notebooks/natural_language_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Natural Language Preprocessing and Word Vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we clean up a dataset of natural language data (books from Project Gutenberg) and use word2vec to embed the language in word vectors.\n",
    "\n",
    "**N.B.:** Some, all or none of these preprocessing steps may be helpful to a given downstream application. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/the-deep-learners/deep-learning-illustrated/blob/master/notebooks/natural_language_preprocessing.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package gutenberg to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package gutenberg is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk import word_tokenize, sent_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import *\n",
    "nltk.download('gutenberg')\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "\n",
    "import string\n",
    "\n",
    "import gensim\n",
    "from gensim.models.phrases import Phraser, Phrases\n",
    "from gensim.models.word2vec import Word2Vec\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "import pandas as pd\n",
    "from bokeh.io import output_notebook, output_file\n",
    "from bokeh.plotting import show, figure\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import gutenberg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gutenberg.fileids())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['austen-emma.txt',\n",
       " 'austen-persuasion.txt',\n",
       " 'austen-sense.txt',\n",
       " 'bible-kjv.txt',\n",
       " 'blake-poems.txt',\n",
       " 'bryant-stories.txt',\n",
       " 'burgess-busterbrown.txt',\n",
       " 'carroll-alice.txt',\n",
       " 'chesterton-ball.txt',\n",
       " 'chesterton-brown.txt',\n",
       " 'chesterton-thursday.txt',\n",
       " 'edgeworth-parents.txt',\n",
       " 'melville-moby_dick.txt',\n",
       " 'milton-paradise.txt',\n",
       " 'shakespeare-caesar.txt',\n",
       " 'shakespeare-hamlet.txt',\n",
       " 'shakespeare-macbeth.txt',\n",
       " 'whitman-leaves.txt']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gutenberg.fileids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2621613"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gutenberg.words())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gberg_sent_tokens = sent_tokenize(gutenberg.raw())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[Emma by Jane Austen 1816]\\n\\nVOLUME I\\n\\nCHAPTER I\\n\\n\\nEmma Woodhouse, handsome, clever, and rich, with a comfortable home\\nand happy disposition, seemed to unite some of the best blessings\\nof existence; and had lived nearly twenty-one years in the world\\nwith very little to distress or vex her.',\n",
       " \"She was the youngest of the two daughters of a most affectionate,\\nindulgent father; and had, in consequence of her sister's marriage,\\nbeen mistress of his house from a very early period.\",\n",
       " 'Her mother\\nhad died too long ago for her to have more than an indistinct\\nremembrance of her caresses; and her place had been supplied\\nby an excellent woman as governess, who had fallen little short\\nof a mother in affection.',\n",
       " \"Sixteen years had Miss Taylor been in Mr. Woodhouse's family,\\nless as a governess than a friend, very fond of both daughters,\\nbut particularly of Emma.\",\n",
       " 'Between _them_ it was more the intimacy\\nof sisters.',\n",
       " \"Even before Miss Taylor had ceased to hold the nominal\\noffice of governess, the mildness of her temper had hardly allowed\\nher to impose any restraint; and the shadow of authority being\\nnow long passed away, they had been living together as friend and\\nfriend very mutually attached, and Emma doing just what she liked;\\nhighly esteeming Miss Taylor's judgment, but directed chiefly by\\nher own.\"]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gberg_sent_tokens[0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"She was the youngest of the two daughters of a most affectionate,\\nindulgent father; and had, in consequence of her sister's marriage,\\nbeen mistress of his house from a very early period.\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gberg_sent_tokens[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['She',\n",
       " 'was',\n",
       " 'the',\n",
       " 'youngest',\n",
       " 'of',\n",
       " 'the',\n",
       " 'two',\n",
       " 'daughters',\n",
       " 'of',\n",
       " 'a',\n",
       " 'most',\n",
       " 'affectionate',\n",
       " ',',\n",
       " 'indulgent',\n",
       " 'father',\n",
       " ';',\n",
       " 'and',\n",
       " 'had',\n",
       " ',',\n",
       " 'in',\n",
       " 'consequence',\n",
       " 'of',\n",
       " 'her',\n",
       " 'sister',\n",
       " \"'s\",\n",
       " 'marriage',\n",
       " ',',\n",
       " 'been',\n",
       " 'mistress',\n",
       " 'of',\n",
       " 'his',\n",
       " 'house',\n",
       " 'from',\n",
       " 'a',\n",
       " 'very',\n",
       " 'early',\n",
       " 'period',\n",
       " '.']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_tokenize(gberg_sent_tokens[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'father'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_tokenize(gberg_sent_tokens[1])[14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a convenient method that handles newlines, as well as tokenizing sentences and words in one shot\n",
    "gberg_sents = gutenberg.sents()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ']'],\n",
       " ['VOLUME', 'I'],\n",
       " ['CHAPTER', 'I'],\n",
       " ['Emma',\n",
       "  'Woodhouse',\n",
       "  ',',\n",
       "  'handsome',\n",
       "  ',',\n",
       "  'clever',\n",
       "  ',',\n",
       "  'and',\n",
       "  'rich',\n",
       "  ',',\n",
       "  'with',\n",
       "  'a',\n",
       "  'comfortable',\n",
       "  'home',\n",
       "  'and',\n",
       "  'happy',\n",
       "  'disposition',\n",
       "  ',',\n",
       "  'seemed',\n",
       "  'to',\n",
       "  'unite',\n",
       "  'some',\n",
       "  'of',\n",
       "  'the',\n",
       "  'best',\n",
       "  'blessings',\n",
       "  'of',\n",
       "  'existence',\n",
       "  ';',\n",
       "  'and',\n",
       "  'had',\n",
       "  'lived',\n",
       "  'nearly',\n",
       "  'twenty',\n",
       "  '-',\n",
       "  'one',\n",
       "  'years',\n",
       "  'in',\n",
       "  'the',\n",
       "  'world',\n",
       "  'with',\n",
       "  'very',\n",
       "  'little',\n",
       "  'to',\n",
       "  'distress',\n",
       "  'or',\n",
       "  'vex',\n",
       "  'her',\n",
       "  '.'],\n",
       " ['She',\n",
       "  'was',\n",
       "  'the',\n",
       "  'youngest',\n",
       "  'of',\n",
       "  'the',\n",
       "  'two',\n",
       "  'daughters',\n",
       "  'of',\n",
       "  'a',\n",
       "  'most',\n",
       "  'affectionate',\n",
       "  ',',\n",
       "  'indulgent',\n",
       "  'father',\n",
       "  ';',\n",
       "  'and',\n",
       "  'had',\n",
       "  ',',\n",
       "  'in',\n",
       "  'consequence',\n",
       "  'of',\n",
       "  'her',\n",
       "  'sister',\n",
       "  \"'\",\n",
       "  's',\n",
       "  'marriage',\n",
       "  ',',\n",
       "  'been',\n",
       "  'mistress',\n",
       "  'of',\n",
       "  'his',\n",
       "  'house',\n",
       "  'from',\n",
       "  'a',\n",
       "  'very',\n",
       "  'early',\n",
       "  'period',\n",
       "  '.'],\n",
       " ['Her',\n",
       "  'mother',\n",
       "  'had',\n",
       "  'died',\n",
       "  'too',\n",
       "  'long',\n",
       "  'ago',\n",
       "  'for',\n",
       "  'her',\n",
       "  'to',\n",
       "  'have',\n",
       "  'more',\n",
       "  'than',\n",
       "  'an',\n",
       "  'indistinct',\n",
       "  'remembrance',\n",
       "  'of',\n",
       "  'her',\n",
       "  'caresses',\n",
       "  ';',\n",
       "  'and',\n",
       "  'her',\n",
       "  'place',\n",
       "  'had',\n",
       "  'been',\n",
       "  'supplied',\n",
       "  'by',\n",
       "  'an',\n",
       "  'excellent',\n",
       "  'woman',\n",
       "  'as',\n",
       "  'governess',\n",
       "  ',',\n",
       "  'who',\n",
       "  'had',\n",
       "  'fallen',\n",
       "  'little',\n",
       "  'short',\n",
       "  'of',\n",
       "  'a',\n",
       "  'mother',\n",
       "  'in',\n",
       "  'affection',\n",
       "  '.']]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gberg_sents[0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'father'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gberg_sents[4][14]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Iteratively preprocess a sentence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### a tokenized sentence: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['She',\n",
       " 'was',\n",
       " 'the',\n",
       " 'youngest',\n",
       " 'of',\n",
       " 'the',\n",
       " 'two',\n",
       " 'daughters',\n",
       " 'of',\n",
       " 'a',\n",
       " 'most',\n",
       " 'affectionate',\n",
       " ',',\n",
       " 'indulgent',\n",
       " 'father',\n",
       " ';',\n",
       " 'and',\n",
       " 'had',\n",
       " ',',\n",
       " 'in',\n",
       " 'consequence',\n",
