3_Doc2Vec_scripts-Executed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d62e44f",
   "metadata": {},
   "source": [
    "# Doc2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "bcac976c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "# nltk.download('all')\n",
    "from nltk.tokenize import sent_tokenize\n",
    "import re\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "debc33c6",
   "metadata": {},
   "source": [
    "## Embedding the text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ebf627",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "b04d84dd",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['402.txt',\n",
       " '403.txt',\n",
       " '404.txt',\n",
       " '405.txt',\n",
       " '406.txt',\n",
       " '407.txt',\n",
       " '408.txt',\n",
       " '409.txt',\n",
       " '410.txt',\n",
       " '411.txt']"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.chdir('/Users/gretel.depaepe/Documents/Projects/deep_space_9')\n",
    "PATH = 'resources/ScriptsDS9/'\n",
    "trn_files = !ls {PATH}\n",
    "trn_files[:10] # as a test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "9ebf1afa",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK Emissary\n",
      "OK A Man Alone\n",
      "OK Past Prologue\n",
      "OK Babel\n",
      "OK Captive Pursuit\n",
      "OK Q-Less\n",
      "OK Dax\n",
      "OK The Passenger\n",
      "OK Move Along Home\n",
      "OK The Nagus\n",
      "OK Vortex\n",
      "OK Battle Lines\n",
      "414.txt Error\n",
      "OK Progress\n",
      "OK If Wishes Were Horses\n",
      "OK The Forsaken\n",
      "OK Dramatis Personae\n",
      "OK Duet\n",
      "420.txt Error\n",
      "OK The Homecoming\n",
      "OK The Circle\n",
      "OK The Siege\n",
      "OK Invasive Procedures\n",
      "OK Cardassians\n",
      "OK Melora\n",
      "OK Rules of Acquisition\n",
      "OK Necessary Evil\n",
      "OK Second Sight\n",
      "OK Sanctuary\n",
      "OK Rivals\n",
      "OK The Alternate\n",
      "OK Armageddon Game\n",
      "OK Whispers\n",
      "OK Paradise\n",
      "OK Shadowplay\n",
      "OK Playing God\n",
      "OK Profit and Loss\n",
      "OK Blood Oath\n",
      "OK The Maquis, Part I\n",
      "OK The Maquis, Part II\n",
      "OK The Wire\n",
      "OK Crossover\n",
      "OK The Collaborator\n",
      "OK Tribunal\n",
      "OK The Jem'Hadar\n",
      "OK The Search\n",
      "OK The Search, Part II\n",
      "OK The House of Quark\n",
      "OK Equilibrium\n",
      "OK Second Skin\n",
      "OK The Abandoned\n",
      "OK Civil Defense\n",
      "OK Meridian\n",
      "OK Defiant\n",
      "OK Fascination\n",
      "OK Past Tense, Part I\n",
      "OK Past Tense, Part II\n",
      "459.txt Error\n",
      "OK Heart of Stone\n",
      "OK Destiny\n",
      "OK Prophet Motive\n",
      "OK Visionary\n",
      "464.txt Error\n",
      "OK Improbable Cause\n",
      "OK Through the Looking Glass\n",
      "OK The Die is Cast\n",
      "468.txt Error\n",
      "OK Family Business\n",
      "470.txt Error\n",
      "OK Facets\n",
      "472.txt Error\n",
      "OK The Way of the Warrior\n",
      "OK Hippocratic oath\n",
      "OK The Visitor\n",
      "OK Indiscretion\n",
      "OK Rejoined\n",
      "OK Starship Down\n",
      "OK Little Green Men\n",
      "OK The Sword of Kahless\n",
      "OK Our Man Bashir\n",
      "OK Homefront\n",
      "OK Paradise Lost\n",
      "OK Crossfire\n",
      "OK Return to Grace\n",
      "OK The Sons of Mogh\n",
      "OK The Bar Association\n",
      "OK Accession\n",
      "OK Rules of Engagement\n",
      "OK Hard Time\n",
      "OK Shattered Mirror\n",
      "OK The Muse\n",
      "OK For the Cause\n",
      "OK The Quickening\n",
      "OK To The Death\n",
      "OK Body Parts\n",
      "OK Broken Link\n",
      "OK Apocalypse Rising\n",
      "OK The Ship\n",
      "OK Looking For par'Mach in All the Wrong Places\n",
      "OK ... Nor the Battle to the Strong\n",
      "OK Trials and Tribble-ations\n",
      "OK The Assignment\n",
      "OK Let He Who Is Without Sin ... \n",
      "OK Things Past\n",
      "OK The Ascent\n",
      "OK Rapture\n",
      "OK The Darkness and the Light\n",
      "OK The Begotten\n",
      "OK For the Uniform\n",
      "OK In Purgatory's Shadow\n",
      "OK By Inferno's Light\n",
      "OK Doctor Bashir, I Presume?\n",
      "OK A Simple Investigation\n",
      "OK Business As Usual \n",
      "OK Ties of Blood and Water\n",
      "OK Ferengi Love Songs\n",
      "OK Soldiers of the Empire\n",
      "OK Children of Time\n",
      "OK Blaze of Glory\n",
      "OK Empok Nor\n",
      "OK In the Cards\n",
      "OK Call to Arms\n",
      "OK A Time to Stand\n",
      "OK Sons and Daughters\n",
      "OK Rocks and Shoals\n",
      "OK Behind the Lines\n",
      "OK Favor the Bold\n",
      "OK The Sacrifice of Angels\n",
      "OK You Are Cordially Invited... \n",
      "OK Resurrection\n",
      "OK Statistical Probabilities\n",
      "OK The Magnificent Ferengi\n",
      "OK Waltz\n",
      "OK Who Mourns for Morn?\n",
      "OK One Little Ship\n",
      "OK Far Beyond the Stars\n",
      "OK Honor Among Thieves\n",
      "OK Change of Heart\n",
      "OK Wrongs Darker Than Death or Night\n",
      "OK Inquisition\n",
      "OK In the Pale Moonlight\n",
      "OK His Way\n",
      "OK The Reckoning\n",
      "OK Valiant\n",
      "OK Profit and Lace\n",
      "OK Time's Orphan\n",
      "OK The Sound of Her Voice\n",
      "OK Tears of the Prophets\n",
      "OK Image in the Sand\n",
      "OK Shadows and Symbols\n",
      "OK Afterimage\n",
      "OK Take Me Out to the Holosuite\n",
      "OK Chrysalis\n",
