a1/exploring_word_vectors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CS224N Assignment 1: Exploring Word Vectors (25 Points)\n",
    "\n",
    "Welcome to CS224n! \n",
    "\n",
    "Before you start, make sure you read the README.txt in the same directory as this notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\gensim\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n",
      "[nltk_data] Downloading package reuters to\n",
      "[nltk_data]     C:\\Users\\debow\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package reuters is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# All Import Statements Defined Here\n",
    "# Note: Do not add to this list.\n",
    "# All the dependencies you need, can be installed by running .\n",
    "# ----------------\n",
    "\n",
    "import sys\n",
    "assert sys.version_info[0]==3\n",
    "assert sys.version_info[1] >= 5\n",
    "\n",
    "from gensim.models import KeyedVectors\n",
    "from gensim.test.utils import datapath\n",
    "import pprint\n",
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = [10, 5]\n",
    "import nltk\n",
    "nltk.download('reuters')\n",
    "from nltk.corpus import reuters\n",
    "import numpy as np\n",
    "import random\n",
    "import scipy as sp\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "START_TOKEN = '<START>'\n",
    "END_TOKEN = '<END>'\n",
    "\n",
    "np.random.seed(0)\n",
    "random.seed(0)\n",
    "# ----------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Please Write Your SUNet ID Here: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Vectors\n",
    "\n",
    "Word Vectors are often used as a fundamental component for downstream NLP tasks, e.g. question answering, text generation, translation, etc., so it is important to build some intuitions as to their strengths and weaknesses. Here, you will explore two types of word vectors: those derived from *co-occurrence matrices*, and those derived via *word2vec*. \n",
    "\n",
    "**Assignment Notes:** Please make sure to save the notebook as you go along. Submission Instructions are located at the bottom of the notebook.\n",
    "\n",
    "**Note on Terminology:** The terms \"word vectors\" and \"word embeddings\" are often used interchangeably. The term \"embedding\" refers to the fact that we are encoding aspects of a word's meaning in a lower dimensional space. As [Wikipedia](https://en.wikipedia.org/wiki/Word_embedding) states, \"*conceptually it involves a mathematical embedding from a space with one dimension per word to a continuous vector space with a much lower dimension*\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Count-Based Word Vectors (10 points)\n",
    "\n",
    "Most word vector models start from the following idea:\n",
    "\n",
    "*You shall know a word by the company it keeps ([Firth, J. R. 1957:11](https://en.wikipedia.org/wiki/John_Rupert_Firth))*\n",
    "\n",
    "Many word vector implementations are driven by the idea that similar words, i.e., (near) synonyms, will be used in similar contexts. As a result, similar words will often be spoken or written along with a shared subset of words, i.e., contexts. By examining these contexts, we can try to develop embeddings for our words. With this intuition in mind, many \"old school\" approaches to constructing word vectors relied on word counts. Here we elaborate upon one of those strategies, *co-occurrence matrices* (for more information, see [here](http://web.stanford.edu/class/cs124/lec/vectorsemantics.video.pdf) or [here](https://medium.com/data-science-group-iitr/word-embedding-2d05d270b285))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Co-Occurrence\n",
    "\n",