       " 'of',\n",
       " 'her',\n",
       " 'sister',\n",
       " \"'\",\n",
       " 's',\n",
       " 'marriage',\n",
       " ',',\n",
       " 'been',\n",
       " 'mistress',\n",
       " 'of',\n",
       " 'his',\n",
       " 'house',\n",
       " 'from',\n",
       " 'a',\n",
       " 'very',\n",
       " 'early',\n",
       " 'period',\n",
       " '.']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gberg_sents[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### to lowercase: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['she',\n",
       " 'was',\n",
       " 'the',\n",
       " 'youngest',\n",
       " 'of',\n",
       " 'the',\n",
       " 'two',\n",
       " 'daughters',\n",
       " 'of',\n",
       " 'a',\n",
       " 'most',\n",
       " 'affectionate',\n",
       " ',',\n",
       " 'indulgent',\n",
       " 'father',\n",
       " ';',\n",
       " 'and',\n",
       " 'had',\n",
       " ',',\n",
       " 'in',\n",
       " 'consequence',\n",
       " 'of',\n",
       " 'her',\n",
       " 'sister',\n",
       " \"'\",\n",
       " 's',\n",
       " 'marriage',\n",
       " ',',\n",
       " 'been',\n",
       " 'mistress',\n",
       " 'of',\n",
       " 'his',\n",
       " 'house',\n",
       " 'from',\n",
       " 'a',\n",
       " 'very',\n",
       " 'early',\n",
       " 'period',\n",
       " '.']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[w.lower() for w in gberg_sents[4]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### remove stopwords and punctuation: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "stpwrds = stopwords.words('english') + list(string.punctuation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " \"you'll\",\n",
       " \"you'd\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'her',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'they',\n",
       " 'them',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'themselves',\n",
       " 'what',\n",
       " 'which',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'this',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'these',\n",
       " 'those',\n",
       " 'am',\n",
       " 'is',\n",
       " 'are',\n",
       " 'was',\n",
       " 'were',\n",
       " 'be',\n",
       " 'been',\n",
       " 'being',\n",
       " 'have',\n",
       " 'has',\n",
       " 'had',\n",
       " 'having',\n",
       " 'do',\n",
       " 'does',\n",
       " 'did',\n",
       " 'doing',\n",
       " 'a',\n",
       " 'an',\n",
       " 'the',\n",
       " 'and',\n",
       " 'but',\n",
       " 'if',\n",
       " 'or',\n",
       " 'because',\n",
       " 'as',\n",
       " 'until',\n",
       " 'while',\n",
       " 'of',\n",
       " 'at',\n",
       " 'by',\n",
       " 'for',\n",
       " 'with',\n",
       " 'about',\n",
       " 'against',\n",
       " 'between',\n",
       " 'into',\n",
       " 'through',\n",
       " 'during',\n",
       " 'before',\n",
       " 'after',\n",
       " 'above',\n",
       " 'below',\n",
       " 'to',\n",
       " 'from',\n",
       " 'up',\n",
       " 'down',\n",
       " 'in',\n",
       " 'out',\n",
       " 'on',\n",
       " 'off',\n",
       " 'over',\n",
       " 'under',\n",
       " 'again',\n",
       " 'further',\n",
       " 'then',\n",
       " 'once',\n",
       " 'here',\n",
       " 'there',\n",
       " 'when',\n",
       " 'where',\n",
       " 'why',\n",
       " 'how',\n",
       " 'all',\n",
       " 'any',\n",
       " 'both',\n",
       " 'each',\n",
       " 'few',\n",
       " 'more',\n",
       " 'most',\n",
       " 'other',\n",
       " 'some',\n",
       " 'such',\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'only',\n",
       " 'own',\n",
       " 'same',\n",
       " 'so',\n",
       " 'than',\n",
       " 'too',\n",
       " 'very',\n",
       " 's',\n",
       " 't',\n",
       " 'can',\n",
       " 'will',\n",
       " 'just',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'now',\n",
       " 'd',\n",
       " 'll',\n",
       " 'm',\n",
       " 'o',\n",
       " 're',\n",
       " 've',\n",
       " 'y',\n",
       " 'ain',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'ma',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\",\n",
       " '!',\n",
       " '\"',\n",
       " '#',\n",
       " '$',\n",
       " '%',\n",
       " '&',\n",
       " \"'\",\n",
       " '(',\n",
       " ')',\n",
       " '*',\n",
       " '+',\n",
       " ',',\n",
       " '-',\n",
       " '.',\n",
       " '/',\n",
       " ':',\n",
       " ';',\n",
       " '<',\n",
       " '=',\n",
       " '>',\n",
       " '?',\n",
       " '@',\n",
       " '[',\n",
       " '\\\\',\n",
       " ']',\n",
       " '^',\n",
       " '_',\n",
       " '`',\n",
       " '{',\n",
       " '|',\n",
       " '}',\n",
       " '~']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stpwrds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['youngest',\n",
       " 'two',\n",
       " 'daughters',\n",
       " 'affectionate',\n",
       " 'indulgent',\n",
       " 'father',\n",
       " 'consequence',\n",
       " 'sister',\n",
       " 'marriage',\n",
       " 'mistress',\n",
       " 'house',\n",
       " 'early',\n",
       " 'period']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[w.lower() for w in gberg_sents[4] if w.lower() not in stpwrds]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### stem words: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['youngest',\n",
       " 'two',\n",
       " 'daughter',\n",
       " 'affection',\n",
       " 'indulg',\n",
       " 'father',\n",
       " 'consequ',\n",
       " 'sister',\n",
       " 'marriag',\n",
       " 'mistress',\n",
       " 'hous',\n",
       " 'earli',\n",
       " 'period']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[stemmer.stem(w.lower()) for w in gberg_sents[4] \n",
    " if w.lower() not in stpwrds]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### handle bigram collocations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "phrases = Phrases(gberg_sents) # train detector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigram = Phraser(phrases) # create a more efficient Phraser object for transforming sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(b'two', b'daughters'): (19, 11.966813731181546),\n",
       " (b'her', b'sister'): (195, 17.7960829227865),\n",
       " (b\"'\", b's'): (9781, 31.066242737744524),\n",
       " (b'very', b'early'): (24, 11.01214147275924),\n",
       " (b'Her', b'mother'): (14, 13.529425062715127),\n",
       " (b'long', b'ago'): (38, 63.22343628984788),\n",
       " (b'more', b'than'): (541, 29.023584433996874),\n",
       " (b'had', b'been'): (1256, 22.306024648925288),\n",
       " (b'an', b'excellent'): (54, 39.063874851750626),\n",
       " (b'Miss', b'Taylor'): (48, 453.75918026073305),\n",
       " (b'very', b'fond'): (28, 24.134280468850747),\n",
       " (b'passed', b'away'): (25, 12.35053642325912),\n",
       " (b'too', b'much'): (173, 31.376002029426687),\n",
       " (b'did', b'not'): (935, 11.728416217142811),\n",
       " (b'any', b'means'): (27, 14.096964108090186),\n",
       " (b'wedding', b'-'): (15, 17.4695197740113),\n",
       " (b'Her', b'father'): (18, 13.129571562488772),\n",
       " (b'after', b'dinner'): (21, 21.5285481168817),\n",
       " (b'self', b'-'): (124, 47.79018053120332),\n",
       " (b'sixteen', b'years'): (12, 107.0461671612265),\n",
       " (b'five', b'years'): (42, 40.128755673408115),\n",
       " (b'years', b'old'): (176, 54.735425236061104),\n",
       " (b'seven', b'years'): (51, 52.59411150244507),\n",
       " (b'each', b'other'): (236, 79.4168405322873),\n",
       " (b'a', b'mile'): (48, 12.783091600264584),\n",
       " (b'must', b'be'): (601, 10.229989650632808),\n",
       " (b'difference', b'between'): (44, 220.5253730524468),\n",
       " (b'could', b'not'): (1049, 10.870983286982371),\n",
       " (b'having', b'been'): (49, 11.538018331569376),\n",
       " (b'miles', b'off'): (16, 34.7868137375225),\n",
       " (b'at', b'Hartfield'): (66, 27.282227059708397),\n",
       " (b'her', b'husband'): (158, 27.544395195049724),\n",
       " (b'in', b'spite'): (96, 13.441914963088442),\n",
       " (b'Emma', b'could'): (61, 11.335111257744053),\n",
       " (b'every', b'body'): (127, 36.972582976825784),\n",
       " (b'no', b'means'): (80, 32.57361823161739),\n",
       " (b'his', b'own'): (773, 10.402387689390995),\n",
       " (b'obliged', b'to'): (179, 10.43662879984823),\n",
       " (b'able', b'to'): (348, 11.446828804673226),\n",
       " (b'very', b'much'): (234, 16.21027499400206),\n",
       " (b'have', b'been'): (986, 17.981191047801964),\n",
       " (b'great', b'deal'): (181, 118.04013764577145),\n",
       " (b'\"', b'Poor'): (30, 10.125586409383336),\n",
       " (b'agree', b'with'): (25, 13.611743912451109),\n",
       " (b'-', b'humoured'): (22, 33.94078127522195),\n",
       " (b'for', b'ever'): (555, 12.4761138144463),\n",
       " (b'This', b'is'): (353, 11.381028160111825),\n",
       " (b'three', b'times'): (36, 35.42578086743132),\n",
       " (b'my', b'dear'): (253, 24.47894249615343),\n",
       " (b'How', b'often'): (12, 12.377968718725876),\n",
       " (b'My', b'dear'): (85, 84.80698289930197),\n",
       " (b'so', b'far'): (98, 10.161632478973766),\n",
       " (b'\"', b'No'): (351, 15.063706270011586),\n",
       " (b'We', b'must'): (68, 18.765646900386386),\n",
       " (b'last', b'night'): (63, 23.592598950882213),\n",