      "OK Treachery, Faith, and the Great River\n",
      "OK Once More Unto the Breach\n",
      "OK The Siege of AR-558\n",
      "OK Covenant\n",
      "OK It's Only a Paper Moon\n",
      "OK Prodigal Daughter\n",
      "OK The Emperor's New Cloak\n",
      "OK Field of Fire\n",
      "OK Chimera\n",
      "OK Inter Arma Enim Silent Leges\n",
      "OK Badda-bing Badda-bang\n",
      "OK Penumbra\n",
      "OK 'Til Death Do Us Part\n",
      "OK Strange Bedfellows\n",
      "OK The Changing Face of Evil\n",
      "OK When it Rains... \n",
      "OK Tacking into the Wind\n",
      "OK Extreme Measures\n",
      "OK The Dogs of War\n",
      "OK What You Leave Behind\n",
      "7 files not read\n"
     ]
    }
   ],
   "source": [
    "scripts = []\n",
    "counter = 0\n",
    "for f in trn_files:\n",
    "    script = {}\n",
    "    if f != 'all_scripts.json':\n",
    "        try:\n",
    "            text = open(PATH+f, 'r').read()\n",
    "            text = re.sub(\"\\t\",' ',text)\n",
    "            text = re.sub(\"\\n\",' ',text)\n",
    "            text = re.sub(' +',' ',text)\n",
    "            title = text.split(\"#\")[0].split('\"')[1]\n",
    "            script['title'] = title\n",
    "            script['text'] = text\n",
    "            scripts.append(script)\n",
    "            print(\"OK\", title)\n",
    "        except:\n",
    "            print(f, 'Error')\n",
    "            counter += 1\n",
    "print(counter, 'files not read')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "18fadad0",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(scripts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "755f30f2",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df['length'] = df['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "78d14b71",
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>His Way</td>\n",
       "      <td>STAR TREK: DEEP SPACE NINE \"His Way\" #40510-5...</td>\n",
       "      <td>46849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>It's Only a Paper Moon</td>\n",
       "      <td>STAR TREK: DEEP SPACE NINE \"It's Only a Paper...</td>\n",
       "      <td>49701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Chimera</td>\n",
       "      <td>STAR TREK: DEEP SPACE NINE \"Chimera\" (fka \"Un...</td>\n",
       "      <td>49790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>The Forsaken</td>\n",
       "      <td>STAR TREK: DEEP SPACE NINE \"The Forsaken\" (fk...</td>\n",
       "      <td>50254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vortex</td>\n",
       "      <td>STAR TREK: DEEP SPACE NINE \"Vortex\" #40511-41...</td>\n",
       "      <td>50302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>One Little Ship</td>\n",
       "      <td>STAR TREK: DEEP SPACE NINE \"One Little Ship\" ...</td>\n",
       "      <td>74641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>... Nor the Battle to the Strong</td>\n",
       "      <td>\u0000\u0000 STAR TREK: DEEP SPACE NINE \"... Nor the Ba...</td>\n",
       "      <td>74663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>What You Leave Behind</td>\n",
       "      <td>STAR TREK: DEEP SPACE NINE \"What You Leave Be...</td>\n",
       "      <td>116256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emissary</td>\n",
       "      <td>STAR TREK: DEEP SPACE NINE \"Emissary\" #40511-...</td>\n",
       "      <td>124436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>The Way of the Warrior</td>\n",
       "      <td>STAR TREK: DEEP SPACE NINE \"The Way of the Wa...</td>\n",
       "      <td>126270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                title  \\\n",
       "134                           His Way   \n",
       "150            It's Only a Paper Moon   \n",
       "154                           Chimera   \n",
       "14                       The Forsaken   \n",
       "10                             Vortex   \n",
       "..                                ...   \n",
       "127                   One Little Ship   \n",
       "92   ... Nor the Battle to the Strong   \n",
       "165             What You Leave Behind   \n",
       "0                            Emissary   \n",
       "64             The Way of the Warrior   \n",
       "\n",
       "                                                  text  length  \n",
       "134   STAR TREK: DEEP SPACE NINE \"His Way\" #40510-5...   46849  \n",
       "150   STAR TREK: DEEP SPACE NINE \"It's Only a Paper...   49701  \n",
       "154   STAR TREK: DEEP SPACE NINE \"Chimera\" (fka \"Un...   49790  \n",
       "14    STAR TREK: DEEP SPACE NINE \"The Forsaken\" (fk...   50254  \n",
       "10    STAR TREK: DEEP SPACE NINE \"Vortex\" #40511-41...   50302  \n",
       "..                                                 ...     ...  \n",
       "127   STAR TREK: DEEP SPACE NINE \"One Little Ship\" ...   74641  \n",
       "92    \u0000\u0000 STAR TREK: DEEP SPACE NINE \"... Nor the Ba...   74663  \n",
       "165   STAR TREK: DEEP SPACE NINE \"What You Leave Be...  116256  \n",
       "0     STAR TREK: DEEP SPACE NINE \"Emissary\" #40511-...  124436  \n",
       "64    STAR TREK: DEEP SPACE NINE \"The Way of the Wa...  126270  \n",
       "\n",
       "[166 rows x 3 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "4862fa82",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       166.000000\n",
       "mean      59776.216867\n",
       "std        9760.980123\n",
       "min       46849.000000\n",
       "25%       55602.000000\n",
       "50%       58310.500000\n",
       "75%       60963.000000\n",