    "A co-occurrence matrix counts how often things co-occur in some environment. Given some word $w_i$ occurring in the document, we consider the *context window* surrounding $w_i$. Supposing our fixed window size is $n$, then this is the $n$ preceding and $n$ subsequent words in that document, i.e. words $w_{i-n} \\dots w_{i-1}$ and $w_{i+1} \\dots w_{i+n}$. We build a *co-occurrence matrix* $M$, which is a symmetric word-by-word matrix in which $M_{ij}$ is the number of times $w_j$ appears inside $w_i$'s window.\n",
    "\n",
    "**Example: Co-Occurrence with Fixed Window of n=1**:\n",
    "\n",
    "Document 1: \"all that glitters is not gold\"\n",
    "\n",
    "Document 2: \"all is well that ends well\"\n",
    "\n",
    "\n",
    "|     *    | START | all | that | glitters | is   | not  | gold  | well | ends | END |\n",
    "|----------|-------|-----|------|----------|------|------|-------|------|------|-----|\n",
    "| START    | 0     | 2   | 0    | 0        | 0    | 0    | 0     | 0    | 0    | 0   |\n",
    "| all      | 2     | 0   | 1    | 0        | 1    | 0    | 0     | 0    | 0    | 0   |\n",
    "| that     | 0     | 1   | 0    | 1        | 0    | 0    | 0     | 1    | 1    | 0   |\n",
    "| glitters | 0     | 0   | 1    | 0        | 1    | 0    | 0     | 0    | 0    | 0   |\n",
    "| is       | 0     | 1   | 0    | 1        | 0    | 1    | 0     | 1    | 0    | 0   |\n",
    "| not      | 0     | 0   | 0    | 0        | 1    | 0    | 1     | 0    | 0    | 0   |\n",
    "| gold     | 0     | 0   | 0    | 0        | 0    | 1    | 0     | 0    | 0    | 1   |\n",
    "| well     | 0     | 0   | 1    | 0        | 1    | 0    | 0     | 0    | 1    | 1   |\n",
    "| ends     | 0     | 0   | 1    | 0        | 0    | 0    | 0     | 1    | 0    | 0   |\n",
    "| END      | 0     | 0   | 0    | 0        | 0    | 0    | 1     | 1    | 0    | 0   |\n",
    "\n",
    "**Note:** In NLP, we often add START and END tokens to represent the beginning and end of sentences, paragraphs or documents. In thise case we imagine START and END tokens encapsulating each document, e.g., \"START All that glitters is not gold END\", and include these tokens in our co-occurrence counts.\n",
    "\n",
    "The rows (or columns) of this matrix provide one type of word vectors (those based on word-word co-occurrence), but the vectors will be large in general (linear in the number of distinct words in a corpus). Thus, our next step is to run *dimensionality reduction*. In particular, we will run *SVD (Singular Value Decomposition)*, which is a kind of generalized *PCA (Principal Components Analysis)* to select the top $k$ principal components. Here's a visualization of dimensionality reduction with SVD. In this picture our co-occurrence matrix is $A$ with $n$ rows corresponding to $n$ words. We obtain a full matrix decomposition, with the singular values ordered in the diagonal $S$ matrix, and our new, shorter length-$k$ word vectors in $U_k$.\n",
    "\n",
    "![Picture of an SVD](imgs/svd.png \"SVD\")\n",
    "\n",
    "This reduced-dimensionality co-occurrence representation preserves semantic relationships between words, e.g. *doctor* and *hospital* will be closer than *doctor* and *dog*. \n",
    "\n",