       " (b'doubt', b'whether'): (12, 22.924130736398396),\n",
       " (b'anywhere', b'else'): (6, 16.100101533414907),\n",
       " (b'I', b'am'): (2428, 16.951297329049222),\n",
       " (b'very', b'glad'): (46, 18.28434074650975),\n",
       " (b'am', b'sure'): (282, 65.1446020744797),\n",
       " (b'very', b'pretty'): (39, 20.068178868120455),\n",
       " (b'be', b'able'): (121, 11.347612277348256),\n",
       " (b'immediately', b'afterwards'): (10, 41.06053966483414),\n",
       " (b'sensible', b'man'): (17, 14.541388094211703),\n",
       " (b'intimate', b'friend'): (6, 21.898760623229464),\n",
       " (b'connected', b'with'): (31, 18.375854281808998),\n",
       " (b'than', b'usual'): (30, 28.951968704471486),\n",
       " (b'Brunswick', b'Square'): (11, 10881.307917888562),\n",
       " (b'some', b'time'): (146, 12.926553753557874),\n",
       " (b'poor', b'Isabella'): (10, 41.30241100647832),\n",
       " (b'It', b'is'): (777, 11.705870174225897),\n",
       " (b'am', b'afraid'): (65, 25.627391867391864),\n",
       " (b'moonlight', b'night'): (6, 14.745374344301382),\n",
       " (b'Look', b'at'): (33, 13.630464729284446),\n",
       " (b'\"', b'Well'): (311, 21.191330893669),\n",
       " (b'vast', b'deal'): (11, 61.90400400400401),\n",
       " (b'an', b'hour'): (150, 41.75757187695002),\n",
       " (b'pretty', b'well'): (20, 17.716415202444615),\n",
       " (b'tolerably', b'well'): (7, 18.357580705009276),\n",
       " (b'\"', b'Ah'): (83, 17.279509006244886),\n",
       " (b'Ah', b'!'): (68, 37.53295698121932),\n",
       " (b\"'\", b'Tis'): (64, 23.239343942788935),\n",
       " (b'Miss', b'Woodhouse'): (173, 294.52709701744294),\n",
       " (b'you', b'please'): (93, 13.035980721875688),\n",
       " (b'any', b'rate'): (47, 83.92034351736973),\n",
       " (b',\"', b'said'): (2583, 36.03254133384804),\n",
       " (b'My', b'dearest'): (7, 26.665272507761294),\n",
       " (b'so', b'much'): (483, 20.56443776042493),\n",
       " (b'much', b'less'): (38, 19.104435569934505),\n",
       " (b'any', b'body'): (93, 21.716438732250484),\n",
       " (b'has', b'been'): (263, 29.2606767165493),\n",
       " (b'been', b'used'): (29, 14.09410182779525),\n",
       " (b'Well', b',\"'): (60, 12.493546273257474),\n",
       " (b'tell', b'you'): (296, 11.612165547868624),\n",
       " (b'Every', b'body'): (21, 72.2001079929367),\n",
       " (b'\"', b'Dear'): (39, 20.048661090579007),\n",
       " (b'every', b'thing'): (240, 27.277168506679985),\n",
       " (b'very', b'sorry'): (32, 20.255731941654293),\n",
       " (b'turned', b'away'): (50, 19.3446247292484),\n",
       " (b'divided', b'between'): (10, 35.82806127178346),\n",
       " (b'knows', b'how'): (13, 14.800957338598696),\n",
       " (b'how', b'much'): (110, 15.417663894373641),\n",
       " (b'four', b'years'): (21, 16.257604884476734),\n",
       " (b'years', b'ago'): (56, 163.33147420261938),\n",
       " (b'any', b'thing'): (383, 35.72804044966351),\n",
       " (b'need', b'not'): (107, 13.478832663938945),\n",
       " (b'his', b'wife'): (263, 10.870850757012368),\n",
       " (b'Ever', b'since'): (8, 99.63818474758324),\n",
       " (b'leave', b'off'): (18, 10.507247077643713),\n",
       " (b'you', b'mean'): (142, 10.573995913362895),\n",
       " (b'young', b'lady'): (73, 113.30511794581632),\n",
       " (b'depend', b'upon'): (28, 66.33685452578166),\n",
       " (b'quarrel', b'with'): (21, 10.691406127597961),\n",
       " (b'-', b'hearted'): (45, 49.037248488452775),\n",
       " (b'their', b'own'): (279, 10.164510720981893),\n",
       " (b'You', b'are'): (231, 12.60019671587268),\n",
       " (b'more', b'likely'): (16, 11.176882987148272),\n",
       " (b'have', b'done'): (272, 12.664105519891397),\n",
       " (b',\"', b'rejoined'): (6, 11.956633540853023),\n",
       " (b'any', b'longer'): (32, 16.396201569570135),\n",
       " (b'very', b'well'): (171, 13.844437162007257),\n",
       " (b'young', b'man'): (260, 25.863812524396362),\n",
       " (b'dine', b'with'): (22, 13.88397879070013),\n",
       " (b'much', b'better'): (38, 10.763384154831522),\n",
       " (b'I', b'dare'): (138, 13.676468274997303),\n",
       " (b'dare', b'say'): (114, 128.21086697682205),\n",
       " (b'Depend', b'upon'): (17, 92.29475412282665),\n",
       " (b'take', b'care'): (59, 72.93974751004718),\n",
       " (b'CHAPTER', b'II'): (11, 335.5512750949539),\n",
       " (b'entering', b'into'): (14, 16.437457915441044),\n",
       " (b'never', b'seen'): (42, 14.015206798866856),\n",
       " (b'refrain', b'from'): (9, 12.438010669696954),\n",
       " (b'at', b'once'): (263, 21.418172245725966),\n",
       " (b'three', b'years'): (77, 37.3552600780725),\n",
       " (b'any', b'other'): (138, 10.208401968552408),\n",
       " (b'twenty', b'years'): (69, 85.29044131115464),\n",
       " (b'an', b'easy'): (18, 10.427467282325322),\n",
       " (b'according', b'to'): (747, 12.093327441482012),\n",
       " (b'had', b'begun'): (25, 12.032976708302648),\n",
       " (b'passed', b'through'): (43, 31.462199837199837),\n",
       " (b'its', b'being'): (58, 16.06447342496047),\n",
       " (b'deal', b'better'): (14, 19.992919953446272),\n",
       " (b'fine', b'young'): (13, 10.403137320870965),\n",
       " (b'belonging', b'to'): (35, 10.512393800210106),\n",
       " (b'Frank', b'Churchill'): (151, 1750.6779772753712),\n",
       " (b'Miss', b'Bates'): (113, 400.4260773639656),\n",
       " (b'a', b'few'): (404, 11.554600796586188),\n",
       " (b'few', b'days'): (53, 35.91529644067919),\n",
       " (b'I', b'suppose'): (210, 12.338158409520457),\n",
       " (b'very', b'handsome'): (21, 19.759437654764756),\n",
       " (b'an', b'irresistible'): (7, 11.369078040261053),\n",
       " (b'good', b'sense'): (28, 17.373370904818092),\n",
       " (b'had', b'already'): (64, 11.990720435581183),\n",
       " (b'She', b'felt'): (26, 13.338332744482605),\n",
       " (b'most', b'fortunate'): (6, 11.471572464709046),\n",
       " (b'long', b'enough'): (38, 15.189529976554649),\n",
       " (b'know', b'how'): (120, 12.782869530582152),\n",
       " (b'dear', b'Emma'): (31, 28.38977406756079),\n",
       " (b'at', b'Randalls'): (39, 27.033755046414154),\n",
       " (b'few', b'weeks'): (19, 134.46858012611438),\n",
       " (b'no', b'longer'): (113, 44.45340841020726),\n",
       " (b'CHAPTER', b'III'): (10, 354.1930126002291),\n",
       " (b'Donwell', b'Abbey'): (9, 753.4827901309776),\n",
       " (b'card', b'-'): (18, 15.66232807325151),\n",
       " (b'drawing', b'-'): (53, 20.08471734497107),\n",
       " (b'-', b'room'): (116, 10.86339885106585),\n",
       " (b'thrown', b'away'): (11, 14.820643707910945),\n",
       " (b'After', b'these'): (18, 11.091988134657838),\n",
       " (b'an', b'invitation'): (11, 10.459551797040168),\n",
       " (b'old', b'lady'): (16, 10.886003613395488),\n",
       " (b'those', b'who'): (150, 15.975643084740117),\n",
       " (b'as', b'possible'): (81, 11.70949877112537),\n",
       " (b'young', b'ladies'): (44, 113.63480411788262),\n",
       " (b'-', b'fashioned'): (31, 34.9390395480226),\n",
       " (b'Goddard', b\"'\"): (34, 15.29483969345862),\n",
       " (b'found', b'herself'): (27, 11.22605649143189),\n",
       " (b's', b'sake'): (142, 28.092000957580005),\n",
       " (b'much', b'pleased'): (18, 13.276184200965723),\n",
       " (b'be', b'allowed'): (32, 10.133274789777253),\n",
       " (b'Miss', b'Smith'): (58, 165.24316871017183),\n",
       " (b'Harriet', b'Smith'): (31, 180.54871092346391),\n",
       " (b'several', b'years'): (10, 17.577367349955093),\n",
       " (b'pretty', b'girl'): (10, 40.4556337659619),\n",
       " (b'blue', b'eyes'): (28, 35.59533676681832),\n",
       " (b'They', b'were'): (188, 10.653371553453791),\n",
       " (b'due', b'time'): (18, 21.04160963161683),\n",
       " (b'its', b'own'): (54, 10.834370434674414),\n",
       " (b'better', b'than'): (170, 42.51368036077655),\n",
       " (b'body', b'else'): (31, 39.469837225343845),\n",
       " (b'apple', b'-'): (26, 28.21999348109518),\n",
       " (b'You', b'need'): (16, 14.652632145780382),\n",
       " (b'half', b'-'): (179, 14.007817698976801),\n",
       " (b'much', b'more'): (159, 10.555945043417811),\n",
       " (b'little', b'girl'): (50, 35.05677573772557),\n",
       " (b'at', b'last'): (420, 22.75660921756972),\n",
       " (b'CHAPTER', b'IV'): (8, 335.5512750949539),\n",
       " (b'every', b'respect'): (14, 12.224980231945178),\n",
       " (b'guided', b'by'): (14, 23.954538020555372),\n",
       " (b'different', b'sort'): (8, 14.490845895493244),\n",
       " (b'-', b'Mill'): (7, 12.705105290190035),\n",
       " (b'good', b'deal'): (62, 39.176849314155014),\n",
       " (b'very', b'happy'): (43, 11.360756752157577),\n",
       " (b'drink', b'tea'): (7, 32.50399453379586),\n",
       " (b'large', b'enough'): (11, 10.829067985816224),\n",
       " (b'had', b'taken'): (121, 10.962547054002629),\n",
       " (b'doing', b'something'): (9, 10.716846747710356),\n",
       " (b'three', b'miles'): (9, 16.651749532156657),\n",
       " (b'thing', b'else'): (26, 12.21019907747794),\n",
       " (b'very', b'obliging'): (14, 25.349278570257418),\n",
       " (b'on', b'purpose'): (35, 10.833361556419508),\n",
       " (b'very', b'clever'): (15, 21.69532850607617),\n",
       " (b'\"', b'You'): (493, 11.717061811021022),\n",
       " (b'know', b'what'): (219, 10.687860772182088),\n",
       " (b'Miss', b'Nash'): (13, 337.68125042659204),\n",