       "max      126270.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['length'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "1e978ba3",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import smart_open\n",
    "import gensim\n",
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
    "\n",
    "\n",
    "def read_corpus(df, tokens_only=False):\n",
    "    for i, row in df.iterrows():\n",
    "        tokens = gensim.utils.simple_preprocess(row['text'],\n",
    "                                                min_len=1, max_len=30)\n",
    "        if tokens_only:\n",
    "            yield tokens\n",
    "        else:\n",
    "            # For training data, add tags\n",
    "            yield gensim.models.doc2vec.TaggedDocument(tokens, [i])\n",
    "\n",
    "corpus = list(read_corpus(pd.DataFrame(df['text'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "9f470a73",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "166"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5892862e",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Train Doc2Vec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0357b06f",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<font color='purple' size=4>While Word2Vec computes a feature vector for every word in the corpus, Gensim’s Doc2Vec computes a feature vector for every document in the corpus.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "5d21eef0",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-29 14:10:21,964 : INFO : Doc2Vec lifecycle event {'params': 'Doc2Vec<dm/m,d50,n5,w5,s0.001,t3>', 'datetime': '2022-09-29T14:10:21.964148', 'gensim': '4.2.0', 'python': '3.9.13 (main, Aug 25 2022, 18:29:29) \\n[Clang 12.0.0 ]', 'platform': 'macOS-10.16-x86_64-i386-64bit', 'event': 'created'}\n"
     ]
    }
   ],
   "source": [
    "model = gensim.models.doc2vec.Doc2Vec(vector_size=50,\n",
    "                                      min_count=1,\n",
    "                                      epochs=50,\n",
    "                                      seed=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "1d043c2a",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-29 14:10:21,970 : INFO : collecting all words and their counts\n",
      "2022-09-29 14:10:21,971 : INFO : PROGRESS: at example #0, processed 0 words (0 words/s), 0 word types, 0 tags\n",
      "2022-09-29 14:10:22,188 : INFO : collected 26488 word types and 166 unique tags from a corpus of 166 examples and 1772329 words\n",
      "2022-09-29 14:10:22,189 : INFO : Creating a fresh vocabulary\n",
      "2022-09-29 14:10:22,249 : INFO : Doc2Vec lifecycle event {'msg': 'effective_min_count=1 retains 26488 unique words (100.00% of original 26488, drops 0)', 'datetime': '2022-09-29T14:10:22.249855', 'gensim': '4.2.0', 'python': '3.9.13 (main, Aug 25 2022, 18:29:29) \\n[Clang 12.0.0 ]', 'platform': 'macOS-10.16-x86_64-i386-64bit', 'event': 'prepare_vocab'}\n",
      "2022-09-29 14:10:22,250 : INFO : Doc2Vec lifecycle event {'msg': 'effective_min_count=1 leaves 1772329 word corpus (100.00% of original 1772329, drops 0)', 'datetime': '2022-09-29T14:10:22.250494', 'gensim': '4.2.0', 'python': '3.9.13 (main, Aug 25 2022, 18:29:29) \\n[Clang 12.0.0 ]', 'platform': 'macOS-10.16-x86_64-i386-64bit', 'event': 'prepare_vocab'}\n",
      "2022-09-29 14:10:22,351 : INFO : deleting the raw counts dictionary of 26488 items\n",
      "2022-09-29 14:10:22,352 : INFO : sample=0.001 downsamples 65 most-common words\n",
      "2022-09-29 14:10:22,353 : INFO : Doc2Vec lifecycle event {'msg': 'downsampling leaves estimated 1343341.9169828102 word corpus (75.8%% of prior 1772329)', 'datetime': '2022-09-29T14:10:22.353317', 'gensim': '4.2.0', 'python': '3.9.13 (main, Aug 25 2022, 18:29:29) \\n[Clang 12.0.0 ]', 'platform': 'macOS-10.16-x86_64-i386-64bit', 'event': 'prepare_vocab'}\n",
      "2022-09-29 14:10:22,519 : INFO : estimated required memory for 26488 words and 50 dimensions: 23905600 bytes\n",
      "2022-09-29 14:10:22,519 : INFO : resetting layer weights\n"
     ]
    }
   ],
   "source": [
    "model.build_vocab(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "c9064936",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word 'raktajino' appeared 98 times in the training corpus.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Word 'raktajino' appeared {model.wv.get_vecattr('raktajino', 'count')} times in the training corpus.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "e5ade10b",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-29 14:10:22,533 : INFO : Doc2Vec lifecycle event {'msg': 'training model with 3 workers on 26488 vocabulary and 50 features, using sg=0 hs=0 sample=0.001 negative=5 window=5 shrink_windows=True', 'datetime': '2022-09-29T14:10:22.533079', 'gensim': '4.2.0', 'python': '3.9.13 (main, Aug 25 2022, 18:29:29) \\n[Clang 12.0.0 ]', 'platform': 'macOS-10.16-x86_64-i386-64bit', 'event': 'train'}\n",
      "2022-09-29 14:10:23,157 : INFO : EPOCH 0: training on 1772329 raw words (1324346 effective words) took 0.6s, 2130298 effective words/s\n",
      "2022-09-29 14:10:23,786 : INFO : EPOCH 1: training on 1772329 raw words (1324744 effective words) took 0.6s, 2116918 effective words/s\n",
      "2022-09-29 14:10:24,415 : INFO : EPOCH 2: training on 1772329 raw words (1324555 effective words) took 0.6s, 2117237 effective words/s\n",
      "2022-09-29 14:10:25,037 : INFO : EPOCH 3: training on 1772329 raw words (1324662 effective words) took 0.6s, 2133932 effective words/s\n",
      "2022-09-29 14:10:25,666 : INFO : EPOCH 4: training on 1772329 raw words (1324730 effective words) took 0.6s, 2116481 effective words/s\n",
      "2022-09-29 14:10:26,288 : INFO : EPOCH 5: training on 1772329 raw words (1324872 effective words) took 0.6s, 2135169 effective words/s\n",
