td>0.023044</td>\n",
       "      <td>0.085933</td>\n",
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       "    <tr>\n",
       "      <th>be</th>\n",
       "      <td>0.005479</td>\n",
       "      <td>0.063909</td>\n",
       "      <td>0.010632</td>\n",
       "      <td>0.042974</td>\n",
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       "      <td>0.120524</td>\n",
       "      <td>-0.091634</td>\n",
       "      <td>-0.005393</td>\n",
       "      <td>0.098206</td>\n",
       "      <td>0.191996</td>\n",
       "      <td>...</td>\n",
       "      <td>0.083242</td>\n",
       "      <td>0.081308</td>\n",
       "      <td>0.050597</td>\n",
       "      <td>0.069064</td>\n",
       "      <td>-0.014169</td>\n",
       "      <td>0.131986</td>\n",
       "      <td>-0.154185</td>\n",
       "      <td>0.138034</td>\n",
       "      <td>-0.064113</td>\n",
       "      <td>0.163660</td>\n",
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       "    <tr>\n",
       "      <th>the</th>\n",
       "      <td>-0.019968</td>\n",
       "      <td>-0.018599</td>\n",
       "      <td>0.030403</td>\n",
       "      <td>0.091279</td>\n",
       "      <td>0.090386</td>\n",
       "      <td>0.141655</td>\n",
       "      <td>0.115172</td>\n",
       "      <td>-0.087189</td>\n",
       "      <td>0.027388</td>\n",
       "      <td>0.157450</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100098</td>\n",
       "      <td>0.093134</td>\n",
       "      <td>0.026690</td>\n",
       "      <td>0.066760</td>\n",
       "      <td>-0.089390</td>\n",
       "      <td>-0.100121</td>\n",
       "      <td>-0.186524</td>\n",
       "      <td>0.088263</td>\n",
       "      <td>-0.226336</td>\n",
       "      <td>0.263659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>and</th>\n",
       "      <td>0.081139</td>\n",
       "      <td>0.013136</td>\n",
       "      <td>0.106516</td>\n",
       "      <td>0.099658</td>\n",
       "      <td>-0.019699</td>\n",
       "      <td>0.056712</td>\n",
       "      <td>-0.047497</td>\n",
       "      <td>-0.051626</td>\n",
       "      <td>0.069902</td>\n",
       "      <td>0.082603</td>\n",
       "      <td>...</td>\n",
       "      <td>0.091325</td>\n",
       "      <td>0.218942</td>\n",
       "      <td>0.044745</td>\n",
       "      <td>0.031279</td>\n",
       "      <td>0.111612</td>\n",
       "      <td>0.032223</td>\n",
       "      <td>-0.146463</td>\n",
       "      <td>0.064543</td>\n",
       "      <td>-0.198934</td>\n",
       "      <td>0.187167</td>\n",
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       "    <tr>\n",
       "      <th>a</th>\n",
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       "      <td>0.201263</td>\n",
       "      <td>-0.025526</td>\n",
       "      <td>0.072634</td>\n",
       "      <td>-0.027957</td>\n",
       "      <td>-0.021553</td>\n",
       "      <td>0.203276</td>\n",
       "      <td>-0.073604</td>\n",
       "      <td>0.015611</td>\n",
       "      <td>0.081752</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113039</td>\n",
       "      <td>-0.065582</td>\n",
       "      <td>0.099271</td>\n",
       "      <td>0.013485</td>\n",
       "      <td>-0.096199</td>\n",
       "      <td>-0.034326</td>\n",
       "      <td>-0.162997</td>\n",
       "      <td>0.027286</td>\n",
       "      <td>-0.093052</td>\n",
       "      <td>0.245475</td>\n",
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       "    <tr>\n",
       "      <th>to</th>\n",
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       "      <td>-0.102969</td>\n",
       "      <td>0.153912</td>\n",
       "      <td>0.103185</td>\n",
       "      <td>-0.035485</td>\n",
       "      <td>-0.072193</td>\n",
       "      <td>0.197997</td>\n",
       "      <td>-0.077186</td>\n",
       "      <td>-0.057525</td>\n",
       "      <td>0.174217</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030722</td>\n",
       "      <td>0.134326</td>\n",
       "      <td>0.116173</td>\n",
       "      <td>0.024675</td>\n",
       "      <td>0.056588</td>\n",
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       "      <td>-0.110054</td>\n",
       "      <td>-0.084305</td>\n",
       "      <td>-0.085341</td>\n",
       "      <td>0.235801</td>\n",
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       "    <tr>\n",
       "      <th>have</th>\n",
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       "      <td>0.125542</td>\n",
       "      <td>0.245807</td>\n",
       "      <td>0.060274</td>\n",
       "      <td>0.017346</td>\n",
       "      <td>-0.093866</td>\n",
       "      <td>0.053227</td>\n",
       "      <td>0.001765</td>\n",
       "      <td>-0.009886</td>\n",
       "      <td>0.054052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056817</td>\n",
       "      <td>0.280355</td>\n",
       "      <td>-0.028588</td>\n",
       "      <td>0.097306</td>\n",
       "      <td>0.054263</td>\n",
       "      <td>0.056908</td>\n",
       "      <td>-0.055594</td>\n",
       "      <td>-0.134492</td>\n",
       "      <td>0.034391</td>\n",
       "      <td>0.244539</td>\n",
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       "    <tr>\n",
       "      <th>of</th>\n",
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       "      <td>0.168838</td>\n",
       "      <td>-0.179161</td>\n",
       "      <td>0.054272</td>\n",
       "      <td>0.026726</td>\n",
       "      <td>0.031894</td>\n",
       "      <td>0.017067</td>\n",
       "      <td>-0.022372</td>\n",
       "      <td>-0.134089</td>\n",
       "      <td>0.005996</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086960</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>0.155634</td>\n",
       "      <td>0.062147</td>\n",
       "      <td>-0.112930</td>\n",
       "      <td>0.002126</td>\n",
       "      <td>-0.072175</td>\n",
       "      <td>0.048358</td>\n",
       "      <td>-0.145528</td>\n",
       "      <td>0.135202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for</th>\n",
       "      <td>-0.074182</td>\n",
       "      <td>0.045114</td>\n",
       "      <td>0.150311</td>\n",
       "      <td>0.002723</td>\n",
       "      <td>0.157037</td>\n",
       "      <td>-0.103920</td>\n",
       "      <td>-0.029432</td>\n",
       "      <td>0.025852</td>\n",
       "      <td>-0.048811</td>\n",
       "      <td>0.128088</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.049487</td>\n",
       "      <td>0.116073</td>\n",
       "      <td>0.098516</td>\n",
       "      <td>0.153534</td>\n",
       "      <td>-0.054499</td>\n",
       "      <td>-0.157289</td>\n",
       "      <td>-0.206760</td>\n",
       "      <td>-0.135975</td>\n",
       "      <td>-0.038987</td>\n",
       "      <td>0.024615</td>\n",
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       "    <tr>\n",
       "      <th>in</th>\n",
       "      <td>-0.063061</td>\n",
       "      <td>0.038797</td>\n",
       "      <td>-0.090068</td>\n",
       "      <td>0.130691</td>\n",
       "      <td>0.201582</td>\n",
       "      <td>-0.088885</td>\n",
       "      <td>0.191099</td>\n",
       "      <td>-0.017693</td>\n",
       "      <td>0.011806</td>\n",
       "      <td>-0.053722</td>\n",
       "      <td>...</td>\n",
       "      <td>0.163475</td>\n",
       "      <td>0.150726</td>\n",
       "      <td>0.103329</td>\n",
       "      <td>0.043864</td>\n",
       "      <td>0.089603</td>\n",
       "      <td>0.017072</td>\n",
       "      <td>-0.148797</td>\n",
       "      <td>0.026060</td>\n",
       "      <td>-0.086892</td>\n",
       "      <td>0.099905</td>\n",
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       "    <tr>\n",
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       "      <td>0.019333</td>\n",
       "      <td>-0.033297</td>\n",
       "      <td>0.107993</td>\n",
       "      <td>0.073116</td>\n",
       "      <td>0.128141</td>\n",
       "      <td>0.065643</td>\n",
       "      <td>-0.145466</td>\n",
       "      <td>0.073278</td>\n",
       "      <td>0.253649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.079743</td>\n",
       "      <td>0.154641</td>\n",
       "      <td>-0.046880</td>\n",
       "      <td>0.110583</td>\n",
       "      <td>-0.024271</td>\n",
       "      <td>0.125384</td>\n",
       "      <td>-0.119232</td>\n",
       "      <td>0.210893</td>\n",
       "      <td>0.010013</td>\n",
       "      <td>0.192984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not</th>\n",
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       "      <td>0.000039</td>\n",
       "      <td>0.107877</td>\n",
       "      <td>0.047191</td>\n",
       "      <td>-0.027561</td>\n",
       "      <td>0.077486</td>\n",
       "      <td>0.080978</td>\n",
       "      <td>0.072695</td>\n",
       "      <td>0.100900</td>\n",
       "      <td>0.154377</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101016</td>\n",
       "      <td>0.049112</td>\n",
       "      <td>-0.038364</td>\n",
       "      <td>0.145842</td>\n",
       "      <td>-0.224294</td>\n",
       "      <td>0.137787</td>\n",
       "      <td>-0.062697</td>\n",
       "      <td>-0.007003</td>\n",
       "      <td>0.027755</td>\n",
       "      <td>-0.024999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>with</th>\n",
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       "      <td>-0.062618</td>\n",
       "      <td>0.045002</td>\n",
       "      <td>-0.085068</td>\n",
       "      <td>0.034564</td>\n",
       "      <td>-0.008137</td>\n",
       "      <td>0.045573</td>\n",
       "      <td>-0.026095</td>\n",
       "      <td>-0.065259</td>\n",
       "      <td>-0.099804</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101140</td>\n",
       "      <td>0.192045</td>\n",
       "      <td>-0.030208</td>\n",
       "      <td>0.099976</td>\n",
       "      <td>0.064068</td>\n",
       "      <td>-0.003784</td>\n",
       "      <td>-0.110250</td>\n",
       "      <td>-0.084829</td>\n",
       "      <td>-0.165389</td>\n",
       "      <td>0.137321</td>\n",
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       "    <tr>\n",
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       "      <td>-0.048688</td>\n",
       "      <td>0.033159</td>\n",
       "      <td>0.150882</td>\n",
       "      <td>-0.116586</td>\n",
       "      <td>0.037372</td>\n",
       "      <td>0.237538</td>\n",
       "      <td>-0.040569</td>\n",
       "      <td>-0.057240</td>\n",
       "      <td>0.185601</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.179260</td>\n",
       "      <td>0.142818</td>\n",
       "      <td>-0.034827</td>\n",
       "      <td>0.064473</td>\n",
       "      <td>-0.242229</td>\n",
       "      <td>0.079056</td>\n",
       "      <td>-0.048935</td>\n",
       "      <td>-0.030873</td>\n",
       "      <td>-0.120422</td>\n",
       "      <td>0.171408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>-0.077929</td>\n",
       "      <td>0.072339</td>\n",
       "      <td>-0.064739</td>\n",
       "      <td>0.006267</td>\n",
       "      <td>-0.084166</td>\n",
       "      <td>0.172318</td>\n",
       "      <td>-0.035833</td>\n",
       "      <td>0.086935</td>\n",
       "      <td>-0.009773</td>\n",
       "      <td>0.031513</td>\n",
       "      <td>...</td>\n",
       "      <td>0.175564</td>\n",
       "      <td>0.183678</td>\n",
       "      <td>0.141093</td>\n",
       "      <td>0.049934</td>\n",