       " (b'does', b'not'): (211, 13.23025142257054),\n",
       " (b'\"', b'Oh'): (496, 20.296685763864517),\n",
       " (b'Oh', b'yes'): (11, 23.468003288849534),\n",
       " (b'very', b'entertaining'): (7, 16.054543094496367),\n",
       " (b'soon', b'as'): (271, 12.01164260504438),\n",
       " (b'Oh', b'!'): (285, 31.12698837846826),\n",
       " (b'have', b'seen'): (204, 13.438796505596505),\n",
       " (b'on', b'horseback'): (21, 54.889031885858834),\n",
       " (b'their', b'families'): (95, 35.26311487873489),\n",
       " (b'no', b'doubt'): (117, 40.18959620751882),\n",
       " (b'very', b'respectable'): (9, 10.88443599626872),\n",
       " (b'respectable', b'young'): (8, 27.70496528037034),\n",
       " (b'very', b'odd'): (16, 18.206182890665982),\n",
       " (b'perfectly', b'right'): (12, 16.998927982407917),\n",
       " (b'years', b'hence'): (9, 17.990952464071682),\n",
       " (b'young', b'woman'): (57, 30.4001550358284),\n",
       " (b'very', b'desirable'): (9, 14.595039176814876),\n",
       " (b'Dear', b'Miss'): (9, 32.278354820188945),\n",
       " (b'thirty', b'years'): (35, 72.53269372866845),\n",
       " (b'can', b'afford'): (11, 26.391592873146273),\n",
       " (b'good', b'luck'): (21, 51.57752734020704),\n",
       " (b'acquainted', b'with'): (88, 27.73083464345721),\n",
       " (b'your', b'own'): (181, 10.134692346685467),\n",
       " (b'\"', b'Yes'): (349, 27.046036922192854),\n",
       " (b'next', b'day'): (100, 33.66839067944251),\n",
       " (b'an', b'opportunity'): (34, 39.49570340028189),\n",
       " (b'few', b'yards'): (15, 127.20000822740548),\n",
       " (b'Robert', b'Martin'): (31, 1963.7208109428432),\n",
       " (b'few', b'minutes'): (86, 316.36383789006993),\n",
       " (b'Only', b'think'): (9, 11.416616668358916),\n",
       " (b'been', b'able'): (40, 15.91768079235648),\n",
       " (b'-', b'morrow'): (134, 31.191253298926746),\n",
       " (b'should', b'happen'): (13, 20.434212265397832),\n",
       " (b'Do', b'you'): (187, 17.542972188763283),\n",
       " (b'compared', b'with'): (25, 15.313211901507499),\n",
       " (b'\"', b'Certainly'): (39, 25.246462114062453),\n",
       " (b'You', b'must'): (84, 12.864839977038008),\n",
       " (b'an', b'old'): (158, 10.467761806300013),\n",
       " (b'old', b'man'): (201, 11.807109991026596),\n",
       " (b'more', b'valuable'): (10, 17.665941085058734),\n",
       " (b',\"', b'replied'): (256, 68.6325679964557),\n",
       " (b'very', b'bad'): (36, 15.601593602802112),\n",
       " (b'deal', b'too'): (15, 12.720000822740548),\n",
       " (b'no', b'more'): (553, 17.350760547573774),\n",
       " (b'very', b'agreeable'): (21, 21.406057459328487),\n",
       " (b'fixed', b'on'): (31, 10.722857321964225),\n",
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       " (b'Kellynch', b'Hall'): (24, 4945.285774410774),\n",
       " (b'arising', b'from'): (7, 10.216937335822498),\n",
       " (b'Charles', b'Musgrove'): (14, 248.91721824686942),\n",
       " (b'first', b'year'): (71, 36.52536994480359),\n",
       " (b'Lady', b'Elliot'): (12, 34.95596737726098),\n",
       " (b'seventeen', b'years'): (7, 50.974365314869765),\n",
       " (b'an', b'awful'): (13, 15.611271338865924),\n",
       " (b'Lady', b'Russell'): (147, 1370.6224754175123),\n",
       " (b'Anne', b'Elliot'): (23, 69.51674910071942),\n",
       " (b'Miss', b'Elliot'): (48, 81.92874088041015),\n",
       " (b'everybody', b'else'): (20, 116.64359274208759),\n",
       " (b'her', b'mistress'): (30, 10.11840289117327),\n",
       " (b'Mr', b'Elliot'): (174, 154.42250147754135),\n",
       " (b'Mr', b'Shepherd'): (26, 153.50875886524824),\n",
       " (b'anybody', b'else'): (21, 167.79555359595722),\n",
       " (b'reference', b'to'): (30, 10.087650616363232),\n",
       " (b'an', b'honest'): (28, 22.111184865066537),\n",
       " (b'descend', b'into'): (11, 19.585056239674437),\n",
       " (b'Mrs', b'Clay'): (66, 287.0445438704621),\n",
       " (b'Miss', b'Anne'): (19, 13.816993610080877),\n",
       " (b'their', b'fathers'): (151, 21.03847250792006),\n",
       " (b'an', b'example'): (14, 17.82878147222756),\n",
       " (b'Admiral', b'Croft'): (14, 1020.8710564930301),\n",
       " (b'Mrs', b'Croft'): (41, 207.37305091376518),\n",
       " (b'walked', b'along'): (8, 11.422274896105895),\n",
       " (b'Frederick', b'Wentworth'): (6, 23.252406376898783),\n",
       " (b'either', b'side'): (18, 19.363312306866),\n",
       " (b'Captain', b'Wentworth'): (196, 976.2658980080998),\n",
       " (b'eldest', b'son'): (15, 39.758333601208655),\n",
       " (b'removed', b'from'): (36, 14.303712270151498),\n",
       " (b'good', b'humour'): (23, 57.21881939304219),\n",
       " (b'The', b'Crofts'): (8, 10.249618801378945),\n",
       " (b'startled', b'by'): (14, 14.177175563185834),\n",
       " (b'most', b'important'): (14, 33.80560735175321),\n",
       " (b'replied', b'Anne'): (11, 13.874444726962713),\n",
       " (b'at', b'Uppercross'): (20, 13.940248018586365),\n",
       " (b'Great', b'House'): (13, 1177.944761904762),\n",
       " (b'left', b'alone'): (16, 14.13574836462775),\n",
       " (b'Mr', b'Musgrove'): (21, 32.388661211129296),\n",
       " (b'Miss', b'Musgroves'): (22, 227.52303763499037),\n",
       " (b'Mrs', b'Musgrove'): (66, 156.77048165232932),\n",
       " (b'flower', b'-'): (23, 13.52478950246036),\n",
       " (b'grown', b'up'): (19, 12.21891287320147),\n",
       " (b'their', b'faces'): (62, 22.656977192617102),\n",
       " (b'surprised', b'at'): (27, 14.056416752074586),\n",
       " (b'ere', b'long'): (20, 33.88236905544598),\n",
       " (b'anything', b'else'): (30, 74.81068120893052),\n",
       " (b'quite', b'different'): (12, 19.349238133975785),\n",
       " (b'their', b'sakes'): (13, 16.878072078710716),\n",
       " (b'twentieth', b'year'): (13, 185.54485448544855),\n",
       " (b'on', b'board'): (69, 34.25698443940126),\n",
       " (b'eight', b'years'): (22, 61.89744359662757),\n",
       " (b'-', b'bone'): (22, 13.499174370826912),\n",
       " (b'their', b'heads'): (77, 21.62117992564767),\n",
       " (b'Your', b'sister'): (11, 24.014484311898105),\n",
       " (b'dressing', b'-'): (19, 29.645245677110086),\n",
       " (b'up', b'stairs'): (15, 14.51249618660972),\n",
       " (b'waited', b'till'): (7, 12.054520291606565),\n",
       " (b'third', b'part'): (39, 80.67867146551659),\n",
       " (b'Phoo', b'!'): (7, 23.96284025961445),\n",
       " (b'dear', b'fellow'): (11, 20.63122602168474),\n",
       " (b'good', b'cheer'): (14, 58.85364280427197),\n",
       " (b'Mrs', b'Harville'): (24, 84.63892670628489),\n",
       " (b'\"', b'Ay'): (34, 18.457497511961623),\n",
       " (b'fifteen', b'years'): (9, 52.05892627901593),\n",
       " (b'Charles', b'Hayter'): (33, 2649.294369645043),\n",
       " (b'came', b'near'): (42, 11.627278418555207),\n",
       " (b'Her', b'husband'): (9, 21.943829394649068),\n",
       " (b'two', b'hundred'): (102, 34.52901130958137),\n",
       " (b'Dr', b'Shirley'): (9, 1086.3785682916116),\n",
       " (b'went', b'up'): (206, 10.892879248012838),\n",
       " (b'within', b'reach'): (7, 18.479083248670293),\n",
       " (b'-', b'yard'): (19, 16.037591923682502),\n",
       " (b'turn', b'back'): (15, 10.596023199431151),\n",
       " (b'walking', b'along'): (8, 19.12445108751675),\n",
       " (b'leaning', b'against'): (12, 23.700128657852233),\n",
       " (b'trodden', b'under'): (9, 72.24544392523364),\n",
       " (b'under', b'foot'): (15, 22.22936736161035),\n",
       " (b'Louisa', b'Musgrove'): (15, 189.52528348145879),\n",
       " (b'provoke', b'me'): (18, 16.48946779010239),\n",
       " (b'Very', b'good'): (11, 10.325200491977538),\n",
       " (b'good', b'humoured'): (9, 26.15717457967643),\n",
       " (b'Captain', b'Harville'): (37, 475.42275075075077),\n",
       " (b'at', b'Lyme'): (24, 20.293117264867515),\n",
       " (b'earnest', b'desire'): (6, 32.79008483563097),\n",
       " (b'Captain', b'Benwick'): (56, 811.8267953667954),\n",
       " (b'an', b'officer'): (9, 10.895366455250176),\n",
       " (b'place', b'where'): (114, 29.88335727268697),\n",
       " (b'-', b'coat'): (34, 13.073963185711682),\n",
       " (b'an', b'introduction'): (7, 10.895366455250176),\n",
       " (b'preceding', b'evening'): (6, 48.9411997467553),\n",
       " (b'an', b'agony'): (10, 15.202836914302573),\n",
       " (b'catching', b'hold'): (10, 135.5294762217839),\n",
       " (b'raised', b'up'): (35, 20.917452604163728),\n",
       " (b'could', b'scarcely'): (17, 17.384072637319388),\n",
       " (b'passed', b'along'): (11, 16.544168004280362),\n",
       " (b'leaning', b'over'): (16, 33.66056062742769),\n",
       " (b't', b'talk'): (19, 11.57051713815297),\n",
       " (b'Camden', b'Place'): (29, 11505.506976744186),\n",
       " (b'straight', b'forward'): (6, 11.997691337666117),\n",
       " (b'same', b'hour'): (17, 12.921683411398027),\n",
       " (b'-', b'glasses'): (16, 16.35444404375526),\n",
       " (b'poring', b'over'): (6, 41.31068804275217),\n",
       " (b'thirty', b'feet'): (8, 12.120752621435336),\n",
       " (b'Colonel', b'Wallis'): (23, 967.3744677153037),\n",
       " (b'Mrs', b'Wallis'): (11, 54.17854483332603),\n",
       " (b'-', b'haired'): (38, 60.68359500446031),\n",
       " (b'at', b'length'): (74, 17.88998495718584),\n",
       " (b'carried', b'away'): (73, 68.93771731236751),\n",
       " (b'greater', b'than'): (56, 48.182171075546755),\n",