      "2022-09-29 14:10:26,927 : INFO : EPOCH 6: training on 1772329 raw words (1325559 effective words) took 0.6s, 2086027 effective words/s\n",
      "2022-09-29 14:10:27,543 : INFO : EPOCH 7: training on 1772329 raw words (1324435 effective words) took 0.6s, 2152969 effective words/s\n",
      "2022-09-29 14:10:28,172 : INFO : EPOCH 8: training on 1772329 raw words (1324813 effective words) took 0.6s, 2110669 effective words/s\n",
      "2022-09-29 14:10:28,805 : INFO : EPOCH 9: training on 1772329 raw words (1324703 effective words) took 0.6s, 2097788 effective words/s\n",
      "2022-09-29 14:10:29,441 : INFO : EPOCH 10: training on 1772329 raw words (1324375 effective words) took 0.6s, 2088854 effective words/s\n",
      "2022-09-29 14:10:30,069 : INFO : EPOCH 11: training on 1772329 raw words (1325233 effective words) took 0.6s, 2112401 effective words/s\n",
      "2022-09-29 14:10:30,696 : INFO : EPOCH 12: training on 1772329 raw words (1323805 effective words) took 0.6s, 2117389 effective words/s\n",
      "2022-09-29 14:10:31,303 : INFO : EPOCH 13: training on 1772329 raw words (1324411 effective words) took 0.6s, 2187905 effective words/s\n",
      "2022-09-29 14:10:31,911 : INFO : EPOCH 14: training on 1772329 raw words (1324854 effective words) took 0.6s, 2189085 effective words/s\n",
      "2022-09-29 14:10:32,520 : INFO : EPOCH 15: training on 1772329 raw words (1324668 effective words) took 0.6s, 2180815 effective words/s\n",
      "2022-09-29 14:10:33,135 : INFO : EPOCH 16: training on 1772329 raw words (1324855 effective words) took 0.6s, 2163913 effective words/s\n",
      "2022-09-29 14:10:33,737 : INFO : EPOCH 17: training on 1772329 raw words (1324922 effective words) took 0.6s, 2205401 effective words/s\n",
      "2022-09-29 14:10:34,352 : INFO : EPOCH 18: training on 1772329 raw words (1324297 effective words) took 0.6s, 2159168 effective words/s\n",
      "2022-09-29 14:10:35,027 : INFO : EPOCH 19: training on 1772329 raw words (1324403 effective words) took 0.7s, 1966484 effective words/s\n",
      "2022-09-29 14:10:35,652 : INFO : EPOCH 20: training on 1772329 raw words (1324189 effective words) took 0.6s, 2124611 effective words/s\n",
      "2022-09-29 14:10:36,248 : INFO : EPOCH 21: training on 1772329 raw words (1324890 effective words) took 0.6s, 2228468 effective words/s\n",
      "2022-09-29 14:10:36,860 : INFO : EPOCH 22: training on 1772329 raw words (1324229 effective words) took 0.6s, 2166346 effective words/s\n",
      "2022-09-29 14:10:37,454 : INFO : EPOCH 23: training on 1772329 raw words (1324570 effective words) took 0.6s, 2244210 effective words/s\n",
      "2022-09-29 14:10:38,059 : INFO : EPOCH 24: training on 1772329 raw words (1324821 effective words) took 0.6s, 2191275 effective words/s\n",
      "2022-09-29 14:10:38,651 : INFO : EPOCH 25: training on 1772329 raw words (1324623 effective words) took 0.6s, 2249936 effective words/s\n",
      "2022-09-29 14:10:39,270 : INFO : EPOCH 26: training on 1772329 raw words (1325292 effective words) took 0.6s, 2154764 effective words/s\n",
      "2022-09-29 14:10:39,864 : INFO : EPOCH 27: training on 1772329 raw words (1324011 effective words) took 0.6s, 2239667 effective words/s\n",
      "2022-09-29 14:10:40,449 : INFO : EPOCH 28: training on 1772329 raw words (1324803 effective words) took 0.6s, 2277187 effective words/s\n",
      "2022-09-29 14:10:41,046 : INFO : EPOCH 29: training on 1772329 raw words (1324603 effective words) took 0.6s, 2230560 effective words/s\n",
      "2022-09-29 14:10:41,642 : INFO : EPOCH 30: training on 1772329 raw words (1325478 effective words) took 0.6s, 2228209 effective words/s\n",
      "2022-09-29 14:10:42,247 : INFO : EPOCH 31: training on 1772329 raw words (1324587 effective words) took 0.6s, 2200984 effective words/s\n",
      "2022-09-29 14:10:42,853 : INFO : EPOCH 32: training on 1772329 raw words (1324661 effective words) took 0.6s, 2193433 effective words/s\n",
      "2022-09-29 14:10:43,451 : INFO : EPOCH 33: training on 1772329 raw words (1324876 effective words) took 0.6s, 2225965 effective words/s\n",
      "2022-09-29 14:10:44,054 : INFO : EPOCH 34: training on 1772329 raw words (1324973 effective words) took 0.6s, 2202287 effective words/s\n",
      "2022-09-29 14:10:44,651 : INFO : EPOCH 35: training on 1772329 raw words (1324500 effective words) took 0.6s, 2223584 effective words/s\n",
      "2022-09-29 14:10:45,258 : INFO : EPOCH 36: training on 1772329 raw words (1324254 effective words) took 0.6s, 2193889 effective words/s\n",
      "2022-09-29 14:10:45,868 : INFO : EPOCH 37: training on 1772329 raw words (1324564 effective words) took 0.6s, 2176523 effective words/s\n",
      "2022-09-29 14:10:46,475 : INFO : EPOCH 38: training on 1772329 raw words (1325021 effective words) took 0.6s, 2192949 effective words/s\n",
      "2022-09-29 14:10:47,073 : INFO : EPOCH 39: training on 1772329 raw words (1324492 effective words) took 0.6s, 2228774 effective words/s\n",
      "2022-09-29 14:10:47,669 : INFO : EPOCH 40: training on 1772329 raw words (1325140 effective words) took 0.6s, 2227823 effective words/s\n",
      "2022-09-29 14:10:48,273 : INFO : EPOCH 41: training on 1772329 raw words (1324208 effective words) took 0.6s, 2196309 effective words/s\n",
      "2022-09-29 14:10:48,878 : INFO : EPOCH 42: training on 1772329 raw words (1325013 effective words) took 0.6s, 2198724 effective words/s\n",
      "2022-09-29 14:10:49,486 : INFO : EPOCH 43: training on 1772329 raw words (1325095 effective words) took 0.6s, 2182707 effective words/s\n",