       "      <td>-0.076101</td>\n",
       "      <td>0.125575</td>\n",
       "      <td>-0.187526</td>\n",
       "      <td>-0.026195</td>\n",
       "      <td>-0.001615</td>\n",
       "      <td>0.046976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>on</th>\n",
       "      <td>0.048296</td>\n",
       "      <td>0.000741</td>\n",
       "      <td>-0.140199</td>\n",
       "      <td>0.051203</td>\n",
       "      <td>0.035675</td>\n",
       "      <td>-0.011357</td>\n",
       "      <td>0.158990</td>\n",
       "      <td>-0.008067</td>\n",
       "      <td>0.032794</td>\n",
       "      <td>0.032604</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132632</td>\n",
       "      <td>0.103436</td>\n",
       "      <td>-0.033376</td>\n",
       "      <td>0.022920</td>\n",
       "      <td>-0.013968</td>\n",
       "      <td>-0.148388</td>\n",
       "      <td>0.058879</td>\n",
       "      <td>-0.194498</td>\n",
       "      <td>-0.022756</td>\n",
       "      <td>0.188520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food</th>\n",
       "      <td>0.099461</td>\n",
       "      <td>-0.074523</td>\n",
       "      <td>-0.006512</td>\n",
       "      <td>0.154290</td>\n",
       "      <td>0.002714</td>\n",
       "      <td>-0.018780</td>\n",
       "      <td>-0.025005</td>\n",
       "      <td>-0.078093</td>\n",
       "      <td>-0.109838</td>\n",
       "      <td>0.151286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.083410</td>\n",
       "      <td>0.146970</td>\n",
       "      <td>-0.043503</td>\n",
       "      <td>0.076456</td>\n",
       "      <td>-0.015922</td>\n",
       "      <td>0.014881</td>\n",
       "      <td>-0.066324</td>\n",
       "      <td>0.037083</td>\n",
       "      <td>-0.087986</td>\n",
       "      <td>0.025558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>so</th>\n",
       "      <td>0.099528</td>\n",
       "      <td>0.115163</td>\n",
       "      <td>0.036966</td>\n",
       "      <td>0.166307</td>\n",
       "      <td>-0.070065</td>\n",
       "      <td>0.049459</td>\n",
       "      <td>0.064828</td>\n",
       "      <td>0.027711</td>\n",
       "      <td>0.074824</td>\n",
       "      <td>0.250801</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.022751</td>\n",
       "      <td>0.223825</td>\n",
       "      <td>-0.044192</td>\n",
       "      <td>-0.067972</td>\n",
       "      <td>-0.081294</td>\n",
       "      <td>0.043588</td>\n",
       "      <td>-0.085931</td>\n",
       "      <td>0.014199</td>\n",
       "      <td>-0.002011</td>\n",
       "      <td>0.136697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>this</th>\n",
       "      <td>0.016207</td>\n",
       "      <td>0.113837</td>\n",
       "      <td>0.144200</td>\n",
       "      <td>-0.064574</td>\n",
       "      <td>0.085020</td>\n",
       "      <td>0.030643</td>\n",
       "      <td>0.307209</td>\n",
       "      <td>-0.046145</td>\n",
       "      <td>-0.039162</td>\n",
       "      <td>0.003456</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.134705</td>\n",
       "      <td>0.121690</td>\n",
       "      <td>0.053272</td>\n",
       "      <td>0.031826</td>\n",
       "      <td>-0.123301</td>\n",
       "      <td>0.098906</td>\n",
       "      <td>-0.082409</td>\n",
       "      <td>0.112495</td>\n",
       "      <td>-0.119913</td>\n",
       "      <td>0.054615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>get</th>\n",
       "      <td>-0.089246</td>\n",
       "      <td>0.012672</td>\n",
       "      <td>0.117417</td>\n",
       "      <td>-0.070573</td>\n",
       "      <td>0.167819</td>\n",
       "      <td>0.021411</td>\n",
       "      <td>0.050478</td>\n",
       "      <td>-0.152990</td>\n",
       "      <td>0.116210</td>\n",
       "      <td>0.130808</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100006</td>\n",
       "      <td>0.095062</td>\n",
       "      <td>-0.127962</td>\n",
       "      <td>0.041920</td>\n",
       "      <td>-0.059564</td>\n",
       "      <td>0.011884</td>\n",
       "      <td>-0.193332</td>\n",
       "      <td>0.034122</td>\n",
       "      <td>0.072253</td>\n",
       "      <td>0.234420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order</th>\n",
       "      <td>0.015184</td>\n",
       "      <td>0.061463</td>\n",
       "      <td>0.239002</td>\n",
       "      <td>-0.100282</td>\n",
       "      <td>0.026302</td>\n",
       "      <td>-0.062340</td>\n",
       "      <td>-0.048245</td>\n",
       "      <td>-0.196410</td>\n",
       "      <td>0.010889</td>\n",
       "      <td>0.068525</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101694</td>\n",
       "      <td>0.067599</td>\n",
       "      <td>-0.072483</td>\n",
       "      <td>-0.070933</td>\n",
       "      <td>-0.096777</td>\n",
       "      <td>-0.102682</td>\n",
       "      <td>0.093476</td>\n",
       "      <td>0.140417</td>\n",
       "      <td>0.022165</td>\n",
       "      <td>0.053388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>at</th>\n",
       "      <td>0.007245</td>\n",
       "      <td>-0.032088</td>\n",
       "      <td>-0.124758</td>\n",
       "      <td>0.137440</td>\n",
       "      <td>0.165749</td>\n",
       "      <td>-0.029296</td>\n",
       "      <td>0.082775</td>\n",
       "      <td>0.118589</td>\n",
       "      <td>-0.014189</td>\n",
       "      <td>0.188096</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066598</td>\n",
       "      <td>0.125595</td>\n",
       "      <td>0.113031</td>\n",
       "      <td>0.076517</td>\n",
       "      <td>0.001163</td>\n",
       "      <td>-0.090803</td>\n",
       "      <td>-0.037612</td>\n",
       "      <td>-0.028023</td>\n",
       "      <td>-0.048440</td>\n",
       "      <td>-0.055780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go</th>\n",
       "      <td>0.091923</td>\n",
       "      <td>0.079501</td>\n",
       "      <td>0.117881</td>\n",
       "      <td>-0.011847</td>\n",
       "      <td>0.211258</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>0.116285</td>\n",
       "      <td>-0.061170</td>\n",
       "      <td>0.093749</td>\n",
       "      <td>0.135111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069980</td>\n",
       "      <td>0.030251</td>\n",
       "      <td>-0.028685</td>\n",
       "      <td>-0.020237</td>\n",
       "      <td>-0.177914</td>\n",
       "      <td>0.005006</td>\n",
       "      <td>-0.105433</td>\n",
       "      <td>-0.107257</td>\n",
       "      <td>-0.077823</td>\n",
       "      <td>0.133260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>there</th>\n",
       "      <td>-0.040784</td>\n",
       "      <td>-0.104618</td>\n",
       "      <td>0.036954</td>\n",
       "      <td>0.030026</td>\n",
       "      <td>-0.035136</td>\n",
       "      <td>-0.079379</td>\n",
       "      <td>0.351998</td>\n",
       "      <td>-0.080119</td>\n",
       "      <td>-0.025872</td>\n",
       "      <td>0.042685</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007807</td>\n",
       "      <td>0.258627</td>\n",
       "      <td>0.025547</td>\n",
       "      <td>0.064562</td>\n",
       "      <td>-0.033229</td>\n",
       "      <td>0.028779</td>\n",
       "      <td>0.142533</td>\n",
       "      <td>0.001533</td>\n",
       "      <td>0.103356</td>\n",
       "      <td>0.202158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>great</th>\n",
       "      <td>0.025892</td>\n",
       "      <td>-0.084992</td>\n",
       "      <td>-0.093094</td>\n",
       "      <td>0.066660</td>\n",
       "      <td>-0.036939</td>\n",
       "      <td>0.164280</td>\n",
       "      <td>0.029153</td>\n",
       "      <td>0.108697</td>\n",
       "      <td>-0.112106</td>\n",
       "      <td>0.043987</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132083</td>\n",
       "      <td>0.085874</td>\n",
       "      <td>0.133308</td>\n",
       "      <td>-0.006298</td>\n",
       "      <td>0.051901</td>\n",
       "      <td>0.014055</td>\n",
       "      <td>-0.079027</td>\n",
       "      <td>-0.005570</td>\n",
       "      <td>-0.040190</td>\n",
       "      <td>0.161477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>like</th>\n",
       "      <td>-0.056245</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.073441</td>\n",
       "      <td>-0.003146</td>\n",
       "      <td>-0.043449</td>\n",
       "      <td>0.170391</td>\n",
       "      <td>0.025431</td>\n",
       "      <td>-0.147248</td>\n",
       "      <td>0.022927</td>\n",
       "      <td>0.010088</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019163</td>\n",
       "      <td>-0.066823</td>\n",
       "      <td>0.086806</td>\n",
       "      <td>0.056701</td>\n",
       "      <td>-0.075759</td>\n",
       "      <td>0.085603</td>\n",
       "      <td>-0.009358</td>\n",
       "      <td>0.052090</td>\n",
       "      <td>-0.156967</td>\n",
       "      <td>0.121180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>do_not</th>\n",
       "      <td>-0.109633</td>\n",
       "      <td>-0.087489</td>\n",
       "      <td>-0.005181</td>\n",
       "      <td>0.057777</td>\n",
       "      <td>-0.002973</td>\n",
       "      <td>0.052368</td>\n",
       "      <td>0.095269</td>\n",
       "      <td>0.091452</td>\n",
       "      <td>0.064375</td>\n",
       "      <td>0.128677</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107150</td>\n",
       "      <td>0.047212</td>\n",
       "      <td>-0.034534</td>\n",
       "      <td>0.098393</td>\n",
       "      <td>-0.190022</td>\n",
       "      <td>-0.130341</td>\n",
       "      <td>0.057239</td>\n",
       "      <td>0.036072</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.033032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>as</th>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.131824</td>\n",
       "      <td>-0.066155</td>\n",
       "      <td>0.126328</td>\n",
       "      <td>0.110641</td>\n",
       "      <td>0.036445</td>\n",
       "      <td>-0.005161</td>\n",
       "      <td>-0.070224</td>\n",
       "      <td>0.002621</td>\n",
       "      <td>0.357644</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.040760</td>\n",
       "      <td>0.072114</td>\n",
       "      <td>0.075446</td>\n",
       "      <td>0.033452</td>\n",
       "      <td>-0.005070</td>\n",
       "      <td>-0.032647</td>\n",
       "      <td>0.066569</td>\n",
       "      <td>-0.025970</td>\n",
       "      <td>-0.193730</td>\n",
       "      <td>0.104274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>just</th>\n",
       "      <td>-0.074791</td>\n",
       "      <td>0.026678</td>\n",
       "      <td>0.049162</td>\n",
       "      <td>0.052487</td>\n",
       "      <td>0.010361</td>\n",
       "      <td>-0.078894</td>\n",
       "      <td>0.115006</td>\n",
       "      <td>-0.084656</td>\n",
       "      <td>-0.146825</td>\n",
       "      <td>0.127160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032737</td>\n",
       "      <td>0.078109</td>\n",
       "      <td>-0.150968</td>\n",
       "      <td>-0.131558</td>\n",
       "      <td>-0.008350</td>\n",
       "      <td>0.010902</td>\n",
       "      <td>0.092659</td>\n",
       "      <td>-0.082724</td>\n",
       "      <td>-0.054292</td>\n",
       "      <td>0.139054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>0.058144</td>\n",
       "      <td>0.099117</td>\n",
       "      <td>0.063235</td>\n",
       "      <td>0.096030</td>\n",