       " (b'Miss', b'Carteret'): (12, 320.09368530020697),\n",
       " (b'contact', b'with'): (11, 11.025512569085398),\n",
       " (b'Lady', b'Dalrymple'): (25, 1027.2774086378738),\n",
       " (b'Laura', b'Place'): (7, 777.3991200502828),\n",
       " (b'be', b'established'): (41, 13.256107901092253),\n",
       " (b'Mrs', b'Smith'): (64, 111.99977591965032),\n",
       " (b'Westgate', b'Buildings'): (7, 8589.180555555555),\n",
       " (b'buried', b'him'): (40, 10.212015743068294),\n",
       " (b'at', b'liberty'): (25, 14.03136075073399),\n",
       " (b'human', b'nature'): (9, 43.51094068810244),\n",
       " (b'five', b'thousand'): (31, 37.910597744077265),\n",
       " (b'whose', b'names'): (9, 20.29106718070708),\n",
       " (b'her', b'ladyship'): (21, 34.12236790377201),\n",
       " (b'-', b'maker'): (21, 26.620220608017217),\n",
       " (b'old', b'gentleman'): (31, 27.054312400201237),\n",
       " (b'almost', b'entirely'): (13, 36.10558657179347),\n",
       " (b'lower', b'part'): (8, 15.927465187754331),\n",
       " (b'staring', b'at'): (33, 25.04597894006017),\n",
       " (b'an', b'oath'): (37, 44.98731955716202),\n",
       " (b'wiser', b'than'): (8, 29.373088249263795),\n",
       " (b'prejudice', b'against'): (7, 39.177763699714916),\n",
       " (b'both', b'sides'): (30, 99.67073029216844),\n",
       " (b'my', b'soul'): (234, 16.443509373671976),\n",
       " (b'rejoice', b'over'): (13, 10.116903194143386),\n",
       " (b'same', b'instant'): (19, 25.162444780283654),\n",
       " (b'every', b'one'): (375, 14.671392435905247),\n",
       " (b'their', b'seats'): (11, 12.658554059033037),\n",
       " (b'their', b'mouths'): (24, 26.497142818484406),\n",
       " (b'short', b'silence'): (9, 19.427041120849434),\n",
       " (b'-', b'blooded'): (7, 17.4695197740113),\n",
       " (b'general', b'character'): (6, 10.833526031812768),\n",
       " (b'fifty', b'pounds'): (6, 35.380799816923165),\n",
       " (b'be', b'saved'): (61, 13.4097233300756),\n",
       " (b'threw', b'himself'): (8, 10.02991247371238),\n",
       " (b'some', b'moments'): (13, 21.006148097826088),\n",
       " (b'exclaimed', b'Mrs'): (11, 12.149128235351897),\n",
       " (b'compassion', b'on'): (20, 13.01248600742343),\n",
       " (b'an', b'explanation'): (12, 16.343049682875264),\n",
       " (b'our', b'hearts'): (17, 14.94420279348509),\n",
       " (b'minutes', b'afterwards'): (7, 22.216989096657148),\n",
       " (b'make', b'haste'): (26, 50.3846341860844),\n",
       " (b\"'\", b'n'): (26, 22.533063472289214),\n",
       " (b'n', b\"'\"): (20, 16.09504533734944),\n",
       " (b'rising', b'sun'): (7, 13.06573846170098),\n",
       " (b'-', b'faced'): (30, 42.60858481466171),\n",
       " (b'an', b'atonement'): (66, 89.61132859823739),\n",
       " (b'atonement', b'for'): (64, 24.315805643301744),\n",
       " (b'\"', b'Look'): (42, 10.09252327008739),\n",
       " (b'Look', b'here'): (14, 26.311681865240885),\n",
       " ...}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigram.phrasegrams # output count and score of each bigram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_sentence = \"Jon lives in New York City\".split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Jon', 'lives', 'in', 'New', 'York', 'City']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Jon', 'lives', 'in', 'New_York', 'City']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigram[tokenized_sentence]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocess the corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# as in Maas et al. (2001):\n",
    "# - leave in stop words (\"indicative of sentiment\")\n",
    "# - no stemming (\"model learns similar representations of words of the same stem when data suggests it\")\n",
    "lower_sents = []\n",
    "for s in gberg_sents:\n",
    "    lower_sents.append([w.lower() for w in s if w.lower()\n",
    "                        not in list(string.punctuation)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['emma', 'by', 'jane', 'austen', '1816'],\n",
       " ['volume', 'i'],\n",
       " ['chapter', 'i'],\n",
       " ['emma',\n",
       "  'woodhouse',\n",
       "  'handsome',\n",
       "  'clever',\n",
       "  'and',\n",
       "  'rich',\n",
       "  'with',\n",
       "  'a',\n",
       "  'comfortable',\n",
       "  'home',\n",
       "  'and',\n",
       "  'happy',\n",
       "  'disposition',\n",
       "  'seemed',\n",
       "  'to',\n",
       "  'unite',\n",
       "  'some',\n",
       "  'of',\n",
       "  'the',\n",
       "  'best',\n",
       "  'blessings',\n",
       "  'of',\n",
       "  'existence',\n",
       "  'and',\n",
       "  'had',\n",
       "  'lived',\n",
       "  'nearly',\n",
       "  'twenty',\n",
       "  'one',\n",
       "  'years',\n",
       "  'in',\n",
       "  'the',\n",
       "  'world',\n",
       "  'with',\n",
       "  'very',\n",
       "  'little',\n",
       "  'to',\n",
       "  'distress',\n",
       "  'or',\n",
       "  'vex',\n",
       "  'her'],\n",
       " ['she',\n",
       "  'was',\n",
       "  'the',\n",
       "  'youngest',\n",
       "  'of',\n",
       "  'the',\n",
       "  'two',\n",
       "  'daughters',\n",
       "  'of',\n",
       "  'a',\n",
       "  'most',\n",
       "  'affectionate',\n",
       "  'indulgent',\n",
       "  'father',\n",
       "  'and',\n",
       "  'had',\n",
       "  'in',\n",
       "  'consequence',\n",
       "  'of',\n",
       "  'her',\n",
       "  'sister',\n",
       "  's',\n",
       "  'marriage',\n",
       "  'been',\n",
       "  'mistress',\n",
       "  'of',\n",
       "  'his',\n",
       "  'house',\n",
       "  'from',\n",
       "  'a',\n",
       "  'very',\n",
       "  'early',\n",
       "  'period']]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_sents[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bigram = Phraser(Phrases(lower_sents))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(b'two', b'daughters'): (19, 11.080802900992637),\n",
       " (b'her', b'sister'): (201, 16.93971298099339),\n",
       " (b'very', b'early'): (25, 10.516998773665177),\n",
       " (b'her', b'mother'): (253, 10.70812618607742),\n",
       " (b'long', b'ago'): (38, 59.226442015336005),\n",
       " (b'more', b'than'): (562, 28.529926612065935),\n",
       " (b'had', b'been'): (1260, 21.583193129694834),\n",
       " (b'an', b'excellent'): (58, 37.41859680854167),\n",
       " (b'sixteen', b'years'): (15, 131.42913000977515),\n",
       " (b'miss', b'taylor'): (48, 420.4340982546865),\n",
       " (b'mr', b'woodhouse'): (132, 104.19907841850323),\n",
       " (b'very', b'fond'): (30, 24.185726346489627),\n",
       " (b'passed', b'away'): (25, 11.751473221742694),\n",
       " (b'too', b'much'): (177, 30.36309017383541),\n",
       " (b'did', b'not'): (977, 10.846196223896685),\n",
       " (b'any', b'means'): (28, 14.294148100212627),\n",
       " (b'after', b'dinner'): (22, 18.60737125272944),\n",
       " (b'mr', b'weston'): (162, 91.63290824201266),\n",
       " (b'five', b'years'): (42, 37.66428596665674),\n",
       " (b'years', b'old'): (176, 48.599094446190286),\n",
       " (b'seven', b'years'): (53, 50.3345604292756),\n",
       " (b'each', b'other'): (239, 71.31277029783762),\n",
       " (b'well', b'informed'): (8, 14.185028016786625),\n",
       " (b'a', b'mile'): (49, 11.700110753652233),\n",
       " (b'difference', b'between'): (44, 207.86784241868986),\n",
       " (b'mrs', b'weston'): (249, 180.6778969011602),\n",
       " (b'could', b'not'): (1059, 10.213333164207079),\n",
       " (b'having', b'been'): (49, 10.723750443105281),\n",
       " (b'sixteen', b'miles'): (6, 105.040625),\n",
       " (b'miles', b'off'): (16, 32.99182066941624),\n",
       " (b'at', b'hartfield'): (67, 25.555992478744276),\n",
       " (b'her', b'husband'): (168, 26.67842743680748),\n",
       " (b'in', b'spite'): (105, 13.346436370855669),\n",
       " (b'emma', b'could'): (61, 10.88608805283633),\n",
       " (b'every', b'body'): (148, 39.26110856993498),\n",
       " (b'no', b'means'): (80, 26.766046928639682),\n",
       " (b'able', b'to'): (349, 10.854471217684639),\n",
       " (b'very', b'much'): (241, 15.43191856451234),\n",
       " (b'have', b'been'): (986, 17.20622462716941),\n",
       " (b'great', b'deal'): (182, 110.16914388000741),\n",
       " (b'agree', b'with'): (26, 13.12648342622773),\n",
       " (b'good', b'humoured'): (30, 149.07455772926625),\n",
       " (b'for', b'ever'): (565, 10.477844500702485),\n",
       " (b'three', b'times'): (41, 38.14441525690869),\n",
       " (b'my', b'dear'): (340, 26.343257103019322),\n",
       " (b'last', b'night'): (70, 23.230537949447992),\n",
       " (b'doubt', b'whether'): (12, 19.564378663784144),\n",
       " (b'anywhere', b'else'): (6, 15.30646630236794),\n",
       " (b'i', b'am'): (2445, 16.330282814272387),\n",
       " (b'very', b'glad'): (46, 16.952537612290158),\n",
       " (b'am', b'sure'): (282, 60.9206736247954),\n",
       " (b'very', b'pretty'): (40, 18.02785881936292),\n",
       " (b'be', b'able'): (121, 10.915272046078586),\n",
       " (b'immediately', b'afterwards'): (10, 37.530798337572115),\n",
       " (b'mr', b'knightley'): (277, 179.5658713081734),\n",
       " (b'sensible', b'man'): (17, 13.469145146927872),\n",
       " (b'intimate', b'friend'): (6, 20.19472630173565),\n",
       " (b'connected', b'with'): (31, 16.865113476644794),\n",