      "2022-09-29 14:10:50,086 : INFO : EPOCH 44: training on 1772329 raw words (1325360 effective words) took 0.6s, 2213380 effective words/s\n",
      "2022-09-29 14:10:50,685 : INFO : EPOCH 45: training on 1772329 raw words (1324757 effective words) took 0.6s, 2220353 effective words/s\n",
      "2022-09-29 14:10:51,282 : INFO : EPOCH 46: training on 1772329 raw words (1324439 effective words) took 0.6s, 2223184 effective words/s\n",
      "2022-09-29 14:10:51,926 : INFO : EPOCH 47: training on 1772329 raw words (1324225 effective words) took 0.6s, 2059059 effective words/s\n",
      "2022-09-29 14:10:52,563 : INFO : EPOCH 48: training on 1772329 raw words (1324070 effective words) took 0.6s, 2085573 effective words/s\n",
      "2022-09-29 14:10:53,157 : INFO : EPOCH 49: training on 1772329 raw words (1324686 effective words) took 0.6s, 2240562 effective words/s\n",
      "2022-09-29 14:10:53,158 : INFO : Doc2Vec lifecycle event {'msg': 'training on 88616450 raw words (66233672 effective words) took 30.6s, 2162803 effective words/s', 'datetime': '2022-09-29T14:10:53.158345', 'gensim': '4.2.0', 'python': '3.9.13 (main, Aug 25 2022, 18:29:29) \\n[Clang 12.0.0 ]', 'platform': 'macOS-10.16-x86_64-i386-64bit', 'event': 'train'}\n"
     ]
    }
   ],
   "source": [
    "model.train(corpus,\n",
    "            total_examples=model.corpus_count,\n",
    "            epochs=model.epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ad0ba8",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<font color='purple' size=4>We trained the model for 50 epoch and requested to embed each document in a 50-dimensional vector. This took us half a minute using a normal laptop.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f1c2bfd7",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most similar scripts compared to 'The Nagus' \n",
      "          are: \n",
      "'The Nagus'\n",
      "'Ferengi Love Songs'\n",
      "'Profit and Lace'\n",
      "'Prophet Motive'\n",
      "'Little Green Men'\n"
     ]
    }
   ],
   "source": [
    "doc_id = 9\n",
    "\n",
    "inferred_vector = model.infer_vector(corpus[doc_id].words)\n",
    "sims = model.dv.most_similar([inferred_vector], topn=5)\n",
    "print(f\"\"\"The most similar scripts compared to '{df['title'].iloc[doc_id]}' \n",
    "          are: \"\"\")\n",
    "for i in range(5):\n",
    "    print(f\"'{df['title'].iloc[sims[i][0]]}'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "f8fd9d80",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most similar scripts compared to 'The Homecoming' \n",
      "          are: \n",
      "'The Homecoming'\n",
      "'The Circle'\n",
      "'The Siege'\n",
      "'Sanctuary'\n",
      "'Progress'\n"
     ]
    }
   ],
   "source": [
    "doc_id = 17\n",
    "\n",
    "inferred_vector = model.infer_vector(corpus[doc_id].words)\n",
    "sims = model.dv.most_similar([inferred_vector], topn=5)\n",
    "print(f\"\"\"The most similar scripts compared to '{df['title'].iloc[doc_id]}' \n",
    "          are: \"\"\")\n",
    "for i in range(5):\n",
    "    print(f\"'{df['title'].iloc[sims[i][0]]}'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "f619f008",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inferring vector for Emissary\n",
      "Inferring vector for A Man Alone\n",
      "Inferring vector for Past Prologue\n",
      "Inferring vector for Babel\n",
      "Inferring vector for Captive Pursuit\n",
      "Inferring vector for Q-Less\n",
      "Inferring vector for Dax\n",
      "Inferring vector for The Passenger\n",
      "Inferring vector for Move Along Home\n",
      "Inferring vector for The Nagus\n",
      "Inferring vector for Vortex\n",
      "Inferring vector for Battle Lines\n",
      "Inferring vector for Progress\n",
      "Inferring vector for If Wishes Were Horses\n",
      "Inferring vector for The Forsaken\n",
      "Inferring vector for Dramatis Personae\n",
      "Inferring vector for Duet\n",
      "Inferring vector for The Homecoming\n",
      "Inferring vector for The Circle\n",
      "Inferring vector for The Siege\n",
      "Inferring vector for Invasive Procedures\n",
      "Inferring vector for Cardassians\n",
      "Inferring vector for Melora\n",
      "Inferring vector for Rules of Acquisition\n",
      "Inferring vector for Necessary Evil\n",
      "Inferring vector for Second Sight\n",
      "Inferring vector for Sanctuary\n",
      "Inferring vector for Rivals\n",
      "Inferring vector for The Alternate\n",
      "Inferring vector for Armageddon Game\n",
      "Inferring vector for Whispers\n",
      "Inferring vector for Paradise\n",
      "Inferring vector for Shadowplay\n",
      "Inferring vector for Playing God\n",
      "Inferring vector for Profit and Loss\n",
      "Inferring vector for Blood Oath\n",
      "Inferring vector for The Maquis, Part I\n",
      "Inferring vector for The Maquis, Part II\n",
      "Inferring vector for The Wire\n",
      "Inferring vector for Crossover\n",
      "Inferring vector for The Collaborator\n",
      "Inferring vector for Tribunal\n",
      "Inferring vector for The Jem'Hadar\n",
      "Inferring vector for The Search\n",
      "Inferring vector for The Search, Part II\n",
      "Inferring vector for The House of Quark\n",
      "Inferring vector for Equilibrium\n",
      "Inferring vector for Second Skin\n",
      "Inferring vector for The Abandoned\n",
      "Inferring vector for Civil Defense\n",
      "Inferring vector for Meridian\n",
      "Inferring vector for Defiant\n",
      "Inferring vector for Fascination\n",
      "Inferring vector for Past Tense, Part I\n",
      "Inferring vector for Past Tense, Part II\n",
      "Inferring vector for Heart of Stone\n",