       "      <td>0.015492</td>\n",
       "      <td>0.085599</td>\n",
       "      <td>-0.056118</td>\n",
       "      <td>0.134822</td>\n",
       "      <td>-0.024651</td>\n",
       "      <td>0.189896</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069615</td>\n",
       "      <td>0.031634</td>\n",
       "      <td>-0.090408</td>\n",
       "      <td>0.146665</td>\n",
       "      <td>-0.260102</td>\n",
       "      <td>0.102306</td>\n",
       "      <td>-0.155521</td>\n",
       "      <td>-0.168454</td>\n",
       "      <td>-0.076448</td>\n",
       "      <td>0.088624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soupe</th>\n",
       "      <td>0.119043</td>\n",
       "      <td>0.111006</td>\n",
       "      <td>0.015941</td>\n",
       "      <td>-0.069399</td>\n",
       "      <td>0.248693</td>\n",
       "      <td>0.099899</td>\n",
       "      <td>-0.042241</td>\n",
       "      <td>-0.094774</td>\n",
       "      <td>0.010692</td>\n",
       "      <td>-0.089792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116944</td>\n",
       "      <td>-0.084932</td>\n",
       "      <td>-0.074936</td>\n",
       "      <td>0.028713</td>\n",
       "      <td>-0.068929</td>\n",
       "      <td>0.027130</td>\n",
       "      <td>0.069189</td>\n",
       "      <td>-0.103308</td>\n",
       "      <td>-0.052373</td>\n",
       "      <td>-0.013451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caviar</th>\n",
       "      <td>-0.093376</td>\n",
       "      <td>0.094029</td>\n",
       "      <td>0.020091</td>\n",
       "      <td>0.071385</td>\n",
       "      <td>-0.103909</td>\n",
       "      <td>0.197896</td>\n",
       "      <td>-0.084370</td>\n",
       "      <td>-0.046042</td>\n",
       "      <td>-0.000263</td>\n",
       "      <td>-0.097974</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038786</td>\n",
       "      <td>0.007426</td>\n",
       "      <td>-0.096449</td>\n",
       "      <td>0.118782</td>\n",
       "      <td>0.017474</td>\n",
       "      <td>-0.079687</td>\n",
       "      <td>0.110128</td>\n",
       "      <td>0.085168</td>\n",
       "      <td>-0.148457</td>\n",
       "      <td>-0.109188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>too_greasy</th>\n",
       "      <td>-0.066241</td>\n",
       "      <td>0.013552</td>\n",
       "      <td>0.217268</td>\n",
       "      <td>-0.019829</td>\n",
       "      <td>-0.097052</td>\n",
       "      <td>-0.179911</td>\n",
       "      <td>-0.055463</td>\n",
       "      <td>-0.016201</td>\n",
       "      <td>-0.149123</td>\n",
       "      <td>-0.067498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100175</td>\n",
       "      <td>0.003434</td>\n",
       "      <td>0.077780</td>\n",
       "      <td>-0.139763</td>\n",
       "      <td>0.003994</td>\n",
       "      <td>0.013410</td>\n",
       "      <td>-0.047397</td>\n",
       "      <td>0.127584</td>\n",
       "      <td>-0.050304</td>\n",
       "      <td>0.112870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>turkey_burger</th>\n",
       "      <td>0.139216</td>\n",
       "      <td>0.072941</td>\n",
       "      <td>0.061318</td>\n",
       "      <td>-0.003683</td>\n",
       "      <td>-0.049586</td>\n",
       "      <td>-0.065391</td>\n",
       "      <td>0.060141</td>\n",
       "      <td>-0.008932</td>\n",
       "      <td>0.024156</td>\n",
       "      <td>-0.075696</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146754</td>\n",
       "      <td>0.096004</td>\n",
       "      <td>0.079550</td>\n",
       "      <td>-0.030298</td>\n",
       "      <td>0.003457</td>\n",
       "      <td>0.156391</td>\n",
       "      <td>-0.029296</td>\n",
       "      <td>0.012777</td>\n",
       "      <td>0.021361</td>\n",
       "      <td>-0.351614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>about_8</th>\n",
       "      <td>0.018403</td>\n",
       "      <td>0.181494</td>\n",
       "      <td>-0.001897</td>\n",
       "      <td>-0.016298</td>\n",
       "      <td>-0.257904</td>\n",
       "      <td>0.004929</td>\n",
       "      <td>0.039899</td>\n",
       "      <td>0.164419</td>\n",
       "      <td>0.081270</td>\n",
       "      <td>-0.043134</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002133</td>\n",
       "      <td>0.018248</td>\n",
       "      <td>-0.170031</td>\n",
       "      <td>-0.037194</td>\n",
       "      <td>-0.058689</td>\n",
       "      <td>-0.040005</td>\n",
       "      <td>0.038772</td>\n",
       "      <td>-0.004713</td>\n",
       "      <td>0.004164</td>\n",
       "      <td>0.149236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popcorn_chicken</th>\n",
       "      <td>-0.018040</td>\n",
       "      <td>-0.060433</td>\n",
       "      <td>-0.051168</td>\n",
       "      <td>-0.162396</td>\n",
       "      <td>-0.015527</td>\n",
       "      <td>0.156524</td>\n",
       "      <td>-0.191176</td>\n",
       "      <td>0.004087</td>\n",
       "      <td>-0.008966</td>\n",
       "      <td>-0.007669</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167326</td>\n",
       "      <td>-0.048665</td>\n",
       "      <td>-0.031060</td>\n",
       "      <td>-0.126178</td>\n",
       "      <td>0.045874</td>\n",
       "      <td>-0.001073</td>\n",
       "      <td>-0.021110</td>\n",
       "      <td>0.173797</td>\n",
       "      <td>-0.056659</td>\n",
       "      <td>0.005796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>theatre</th>\n",
       "      <td>0.012417</td>\n",
       "      <td>-0.234724</td>\n",
       "      <td>0.081609</td>\n",
       "      <td>0.030557</td>\n",
       "      <td>0.121828</td>\n",
       "      <td>-0.146305</td>\n",
       "      <td>0.188331</td>\n",
       "      <td>0.017656</td>\n",
       "      <td>-0.007008</td>\n",
       "      <td>0.251279</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007571</td>\n",
       "      <td>-0.110126</td>\n",
       "      <td>0.092264</td>\n",
       "      <td>0.005960</td>\n",
       "      <td>0.079229</td>\n",
       "      <td>-0.093480</td>\n",
       "      <td>0.038719</td>\n",
       "      <td>-0.036078</td>\n",
       "      <td>0.059175</td>\n",
       "      <td>0.037637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>star_rating</th>\n",
       "      <td>0.078249</td>\n",
       "      <td>0.156049</td>\n",
       "      <td>-0.069289</td>\n",
       "      <td>-0.011360</td>\n",
       "      <td>0.056457</td>\n",
       "      <td>0.070278</td>\n",
       "      <td>-0.178994</td>\n",
       "      <td>-0.018324</td>\n",
       "      <td>0.061703</td>\n",
       "      <td>-0.152503</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.159677</td>\n",
       "      <td>-0.120174</td>\n",
       "      <td>-0.031306</td>\n",
       "      <td>-0.021414</td>\n",
       "      <td>-0.009382</td>\n",
       "      <td>0.195018</td>\n",
       "      <td>0.120424</td>\n",
       "      <td>-0.024950</td>\n",
       "      <td>-0.175421</td>\n",
       "      <td>0.042044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>about_$_10</th>\n",
       "      <td>-0.103359</td>\n",
       "      <td>-0.126082</td>\n",
       "      <td>0.006920</td>\n",
       "      <td>0.071138</td>\n",
       "      <td>-0.013155</td>\n",
       "      <td>-0.009906</td>\n",
       "      <td>-0.021076</td>\n",
       "      <td>-0.017437</td>\n",
       "      <td>-0.187457</td>\n",
       "      <td>0.101457</td>\n",
       "      <td>...</td>\n",
       "      <td>0.068171</td>\n",
       "      <td>-0.135463</td>\n",
       "      <td>-0.003174</td>\n",
       "      <td>-0.087587</td>\n",
       "      <td>-0.086760</td>\n",
       "      <td>-0.014259</td>\n",
       "      <td>-0.144352</td>\n",
       "      <td>0.197767</td>\n",
       "      <td>-0.054959</td>\n",
       "      <td>-0.026898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cheat</th>\n",
       "      <td>-0.161958</td>\n",
       "      <td>0.031013</td>\n",
       "      <td>-0.125746</td>\n",
       "      <td>-0.055058</td>\n",
       "      <td>0.075479</td>\n",
       "      <td>-0.038117</td>\n",
       "      <td>0.115853</td>\n",
       "      <td>0.078113</td>\n",
       "      <td>-0.055405</td>\n",
       "      <td>-0.064879</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013509</td>\n",
       "      <td>-0.031727</td>\n",
       "      <td>0.003205</td>\n",
       "      <td>-0.020716</td>\n",
       "      <td>-0.209662</td>\n",
       "      <td>0.066677</td>\n",
       "      <td>-0.001764</td>\n",
       "      <td>-0.124963</td>\n",
       "      <td>0.052039</td>\n",
       "      <td>-0.054816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stair</th>\n",
       "      <td>-0.046825</td>\n",
       "      <td>-0.102849</td>\n",
       "      <td>0.014472</td>\n",
       "      <td>0.060148</td>\n",
       "      <td>0.189276</td>\n",
       "      <td>-0.067087</td>\n",
       "      <td>0.244787</td>\n",
       "      <td>-0.143896</td>\n",
       "      <td>-0.120897</td>\n",
       "      <td>0.095147</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067247</td>\n",
       "      <td>0.079679</td>\n",
       "      <td>0.179751</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>-0.011329</td>\n",
       "      <td>-0.059856</td>\n",
       "      <td>-0.084733</td>\n",
       "      <td>0.124362</td>\n",
       "      <td>0.042136</td>\n",
       "      <td>0.102719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes_please</th>\n",
       "      <td>0.049617</td>\n",
       "      <td>0.050426</td>\n",
       "      <td>-0.017085</td>\n",
       "      <td>0.002076</td>\n",
       "      <td>-0.118133</td>\n",
       "      <td>0.009679</td>\n",
       "      <td>0.003497</td>\n",
       "      <td>0.015789</td>\n",
       "      <td>0.138569</td>\n",
       "      <td>0.114994</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.040168</td>\n",
       "      <td>0.019519</td>\n",
       "      <td>-0.048639</td>\n",
       "      <td>0.178955</td>\n",
       "      <td>0.073848</td>\n",
       "      <td>-0.107971</td>\n",
       "      <td>-0.134588</td>\n",
       "      <td>-0.060265</td>\n",
       "      <td>0.033388</td>\n",
       "      <td>0.009892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>town_square</th>\n",
       "      <td>-0.050668</td>\n",
       "      <td>0.070783</td>\n",
       "      <td>-0.087218</td>\n",
       "      <td>-0.009110</td>\n",
       "      <td>-0.013630</td>\n",
       "      <td>0.154667</td>\n",
       "      <td>0.191121</td>\n",
       "      <td>0.181119</td>\n",
       "      <td>-0.153970</td>\n",
       "      <td>0.108076</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058567</td>\n",
       "      <td>-0.097777</td>\n",
       "      <td>0.260993</td>\n",
       "      <td>-0.021351</td>\n",
       "      <td>-0.061542</td>\n",
       "      <td>0.105708</td>\n",
       "      <td>-0.000083</td>\n",
       "      <td>0.073660</td>\n",
       "      <td>-0.015582</td>\n",
       "      <td>0.025696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_theater</th>\n",
       "      <td>-0.017301</td>\n",
       "      <td>-0.084452</td>\n",
       "      <td>-0.120174</td>\n",
       "      <td>0.124902</td>\n",
       "      <td>0.013372</td>\n",
       "      <td>0.074914</td>\n",
       "      <td>0.257524</td>\n",
       "      <td>-0.056451</td>\n",
       "      <td>-0.097877</td>\n",
       "      <td>0.012137</td>\n",
       "      <td>...</td>\n",