    "**Notes:** If you can barely remember what an eigenvalue is, here's [a slow, friendly introduction to SVD](https://davetang.org/file/Singular_Value_Decomposition_Tutorial.pdf). If you want to learn more thoroughly about PCA or SVD, feel free to check out lectures [7](https://web.stanford.edu/class/cs168/l/l7.pdf), [8](http://theory.stanford.edu/~tim/s15/l/l8.pdf), and [9](https://web.stanford.edu/class/cs168/l/l9.pdf) of CS168. These course notes provide a great high-level treatment of these general purpose algorithms. Though, for the purpose of this class, you only need to know how to extract the k-dimensional embeddings by utilizing pre-programmed implementations of these algorithms from the numpy, scipy, or sklearn python packages. In practice, it is challenging to apply full SVD to large corpora because of the memory needed to perform PCA or SVD. However, if you only want the top $k$ vector components for relatively small $k$ — known as *[Truncated SVD](https://en.wikipedia.org/wiki/Singular_value_decomposition#Truncated_SVD)* — then there are reasonably scalable techniques to compute those iteratively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Co-Occurrence Word Embeddings\n",
    "\n",
    "Here, we will be using the Reuters (business and financial news) corpus. If you haven't run the import cell at the top of this page, please run it now (click it and press SHIFT-RETURN). The corpus consists of 10,788 news documents totaling 1.3 million words. These documents span 90 categories and are split into train and test. For more details, please see https://www.nltk.org/book/ch02.html. We provide a `read_corpus` function below that pulls out only articles from the \"crude\" (i.e. news articles about oil, gas, etc.) category. The function also adds START and END tokens to each of the documents, and lowercases words. You do **not** have perform any other kind of pre-processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_corpus(category=\"crude\"):\n",
    "    \"\"\" Read files from the specified Reuter's category.\n",
    "        Params:\n",
    "            category (string): category name\n",
    "        Return:\n",
    "            list of lists, with words from each of the processed files\n",
    "    \"\"\"\n",
    "    files = reuters.fileids(category)\n",
    "    return [[START_TOKEN] + [w.lower() for w in list(reuters.words(f))] + [END_TOKEN] for f in files]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look what these documents are like…."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['<START>', 'japan', 'to', 'revise', 'long', '-', 'term', 'energy', 'demand', 'downwards', 'the',\n",
      "  'ministry', 'of', 'international', 'trade', 'and', 'industry', '(', 'miti', ')', 'will', 'revise',\n",
      "  'its', 'long', '-', 'term', 'energy', 'supply', '/', 'demand', 'outlook', 'by', 'august', 'to',\n",
      "  'meet', 'a', 'forecast', 'downtrend', 'in', 'japanese', 'energy', 'demand', ',', 'ministry',\n",
      "  'officials', 'said', '.', 'miti', 'is', 'expected', 'to', 'lower', 'the', 'projection', 'for',\n",
      "  'primary', 'energy', 'supplies', 'in', 'the', 'year', '2000', 'to', '550', 'mln', 'kilolitres',\n",
      "  '(', 'kl', ')', 'from', '600', 'mln', ',', 'they', 'said', '.', 'the', 'decision', 'follows',\n",
      "  'the', 'emergence', 'of', 'structural', 'changes', 'in', 'japanese', 'industry', 'following',\n",
      "  'the', 'rise', 'in', 'the', 'value', 'of', 'the', 'yen', 'and', 'a', 'decline', 'in', 'domestic',\n",
      "  'electric', 'power', 'demand', '.', 'miti', 'is', 'planning', 'to', 'work', 'out', 'a', 'revised',\n",
      "  'energy', 'supply', '/', 'demand', 'outlook', 'through', 'deliberations', 'of', 'committee',\n",
      "  'meetings', 'of', 'the', 'agency', 'of', 'natural', 'resources', 'and', 'energy', ',', 'the',\n",
      "  'officials', 'said', '.', 'they', 'said', 'miti', 'will', 'also', 'review', 'the', 'breakdown',\n",
      "  'of', 'energy', 'supply', 'sources', ',', 'including', 'oil', ',', 'nuclear', ',', 'coal', 'and',\n",
      "  'natural', 'gas', '.', 'nuclear', 'energy', 'provided', 'the', 'bulk', 'of', 'japan', \"'\", 's',\n",
      "  'electric', 'power', 'in', 'the', 'fiscal', 'year', 'ended', 'march', '31', ',', 'supplying',\n",