      "Inferring vector for Destiny\n",
      "Inferring vector for Prophet Motive\n",
      "Inferring vector for Visionary\n",
      "Inferring vector for Improbable Cause\n",
      "Inferring vector for Through the Looking Glass\n",
      "Inferring vector for The Die is Cast\n",
      "Inferring vector for Family Business\n",
      "Inferring vector for Facets\n",
      "Inferring vector for The Way of the Warrior\n",
      "Inferring vector for Hippocratic oath\n",
      "Inferring vector for The Visitor\n",
      "Inferring vector for Indiscretion\n",
      "Inferring vector for Rejoined\n",
      "Inferring vector for Starship Down\n",
      "Inferring vector for Little Green Men\n",
      "Inferring vector for The Sword of Kahless\n",
      "Inferring vector for Our Man Bashir\n",
      "Inferring vector for Homefront\n",
      "Inferring vector for Paradise Lost\n",
      "Inferring vector for Crossfire\n",
      "Inferring vector for Return to Grace\n",
      "Inferring vector for The Sons of Mogh\n",
      "Inferring vector for The Bar Association\n",
      "Inferring vector for Accession\n",
      "Inferring vector for Rules of Engagement\n",
      "Inferring vector for Hard Time\n",
      "Inferring vector for Shattered Mirror\n",
      "Inferring vector for The Muse\n",
      "Inferring vector for For the Cause\n",
      "Inferring vector for The Quickening\n",
      "Inferring vector for To The Death\n",
      "Inferring vector for Body Parts\n",
      "Inferring vector for Broken Link\n",
      "Inferring vector for Apocalypse Rising\n",
      "Inferring vector for The Ship\n",
      "Inferring vector for Looking For par'Mach in All the Wrong Places\n",
      "Inferring vector for ... Nor the Battle to the Strong\n",
      "Inferring vector for Trials and Tribble-ations\n",
      "Inferring vector for The Assignment\n",
      "Inferring vector for Let He Who Is Without Sin ... \n",
      "Inferring vector for Things Past\n",
      "Inferring vector for The Ascent\n",
      "Inferring vector for Rapture\n",
      "Inferring vector for The Darkness and the Light\n",
      "Inferring vector for The Begotten\n",
      "Inferring vector for For the Uniform\n",
      "Inferring vector for In Purgatory's Shadow\n",
      "Inferring vector for By Inferno's Light\n",
      "Inferring vector for Doctor Bashir, I Presume?\n",
      "Inferring vector for A Simple Investigation\n",
      "Inferring vector for Business As Usual \n",
      "Inferring vector for Ties of Blood and Water\n",
      "Inferring vector for Ferengi Love Songs\n",
      "Inferring vector for Soldiers of the Empire\n",
      "Inferring vector for Children of Time\n",
      "Inferring vector for Blaze of Glory\n",
      "Inferring vector for Empok Nor\n",
      "Inferring vector for In the Cards\n",
      "Inferring vector for Call to Arms\n",
      "Inferring vector for A Time to Stand\n",
      "Inferring vector for Sons and Daughters\n",
      "Inferring vector for Rocks and Shoals\n",
      "Inferring vector for Behind the Lines\n",
      "Inferring vector for Favor the Bold\n",
      "Inferring vector for The Sacrifice of Angels\n",
      "Inferring vector for You Are Cordially Invited... \n",
      "Inferring vector for Resurrection\n",
      "Inferring vector for Statistical Probabilities\n",
      "Inferring vector for The Magnificent Ferengi\n",
      "Inferring vector for Waltz\n",
      "Inferring vector for Who Mourns for Morn?\n",
      "Inferring vector for One Little Ship\n",
      "Inferring vector for Far Beyond the Stars\n",
      "Inferring vector for Honor Among Thieves\n",
      "Inferring vector for Change of Heart\n",
      "Inferring vector for Wrongs Darker Than Death or Night\n",
      "Inferring vector for Inquisition\n",
      "Inferring vector for In the Pale Moonlight\n",
      "Inferring vector for His Way\n",
      "Inferring vector for The Reckoning\n",
      "Inferring vector for Valiant\n",
      "Inferring vector for Profit and Lace\n",
      "Inferring vector for Time's Orphan\n",
      "Inferring vector for The Sound of Her Voice\n",
      "Inferring vector for Tears of the Prophets\n",
      "Inferring vector for Image in the Sand\n",
      "Inferring vector for Shadows and Symbols\n",
      "Inferring vector for Afterimage\n",
      "Inferring vector for Take Me Out to the Holosuite\n",
      "Inferring vector for Chrysalis\n",
      "Inferring vector for Treachery, Faith, and the Great River\n",
      "Inferring vector for Once More Unto the Breach\n",
      "Inferring vector for The Siege of AR-558\n",
      "Inferring vector for Covenant\n",
      "Inferring vector for It's Only a Paper Moon\n",
      "Inferring vector for Prodigal Daughter\n",
      "Inferring vector for The Emperor's New Cloak\n",
      "Inferring vector for Field of Fire\n",
      "Inferring vector for Chimera\n",
      "Inferring vector for Inter Arma Enim Silent Leges\n",
      "Inferring vector for Badda-bing Badda-bang\n",
      "Inferring vector for Penumbra\n",
      "Inferring vector for 'Til Death Do Us Part\n",
      "Inferring vector for Strange Bedfellows\n",
      "Inferring vector for The Changing Face of Evil\n",
      "Inferring vector for When it Rains... \n",
      "Inferring vector for Tacking into the Wind\n",
      "Inferring vector for Extreme Measures\n",
      "Inferring vector for The Dogs of War\n",
      "Inferring vector for What You Leave Behind\n"
     ]
    }
   ],
   "source": [
    "all_sims = pd.DataFrame()\n",
    "doc_embeddings = []\n",
    "for i, row in df.iterrows():\n",
    "    print(f\"Inferring vector for {row['title']}\")\n",
    "    doc_id = i\n",