       "      <td>0.177486</td>\n",
       "      <td>0.126203</td>\n",
       "      <td>-0.048961</td>\n",
       "      <td>-0.126829</td>\n",
       "      <td>0.019640</td>\n",
       "      <td>-0.046862</td>\n",
       "      <td>-0.074460</td>\n",
       "      <td>0.209412</td>\n",
       "      <td>-0.025491</td>\n",
       "      <td>-0.029449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not_crazy_about</th>\n",
       "      <td>-0.068309</td>\n",
       "      <td>0.158546</td>\n",
       "      <td>0.111879</td>\n",
       "      <td>-0.053368</td>\n",
       "      <td>-0.204698</td>\n",
       "      <td>0.058277</td>\n",
       "      <td>0.072791</td>\n",
       "      <td>-0.042489</td>\n",
       "      <td>-0.106594</td>\n",
       "      <td>-0.009639</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.059235</td>\n",
       "      <td>-0.032113</td>\n",
       "      <td>0.059571</td>\n",
       "      <td>0.051386</td>\n",
       "      <td>-0.127157</td>\n",
       "      <td>-0.081863</td>\n",
       "      <td>-0.028400</td>\n",
       "      <td>-0.114657</td>\n",
       "      <td>0.185640</td>\n",
       "      <td>-0.035354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arepa</th>\n",
       "      <td>0.163028</td>\n",
       "      <td>-0.146583</td>\n",
       "      <td>-0.040513</td>\n",
       "      <td>0.030970</td>\n",
       "      <td>-0.029102</td>\n",
       "      <td>-0.024530</td>\n",
       "      <td>-0.230694</td>\n",
       "      <td>-0.126618</td>\n",
       "      <td>-0.118695</td>\n",
       "      <td>-0.038135</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069418</td>\n",
       "      <td>-0.030860</td>\n",
       "      <td>-0.040078</td>\n",
       "      <td>-0.180366</td>\n",
       "      <td>-0.046380</td>\n",
       "      <td>0.079982</td>\n",
       "      <td>-0.144685</td>\n",
       "      <td>0.057547</td>\n",
       "      <td>-0.029957</td>\n",
       "      <td>0.075641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_doubt</th>\n",
       "      <td>0.194549</td>\n",
       "      <td>0.065102</td>\n",
       "      <td>-0.063937</td>\n",
       "      <td>0.012639</td>\n",
       "      <td>-0.096712</td>\n",
       "      <td>-0.142001</td>\n",
       "      <td>-0.121815</td>\n",
       "      <td>0.092741</td>\n",
       "      <td>0.012027</td>\n",
       "      <td>0.006602</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167484</td>\n",
       "      <td>0.069349</td>\n",
       "      <td>-0.007034</td>\n",
       "      <td>0.112481</td>\n",
       "      <td>-0.008648</td>\n",
       "      <td>0.044351</td>\n",
       "      <td>-0.077816</td>\n",
       "      <td>-0.153674</td>\n",
       "      <td>-0.109181</td>\n",
       "      <td>0.154344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feedback</th>\n",
       "      <td>0.204531</td>\n",
       "      <td>0.000869</td>\n",
       "      <td>0.132254</td>\n",
       "      <td>0.099384</td>\n",
       "      <td>-0.099042</td>\n",
       "      <td>-0.025646</td>\n",
       "      <td>0.136957</td>\n",
       "      <td>-0.007597</td>\n",
       "      <td>-0.112580</td>\n",
       "      <td>0.109925</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.179941</td>\n",
       "      <td>0.182507</td>\n",
       "      <td>0.020706</td>\n",
       "      <td>0.015952</td>\n",
       "      <td>-0.133618</td>\n",
       "      <td>-0.135532</td>\n",
       "      <td>0.021144</td>\n",
       "      <td>-0.128339</td>\n",
       "      <td>-0.144952</td>\n",
       "      <td>0.076409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yard_house</th>\n",
       "      <td>0.097059</td>\n",
       "      <td>0.100897</td>\n",
       "      <td>-0.224217</td>\n",
       "      <td>0.035832</td>\n",
       "      <td>0.016492</td>\n",
       "      <td>-0.028327</td>\n",
       "      <td>-0.028672</td>\n",
       "      <td>-0.108547</td>\n",
       "      <td>-0.116689</td>\n",
       "      <td>-0.146143</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.075882</td>\n",
       "      <td>-0.112375</td>\n",
       "      <td>0.122241</td>\n",
       "      <td>0.101566</td>\n",
       "      <td>-0.138232</td>\n",
       "      <td>-0.012017</td>\n",
       "      <td>-0.232182</td>\n",
       "      <td>-0.034668</td>\n",
       "      <td>0.125568</td>\n",
       "      <td>-0.060757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liquid</th>\n",
       "      <td>-0.156451</td>\n",
       "      <td>-0.046418</td>\n",
       "      <td>0.022691</td>\n",
       "      <td>-0.076202</td>\n",
       "      <td>-0.072052</td>\n",
       "      <td>-0.171949</td>\n",
       "      <td>0.074207</td>\n",
       "      <td>-0.001681</td>\n",
       "      <td>-0.122432</td>\n",
       "      <td>-0.050786</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019585</td>\n",
       "      <td>-0.075008</td>\n",
       "      <td>-0.038078</td>\n",
       "      <td>-0.081612</td>\n",
       "      <td>-0.063549</td>\n",
       "      <td>-0.057237</td>\n",
       "      <td>-0.029502</td>\n",
       "      <td>0.048064</td>\n",
       "      <td>-0.101491</td>\n",
       "      <td>0.200555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>niece</th>\n",
       "      <td>0.159951</td>\n",
       "      <td>0.135519</td>\n",
       "      <td>0.043976</td>\n",
       "      <td>0.102877</td>\n",
       "      <td>0.014138</td>\n",
       "      <td>-0.017335</td>\n",
       "      <td>0.220209</td>\n",
       "      <td>0.086226</td>\n",
       "      <td>-0.048767</td>\n",
       "      <td>-0.112097</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074138</td>\n",
       "      <td>-0.009747</td>\n",
       "      <td>0.008809</td>\n",
       "      <td>-0.095215</td>\n",
       "      <td>0.074008</td>\n",
       "      <td>-0.021332</td>\n",
       "      <td>0.132398</td>\n",
       "      <td>0.069399</td>\n",
       "      <td>-0.078348</td>\n",
       "      <td>-0.054140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go_wrong</th>\n",
       "      <td>-0.067835</td>\n",
       "      <td>-0.005643</td>\n",
       "      <td>0.048012</td>\n",
       "      <td>0.021519</td>\n",
       "      <td>0.055329</td>\n",
       "      <td>0.189307</td>\n",
       "      <td>-0.024489</td>\n",
       "      <td>0.019939</td>\n",
       "      <td>0.068217</td>\n",
       "      <td>0.108768</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.000789</td>\n",
       "      <td>-0.146820</td>\n",
       "      <td>-0.021005</td>\n",
       "      <td>-0.030786</td>\n",
       "      <td>0.041079</td>\n",
       "      <td>0.030887</td>\n",
       "      <td>-0.074680</td>\n",
       "      <td>0.131629</td>\n",
       "      <td>0.136799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hassle</th>\n",
       "      <td>0.063818</td>\n",
       "      <td>-0.009189</td>\n",
       "      <td>-0.069929</td>\n",
       "      <td>0.049375</td>\n",
       "      <td>0.027215</td>\n",
       "      <td>-0.010845</td>\n",
       "      <td>0.013298</td>\n",
       "      <td>-0.015346</td>\n",
       "      <td>-0.119132</td>\n",
       "      <td>0.106766</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.049192</td>\n",
       "      <td>0.068214</td>\n",
       "      <td>-0.052063</td>\n",
       "      <td>-0.069395</td>\n",
       "      <td>-0.007756</td>\n",
       "      <td>-0.012409</td>\n",
       "      <td>-0.301399</td>\n",
       "      <td>-0.013384</td>\n",
       "      <td>0.159887</td>\n",
       "      <td>-0.015550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kim</th>\n",
       "      <td>0.116404</td>\n",
       "      <td>0.180246</td>\n",
       "      <td>-0.108129</td>\n",
       "      <td>0.162105</td>\n",
       "      <td>0.023455</td>\n",
       "      <td>0.048468</td>\n",
       "      <td>-0.071961</td>\n",
       "      <td>0.001719</td>\n",
       "      <td>0.078302</td>\n",
       "      <td>-0.120150</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.088703</td>\n",
       "      <td>-0.029875</td>\n",
       "      <td>-0.067309</td>\n",
       "      <td>0.168281</td>\n",
       "      <td>-0.081764</td>\n",
       "      <td>0.007857</td>\n",
       "      <td>-0.002197</td>\n",
       "      <td>0.188067</td>\n",
       "      <td>-0.029157</td>\n",
       "      <td>-0.025612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>informative</th>\n",
       "      <td>0.076296</td>\n",
       "      <td>0.105624</td>\n",
       "      <td>-0.042216</td>\n",
       "      <td>0.073638</td>\n",
       "      <td>-0.064488</td>\n",
       "      <td>0.113499</td>\n",
       "      <td>0.067597</td>\n",
       "      <td>-0.060249</td>\n",
       "      <td>-0.112654</td>\n",
       "      <td>-0.084861</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.087383</td>\n",
       "      <td>0.038353</td>\n",
       "      <td>0.012698</td>\n",
       "      <td>-0.048648</td>\n",
       "      <td>0.032426</td>\n",
       "      <td>0.013996</td>\n",
       "      <td>-0.033516</td>\n",
       "      <td>0.023298</td>\n",
       "      <td>-0.072904</td>\n",
       "      <td>-0.076102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cash_register</th>\n",
       "      <td>-0.084403</td>\n",
       "      <td>-0.180934</td>\n",
       "      <td>0.017275</td>\n",
       "      <td>0.078845</td>\n",
       "      <td>0.144804</td>\n",
       "      <td>-0.122969</td>\n",
       "      <td>0.165460</td>\n",
       "      <td>0.091176</td>\n",
       "      <td>-0.091708</td>\n",
       "      <td>0.218862</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.119110</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>0.109356</td>\n",
       "      <td>-0.046450</td>\n",
       "      <td>-0.070741</td>\n",
       "      <td>-0.252635</td>\n",
       "      <td>0.021254</td>\n",
       "      <td>0.088621</td>\n",
       "      <td>-0.012825</td>\n",
       "      <td>-0.054398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edinburgh</th>\n",
       "      <td>0.019272</td>\n",
       "      <td>0.081477</td>\n",
       "      <td>-0.044088</td>\n",
       "      <td>0.045126</td>\n",
       "      <td>0.017116</td>\n",
       "      <td>-0.117002</td>\n",
       "      <td>0.152240</td>\n",
       "      <td>0.051290</td>\n",
       "      <td>0.045804</td>\n",
       "      <td>0.248535</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.081723</td>\n",
       "      <td>-0.074707</td>\n",
       "      <td>0.025100</td>\n",
       "      <td>0.023744</td>\n",
       "      <td>-0.034824</td>\n",
       "      <td>0.055048</td>\n",
       "      <td>-0.104885</td>\n",
       "      <td>-0.054989</td>\n",
       "      <td>-0.054761</td>\n",
       "      <td>-0.099282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seek</th>\n",
       "      <td>0.027544</td>\n",
       "      <td>0.035317</td>\n",
       "      <td>-0.048724</td>\n",
       "      <td>-0.025725</td>\n",
       "      <td>0.022756</td>\n",
       "      <td>-0.080212</td>\n",
       "      <td>-0.051537</td>\n",
       "      <td>0.049947</td>\n",
       "      <td>0.066980</td>\n",
       "      <td>0.079362</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208594</td>\n",
       "      <td>0.078692</td>\n",
       "      <td>0.151242</td>\n",
       "      <td>-0.239776</td>\n",
       "      <td>-0.076414</td>\n",
       "      <td>0.040507</td>\n",
       "      <td>-0.042795</td>\n",
       "      <td>-0.070612</td>\n",