       " (b'elder', b'brother'): (6, 14.417929653989134),\n",
       " (b'than', b'usual'): (30, 27.96484669654346),\n",
       " (b'brunswick', b'square'): (11, 2374.2341399607585),\n",
       " (b'some', b'time'): (149, 11.678170286892607),\n",
       " (b'poor', b'isabella'): (11, 43.036881601877866),\n",
       " (b'am', b'afraid'): (65, 24.40430621044607),\n",
       " (b'moonlight', b'night'): (6, 13.233464566929133),\n",
       " (b'look', b'at'): (188, 10.167669372199262),\n",
       " (b'vast', b'deal'): (11, 58.66522301228184),\n",
       " (b'an', b'hour'): (155, 40.46461919025373),\n",
       " (b'pretty', b'well'): (22, 13.991375757171793),\n",
       " (b'tolerably', b'well'): (7, 13.779741502021295),\n",
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       " (b'miss', b'steele'): (27, 243.16309828279137),\n",
       " (b'lucy', b'steele'): (10, 285.9328922495274),\n",
       " (b'i', b'm'): (438, 16.271042011107397),\n",
       " (b'm', b'sure'): (88, 102.49120766084593),\n",
       " (b'robert', b'ferrars'): (7, 95.96098334655036),\n",
       " (b'mr', b'pratt'): (8, 85.6018675721562),\n",
       " (b'at', b'longstaple'): (7, 16.48773708306082),\n",
       " (b'poor', b'edward'): (10, 12.172840599071293),\n",
       " (b'their', b'names'): (36, 14.302226309432303),\n",
       " (b'latter', b'end'): (12, 42.203822544642854),\n",
       " (b'i', b've'): (218, 13.517866465271059),\n",
       " (b'lifted', b'up'): (151, 72.07416702839072),\n",
       " (b'third', b'day'): (65, 33.7914061089195),\n",
       " (b'starting', b'up'): (9, 11.47168738090498),\n",
       " (b't', b'know'): (147, 12.457577307748522),\n",
       " (b'returned', b'home'): (11, 12.90725432262056),\n",
       " (b'berkeley', b'street'): (16, 1449.9725490196079),\n",
       " (b'conduit', b'street'): (6, 203.71515151515152),\n",
       " (b'lit', b'up'): (23, 36.13581524985069),\n",
       " (b'as', b'follows'): (17, 14.278650094398992),\n",
       " (b'having', b'received'): (10, 11.384803552611771),\n",
       " (b'who', b'cares'): (8, 12.52589229028073),\n",
       " (b'miss', b'grey'): (10, 10.25064380590946),\n",
       " (b'fifty', b'thousand'): (11, 20.37380177348749),\n",
       " (b'why', b'don'): (28, 12.795415425355841),\n",
       " (b'thousand', b'times'): (12, 12.06362743003632),\n",
       " (b'walked', b'across'): (7, 13.91218569999655),\n",
       " (b'fourteen', b'years'): (6, 15.563975922210217),\n",
       " (b'your', b'sakes'): (16, 32.49059753954306),\n",
       " (b'bartlett', b's'): (6, 10.18987469684721),\n",
       " (b'dressing', b'gown'): (6, 509.2878787878788),\n",
       " (b'wild', b'beasts'): (16, 105.2399430740038),\n",
       " (b'miss', b'morton'): (15, 267.5957540911101),\n",
       " (b'six', b'hundred'): (66, 132.00427053900353),\n",
       " (b'harley', b'street'): (16, 1540.5958333333335),\n",
       " (b'most', b'high'): (60, 22.2500544936901),\n",
       " (b'filled', b'with'): (114, 14.087588281459942),\n",
       " (b'two', b'thirds'): (6, 42.47641112047178),\n",
       " (b'public', b'school'): (6, 43.038412291933426),\n",
       " (b\",'\", b'says'): (13, 42.67564803385647),\n",
       " (b'fell', b'upon'): (62, 13.94880910923142),\n",
       " (b's', b'office'): (29, 12.330604675176458),\n",
       " (b'yes', b'ma'): (9, 15.105834768931166),\n",
       " (b'come', b'near'): (47, 11.571024613341859),\n",
       " (b'give', b'ear'): (30, 35.78134979774324),\n",
       " (b'reminds', b'me'): (7, 14.199176259372688),\n",
       " (b'ten', b'guineas'): (6, 26.425314465408807),\n",
       " (b'south', b'east'): (7, 17.11383597097883),\n",
       " (b'mr', b'harris'): (10, 85.6018675721562),\n",
       " (b'quicker', b'than'): (6, 12.238732907193139),\n",
       " (b'bent', b'over'): (10, 11.258750628035504),\n",
       " (b'justified', b'by'): (17, 11.526506299532617),\n",
       " (b'from', b'thence'): (103, 27.334646896223614),\n",
       " (b'latter', b'days'): (12, 29.77529527559055),\n",
       " (b'sprung', b'up'): (13, 24.090543499900463),\n",
       " (b'or', b'later'): (13, 13.913825368307583),\n",
       " (b'living', b'creature'): (16, 82.51963993453354),\n",
       " (b'first', b'month'): (33, 25.952445658418213),\n",
       " (b'have', b'transgressed'): (20, 23.003695592663565),\n",
       " ...}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_bigram.phrasegrams # miss taylor, mr woodhouse, mr weston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['jon', 'lives', 'in', 'new_york', 'city']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_bigram[\"jon lives in new york city\".split()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(b'miss', b'taylor'): (48, 156.44059469941823),\n",
       " (b'mr', b'woodhouse'): (132, 82.04651843976633),\n",
       " (b'mr', b'weston'): (162, 75.87438262077481),\n",
       " (b'mrs', b'weston'): (249, 160.68485093258923),\n",
       " (b'great', b'deal'): (182, 93.36368125424357),\n",
       " (b'mr', b'knightley'): (277, 161.74131790625913),\n",
       " (b'miss', b'woodhouse'): (173, 229.03802722366902),\n",
       " (b'years', b'ago'): (56, 74.31594785893046),\n",
       " (b'mr', b'elton'): (214, 121.3990121932397),\n",
       " (b'dare', b'say'): (115, 89.94000515807346),\n",
       " (b'frank', b'churchill'): (151, 1316.4456593286038),\n",
       " (b'miss', b'bates'): (113, 276.39588291692513),\n",
       " (b'drawing', b'room'): (49, 84.91494947493561),\n",
       " (b'mrs', b'goddard'): (58, 143.57843432545658),\n",
       " (b'miss', b'smith'): (58, 73.03442128232508),\n",
       " (b'few', b'minutes'): (86, 204.16834974753786),\n",
       " (b'john', b'knightley'): (58, 83.03755747111268),\n",
       " (b'don', b't'): (830, 250.30957446808512),\n",
       " (b'good', b'natured'): (66, 88.69936184891343),\n",
       " (b'few', b'moments'): (43, 107.77584531675087),\n",
       " (b'thousand', b'pounds'): (48, 166.51834523092802),\n",
       " (b'o', b'clock'): (67, 89.14789088153573),\n",
       " (b'jane', b'fairfax'): (111, 654.5565917587609),\n",
       " (b'miss', b'fairfax'): (125, 196.19987447261062),\n",
       " (b'ma', b'am'): (216, 157.25846601094193),\n",
       " (b'mrs', b'elton'): (142, 93.08931456265867),\n",
       " (b'forty', b'years'): (68, 90.60220877269607),\n",
       " (b'cut', b'off'): (217, 129.60290535032792),\n",
       " (b'ten', b'thousand'): (82, 84.00099962514057),\n",
       " (b'sir', b'walter'): (136, 399.5145142857143),\n",
       " (b'lady', b'russell'): (147, 613.6301581282135),\n",
       " (b'mr', b'elliot'): (174, 126.18129960463163),\n",
       " (b'mrs', b'clay'): (66, 93.08931456265867),\n",
       " (b'captain', b'wentworth'): (196, 529.8756608388247),\n",
       " (b'mrs', b'musgrove'): (66, 85.21252640735679),\n",
       " (b'charles', b'hayter'): (33, 92.03437785214481),\n",
       " (b'captain', b'benwick'): (56, 241.49037086312987),\n",
       " (b'mrs', b'smith'): (79, 84.60327207490248),\n",
       " (b'mrs', b'dashwood'): (121, 115.06873605661974),\n",
       " (b'mrs', b'ferrars'): (73, 102.75628184416554),\n",
       " (b'sir', b'john'): (113, 95.83994133176884),\n",
       " (b'lady', b'middleton'): (95, 350.26733319243175),\n",
       " (b'mrs', b'jennings'): (229, 279.0655756128398),\n",
       " (b'colonel', b'brandon'): (132, 1313.0078125),\n",
       " (b'miss', b'dashwood'): (70, 76.66830732292917),\n",
       " (b'won', b't'): (219, 189.96708776595744),\n",
       " (b'm', b'sure'): (88, 69.15069432539002),\n",
       " (b'six', b'hundred'): (66, 73.57615079223149),\n",
       " (b'gathered', b'together'): (84, 103.28066074898891),\n",
       " (b'thou', b'shalt'): (1282, 66.88233182614454),\n",
       " (b'burnt', b'offerings'): (86, 299.15702343127646),\n",
       " (b'sweet', b'savour'): (43, 286.17879256965944),\n",
       " (b'unleavened', b'bread'): (43, 237.70041787206688),\n",
       " (b'burnt', b'offering'): (184, 297.524653753951),\n",
       " (b'afar', b'off'): (52, 108.14220347465505),\n",
       " (b'take', b'heed'): (58, 86.38454061712328),\n",
       " (b'sent', b'messengers'): (43, 79.21555418015616),\n",
       " (b'thus', b'saith'): (444, 144.0289127889979),\n",
       " (b'without', b'blemish'): (46, 83.71359108159393),\n",
       " (b'peace', b'offerings'): (83, 176.2577199456205),\n",
       " (b'sin', b'offering'): (118, 129.96079665512747),\n",
       " (b'meat', b'offering'): (122, 210.66724956379437),\n",
       " (b'fine', b'flour'): (36, 86.07682458386685),\n",
       " (b'high', b'places'): (99, 129.8123390846559),\n",
       " (b'fig', b'tree'): (37, 121.73722334004026),\n",
       " (b'fir', b'tree'): (36, 72.6789393074867),\n",
       " (b'mercy', b'endureth'): (41, 269.07674062361025),\n",
       " (b'chief', b'priests'): (65, 116.31947753846512),\n",
       " (b'jesus', b'christ'): (199, 172.16816954974848),\n",
       " (b'holy', b'ghost'): (90, 313.0305073859987),\n",
       " (b'o', b'er'): (82, 108.14993564993564),\n",
       " (b'couldn', b't'): (89, 171.76138536935215),\n",
       " (b'didn', b't'): (180, 220.51081560283686),\n",
       " (b'little', b'jackal'): (61, 69.81254128038833),\n",
       " (b'wasn', b't'): (58, 120.2225788701394),\n",
       " (b'isn', b't'): (63, 131.96593211752787),\n",
       " (b'doesn', b't'): (53, 106.2634985949418),\n",
       " (b'wouldn', b't'): (58, 120.2225788701394),\n",
       " (b'father', b'brown'): (207, 91.68277248710235),\n",
       " (b'buster', b'bear'): (142, 479.87410772225826),\n",