    "    inferred_vector = model.infer_vector(corpus[doc_id].words)\n",
    "    doc_embeddings.append(inferred_vector)\n",
    "    sims = model.dv.most_similar([inferred_vector], topn=len(corpus))\n",
    "    sims_df = pd.DataFrame(sims).sort_values(by=0).set_index(df['title']).drop(0, axis=1).rename({1: row['title']}, axis=1)\n",
    "    all_sims = pd.concat([all_sims, sims_df], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "567bd9a7",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "1185b9cd",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(166, 50)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "doc_embeddings = np.array(doc_embeddings)\n",
    "doc_embeddings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d39c82",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<font color='purple' size=4>For example here is the document vector for episode The Maquis, Part I and The Maquis, Part II.  As we saw in the Bag to Words similarity exercise, multi-part episodes provide a nice way of checking whether the vectors were able to embed anything useful for similarity discovery and the likes.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "cef4676a",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Maquis, Part I\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 3.6415138 , -3.8437922 , -0.41714263,  1.5910972 ,  1.6709527 ,\n",
       "        1.0584058 ,  2.9702353 ,  3.1939042 , -1.3095679 ,  4.5405774 ,\n",
       "        2.0560393 ,  6.089908  , -1.4159873 ,  0.1261762 ,  1.1800828 ,\n",
       "        5.2479186 ,  0.60582685,  3.9294567 ,  8.065656  ,  2.6397734 ,\n",
       "        5.740399  ,  1.5648685 , -2.0495224 ,  0.23161088,  0.4103946 ,\n",
       "        2.8416762 , -1.1840816 , -3.7173266 ,  4.107258  , -0.92860514,\n",
       "       -1.0511168 ,  5.3362026 ,  2.6723378 , -0.50343215,  4.7843204 ,\n",
       "        1.649263  , -4.5519705 ,  2.4291754 ,  5.8891225 , -0.7221585 ,\n",
       "        0.5584846 ,  0.44782704, -0.29252884, -4.8151217 ,  4.128568  ,\n",
       "       -1.2556384 ,  3.3353267 , -5.205019  , -2.2066112 , -0.8151218 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.loc[36]['title'])\n",
    "doc_embeddings[36]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "83ca6ef1",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Maquis, Part II\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-0.75446063, -6.487563  , -1.9830737 ,  2.3375628 , -1.4955459 ,\n",
       "        1.8747909 ,  4.659483  ,  3.2609243 , -2.1075304 ,  3.157266  ,\n",
       "        2.6526926 ,  4.0078373 , -0.71579707,  0.42623955,  2.2828476 ,\n",
       "        4.359351  ,  0.0204813 ,  5.6568217 ,  5.952047  ,  4.8780932 ,\n",
       "        1.9177431 ,  1.8387516 , -2.441538  ,  0.25699607,  1.1681447 ,\n",
       "        2.8455777 ,  0.80487555, -2.607233  ,  5.932713  , -0.3882755 ,\n",
       "       -0.38931736,  4.694014  ,  2.5158448 ,  0.96580535,  2.911487  ,\n",
       "        0.9680258 , -4.4358225 ,  3.3070576 ,  7.504463  , -2.7600331 ,\n",
       "        0.95957243,  1.5437496 , -1.9028528 , -5.1843133 ,  2.48476   ,\n",
       "       -1.508108  , -0.98949146, -4.591182  ,  0.32706228, -1.0453093 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.loc[37]['title'])\n",
    "doc_embeddings[37]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "073b4a02",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Dimensionality Reduction with TSNE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ce631c",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<font color='purple' size=4><font color='purple' size=4>We could of course resort back to calculating pairwise cosine similarity, but just for the heck of it, let’s try another approach.  Let’s see if we can squeeze our 50 dimensional script vectors into just 3 dimensions, since us humans can sadly enough only visualize things at most in 3 dimensions.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "382e20bf",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(166, 3)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "doc_embeddings_tsne = TSNE(n_components=3, learning_rate='auto',\n",
    "                           init='random', random_state=3, perplexity=5).fit_transform(doc_embeddings)\n",
    "doc_embeddings_tsne.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "254372b8",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dim1</th>\n",
       "      <th>dim2</th>\n",
       "      <th>dim3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Emissary</th>\n",
       "      <td>-39.517204</td>\n",
       "      <td>1.043823</td>\n",
       "      <td>3.656014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A Man Alone</th>\n",
       "      <td>-39.255020</td>\n",
       "      <td>-3.463726</td>\n",
       "      <td>-28.837351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Past Prologue</th>\n",
       "      <td>-16.594511</td>\n",
       "      <td>-40.031448</td>\n",
       "      <td>-26.758219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Babel</th>\n",
       "      <td>12.283007</td>\n",
       "      <td>7.270860</td>\n",
       "      <td>5.085349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Captive Pursuit</th>\n",
       "      <td>-45.492222</td>\n",
       "      <td>-14.266699</td>\n",
       "      <td>-24.288284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>When it Rains...</th>\n",
       "      <td>26.265753</td>\n",
       "      <td>-2.899107</td>\n",
       "      <td>-16.521763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tacking into the Wind</th>\n",
       "      <td>23.700840</td>\n",