       "      <td>0.044059</td>\n",
       "      <td>0.158404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>about_15</th>\n",
       "      <td>0.074177</td>\n",
       "      <td>0.242673</td>\n",
       "      <td>-0.039397</td>\n",
       "      <td>-0.066945</td>\n",
       "      <td>-0.143779</td>\n",
       "      <td>-0.195210</td>\n",
       "      <td>0.150586</td>\n",
       "      <td>-0.018438</td>\n",
       "      <td>0.019754</td>\n",
       "      <td>0.123160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026291</td>\n",
       "      <td>-0.023437</td>\n",
       "      <td>-0.037574</td>\n",
       "      <td>0.101363</td>\n",
       "      <td>0.078976</td>\n",
       "      <td>-0.099274</td>\n",
       "      <td>0.036255</td>\n",
       "      <td>0.040220</td>\n",
       "      <td>0.086304</td>\n",
       "      <td>0.079845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_5</th>\n",
       "      <td>-0.128619</td>\n",
       "      <td>-0.024612</td>\n",
       "      <td>-0.053084</td>\n",
       "      <td>0.063273</td>\n",
       "      <td>0.024038</td>\n",
       "      <td>0.081363</td>\n",
       "      <td>-0.111339</td>\n",
       "      <td>-0.051302</td>\n",
       "      <td>-0.104480</td>\n",
       "      <td>0.095948</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.026453</td>\n",
       "      <td>0.083218</td>\n",
       "      <td>-0.044819</td>\n",
       "      <td>0.043364</td>\n",
       "      <td>-0.042922</td>\n",
       "      <td>-0.053264</td>\n",
       "      <td>-0.209178</td>\n",
       "      <td>0.018237</td>\n",
       "      <td>0.051250</td>\n",
       "      <td>0.142535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3629 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0         1         2         3         4         5   \\\n",
       "-PRON-          -0.041108  0.036578  0.254178  0.100293  0.024046 -0.015691   \n",
       "be               0.005479  0.063909  0.010632  0.042974  0.001877  0.120524   \n",
       "the             -0.019968 -0.018599  0.030403  0.091279  0.090386  0.141655   \n",
       "and              0.081139  0.013136  0.106516  0.099658 -0.019699  0.056712   \n",
       "a               -0.001371  0.201263 -0.025526  0.072634 -0.027957 -0.021553   \n",
       "to               0.092182 -0.102969  0.153912  0.103185 -0.035485 -0.072193   \n",
       "have            -0.038690  0.125542  0.245807  0.060274  0.017346 -0.093866   \n",
       "of              -0.090765  0.168838 -0.179161  0.054272  0.026726  0.031894   \n",
       "for             -0.074182  0.045114  0.150311  0.002723  0.157037 -0.103920   \n",
       "in              -0.063061  0.038797 -0.090068  0.130691  0.201582 -0.088885   \n",
       "but             -0.024370  0.019333 -0.033297  0.107993  0.073116  0.128141   \n",
       "not             -0.072597  0.000039  0.107877  0.047191 -0.027561  0.077486   \n",
       "with            -0.006593 -0.062618  0.045002 -0.085068  0.034564 -0.008137   \n",
       "that            -0.110508 -0.048688  0.033159  0.150882 -0.116586  0.037372   \n",
       "good            -0.077929  0.072339 -0.064739  0.006267 -0.084166  0.172318   \n",
       "on               0.048296  0.000741 -0.140199  0.051203  0.035675 -0.011357   \n",
       "food             0.099461 -0.074523 -0.006512  0.154290  0.002714 -0.018780   \n",
       "so               0.099528  0.115163  0.036966  0.166307 -0.070065  0.049459   \n",
       "this             0.016207  0.113837  0.144200 -0.064574  0.085020  0.030643   \n",
       "get             -0.089246  0.012672  0.117417 -0.070573  0.167819  0.021411   \n",
       "order            0.015184  0.061463  0.239002 -0.100282  0.026302 -0.062340   \n",
       "at               0.007245 -0.032088 -0.124758  0.137440  0.165749 -0.029296   \n",
       "go               0.091923  0.079501  0.117881 -0.011847  0.211258  0.019474   \n",
       "there           -0.040784 -0.104618  0.036954  0.030026 -0.035136 -0.079379   \n",
       "great            0.025892 -0.084992 -0.093094  0.066660 -0.036939  0.164280   \n",
       "like            -0.056245  0.000033  0.073441 -0.003146 -0.043449  0.170391   \n",
       "do_not          -0.109633 -0.087489 -0.005181  0.057777 -0.002973  0.052368   \n",
       "as               0.004000  0.131824 -0.066155  0.126328  0.110641  0.036445   \n",
       "just            -0.074791  0.026678  0.049162  0.052487  0.010361 -0.078894   \n",
       "all              0.058144  0.099117  0.063235  0.096030  0.015492  0.085599   \n",
       "...                   ...       ...       ...       ...       ...       ...   \n",
       "soupe            0.119043  0.111006  0.015941 -0.069399  0.248693  0.099899   \n",
       "caviar          -0.093376  0.094029  0.020091  0.071385 -0.103909  0.197896   \n",
       "too_greasy      -0.066241  0.013552  0.217268 -0.019829 -0.097052 -0.179911   \n",
       "turkey_burger    0.139216  0.072941  0.061318 -0.003683 -0.049586 -0.065391   \n",
       "about_8          0.018403  0.181494 -0.001897 -0.016298 -0.257904  0.004929   \n",
       "popcorn_chicken -0.018040 -0.060433 -0.051168 -0.162396 -0.015527  0.156524   \n",
       "theatre          0.012417 -0.234724  0.081609  0.030557  0.121828 -0.146305   \n",
       "star_rating      0.078249  0.156049 -0.069289 -0.011360  0.056457  0.070278   \n",
       "about_$_10      -0.103359 -0.126082  0.006920  0.071138 -0.013155 -0.009906   \n",
       "cheat           -0.161958  0.031013 -0.125746 -0.055058  0.075479 -0.038117   \n",
       "stair           -0.046825 -0.102849  0.014472  0.060148  0.189276 -0.067087   \n",
       "yes_please       0.049617  0.050426 -0.017085  0.002076 -0.118133  0.009679   \n",
       "town_square     -0.050668  0.070783 -0.087218 -0.009110 -0.013630  0.154667   \n",
       "movie_theater   -0.017301 -0.084452 -0.120174  0.124902  0.013372  0.074914   \n",
       "not_crazy_about -0.068309  0.158546  0.111879 -0.053368 -0.204698  0.058277   \n",
       "arepa            0.163028 -0.146583 -0.040513  0.030970 -0.029102 -0.024530   \n",
       "no_doubt         0.194549  0.065102 -0.063937  0.012639 -0.096712 -0.142001   \n",
       "feedback         0.204531  0.000869  0.132254  0.099384 -0.099042 -0.025646   \n",
       "yard_house       0.097059  0.100897 -0.224217  0.035832  0.016492 -0.028327   \n",
       "liquid          -0.156451 -0.046418  0.022691 -0.076202 -0.072052 -0.171949   \n",
       "niece            0.159951  0.135519  0.043976  0.102877  0.014138 -0.017335   \n",
       "go_wrong        -0.067835 -0.005643  0.048012  0.021519  0.055329  0.189307   \n",
       "hassle           0.063818 -0.009189 -0.069929  0.049375  0.027215 -0.010845   \n",
       "kim              0.116404  0.180246 -0.108129  0.162105  0.023455  0.048468   \n",
       "informative      0.076296  0.105624 -0.042216  0.073638 -0.064488  0.113499   \n",
       "cash_register   -0.084403 -0.180934  0.017275  0.078845  0.144804 -0.122969   \n",
       "edinburgh        0.019272  0.081477 -0.044088  0.045126  0.017116 -0.117002   \n",
       "seek             0.027544  0.035317 -0.048724 -0.025725  0.022756 -0.080212   \n",
       "about_15         0.074177  0.242673 -0.039397 -0.066945 -0.143779 -0.195210   \n",
       "5_5             -0.128619 -0.024612 -0.053084  0.063273  0.024038  0.081363   \n",
       "\n",
       "                       6         7         8         9     ...           90  \\\n",
       "-PRON-           0.156560 -0.025353  0.056881  0.251399    ...     0.037885   \n",
       "be              -0.091634 -0.005393  0.098206  0.191996    ...     0.083242   \n",
       "the              0.115172 -0.087189  0.027388  0.157450    ...     0.100098   \n",
       "and             -0.047497 -0.051626  0.069902  0.082603    ...     0.091325   \n",
       "a                0.203276 -0.073604  0.015611  0.081752    ...     0.113039   \n",
       "to               0.197997 -0.077186 -0.057525  0.174217    ...     0.030722   \n",
       "have             0.053227  0.001765 -0.009886  0.054052    ...     0.056817   \n",
       "of               0.017067 -0.022372 -0.134089  0.005996    ...     0.086960   \n",
       "for             -0.029432  0.025852 -0.048811  0.128088    ...    -0.049487   \n",
       "in               0.191099 -0.017693  0.011806 -0.053722    ...     0.163475   \n",
       "but              0.065643 -0.145466  0.073278  0.253649    ...     0.079743   \n",
       "not              0.080978  0.072695  0.100900  0.154377    ...     0.101016   \n",
       "with             0.045573 -0.026095 -0.065259 -0.099804    ...     0.101140   \n",
       "that             0.237538 -0.040569 -0.057240  0.185601    ...    -0.179260   \n",
       "good            -0.035833  0.086935 -0.009773  0.031513    ...     0.175564   \n",
       "on               0.158990 -0.008067  0.032794  0.032604    ...     0.132632   \n",
       "food            -0.025005 -0.078093 -0.109838  0.151286    ...     0.083410   \n",
       "so               0.064828  0.027711  0.074824  0.250801    ...    -0.022751   \n",
       "this             0.307209 -0.046145 -0.039162  0.003456    ...    -0.134705   \n",
       "get              0.050478 -0.152990  0.116210  0.130808    ...     0.100006   \n",
       "order           -0.048245 -0.196410  0.010889  0.068525    ...     0.101694   \n",
       "at               0.082775  0.118589 -0.014189  0.188096    ...     0.066598   \n",
       "go               0.116285 -0.061170  0.093749  0.135111    ...     0.069980   \n",
       "there            0.351998 -0.080119 -0.025872  0.042685    ...    -0.007807   \n",
       "great            0.029153  0.108697 -0.112106  0.043987    ...     0.132083   \n",
       "like             0.025431 -0.147248  0.022927  0.010088    ...     0.019163   \n",
       "do_not           0.095269  0.091452  0.064375  0.128677    ...     0.107150   \n",
       "as              -0.005161 -0.070224  0.002621  0.357644    ...    -0.040760   \n",
       "just             0.115006 -0.084656 -0.146825  0.127160    ...     0.032737   \n",
       "all             -0.056118  0.134822 -0.024651  0.189896    ...     0.069615   \n",
       "...                   ...       ...       ...       ...    ...          ...   \n",
       "soupe           -0.042241 -0.094774  0.010692 -0.089792    ...     0.116944   \n",
       "caviar          -0.084370 -0.046042 -0.000263 -0.097974    ...     0.038786   \n",
       "too_greasy      -0.055463 -0.016201 -0.149123 -0.067498    ...     0.100175   \n",
       "turkey_burger    0.060141 -0.008932  0.024156 -0.075696    ...     0.146754   \n",
       "about_8          0.039899  0.164419  0.081270 -0.043134    ...     0.002133   \n",
       "popcorn_chicken -0.191176  0.004087 -0.008966 -0.007669    ...     0.167326   \n",
       "theatre          0.188331  0.017656 -0.007008  0.251279    ...     0.007571   \n",
       "star_rating     -0.178994 -0.018324  0.061703 -0.152503    ...    -0.159677   \n",
       "about_$_10      -0.021076 -0.017437 -0.187457  0.101457    ...     0.068171   \n",
       "cheat            0.115853  0.078113 -0.055405 -0.064879    ...     0.013509   \n",
       "stair            0.244787 -0.143896 -0.120897  0.095147    ...     0.067247   \n",
       "yes_please       0.003497  0.015789  0.138569  0.114994    ...    -0.040168   \n",
       "town_square      0.191121  0.181119 -0.153970  0.108076    ...     0.058567   \n",
       "movie_theater    0.257524 -0.056451 -0.097877  0.012137    ...     0.177486   \n",
       "not_crazy_about  0.072791 -0.042489 -0.106594 -0.009639    ...    -0.059235   \n",