       " (b'green', b'forest'): (66, 336.37733627667404),\n",
       " (b'little', b'joe'): (111, 133.28784038147822),\n",
       " (b'joe', b'otter'): (47, 1271.6141235813368),\n",
       " (b'farmer', b'brown'): (100, 386.05179596892236),\n",
       " (b'mock', b'turtle'): (56, 2528.877742946708),\n",
       " (b'dr', b'bull'): (65, 680.7870294599019),\n",
       " (b'guinea', b'hen'): (51, 905.8822695035461),\n",
       " (b'sir', b'arthur'): (71, 131.41924812030075),\n",
       " (b'miss', b'somers'): (49, 160.06190476190477),\n",
       " (b'mr', b'gresham'): (49, 87.31390492359931),\n",
       " (b'mrs', b'theresa'): (67, 170.20061244665206),\n",
       " (b'de', b'grey'): (77, 603.2109624246722),\n",
       " (b'dr', b'middleton'): (40, 162.73103819257668),\n",
       " (b'moby', b'dick'): (84, 4115.877551020409),\n",
       " (b'sperm', b'whale'): (183, 297.3672297627184),\n",
       " (b'mast', b'heads'): (37, 77.7358926919519),\n",
       " (b'wee', b'l'): (35, 450.39751861042186)}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_bigram = Phraser(Phrases(lower_sents, \n",
    "                               min_count=32, threshold=64))\n",
    "lower_bigram.phrasegrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_sents = []\n",
    "for s in lower_sents:\n",
    "    clean_sents.append(lower_bigram[s])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['emma', 'by', 'jane', 'austen', '1816'],\n",
       " ['volume', 'i'],\n",
       " ['chapter', 'i'],\n",
       " ['emma',\n",
       "  'woodhouse',\n",
       "  'handsome',\n",
       "  'clever',\n",
       "  'and',\n",
       "  'rich',\n",
       "  'with',\n",
       "  'a',\n",
       "  'comfortable',\n",
       "  'home',\n",
       "  'and',\n",
       "  'happy',\n",
       "  'disposition',\n",
       "  'seemed',\n",
       "  'to',\n",
       "  'unite',\n",
       "  'some',\n",
       "  'of',\n",
       "  'the',\n",
       "  'best',\n",
       "  'blessings',\n",
       "  'of',\n",
       "  'existence',\n",
       "  'and',\n",
       "  'had',\n",
       "  'lived',\n",
       "  'nearly',\n",
       "  'twenty',\n",
       "  'one',\n",
       "  'years',\n",
       "  'in',\n",
       "  'the',\n",
       "  'world',\n",
       "  'with',\n",
       "  'very',\n",
       "  'little',\n",
       "  'to',\n",
       "  'distress',\n",
       "  'or',\n",
       "  'vex',\n",
       "  'her'],\n",
       " ['she',\n",
       "  'was',\n",
       "  'the',\n",
       "  'youngest',\n",
       "  'of',\n",
       "  'the',\n",
       "  'two',\n",
       "  'daughters',\n",
       "  'of',\n",
       "  'a',\n",
       "  'most',\n",
       "  'affectionate',\n",
       "  'indulgent',\n",
       "  'father',\n",
       "  'and',\n",
       "  'had',\n",
       "  'in',\n",
       "  'consequence',\n",
       "  'of',\n",
       "  'her',\n",
       "  'sister',\n",
       "  's',\n",
       "  'marriage',\n",
       "  'been',\n",
       "  'mistress',\n",
       "  'of',\n",
       "  'his',\n",
       "  'house',\n",
       "  'from',\n",
       "  'a',\n",
       "  'very',\n",
       "  'early',\n",
       "  'period'],\n",
       " ['her',\n",
       "  'mother',\n",
       "  'had',\n",
       "  'died',\n",
       "  'too',\n",
       "  'long',\n",
       "  'ago',\n",
       "  'for',\n",
       "  'her',\n",
       "  'to',\n",
       "  'have',\n",
       "  'more',\n",
       "  'than',\n",
       "  'an',\n",
       "  'indistinct',\n",
       "  'remembrance',\n",
       "  'of',\n",
       "  'her',\n",
       "  'caresses',\n",
       "  'and',\n",
       "  'her',\n",
       "  'place',\n",
       "  'had',\n",
       "  'been',\n",
       "  'supplied',\n",
       "  'by',\n",
       "  'an',\n",
       "  'excellent',\n",
       "  'woman',\n",
       "  'as',\n",
       "  'governess',\n",
       "  'who',\n",
       "  'had',\n",
       "  'fallen',\n",
       "  'little',\n",
       "  'short',\n",
       "  'of',\n",
       "  'a',\n",
       "  'mother',\n",
       "  'in',\n",
       "  'affection'],\n",
       " ['sixteen',\n",
       "  'years',\n",
       "  'had',\n",
       "  'miss_taylor',\n",
       "  'been',\n",
       "  'in',\n",
       "  'mr_woodhouse',\n",
       "  's',\n",
       "  'family',\n",
       "  'less',\n",
       "  'as',\n",
       "  'a',\n",
       "  'governess',\n",
       "  'than',\n",
       "  'a',\n",
       "  'friend',\n",
       "  'very',\n",
       "  'fond',\n",
       "  'of',\n",
       "  'both',\n",
       "  'daughters',\n",
       "  'but',\n",
       "  'particularly',\n",
       "  'of',\n",
       "  'emma'],\n",
       " ['between',\n",
       "  '_them_',\n",
       "  'it',\n",
       "  'was',\n",
       "  'more',\n",
       "  'the',\n",
       "  'intimacy',\n",
       "  'of',\n",
       "  'sisters'],\n",
       " ['even',\n",
       "  'before',\n",
       "  'miss_taylor',\n",
       "  'had',\n",
       "  'ceased',\n",
       "  'to',\n",
       "  'hold',\n",
       "  'the',\n",
       "  'nominal',\n",
       "  'office',\n",
       "  'of',\n",
       "  'governess',\n",
       "  'the',\n",
       "  'mildness',\n",
       "  'of',\n",
       "  'her',\n",
       "  'temper',\n",
       "  'had',\n",
       "  'hardly',\n",
       "  'allowed',\n",
       "  'her',\n",
       "  'to',\n",
       "  'impose',\n",
       "  'any',\n",
       "  'restraint',\n",
       "  'and',\n",
       "  'the',\n",
       "  'shadow',\n",
       "  'of',\n",
       "  'authority',\n",
       "  'being',\n",
       "  'now',\n",
       "  'long',\n",
       "  'passed',\n",
       "  'away',\n",
       "  'they',\n",
       "  'had',\n",
       "  'been',\n",
       "  'living',\n",
       "  'together',\n",
       "  'as',\n",
       "  'friend',\n",
       "  'and',\n",
       "  'friend',\n",
       "  'very',\n",
       "  'mutually',\n",
       "  'attached',\n",
       "  'and',\n",
       "  'emma',\n",
       "  'doing',\n",
       "  'just',\n",
       "  'what',\n",
       "  'she',\n",
       "  'liked',\n",
       "  'highly',\n",
       "  'esteeming',\n",
       "  'miss_taylor',\n",
       "  's',\n",
       "  'judgment',\n",
       "  'but',\n",
       "  'directed',\n",
       "  'chiefly',\n",
       "  'by',\n",
       "  'her',\n",
       "  'own']]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_sents[0:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sixteen',\n",
       " 'years',\n",
       " 'had',\n",
       " 'miss_taylor',\n",
       " 'been',\n",
       " 'in',\n",
       " 'mr_woodhouse',\n",
       " 's',\n",
       " 'family',\n",
       " 'less',\n",
       " 'as',\n",
       " 'a',\n",
       " 'governess',\n",
       " 'than',\n",
       " 'a',\n",
       " 'friend',\n",
       " 'very',\n",
       " 'fond',\n",
       " 'of',\n",
       " 'both',\n",
       " 'daughters',\n",
       " 'but',\n",
       " 'particularly',\n",
       " 'of',\n",
       " 'emma']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_sents[6] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run word2vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# max_vocab_size can be used instead of min_count (which has increased here)\n",
    "# model = Word2Vec(sentences=clean_sents, size=64, \n",
    "#                  sg=1, window=10, iter=5,\n",
    "#                  min_count=10, workers=4)\n",
    "# model.save('clean_gutenberg_model.w2v')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# skip re-training the model with the next line:  \n",
    "model = gensim.models.Word2Vec.load('clean_gutenberg_model.w2v') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10329"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.wv.vocab) # would be 17k if we carried out no preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.38401067,  0.01232518, -0.37594706, -0.00112308,  0.38663676,\n",
       "        0.01287549,  0.398965  ,  0.0096426 , -0.10419296, -0.02877572,\n",
       "        0.3207022 ,  0.27838793,  0.62772304,  0.34408906,  0.23356602,\n",
       "        0.24557391,  0.3398472 ,  0.07168821, -0.18941355, -0.10122284,\n",
       "       -0.35172758,  0.4038952 , -0.12179806,  0.096336  ,  0.00641343,\n",
       "        0.02332107,  0.7743452 ,  0.03591069, -0.20103034, -0.1688079 ,\n",
       "       -0.01331445, -0.29832968,  0.08522387, -0.02750671,  0.32494134,\n",
       "       -0.14266558, -0.4192913 , -0.09291836, -0.23813559,  0.38258648,\n",
       "        0.11036541,  0.005807  , -0.16745028,  0.34308755, -0.20224966,\n",
       "       -0.77683043,  0.05146591, -0.5883941 , -0.0718769 , -0.18120563,\n",
       "        0.00358319, -0.29351747,  0.153776  ,  0.48048878,  0.22479494,\n",
       "        0.5465321 ,  0.29695514,  0.00986911, -0.2450937 , -0.19344331,\n",
       "        0.3541134 ,  0.3426432 , -0.10496043,  0.00543602], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv['dog']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.wv['dog'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('puppy', 0.7834004163742065),\n",
       " ('cage', 0.7651870846748352),\n",
       " ('brahmin', 0.7646074295043945)]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar('dog', topn=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('drink', 0.8292896747589111),\n",
       " ('bread', 0.8157557845115662),\n",