       "      <td>-12.714571</td>\n",
       "      <td>-10.770403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extreme Measures</th>\n",
       "      <td>17.768978</td>\n",
       "      <td>42.111938</td>\n",
       "      <td>4.886300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Dogs of War</th>\n",
       "      <td>32.353924</td>\n",
       "      <td>-20.504509</td>\n",
       "      <td>23.069614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>What You Leave Behind</th>\n",
       "      <td>33.293732</td>\n",
       "      <td>-27.047802</td>\n",
       "      <td>14.557134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            dim1       dim2       dim3\n",
       "title                                                 \n",
       "Emissary              -39.517204   1.043823   3.656014\n",
       "A Man Alone           -39.255020  -3.463726 -28.837351\n",
       "Past Prologue         -16.594511 -40.031448 -26.758219\n",
       "Babel                  12.283007   7.270860   5.085349\n",
       "Captive Pursuit       -45.492222 -14.266699 -24.288284\n",
       "...                          ...        ...        ...\n",
       "When it Rains...       26.265753  -2.899107 -16.521763\n",
       "Tacking into the Wind  23.700840 -12.714571 -10.770403\n",
       "Extreme Measures       17.768978  42.111938   4.886300\n",
       "The Dogs of War        32.353924 -20.504509  23.069614\n",
       "What You Leave Behind  33.293732 -27.047802  14.557134\n",
       "\n",
       "[166 rows x 3 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_doc_embeddings_tsne = pd.DataFrame(doc_embeddings_tsne, columns=['dim1', 'dim2', 'dim3'], index=df['title'])\n",
    "df_doc_embeddings_tsne"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a7811c",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<font color='purple' size=4>The verbose bit of code, is my attempt to display all titles of multi-part episodes in a particular color so we can see if they appear close together in the vector space. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "c9a0e574",
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
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       "        fig.send_message('refresh', {});\n",
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       "\n",
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       "            // Full images could contain transparency (where diff images\n",
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       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
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       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
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       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
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       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib notebook\n",
    "\n",
    "sns.set(style = \"darkgrid\")\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10), dpi=80)\n",
    "ax = fig.add_subplot(111, projection = '3d')\n",
    "    \n",
    "for i, row in df_doc_embeddings_tsne.iterrows():\n",
    "    ax.scatter(row['dim1'], row['dim2'], row['dim3'], color='b')\n",
    "    if i in ['The Homecoming', 'The Circle', 'The Siege']:\n",
    "        ax.text(row['dim1'], row['dim2'], row['dim3'],  '%s' % (i), size=8, color='k')\n",
    "    elif i in ['The Maquis, Part I', 'The Maquis, Part II']:\n",
    "        ax.text(row['dim1'], row['dim2'], row['dim3'],  '%s' % (i), size=8, color='b')\n",
    "    elif i in ['The Search', 'The Search, Part II']:\n",
    "        ax.text(row['dim1'], row['dim2'], row['dim3'],  '%s' % (i), size=8, color='r')\n",
    "    elif i in ['Past Tense, Part I', 'Past Tense, Part II']:\n",
    "        ax.text(row['dim1'], row['dim2'], row['dim3'],  '%s' % (i), size=8, color='g')             ,\n",
    "    elif i in ['Improbable Cause', 'The Die is Cast']:\n",
    "        ax.text(row['dim1'], row['dim2'], row['dim3'],  '%s' % (i), size=8, color='grey')               ,\n",
    "    elif i in ['Homefront', 'Paradise Lost']:\n",
    "        ax.text(row['dim1'], row['dim2'], row['dim3'],  '%s' % (i), size=8, color='orange')         ,\n",
    "    elif i in [\"In Purgatory's Shadow\", \"By Inferno's Light\"]:\n",
    "        ax.text(row['dim1'], row['dim2'], row['dim3'],  '%s' % (i), size=8, color='c')                ,\n",
    "    elif i in [\"Favor the Bold\", \"The Sacrifice of Angels\"]:\n",
    "        ax.text(row['dim1'], row['dim2'], row['dim3'],  '%s' % (i), size=8, color='m')          \n",
    "    elif i in [\"Image in the Sand\", \"Shadows and Symbols\"]:\n",
    "        ax.text(row['dim1'], row['dim2'], row['dim3'],  '%s' % (i), size=8, color='plum')   \n",
    "\n",
    "plt.title('Multi-part episodes Deep Space Nine - Doc2Vec reduced to 3 dimensions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c18e298c",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<font color='purple' size=4>Remember The Homecoming, The Circle and The Siege?  As you can see they are nicely clustered together.  But I am not cherry-picking as is often the case.  Homefront and Paradise Lost are quite far apart.  Of course we are trying to capture as much as is possible into 50 dimensions first when creating the document vectors and then we are squeezing this again into 3.  To be honest I am surprised to see anything meaningful at all at the end of this.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "193dcd20",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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