       "arepa           -0.230694 -0.126618 -0.118695 -0.038135    ...     0.069418   \n",
       "no_doubt        -0.121815  0.092741  0.012027  0.006602    ...    -0.167484   \n",
       "feedback         0.136957 -0.007597 -0.112580  0.109925    ...    -0.179941   \n",
       "yard_house      -0.028672 -0.108547 -0.116689 -0.146143    ...    -0.075882   \n",
       "liquid           0.074207 -0.001681 -0.122432 -0.050786    ...     0.019585   \n",
       "niece            0.220209  0.086226 -0.048767 -0.112097    ...     0.074138   \n",
       "go_wrong        -0.024489  0.019939  0.068217  0.108768    ...     0.003280   \n",
       "hassle           0.013298 -0.015346 -0.119132  0.106766    ...    -0.049192   \n",
       "kim             -0.071961  0.001719  0.078302 -0.120150    ...    -0.088703   \n",
       "informative      0.067597 -0.060249 -0.112654 -0.084861    ...    -0.087383   \n",
       "cash_register    0.165460  0.091176 -0.091708  0.218862    ...    -0.119110   \n",
       "edinburgh        0.152240  0.051290  0.045804  0.248535    ...    -0.081723   \n",
       "seek            -0.051537  0.049947  0.066980  0.079362    ...    -0.208594   \n",
       "about_15         0.150586 -0.018438  0.019754  0.123160    ...     0.026291   \n",
       "5_5             -0.111339 -0.051302 -0.104480  0.095948    ...    -0.026453   \n",
       "\n",
       "                       91        92        93        94        95        96  \\\n",
       "-PRON-           0.228471  0.006402  0.044171 -0.117742 -0.040299 -0.077579   \n",
       "be               0.081308  0.050597  0.069064 -0.014169  0.131986 -0.154185   \n",
       "the              0.093134  0.026690  0.066760 -0.089390 -0.100121 -0.186524   \n",
       "and              0.218942  0.044745  0.031279  0.111612  0.032223 -0.146463   \n",
       "a               -0.065582  0.099271  0.013485 -0.096199 -0.034326 -0.162997   \n",
       "to               0.134326  0.116173  0.024675  0.056588 -0.070667 -0.110054   \n",
       "have             0.280355 -0.028588  0.097306  0.054263  0.056908 -0.055594   \n",
       "of              -0.000199  0.155634  0.062147 -0.112930  0.002126 -0.072175   \n",
       "for              0.116073  0.098516  0.153534 -0.054499 -0.157289 -0.206760   \n",
       "in               0.150726  0.103329  0.043864  0.089603  0.017072 -0.148797   \n",
       "but              0.154641 -0.046880  0.110583 -0.024271  0.125384 -0.119232   \n",
       "not              0.049112 -0.038364  0.145842 -0.224294  0.137787 -0.062697   \n",
       "with             0.192045 -0.030208  0.099976  0.064068 -0.003784 -0.110250   \n",
       "that             0.142818 -0.034827  0.064473 -0.242229  0.079056 -0.048935   \n",
       "good             0.183678  0.141093  0.049934 -0.076101  0.125575 -0.187526   \n",
       "on               0.103436 -0.033376  0.022920 -0.013968 -0.148388  0.058879   \n",
       "food             0.146970 -0.043503  0.076456 -0.015922  0.014881 -0.066324   \n",
       "so               0.223825 -0.044192 -0.067972 -0.081294  0.043588 -0.085931   \n",
       "this             0.121690  0.053272  0.031826 -0.123301  0.098906 -0.082409   \n",
       "get              0.095062 -0.127962  0.041920 -0.059564  0.011884 -0.193332   \n",
       "order            0.067599 -0.072483 -0.070933 -0.096777 -0.102682  0.093476   \n",
       "at               0.125595  0.113031  0.076517  0.001163 -0.090803 -0.037612   \n",
       "go               0.030251 -0.028685 -0.020237 -0.177914  0.005006 -0.105433   \n",
       "there            0.258627  0.025547  0.064562 -0.033229  0.028779  0.142533   \n",
       "great            0.085874  0.133308 -0.006298  0.051901  0.014055 -0.079027   \n",
       "like            -0.066823  0.086806  0.056701 -0.075759  0.085603 -0.009358   \n",
       "do_not           0.047212 -0.034534  0.098393 -0.190022 -0.130341  0.057239   \n",
       "as               0.072114  0.075446  0.033452 -0.005070 -0.032647  0.066569   \n",
       "just             0.078109 -0.150968 -0.131558 -0.008350  0.010902  0.092659   \n",
       "all              0.031634 -0.090408  0.146665 -0.260102  0.102306 -0.155521   \n",
       "...                   ...       ...       ...       ...       ...       ...   \n",
       "soupe           -0.084932 -0.074936  0.028713 -0.068929  0.027130  0.069189   \n",
       "caviar           0.007426 -0.096449  0.118782  0.017474 -0.079687  0.110128   \n",
       "too_greasy       0.003434  0.077780 -0.139763  0.003994  0.013410 -0.047397   \n",
       "turkey_burger    0.096004  0.079550 -0.030298  0.003457  0.156391 -0.029296   \n",
       "about_8          0.018248 -0.170031 -0.037194 -0.058689 -0.040005  0.038772   \n",
       "popcorn_chicken -0.048665 -0.031060 -0.126178  0.045874 -0.001073 -0.021110   \n",
       "theatre         -0.110126  0.092264  0.005960  0.079229 -0.093480  0.038719   \n",
       "star_rating     -0.120174 -0.031306 -0.021414 -0.009382  0.195018  0.120424   \n",
       "about_$_10      -0.135463 -0.003174 -0.087587 -0.086760 -0.014259 -0.144352   \n",
       "cheat           -0.031727  0.003205 -0.020716 -0.209662  0.066677 -0.001764   \n",
       "stair            0.079679  0.179751  0.018293 -0.011329 -0.059856 -0.084733   \n",
       "yes_please       0.019519 -0.048639  0.178955  0.073848 -0.107971 -0.134588   \n",
       "town_square     -0.097777  0.260993 -0.021351 -0.061542  0.105708 -0.000083   \n",
       "movie_theater    0.126203 -0.048961 -0.126829  0.019640 -0.046862 -0.074460   \n",
       "not_crazy_about -0.032113  0.059571  0.051386 -0.127157 -0.081863 -0.028400   \n",
       "arepa           -0.030860 -0.040078 -0.180366 -0.046380  0.079982 -0.144685   \n",
       "no_doubt         0.069349 -0.007034  0.112481 -0.008648  0.044351 -0.077816   \n",
       "feedback         0.182507  0.020706  0.015952 -0.133618 -0.135532  0.021144   \n",
       "yard_house      -0.112375  0.122241  0.101566 -0.138232 -0.012017 -0.232182   \n",
       "liquid          -0.075008 -0.038078 -0.081612 -0.063549 -0.057237 -0.029502   \n",
       "niece           -0.009747  0.008809 -0.095215  0.074008 -0.021332  0.132398   \n",
       "go_wrong         0.000789 -0.146820 -0.021005 -0.030786  0.041079  0.030887   \n",
       "hassle           0.068214 -0.052063 -0.069395 -0.007756 -0.012409 -0.301399   \n",
       "kim             -0.029875 -0.067309  0.168281 -0.081764  0.007857 -0.002197   \n",
       "informative      0.038353  0.012698 -0.048648  0.032426  0.013996 -0.033516   \n",
       "cash_register    0.000209  0.109356 -0.046450 -0.070741 -0.252635  0.021254   \n",
       "edinburgh       -0.074707  0.025100  0.023744 -0.034824  0.055048 -0.104885   \n",
       "seek             0.078692  0.151242 -0.239776 -0.076414  0.040507 -0.042795   \n",
       "about_15        -0.023437 -0.037574  0.101363  0.078976 -0.099274  0.036255   \n",
       "5_5              0.083218 -0.044819  0.043364 -0.042922 -0.053264 -0.209178   \n",
       "\n",
       "                       97        98        99  \n",
       "-PRON-           0.083768  0.023044  0.085933  \n",
       "be               0.138034 -0.064113  0.163660  \n",
       "the              0.088263 -0.226336  0.263659  \n",
       "and              0.064543 -0.198934  0.187167  \n",
       "a                0.027286 -0.093052  0.245475  \n",
       "to              -0.084305 -0.085341  0.235801  \n",
       "have            -0.134492  0.034391  0.244539  \n",
       "of               0.048358 -0.145528  0.135202  \n",
       "for             -0.135975 -0.038987  0.024615  \n",
       "in               0.026060 -0.086892  0.099905  \n",
       "but              0.210893  0.010013  0.192984  \n",
       "not             -0.007003  0.027755 -0.024999  \n",
       "with            -0.084829 -0.165389  0.137321  \n",
       "that            -0.030873 -0.120422  0.171408  \n",
       "good            -0.026195 -0.001615  0.046976  \n",
       "on              -0.194498 -0.022756  0.188520  \n",
       "food             0.037083 -0.087986  0.025558  \n",
       "so               0.014199 -0.002011  0.136697  \n",
       "this             0.112495 -0.119913  0.054615  \n",
       "get              0.034122  0.072253  0.234420  \n",
       "order            0.140417  0.022165  0.053388  \n",
       "at              -0.028023 -0.048440 -0.055780  \n",
       "go              -0.107257 -0.077823  0.133260  \n",
       "there            0.001533  0.103356  0.202158  \n",
       "great           -0.005570 -0.040190  0.161477  \n",
       "like             0.052090 -0.156967  0.121180  \n",
       "do_not           0.036072  0.000188  0.033032  \n",
       "as              -0.025970 -0.193730  0.104274  \n",
       "just            -0.082724 -0.054292  0.139054  \n",
       "all             -0.168454 -0.076448  0.088624  \n",
       "...                   ...       ...       ...  \n",
       "soupe           -0.103308 -0.052373 -0.013451  \n",
       "caviar           0.085168 -0.148457 -0.109188  \n",
       "too_greasy       0.127584 -0.050304  0.112870  \n",
       "turkey_burger    0.012777  0.021361 -0.351614  \n",
       "about_8         -0.004713  0.004164  0.149236  \n",
       "popcorn_chicken  0.173797 -0.056659  0.005796  \n",
       "theatre         -0.036078  0.059175  0.037637  \n",
       "star_rating     -0.024950 -0.175421  0.042044  \n",
       "about_$_10       0.197767 -0.054959 -0.026898  \n",
       "cheat           -0.124963  0.052039 -0.054816  \n",
       "stair            0.124362  0.042136  0.102719  \n",
       "yes_please      -0.060265  0.033388  0.009892  \n",
       "town_square      0.073660 -0.015582  0.025696  \n",
       "movie_theater    0.209412 -0.025491 -0.029449  \n",
       "not_crazy_about -0.114657  0.185640 -0.035354  \n",
       "arepa            0.057547 -0.029957  0.075641  \n",
       "no_doubt        -0.153674 -0.109181  0.154344  \n",
       "feedback        -0.128339 -0.144952  0.076409  \n",
       "yard_house      -0.034668  0.125568 -0.060757  \n",
       "liquid           0.048064 -0.101491  0.200555  \n",
       "niece            0.069399 -0.078348 -0.054140  \n",
       "go_wrong        -0.074680  0.131629  0.136799  \n",
       "hassle          -0.013384  0.159887 -0.015550  \n",
       "kim              0.188067 -0.029157 -0.025612  \n",
       "informative      0.023298 -0.072904 -0.076102  \n",
       "cash_register    0.088621 -0.012825 -0.054398  \n",
       "edinburgh       -0.054989 -0.054761 -0.099282  \n",
       "seek            -0.070612  0.044059  0.158404  \n",
       "about_15         0.040220  0.086304  0.079845  \n",
       "5_5              0.018237  0.051250  0.142535  \n",
       "\n",
       "[3629 rows x 100 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build a list of the terms, integer indices,\n",
    "# and term counts from the food2vec model vocabulary\n",
    "ordered_vocab = [(term, voc.index, voc.count)\n",
    "                 for term, voc in food2vec.wv.vocab.items()]\n",
    "\n",
    "def sort_func(tup):\n",
    "    term, indec, count = tup\n",
    "    return -count\n",
    "\n",
    "# sort by the term counts, so the most common terms appear first\n",
    "ordered_vocab = sorted(ordered_vocab, key=sort_func)\n",
    "\n",
    "# unzip the terms, integer indices, and counts into separate lists\n",
    "ordered_terms, term_indices, term_counts = zip(*ordered_vocab)\n",
    "\n",
    "# create a DataFrame with the food2vec vectors as data,\n",
    "# and the terms as row labels\n",
    "word_vectors = pd.DataFrame(food2vec.wv.syn0norm[term_indices, :],\n",