       " ('meat', 0.763256311416626)]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar('eat', topn=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('morning', 0.7578363418579102),\n",
       " ('night', 0.7324314713478088),\n",
       " ('week', 0.7262506484985352)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar('day', topn=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('mother', 0.8257375359535217),\n",
       " ('brother', 0.7275018692016602),\n",
       " ('sister', 0.7177823781967163)]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar('father', topn=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('madam', 0.8472708463668823),\n",
       " ('nancy', 0.8370794057846069),\n",
       " ('betty', 0.8337127566337585)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar('ma_am', topn=3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dog'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.doesnt_match(\"mother father sister brother dog\".split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4423433829436756"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.similarity('father', 'dog')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('mother', 0.7650133371353149),\n",
       " ('husband', 0.7556628584861755),\n",
       " ('sister', 0.7482180595397949),\n",
       " ('daughter', 0.7390402555465698),\n",
       " ('wife', 0.7284981608390808),\n",
       " ('sarah', 0.6856439113616943),\n",
       " ('daughters', 0.6652647256851196),\n",
       " ('conceived', 0.6637862920761108),\n",
       " ('rebekah', 0.6580977439880371),\n",
       " ('dearly', 0.6398962736129761)]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(positive=['father', 'woman'], negative=['man']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('wife', 0.707526445388794),\n",
       " ('sister', 0.6973985433578491),\n",
       " ('maid', 0.6911259889602661),\n",
       " ('daughter', 0.6799546480178833),\n",
       " ('mother', 0.6583081483840942),\n",
       " ('child', 0.6433471441268921),\n",
       " ('conceived', 0.6391384601593018),\n",
       " ('harlot', 0.6089693307876587),\n",
       " ('daughters', 0.6069822907447815),\n",
       " ('marriage', 0.5894294381141663)]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(positive=['husband', 'woman'], negative=['man']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reduce word vector dimensionality with t-SNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tsne = TSNE(n_components=2, n_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_2d = tsne.fit_transform(model.wv[model.wv.vocab])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coords_df = pd.DataFrame(X_2d, columns=['x','y'])\n",
    "# coords_df['token'] = model.wv.vocab.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coords_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coords_df.to_csv('clean_gutenberg_tsne.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualise "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords_df = pd.read_csv('clean_gutenberg_tsne.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd20dedc9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = coords_df.plot.scatter('x', 'y', figsize=(12,12), \n",
    "                           marker='.', s=10, alpha=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"d9636528-f962-4996-9e06-3c1b124bb0f1\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"d9636528-f962-4996-9e06-3c1b124bb0f1\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"d9636528-f962-4996-9e06-3c1b124bb0f1\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'd9636528-f962-4996-9e06-3c1b124bb0f1' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"d9636528-f962-4996-9e06-3c1b124bb0f1\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"d9636528-f962-4996-9e06-3c1b124bb0f1\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"d9636528-f962-4996-9e06-3c1b124bb0f1\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'd9636528-f962-4996-9e06-3c1b124bb0f1' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"d9636528-f962-4996-9e06-3c1b124bb0f1\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_df = coords_df.sample(n=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = figure(plot_width=800, plot_height=800)\n",
    "_ = p.text(x=subset_df.x, y=subset_df.y, text=subset_df.token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"b436bd33-e914-4a33-8981-5cb8664930ef\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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\",\"dtype\":\"float64\",\"shape\":[5000]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[5000]}},\"selected\":{\"id\":\"553d1efc-935d-4ca2-8aa3-b4727acd792c\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"5d7528f1-e63f-44b6-9f15-3c0e78e42d26\",\"type\":\"UnionRenderers\"}},\"id\":\"740dabd9-aade-4034-8d2f-010684737a02\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null},\"id\":\"ad5c5e39-0048-4a1c-bca7-1cbb3cbea3a8\",\"type\":\"DataRange1d\"},{\"attributes\":{\"data_source\":{\"id\":\"740dabd9-aade-4034-8d2f-010684737a02\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"7e900a57-eb89-4c0a-aec5-eec970d7a404\",\"type\":\"Text\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"892c300c-1157-4411-96ef-4204510f5d7d\",\"type\":\"Text\"},\"selection_glyph\":null,\"view\":{\"id\":\"35e6157a-a49d-4c3d-85e2-19483d8817ec\",\"type\":\"CDSView\"}},\"id\":\"8fbd2891-9b81-47d9-90a3-587632a262e8\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1aed8348-df5f-40be-afe7-78d75b4165d4\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"11b967ec-293e-4c8b-bf52-9bf34bc9cc89\",\"type\":\"SaveTool\"},{\"attributes\":{\"text_alpha\":{\"value\":0.1},\"text_color\":{\"value\":\"black\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"892c300c-1157-4411-96ef-4204510f5d7d\",\"type\":\"Text\"},{\"attributes\":{},\"id\":\"fda0f1a5-cefe-45ed-a49f-e1028202ef61\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"553d1efc-935d-4ca2-8aa3-b4727acd792c\",\"type\":\"Selection\"},{\"attributes\":{\"formatter\":{\"id\":\"638774ee-1194-499c-84b9-2a7a880a340d\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"8f7684ec-26f5-43b3-b12e-8d0f4396fe1c\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"dab1680e-7c2a-4817-812f-b061c006fcd9\",\"type\":\"BasicTicker\"}},\"id\":\"e6bc1c87-0202-4240-9613-b08e1790c54c\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"8cc1e825-8c17-44b9-b7f8-6f702472960d\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"638774ee-1194-499c-84b9-2a7a880a340d\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"plot\":null,\"text\":\"\"},\"id\":\"ee42e37e-d7d1-49a0-b5b9-6eefef0c19df\",\"type\":\"Title\"},{\"attributes\":{\"source\":{\"id\":\"740dabd9-aade-4034-8d2f-010684737a02\",\"type\":\"ColumnDataSource\"}},\"id\":\"35e6157a-a49d-4c3d-85e2-19483d8817ec\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"00069be3-dff9-48ae-bc1f-e1967a113752\",\"type\":\"HelpTool\"},{\"attributes\":{\"below\":[{\"id\":\"21f2379c-a73b-4e74-bf77-95f467f225be\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"e6bc1c87-0202-4240-9613-b08e1790c54c\",\"type\":\"LinearAxis\"}],\"plot_height\":800,\"plot_width\":800,\"renderers\":[{\"id\":\"21f2379c-a73b-4e74-bf77-95f467f225be\",\"type\":\"LinearAxis\"},{\"id\":\"a45ee16f-b35b-4cd4-b113-394eaf34877f\",\"type\":\"Grid\"},{\"id\":\"e6bc1c87-0202-4240-9613-b08e1790c54c\",\"type\":\"LinearAxis\"},{\"id\":\"42ab7fbb-3b04-4cce-bd47-570fe5c4ea2e\",\"type\":\"Grid\"},{\"id\":\"1aed8348-df5f-40be-afe7-78d75b4165d4\",\"type\":\"BoxAnnotation\"},{\"id\":\"8fbd2891-9b81-47d9-90a3-587632a262e8\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"ee42e37e-d7d1-49a0-b5b9-6eefef0c19df\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"ca46da8e-4ae0-4f69-b16d-e590108f8f54\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"d42ec76c-440f-425c-8ef8-4da91178a9e5\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"8cc1e825-8c17-44b9-b7f8-6f702472960d\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"ad5c5e39-0048-4a1c-bca7-1cbb3cbea3a8\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"fda0f1a5-cefe-45ed-a49f-e1028202ef61\",\"type\":\"LinearScale\"}},\"id\":\"8f7684ec-26f5-43b3-b12e-8d0f4396fe1c\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"plot\":{\"id\":\"8f7684ec-26f5-43b3-b12e-8d0f4396fe1c\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"76d15e02-2aa9-43dd-8941-ea0cb2062b89\",\"type\":\"BasicTicker\"}},\"id\":\"a45ee16f-b35b-4cd4-b113-394eaf34877f\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"5d7528f1-e63f-44b6-9f15-3c0e78e42d26\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"c6ce946b-043c-462e-ac98-a30388db5a09\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"8f7684ec-26f5-43b3-b12e-8d0f4396fe1c\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"dab1680e-7c2a-4817-812f-b061c006fcd9\",\"type\":\"BasicTicker\"}},\"id\":\"42ab7fbb-3b04-4cce-bd47-570fe5c4ea2e\",\"type\":\"Grid\"}],\"root_ids\":[\"8f7684ec-26f5-43b3-b12e-8d0f4396fe1c\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.16\"}};\n",
       "  var render_items = [{\"docid\":\"76c84f91-4eb0-442d-941c-d3e2c2602066\",\"elementid\":\"b436bd33-e914-4a33-8981-5cb8664930ef\",\"modelid\":\"8f7684ec-26f5-43b3-b12e-8d0f4396fe1c\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "8f7684ec-26f5-43b3-b12e-8d0f4396fe1c"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output_file() here"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}