    "                            index=ordered_terms)\n",
    "\n",
    "word_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `syn0norm` (Attribute will be removed in 4.0.0, use self.wv.vectors_norm instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "word_vectors = pd.DataFrame(food2vec.wv.syn0norm[term_indices, :],\n",
    "                            index=ordered_terms)\n",
    "\n",
    "tsne_input = word_vectors.drop(spacy.lang.en.STOP_WORDS, errors=u'ignore')\n",
    "\n",
    "\n",
    "tsne_vectors = pd.np.load(tsne_vectors_filepath)\n",
    "\n",
    "tsne_vectors = pd.DataFrame(tsne_vectors,\n",
    "                            index=pd.Index(tsne_input.index),\n",
    "                            columns=[u'x_coord', u'y_coord'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>-PRON-</th>\n",
       "      <td>-31.029890</td>\n",
       "      <td>-16.328735</td>\n",
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       "    <tr>\n",
       "      <th>good</th>\n",
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       "      <td>8.958115</td>\n",
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       "    <tr>\n",
       "      <th>food</th>\n",
       "      <td>15.531963</td>\n",
       "      <td>-9.425700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order</th>\n",
       "      <td>0.242420</td>\n",
       "      <td>30.115559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>great</th>\n",
       "      <td>-9.514729</td>\n",
       "      <td>8.594784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          x_coord    y_coord\n",
       "-PRON- -31.029890 -16.328735\n",
       "good    -4.699564   8.958115\n",
       "food    15.531963  -9.425700\n",
       "order    0.242420  30.115559\n",
       "great   -9.514729   8.594784"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsne_vectors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne_vectors[u'word'] = tsne_vectors.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 1\n",
    "%aimport bokeh"
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  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
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       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'be6faef8-6a60-4426-883b-f7f6b0a0bb44' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.14.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"be6faef8-6a60-4426-883b-f7f6b0a0bb44\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"be6faef8-6a60-4426-883b-f7f6b0a0bb44\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"be6faef8-6a60-4426-883b-f7f6b0a0bb44\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'be6faef8-6a60-4426-883b-f7f6b0a0bb44' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.14.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"be6faef8-6a60-4426-883b-f7f6b0a0bb44\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, show, output_notebook\n",
    "from bokeh.models import HoverTool, ColumnDataSource, value\n",
    "\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"2f656f64-bb6a-4288-a71a-c6b9e1100aa9\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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wc8FBkHDJLdAvCA4wb20YL7jJh3BzIC9QDcrk0HsIiLCEJwRQdDNAUE3Js3BBqUfQuC+Gb/RzqXBFxgGwipJLMIc2mVB3DELQQMOzsGtlZJBrIyXQV6RwkF6ZcvAiGnQwWtay8A/bMZBtUUwQZI+OMFPQaDBAXpNwfpu5MEJEZ3AqZAWwQQtwMFWZonBKCcCQoUDFkJ3HWVCuNH5wCrMtEHSmahASMTIQThpC0IuTupB3IRpQuoQGMICxyPCd657QZPV5cEZE2/BUPgowisdw0Hn/RrBo5TFQYe0pMChRw7CVQO3QKpNKsLlxeRA0tIDwu8CfsFF4Hk/LXtgQQquI8KFEpzBChAlQkDy10EAwSLAPPR3QeMfLUKdqLvBN94WwnIzoMH/6+RAPcM1wWfc4kFAVppBRn+oQDtyzsEq5alB++QmQrIu5EGEcjfBJdleQW6fg8HE0p9BleG0QVJCxkClONE+QvZqQpKYxMG7L8xBTq+4QX+Y0kBqVFxAFaopQrMM2sH5y5zBaabVQZMP9kEeiBnBm0BSQIEBhsH75QZBbODnwYH/+UFojO3BwZyKQOsvE8LjvfZAn8P5wb9h98GRtrRBsGKoQQyZokFO5SjCIjTwwem7J0IrKFRBT3Q0whIG2sGUgdZBHWE8Qo09iMDK1llCUIXCwcSbmsDD1KS/tr5QwQgsGcJh6lBATz1MQbIFwkH41R1CGhVvv2UZ8kE7YFNANws6wlj4A8KGqTXBIMN5QfMnLsH7ROVA2z0ywslIDMJ7VDnB3PMPQdzxbMEyHu9BJ+YzwWfJtkGUgV5BnkTKQcGtqcGIayLBORDLQNfXC8GrQi3BScxowW6M8EFkSWpCYt3TwNoLC8JbscLA6ooJwi/+GsHteXXBZY3lwVETA8KgsRBCi+4CwjOcA0JUHwlCJJ6CwUMllED61WPBX2WnQE2QAMLvDuPBJnjgQI2w6EGQuR3B3SKBQJOg/8HPC5VBo/w7wmDH7cANBX5AaLMdwokFh0E95SBBjr4wQtSLLT8DXVDBjecZv+bSjMGSw8bBizj6wb0XI8Lu2JbBS/qFwZ9c48F7bs7BCfmPwGt0OkGx5y/BwkP+wYJok8GgylLBQ2+awWPoLkFNpHnBJhMZQuH1EML+q+9BOiPqQO20/cD6iJ9BkI/mwZyKgMHOuX3BeKobwib9N772eyXChGBaQDT+4sEXeLhBQHuuwMQ5CUECC/LAorIIwrayO8KYOBXCv+6PwazxWcBstSXBVE4lQDjo18EtObfA8db2wTIBz0HIxZPBQhoZwuJcyMAJIfFB6u4AwpmDrUEgrn1B9rwHweCjdEJs7jbCTVXowVP7kMGG9bhBbQuzwWReKkLitGpBQ3r1QBMSccD4IQ/CrRFqQrsXRUEFC+TBN64EwkLGbkG7pupB5nn3wRifxMHCNCTB4sQlwfGeG8FnEE9C6Y3iQaxt9MHQ+8nBiiNIQbkWrb9zLAVC0FeTQQJX9EByrKjB8gvKQcpFu8Fnblk8VWHBQRbeJEJkjqlB/4WUQWo9CEL9sejAR3MFwiJFlMGplMpBrBJZwdjcnUF8oxrC1WVGQeif8kHtIHrBcuIqwS+GpMFTPFvBlSjewRp1EEG5y1XBQDJDQVvcv8ECaqhBuTogwmuihT9AVznAiel7wXbCpEFaFg/BuNXqwGa6ysDsBQTCQXkqQYTSOcKkTSRC1I4owtZ5jsHTuntBVmS1wR48p0BDVIzBshEAwQYZysETHyTCtAdePyc22D8P/vZB3tCiQTpBiMCD3S5Ckv68wQL8x8HuXbvBmEIrwuwRM0HN7gfA2pwYQVW6WT+JeSXBBFIqPwIKzcANB3FCfC+pPxXYp0Ci/RrA1+iGwV5jmcHg0dNBj3PHwfGdFEKjh93BCVYYQQdMY0Kbg0pCqjViQmPqAUGbUo/B+Thtv+BFqsHv6hBAeuyaQQgjnEFLpsnBftccwY0rnsHJzx1BdOu1wef2SUIFagLCa85VQqzAw8G1yRvCyBkDQn2WdkGOjBtCeqGuwW+pD8BgkD/BY0C4PwLsCsJ8a+hBlEHNwXgZMkEtI97BL6nswFBWIcKzIgtCpzudwdhZqcHuP3NCZV2hv5GzgcG7IZjBURgkQUkzukFXU4pBBTEBQujo4sFNH8NBi4uCQWQ/zsHw6gxC9UCfQVKg6EFhJnVC3ipsQhsSZkLF1WpBsX4OQqfA1UFp1cLAhV9bwQRvscGM1ePAzegFwl8H8sCwa8ZBXY/VwdD9s0GzpinB/jvFwYG9AMI3AJFBkMgGQXSBb8HsPpJBVt8+wCQ7y0Ec4gTCqf1pwf9mKMFR7RjCHE2dQCNDv0ATPwlBEcmIQNUJVEF9V9bB9rboQXqiKcIKfdfBXMf5wdtQFcE/eu5BSLokwedSJULVr95BmUyDQfiz3cFCbWxBDwcrwmzJxEHdlgnCqcqEwdFRpsElsC1Cld/cQIi4FUFxrQlC81MIQet2vUFUCRJCx3Z+QVTMjsGi/KXB1hxlwX6x9kGNS7DBYVFXPgYd/cCmTqlB6iYPwaebgMHvpvVBIom1QWBc+MFmM41Bs27UwbI/KsJ7ph5B/HGcQLd6wEBhIfXBJTyOwQxj0UEKBPU+cu4uQgTic8Fhc/PB2GWjQSCvHUAytxbBzvMwQoXkr0G5ek7BSO0PwiazdEAx7uJB02ApwvFuacCTUn5C7KslQuwU98FvYVpBm1REQV+DD0H3+xnCRRPBwFaNHcLzQanAAwsFQrBbLEKbC4vB+txBQXnMFMID9k3BltCUwNFE5sGoeXbBqvMuwrvvKMFomQ1B3ctkP86qEcLd/kfCGY32QddbaEJictjAlmL7we+jj7+eCMnBYv8Gwk8GO0FrE+vB2ZHpvp1a8kFMmMBBjn5sQZvZ7kHabrQ/Z2gPwkIKoMFc65u++o4BQkmaKcHwoZBAn2o6Qd4AjD+97cDBDS/ZQd7H1MEymi/C+3a2QHMGmcFcO0LBo9a5vqh3GkGG4cfBavgSwlY120FYDJ/AM0zLQI40t8FKWQ5BYNYpwcWnOML4F+/AfBvcwV+UQcGp/uRAguQzQWdZE78t3uxA7veYP3iZ90E0ltxA8s4cwqjO1kEliR9BNpCXwfdz1MFO9WpBn5U0QNw3yMHt+OdA7GE1wRc7EsDjAp3Brsu7weXeGUKtvvdBLS3uwfGYYkLWsQxCFHQdwVwn5UHSdAVCaCz2wXM9HkIs17DAWmn8wEkd2UGyLBZC1CW4v5bXzEEx1RLCSFiowMK7AEGYEE7BpNBEwUDBxMHpFtrBQIE4wQMF2MF9JJ7AFbmFQQYTusEl/vRB6UbpQSn3TMEIWNdBGEgEwSRKJkKgayDACrMvQWvQbUFAW+pBGwTdPytOvr8BIZpBS50ZQiEnSkK2abbBB+gEQQP3pj5g3ezBKvUzwoHnvMHAqc9Bz47QwSDEokG0ibLBOzsowJ6GFUHnBq69u/MgQdvSyUHVmoo+8+kcwqt5V8A4ZDHBjjIxwM3cbUFAvP/Bsy+7QDLqA8JeEPhBL4ZCQaavIcGg6C1C/1JaQmUIOMKMnvzAj7+xQEi3vsGBbkTBZmFEQT1UiMHDU3xBfxsBwaLzJMBMTuDBqnSTQAg29cFZU27BTTR7QWox3kEZZJBBpW/YwQKgpMHl3O9BGhAhwbpeW0IwSmhChw7HwTBa2cB8aIXBBuMEwpiHv8E+ioTBH4SrQDoFj8Es9YHBkIXMwAp3s8HdBiVB2vOgQFzvAMK3vPjAWjo8QmP7zkD6/s/BFTGaQTeSrEFaPA7CGbWowThV/MGOCcZBbZrvQUmuYkLW6YfBMEvbQDm1pMBA/ZrBfDD0wQdLnUBc/IzBLYGTwKk1xEBJojzBePbTQVLrFULr3xnC2fCuwXIeFkL42RzCoxDqwLstu0EusVXBE5CnwdQADkErSZdAMco1wjQ6eUAKpMHAYhoswrsmQ8KgCyHAEfOhQS2lK0I2t6zBCNhgwZWJNcIiMwrCAanxwa6HLb94AdPBwk/TQVp5jEHp7vPBY34XwqSgi0H3WYpBnX7Dvz5SsMGqgb3BtAjBQT8H5EENFpHBsAEhwgimW8EZTRFBd+QTwDTr+cGR8lVCWEGRwHLausEqAQxBeVI4wWYykEF83t3B7YEuwlXHusEmZbPB0GokQWqJ1UDXKdxA0/oDwcdo9cHzw6zBOYk2PwLkV8CkcjRBlb3gQUjfWUIVt1VC0ttZQqP83cFBz6PBmZkpwhwYK0KpWrdBWq83Pyrr40EhhTHC5TEbwb7FvsGGT0PBwTCdQRIha0HF2pw/PcPeQbHpaEHTm7TAfKLIwVZkl0Dh+PvBqMc0wokD5kGvjuhBUjUxQfumDMJom4HAqkecv0ODR8Ed4c7BIgYdwZwCjEDe8MTB3rCFQawbEkI6AKfBS5fAwdde4cFZAolB/FgIwJY0HsJRvN/BuklCQskmdsGLxCZB5NClwZ/nmcBlqSXB/r0kwnwBk0FWrDtBfGw6wgJOVj/MpwNCd6GqwZpb6ECjkB9BbPQYwvg9RsBvvlpCkdhcQkw3ZMF0KAhCAOSawR3nGULJ/2hBpaa3QW9iwEGclMbBuHtxP3w9P0EtYPLBwcG0QE43icFgLpNAYUOpwa2mjUEtJhlCfschwtBkj8E/VZBB+D8RwjVJzcAPCA9CdOAkwF5XmsGJrwhBSdHJwYTYAUJJJwbCpXMXQuPrCMKkuRNACw1swd5DzsDJezdBM+WxwVcDDcHus8FBssEZwj6irMAinolBMFpKwcEsQ0IeS8BBqtayQdFP7cFbZSPCAiz6wexOIsKbSNy/9SSZQMREvUFNkIxBG7cZQX3makKDVe6/dHaGwbNBAkGRnRBBiat6QCmzl0G71H9A2itwv8fnr8HWhuHAVXrovylwEMEKvbNBqiACwrlu9MF2h9nAzEHywRIx/EBVrTDCj/j7wXa/AUE+ggxCI3VvwZhJzMHC3edBGR+OwWi+pcHVSiq//S2RwXhlCEL+CP3BOgfKwd8NiEE8ALdBf35FwgtfYUJMNWRCuWMHwr3NJcJtG7nBEA5jQQSPCMLRj6ZBg8pBwtMNAEDwmDrChdvaQbdXF8JkGAJC2pIgQtkVqUG8nERCQVI0Px0eU8EzP9bAP8ucQcOqVUJF97xBZUfFQV+yH8KzviRAvDo3wjBNNMF5BGjB2eaBwFCKAkKmiXZCOh0swv3h/UHeMFdBaJnwwbPXM8JCh+ZBNvYUwlQ4CUEtMhFBEvzoQYMegEBqo7LA1isYwVpLzr+Gzg5C0InsQTCHrcBh1sJAiicZwP2FLsIpaz/CFNkpwPCuPcH/xORBHdffwOxcD8LohhfCfso5wW2R98HMSInAHMQQwXKHJsEr9i9CrbltwZIbJcLngUnBIIlJwQZEsMEHzVdB/nCtwW7g3MGm9BjC+9bQQOLzycHPJIHBuSuRwZhpakGMVbFAI8HEQZBqzsGeN9pAQ6hHQqaQE0E0U6nBkkvSQROb58EYGAxCNP4nQsbjGEJ9KofBkPjfwcOQ5sEhDrRBLr0eQXZtKMHrHRvCfJruQbbBiMHxx0rBOQ9Iwjrstj6fpqPBuOxdQOaHhD/x0CdC++UTQvBDDUIzQlXBsJNkwWcB/sHxi+rABLb5wYvYLcLmx61BbNiKQR54S8GIPqfBHkEkQfwONcLM0wzBxq3wQcptucGx++DBPuuWwJg3IUHPVK3Biuf2QUkWV0G5TIXB8u3kQeSvA8L3UDHBapoCQrbxEsG9Yr3BYAUdwo4x8D/R2RrCKWRbQrGQiUEa64nACmNOQC5UHsHvZxTBN7BfPpL1OEEyeKJBMZ6gwfbTyEHj9xDBdzutwUNTmMH/R8rByFvowXnqxEFbJMFBJx9+wanmqkGreifBXnbYQXMIu0EZwNPBnccrP3IT2EBN6Z3BvA4Nwk3YHUIWWBRC2j+5QQB+2cEvlQLC/+WxQcrq48BcR22+U43owPWmFEL14mpCmY1jwTKRCkEK7S/Ai5/YQRHkBcL7eQrCwj8+QjFp6sHD9ghBfP6kwaFRiEFJiYrBLT82QrkDPcJmVInBv8OKQc3vwMF5rT+/w094QaJU4sHQnHjA9VPLQTLYcUEfR/I/uboJwmONgUFdQBfB3AOVwb7r9cD6/ELABneEwQPfqcEvUk9B1gsVwco1TUAyGorAt7zMwdEZm8DPhY9BRbUKwqmySsDistHBInqAwA8oMkHqe0/BxaNbQZ9qe8FfXY5B01cSQkXR7ME0IEtA3oBqQafvK0EwLu/BngILQl01AkCnHrfBW/UPwglPyr8E6OpBWoETwA4JP8Bzz+rBJuL2we0qtEFt1+LBGgZsQUlpE8Crt+BBmYuiP9HvLUI0+g5CSH/yQcQX/0FaRVdCks7eQK0OsMH83X7Bek/FQZqsGMLfXI1Bt5CgwMlV6kF+LHZBO9qeQHqpxsDi+9c/tP0uwpGq38FE+3zAk3i9wQMzzcA5XplBH1wgQiocm0H9JWRB5+iNQW93g0HkOP1BeGSTwcjcv8D9E37BALoIQkJn7kG5jMBBQ8zFQYpowMF1UDZCxx/APhXglMHaccBBEuMiwnDJzEEGwQzB7pkuQbQZAkL/J7LAqF89waGpwMHtew/Btsq+QQFORcG7Wi7BTI2wQLv+jEBwZZTBfwWTQT7qqb89MdbBOYn6wTAUBcImG8bB4wDOQULtCkE0zINB0LDfQb/5NcEHjY9B4HROQObxMsJHoLtAW4g4QKWGmsA/lSBCSfktwrj7VUF9uRvBW/hSQf0nxEH1YaHB5RshwCjeC8IhnzXB9t94QFH4fMAsml9CdwtiQmgGzUEQN4/BRGmbQf7caUBo5WK/WF/IQOnCwsHyr1PB7zRmwQbnMcAcNZlB128GQQQaM8GMH9C/vdJcwP2LwsBQshBBMz7iQQzqFMKpum/BVRTfwG6OMUEXiCXBK8MDQTevi8EOEuTAP6sXwhFR1j8iYwVB9VwOwiVC+0FBp+XBzo4LwpUESUJ5btPAFpTVv9h0XULUiFtCvL1UQmANykFJik7BU6vVQeLcnsHPcwNC3xY6wjaCD8JQ6wnC3Xy+wQRN8EH+pyXCVYUGwucBz0GryUi9b7PpQRgbg8CR35FBvtyMQSrp+0F3On3B1UbJwC+DnUFk7BnCfSHvwQbsh0Fsvj1B4O8PQnOgAkFhd9PBgFO3QYGmcUJ+XRTCCP36wGY6c0BI1ijBXXsSwgpR/cEZBwzC3xGrQSyoIsFPDH7AlWdsQrka+kGW2IHBl+ftwEOvZkFF5AFCFkp9wT7eBcIl5mBCkIoxQuZS2MG6B59BOg6Fv9kOp0EdZvNANcujwdHlpcCAC8dBby1JwbJU68EvZPdBBWBrwZyz7MHDkRXBCfpQQh8hgcBKQD0++ppcQBi4RkESrO7BsNuWwTMyWUEl+AZBOtTPQS7oyUG/qLrBg6/tQbIjLUFAYIxBKP6BQbsdGMAq8PHBDZzgP1thXUJ46ejBGgV/wfb3xkHq2dLBED7KQZwaRcE6Hi3AN2KjQBSg+L+QkY7BlEODwPL3OD8b4+NAY6YkQsa7p8GVtghCMBcTwjczl0AezGE/BSOwv9YIY0LfFKbBZXOiwYOqDcEe4LJBpg8oQmkxJUHkL9ZBmJZGwSTZ90HyvdlBdeAsQc1mgcF3qAZC0pQSwhDlq0Hz1F9BZavBP7iYr0C/D+1Bi1jcQURnqUEw/qxBm/adwFBW7cGgJPTBWvvKwH4cQkLksVVCeEbRwWjS779d3vZB04JrQjho4EG598zBDWIeQv7WHsCWwbvBKtIGwtO2ncH39G9C35UsQop2m8FqmALCQUmFQHu/E8LZQCVBdkbPwfIij0HP7C9B/w0rQZ1ykMHbXyjC4FDMQeUUUcGGsurBSmTUQH6RJMKTzSm/pLdJQTKhOUGT1wTCiogeQhTk8EHx6OfBolHbwQ6MykHq/htBsy8pwvPDtUG3mTrADzdXwWKzDkG4Fg/CKFc5wavGG8G7XiBBSGsiQspxRUHk9AvBhlPfQca3ysBGlw/CRIgbwh3C3EBHexDCHZ5jQhq7ZUJ4pVdCyGkxwpKcB0HOo19Cg1inveOuwkCRx97AM7s+wdfBU8Ge9CLCoGA/QfNiAsHfZ4/Bx1yNPvmzykEoF6zBNo7NwRHziEHVNpVBg+fYQCYrFcIqvEJBWisjwuftYUHnDQbBn59QQhhVnUG6xQJCdWGdQVPsNECxApXAjgyUwc9AY0F9/QbCZOajwd3e+8HSbpxATOqOv7QINUHLax/Cohw7wWChKMHfDavBfoMLQkFCksF/6lrBhGAEwg8Yn0HKIRhCEHMPwhidv0FH8i5C9ksOwlCk+sAWRPhBP3itwRQlm8GwbwtAruBnQfdcqb9qR41Be2S/P3h3X0KJJWZC3U+vwCexG0ECg3vAdIKPQJiE68FaL9RAtJ0NQm54FsJmJbLBkrKQwYoKEULOcrXBeaLUwe8LMcBiaTRCGdeHwbSAXMGw31jA7g7eQWKP1MFPJzvCXT8vQg/1dkHwijXCzq6/wRYeUUCawvhB0Sy4wLqPP8GADDnCStmewUX0jr87vSFB6owlwS7o90FHjzZCuxDdwG680EHn3pXBzUe6waaB6sFQUqNBHociwt57EUKSo7rBYwlnwXxXecA2UehBTkkUQogsDsJf+UjC8F/IQeXh1MEgDjFBw7sGwocpqcE2PSDBW53TQWwoJUI8TFrBAVGWQFEN50ELvfJAb109wdgr98Fgs1dC5/FjQrOk4UFEhqnATRT8PlspD8EtOZs/KDoswVNPGkJYJDw/DHcwwqA1Fj9nQTdCtQWwwRRRT0J17KjAna2vwYdUvsFDnQ5AT5HGwaCD1sGDoLfBv3AJwX7xIsFMWqfBQWHSQSmbKkIQTn++IQwdQSdx6EGn70FA93vzwGiEGcHucA7CSBfmQRabrEFWG39BJ+iwwZ+OZEE4TrDBcXP+QVsA9cD3WQrBxVsCwrwP+8GUkoPBlBavwfjljUEr0OtB4l3EwNTipUFZbk5BNSEdwSNqL0J2z1RCcC0xQm6yEMKRH8pB+8SdQXT5zkEN9nPBBOjewamRwkHjMDPCrKRyQnXacEKRrQtBxBWIQIifx8E5Vr3BDP3HwRyasMAmSFHAOsoTwgT2K8IYPBDC2pbawX2JxkEOa1HB3JvrQWoxzMBWuxhAyXsowERfF8KhO8hATnTAwVDglEBDt+U/im7EwHEsS0K/DBVBJn12QffQ98DXboVBUR3FwfNxcEG4uY1B4voFwNbgE8I4YyXCEDXYwZKnfMB0XoHBAE9vQXYnAsGC3QpCs6W5wYCsF0IXY1tCLp0vwg8XGsIMaxfCUhJ8PytyhEEphWjBANUkQiKgJ0K1yDbB2HiQwM9JcsGj5NDB9FzUQa5EL0K74mlAWAytP9bKmMFMX4LAEBIiws8ccb+XAy1Bi67bQUlZ0sAEO47BVpsownD2asEAfhJBejoiQCKwRMFOLnfBR6nYwf6xJcLD2WrBW1E0wfhUCkLZBzPCV4T6v2j/vMC4fs0+1MScQZ1dAUEhiShCe0KrwXjlMcLiRDbBCMbkwddsFEJYF+pA1GKkQQq6jMEAtltB4G3jwC10y0CMpnDBRIBjQJDiC0JZCgRBtnziwPrPtUGyGyjCyEXJwVjBZMDnWIvBFCKuwSY2eUGuUQ3CS5iSQVQJJMGQtgrC6PeJwZ7dU0CI3EbC6rZ8v4vT+8BFO8xAWK4JQbzDHkICYapBi+pYQXH1u0GmiM3ABw/2QIBZSD9zhtlB94i9QfH1HsGMkcXBEEdlQo/WVkJCmI1AKdCqwHDhWEFrZ/HBBgzMwdFv8sEjSNPBP3xFQaQMAUK7YHHBbXy7QZE9gkGpeS1BhASlwGflxT7dLzbBsPduQT7+dMHNIINBMBapwUG260G1LgdCbtaWv6aLi0Gw955BHlqVwc1ds741Eg5CUdyDwHmfOMI1BYjAuEUOwKTQ0MFT3gbBaFIHwSGma0DfIBhBub0/QBFNAkGQfiXCJKPwwCwmE8AKAzU+oNgtwjeaD8G/rNFALeK7wf/7NcB9HzdBmjJsQf4QJ8LiRYLB1E5cQS2USEFMlwTBnIWIv1guX0IYnGZCYPrkQRlY5kH/gk1B8Eg2QsXkyMAqSZPBbHPZQAkrbsFcOZzBUHsdwqOs00HqwQFC+YxyQgJvTME5mlTBqTYOQgZenkEWZ63ByPfMQWjJHsEWvrHBzxR9wXfyJ8DbBGvB8kOhwFLCHkFFhLFB2A7KQKbRKcK7ofjB+kgaQlsckUArEFvBzyeSwWXEb8FOwd7BzsOnQcz6ZsHJ/yHBE7XdQHh4jMCLCyhCT1OGQTKL0sDOEhPBSKrWQRe5T8F5HJDBnHaWQXcHPkF4WD9BEVIRQYwyzsFWI0/BoaTyQbV3ksGh0xJBzNCVwQkku8FBQ4DBVw6gQUhAskABht7BDXuJwWGr40Bu+ipB57PwQLcJLELEtwVCyUPRwYEUDUIgoBA/y/MBwvvy7MHYkYNB6MlBwp0adcAGE7LB4CzlwYX/IMA6LR4/kzq+Qdo0KEK7jbvBkVYywg/VgEEv3FPBspzbwYf1wcFcxBzCU3vEQTcsH0JugKJBq0uYwRWA8EDvUN9AomYJQTxqSMJ4ZuLBuIdUQjxOV0L672BC2ZJnQtsnUEKZo1tCP29eQh2aPEGHDK5BRULnQWy060HOapzA3MimviWMXMGbRTrCQebqwa/FqMGY4+dB1w4RQiVkoUFUhJbAJB4OQSJHAMIxFN3AoY2hQdnqTkEm6R9CyxkfQcxYqEHaOYJBhp+bwSVxIUIYztzBYS3RwZK16cDnI2C/CdWUwJ1PAcKGS+HBW/xMwSAK/MAYDyrA6TEJwoQYwkCO2QbCk0JdwSkpS8Ggsq3BtEWJQDN9S8Ege7vBZsXPwZxu2cD4lAJCatHrQfGht8GUw1A//m5Cwh+E0sAthx5B3fVJQnuH6UFBSwfBjsW6QXui9kGadO/B6qyRwTc5hcDvhzxBwv8XwruApkEGqVlBeOtGwE1tq8FVMufBJ1CyQd2f+0F2WA1Cb+iBwRZDoMGZQE9BoAk8wuLg2EFYZavBG9jTwf0cy0HLCStCDGgJwpBB8kCYie1B4gM+vewwqMFpmcBB1SBPQUp1hMEXqqBBdT+WwFZZJMBGWQJBC7bMQddOyUG89ILBMyU2wQoo8UEnDJrBs5vywMBi8MFClwXBVwFYQvpyXEL2lm1CGWdfQpc5W0J7C2JCT5tHQn/NaEL8gA7Cim6hwQ72W0I05vhA2GuCwVAMqMG0zJ1B88Imwl8hTMBHuWrBvbBEQfKQHsJ8BGxBu/JuQa7XHsGlSjNCgmyGQcwGAEK1uhZCfcM0QTePt8EfNUI/bAwwwi/pGUFROe/BwfK/Qc7uHcHWMjxCaOZcQgIGo0G4gRtBTNTIwfHJpMG19YPAS79YwQTbpcHtibBB4OjhQTkP4sHHdNTBr87BQfw62cGlRhnCLR8yQfUpikFhg2ZAcjbVwQIZecHxC9pBE9erQAYsDcI2Z91BE/flwW+ByUFetCBCKIZdwUwyBUHHhlhBlOEpwuRi+EGa/ejBHSW9wZ0YMcKccwhB53ahQaHMCMLN90JBxLGjQMkg0cGY2eJBShCrwUzkO0JVXvtB7muBwam3BEJRQllBo8/qQTPTU0K07JjAURd1wJVCDkGEGdNBnv7NwHBdQsKfc8rB40+3wXtWbUDsUsPBBhKUQZj2dsE/sqtBkFKfwbBNLELc1O3BCnXcv4E96cEEYajAMw41QEk27UE4/+fBtXJVQggEVEJpL0tCdxhQQjksz8G5ixDAe6THQWclNsFcXgFBdPwFwo41uMCPb4PB/y6WwWKDBMFCcca/AzTzQc0WtsFk0ubA/ZgrQhFK/8GmiajA/nT/wWEMmr9ncobA44KHQXOUckLGURFChs9swRQodkF1lAXCTzXUQdeugEFqRBTBKXfQPyKIsUEwkCvCDnWjQYXCYMHCPMLBsiiSwCfrh8GLzAfCVMQNwqhamsDB5UHBBC9/QZzIKcLrKRTBju85wZA0T8Gm2xNCGa7AQfs3JsG5A4/BA+FGQXhroUE8duLB7gfBQU0dgUGwVXDBF6XkwWn7SEFW6ytAIQc9QUudNkGXwETBkP6LP/678cCoLiHC+zo4wuVSAUJLJ7TBVP7DQQG5g0HqbJnBIuY8QfqZLMKv/qtBAbKpQe4BqT+ICzpATJ2bwKzVKUH/p9hBjTclwh4vP8Ajt9pBOm4lwV+Gw0GkEMdBqbwdwUu6ksH6a+3AhiLQwHFpFsG/JBVCNL/pP3Yls8FhuY1AxhG+QTxolEGtY43BJX6TwR7kRcGuFwFCkwFZQvbFSkKTqDvBN72YwaecFMIfjvXBZTHIQauQRMH+ARJCXz4pQiPF/kCO18NBrNILwiwTDEKEGA9CHOsqQsBnkMB0bePBu4gXwjIc/sEb3hhBdUbFwNZx2kEObOFAiLYlwimCO0FBXw3BcqNyQjTF2MAAm/dBp1UxwfQGSkINEbzA33PHwJIHuUEoOSnB7D5gwD1eykGaUcrBwEyJQbMdMkG/5FTBhwcIwns0tkH8IQXCE5AUQZXKr0GtfgzAoY6lQYpnJsL3vDXBjmF7QTljrcDigr9Bu5KCwXARhsBuOw5Cmi+BPqHqskEajLnBu6MEQW8Ar8Fs/QRCaW3jwQP3+8HMfAvCb/v4wUgjr0CNBWhB6Kn6wWKrBsLLMIVALhKXQepjckJp5p7BGEhPwIt6m8HjjOJBfi5qQYCO2cEIPPPB9t6RQQq+TD/kJRVAA4oaQc/SFcFOgtdAhM+eQDYcmzwBewtCmpMmQZHYMMIzyoDBjfW6wd2Nf8F7AAVCbv+GQfqveMBShfZBKw5oQatuu8AJ8uTAckzFQc/iIsLMmBbBo9XRv7HTt8HHKDvBK2kIwWF+2UEfDVNCJ4tPQnDyXELCtFlCJUcdQgDuXkIfeU9CW1rvwIe3vkBzqrBBugtBQppXMELS86TBieD7QWiNO8KdEhpCPogIwiVc/8HynpLBMEGSQavUsMDv3dTBmeU1wf+bEcIQpJPBsjAOQsAgikH1l/HBK/0iwtuGg0FMdyZCByLHQXxn2kHRMNFBxQYqwuuiw8Hz4iRCzPgqQcwvvsGX35Q/MZUuQqAtHMKrdw9BE7OUwdhZ3cFBJg1CY5kEQcJAWUEdatXB9Du0QaRyxcH5bk7BJy6VwFwy60DWWb/Be8phQJrcDED+QozAR3vpQDUxRsGjNDZBo26Jwbgvb8EOw6HBGAiOQce8gsHdLRjB+RVUv+38/MAtXdpBdkO9Qc9En8DR9FlBiUeEwP0abkEjB1TAQkfDQHjzVcFp3gLB0XtLQqgOycGjS+HBUzkNQpM5XME/mQVCh6YlQZZWDMFqj/RBW8kWwpQdCMKNnV7BZiO2v6bsW8Gr7y9BUXdLQqy5IcC5kx7B1ObBwRek68FW9ZjBmQp3wW9aksFFLDpBiEfKwYc0msEOqtrBXwBRQURTK0FBOxrBIUVrwT0VwcExfw5C1ITEwcnyjcDPIFvBtW/ewY5ZCMK4SzfCEY90wdhbJkBP/OVB0MQfwhA8sMAvCohBIb5dQigOTUJ4wlFC/r1iQhE/QUJayl5CWf8NwuioBsIIun3BAdEeQrC3/MDq3oFBdJZcQB370kG3SBnCdek/QSMwlkG8KstB+CreQbM2+D9R1yfBfcTiwc5YzcCNjIhBc0CowbVy6kFGyo7B6FaGQeHgtkFaEjrB/+zlv4+WGkLAJMbBq+EhQiRFhEE=\",\"dtype\":\"float32\",\"shape\":[3413]},\"y_coord\":{\"__ndarray__\":\"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\",\"dtype\":\"float32\",\"shape\":[3413]}}},\"id\":\"fdb10996-cabf-4c0c-a501-3c851e31b57c\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"blue\"},\"line_alpha\":{\"value\":0.2},\"line_color\":{\"value\":\"blue\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x_coord\"},\"y\":{\"field\":\"y_coord\"}},\"id\":\"861ed8b4-8810-4632-9b14-1be17c977c18\",\"type\":\"Circle\"},{\"attributes\":{\"callback\":null},\"id\":\"298f0301-2434-411b-89bc-73059a2d74ae\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"8d6147de-29c4-49ba-ad09-1769b3214a97\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null},\"id\":\"bc515385-a8c5-4c2f-a8e5-b96a5305ee32\",\"type\":\"DataRange1d\"},{\"attributes\":{\"plot\":null,\"text\":\"t-SNE Word Embeddings\",\"text_font_size\":{\"value\":\"16pt\"}},\"id\":\"fba89326-54d1-4ebc-8ff4-924df3094daa\",\"type\":\"Title\"},{\"attributes\":{\"callback\":null,\"tooltips\":\"@word\"},\"id\":\"7ce8eb82-bad0-4395-b6e2-b10c43066fe1\",\"type\":\"HoverTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":{\"id\":\"5f9c1923-fbdc-4329-9283-58d90d6d2d7a\",\"type\":\"WheelZoomTool\"},\"active_tap\":\"auto\",\"tools\":[{\"id\":\"efc4f080-d6d0-4a17-af5e-f09663a8fe57\",\"type\":\"PanTool\"},{\"id\":\"5f9c1923-fbdc-4329-9283-58d90d6d2d7a\",\"type\":\"WheelZoomTool\"},{\"id\":\"86c9f66b-b9ea-4e40-a89b-753d36257fc7\",\"type\":\"BoxZoomTool\"},{\"id\":\"d1709760-4256-47c9-a8d2-e0dafbc5357f\",\"type\":\"BoxSelectTool\"},{\"id\":\"2d2deb3f-1e1b-40a9-af36-c5224eef0bbd\",\"type\":\"ResetTool\"},{\"id\":\"7ce8eb82-bad0-4395-b6e2-b10c43066fe1\",\"type\":\"HoverTool\"}]},\"id\":\"9df0f0d9-2d03-4f5d-9a23-59db582ef756\",\"type\":\"Toolbar\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x_coord\"},\"y\":{\"field\":\"y_coord\"}},\"id\":\"7a7826a4-63be-4957-8dfe-a3aa99cb7c3c\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"f25c9727-cacf-4600-88ea-305c03028fda\",\"type\":\"LinearScale\"},{\"attributes\":{\"grid_line_color\":{\"value\":null},\"plot\":{\"id\":\"fef8d308-eebb-4a79-a0f6-54e15e8514a4\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"010c1620-beee-4e02-bc53-59a3fcf16661\",\"type\":\"BasicTicker\"}},\"id\":\"65c96e85-2359-47e3-9601-9383f9035496\",\"type\":\"Grid\"},{\"attributes\":{\"formatter\":{\"id\":\"77d587ac-045f-46b8-b1bb-83a20810b730\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"fef8d308-eebb-4a79-a0f6-54e15e8514a4\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"010c1620-beee-4e02-bc53-59a3fcf16661\",\"type\":\"BasicTicker\"},\"visible\":false},\"id\":\"655d87bf-710f-4a28-8f47-36079c033566\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"010c1620-beee-4e02-bc53-59a3fcf16661\",\"type\":\"BasicTicker\"},{\"attributes\":{\"formatter\":{\"id\":\"f703b134-2246-4572-86b0-653d8263735d\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"fef8d308-eebb-4a79-a0f6-54e15e8514a4\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"7ec998b4-544e-4209-9a28-1c5faa1124e2\",\"type\":\"BasicTicker\"},\"visible\":false},\"id\":\"1d8405ab-79c7-488d-99af-893d92139cf7\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"7ec998b4-544e-4209-9a28-1c5faa1124e2\",\"type\":\"BasicTicker\"},{\"attributes\":{\"dimension\":1,\"grid_line_color\":{\"value\":null},\"plot\":{\"id\":\"fef8d308-eebb-4a79-a0f6-54e15e8514a4\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"7ec998b4-544e-4209-9a28-1c5faa1124e2\",\"type\":\"BasicTicker\"}},\"id\":\"42688ab8-9f72-46fe-a87c-8d10fc78c75f\",\"type\":\"Grid\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x_coord\"},\"y\":{\"field\":\"y_coord\"}},\"id\":\"1823f604-debe-4cdd-bc29-55e831deee71\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"efc4f080-d6d0-4a17-af5e-f09663a8fe57\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"2d2deb3f-1e1b-40a9-af36-c5224eef0bbd\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"5f9c1923-fbdc-4329-9283-58d90d6d2d7a\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"overlay\":{\"id\":\"9d6574ac-f3d7-4576-b17c-b4016d40b30f\",\"type\":\"BoxAnnotation\"}},\"id\":\"86c9f66b-b9ea-4e40-a89b-753d36257fc7\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"callback\":null,\"overlay\":{\"id\":\"cb4cd21d-85fa-484c-bdd1-e069795f41b0\",\"type\":\"BoxAnnotation\"},\"renderers\":[{\"id\":\"5ce2d413-ccd4-48c9-8c70-1d6ab26c8b08\",\"type\":\"GlyphRenderer\"}]},\"id\":\"d1709760-4256-47c9-a8d2-e0dafbc5357f\",\"type\":\"BoxSelectTool\"}],\"root_ids\":[\"fef8d308-eebb-4a79-a0f6-54e15e8514a4\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.14\"}};\n",
       "  var render_items = [{\"docid\":\"ec7feeef-ed69-4e0b-8fea-b62e4050222e\",\"elementid\":\"2f656f64-bb6a-4288-a71a-c6b9e1100aa9\",\"modelid\":\"fef8d308-eebb-4a79-a0f6-54e15e8514a4\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "fef8d308-eebb-4a79-a0f6-54e15e8514a4"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add our DataFrame as a ColumnDataSource for Bokeh\n",
    "plot_data = ColumnDataSource(tsne_vectors)\n",
    "\n",
    "# create the plot and configure the\n",
    "# title, dimensions, and tools\n",
    "tsne_plot = figure(title=u't-SNE Word Embeddings',\n",
    "                   plot_width = 800,\n",
    "                   plot_height = 800,\n",
    "                   tools= (u'pan, wheel_zoom, box_zoom,'\n",
    "                           u'box_select, reset'),\n",
    "                   active_scroll=u'wheel_zoom')\n",
    "\n",
    "# add a hover tool to display words on roll-over\n",
    "tsne_plot.add_tools( HoverTool(tooltips = u'@word') )\n",
    "\n",
    "# draw the words as circles on the plot\n",
    "tsne_plot.circle(u'x_coord', u'y_coord', source=plot_data,\n",
    "                 color=u'blue', line_alpha=0.2, fill_alpha=0.1,\n",
    "                 size=10, hover_line_color=u'black')\n",
    "\n",
    "# configure visual elements of the plot\n",
    "tsne_plot.title.text_font_size = value(u'16pt')\n",
    "tsne_plot.xaxis.visible = False\n",
    "tsne_plot.yaxis.visible = False\n",
    "tsne_plot.grid.grid_line_color = None\n",
    "tsne_plot.outline_line_color = None\n",
    "\n",
    "# engage!\n",
    "show(tsne_plot);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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