      "  'an', 'estimated', '27', 'pct', 'on', 'a', 'kilowatt', '/', 'hour', 'basis', ',', 'followed',\n",
      "  'by', 'oil', '(', '23', 'pct', ')', 'and', 'liquefied', 'natural', 'gas', '(', '21', 'pct', '),',\n",
      "  'they', 'noted', '.', '<END>'],\n",
      " ['<START>', 'energy', '/', 'u', '.', 's', '.', 'petrochemical', 'industry', 'cheap', 'oil',\n",
      "  'feedstocks', ',', 'the', 'weakened', 'u', '.', 's', '.', 'dollar', 'and', 'a', 'plant',\n",
      "  'utilization', 'rate', 'approaching', '90', 'pct', 'will', 'propel', 'the', 'streamlined', 'u',\n",
      "  '.', 's', '.', 'petrochemical', 'industry', 'to', 'record', 'profits', 'this', 'year', ',',\n",
      "  'with', 'growth', 'expected', 'through', 'at', 'least', '1990', ',', 'major', 'company',\n",
      "  'executives', 'predicted', '.', 'this', 'bullish', 'outlook', 'for', 'chemical', 'manufacturing',\n",
      "  'and', 'an', 'industrywide', 'move', 'to', 'shed', 'unrelated', 'businesses', 'has', 'prompted',\n",
      "  'gaf', 'corp', '&', 'lt', ';', 'gaf', '>,', 'privately', '-', 'held', 'cain', 'chemical', 'inc',\n",
      "  ',', 'and', 'other', 'firms', 'to', 'aggressively', 'seek', 'acquisitions', 'of', 'petrochemical',\n",
      "  'plants', '.', 'oil', 'companies', 'such', 'as', 'ashland', 'oil', 'inc', '&', 'lt', ';', 'ash',\n",
      "  '>,', 'the', 'kentucky', '-', 'based', 'oil', 'refiner', 'and', 'marketer', ',', 'are', 'also',\n",
      "  'shopping', 'for', 'money', '-', 'making', 'petrochemical', 'businesses', 'to', 'buy', '.', '\"',\n",
      "  'i', 'see', 'us', 'poised', 'at', 'the', 'threshold', 'of', 'a', 'golden', 'period', ',\"', 'said',\n",
      "  'paul', 'oreffice', ',', 'chairman', 'of', 'giant', 'dow', 'chemical', 'co', '&', 'lt', ';',\n",
      "  'dow', '>,', 'adding', ',', '\"', 'there', \"'\", 's', 'no', 'major', 'plant', 'capacity', 'being',\n",
      "  'added', 'around', 'the', 'world', 'now', '.', 'the', 'whole', 'game', 'is', 'bringing', 'out',\n",
      "  'new', 'products', 'and', 'improving', 'the', 'old', 'ones', '.\"', 'analysts', 'say', 'the',\n",
      "  'chemical', 'industry', \"'\", 's', 'biggest', 'customers', ',', 'automobile', 'manufacturers',\n",
      "  'and', 'home', 'builders', 'that', 'use', 'a', 'lot', 'of', 'paints', 'and', 'plastics', ',',\n",
      "  'are', 'expected', 'to', 'buy', 'quantities', 'this', 'year', '.', 'u', '.', 's', '.',\n",
      "  'petrochemical', 'plants', 'are', 'currently', 'operating', 'at', 'about', '90', 'pct',\n",
      "  'capacity', ',', 'reflecting', 'tighter', 'supply', 'that', 'could', 'hike', 'product', 'prices',\n",
      "  'by', '30', 'to', '40', 'pct', 'this', 'year', ',', 'said', 'john', 'dosher', ',', 'managing',\n",
      "  'director', 'of', 'pace', 'consultants', 'inc', 'of', 'houston', '.', 'demand', 'for', 'some',\n",
      "  'products', 'such', 'as', 'styrene', 'could', 'push', 'profit', 'margins', 'up', 'by', 'as',\n",
      "  'much', 'as', '300', 'pct', ',', 'he', 'said', '.', 'oreffice', ',', 'speaking', 'at', 'a',\n",
      "  'meeting', 'of', 'chemical', 'engineers', 'in', 'houston', ',', 'said', 'dow', 'would', 'easily',\n",
      "  'top', 'the', '741', 'mln', 'dlrs', 'it', 'earned', 'last', 'year', 'and', 'predicted', 'it',\n",
      "  'would', 'have', 'the', 'best', 'year', 'in', 'its', 'history', '.', 'in', '1985', ',', 'when',\n",
      "  'oil', 'prices', 'were', 'still', 'above', '25', 'dlrs', 'a', 'barrel', 'and', 'chemical',\n",
      "  'exports', 'were', 'adversely', 'affected', 'by', 'the', 'strong', 'u', '.', 's', '.', 'dollar',\n",
      "  ',', 'dow', 'had', 'profits', 'of', '58', 'mln', 'dlrs', '.', '\"', 'i', 'believe', 'the',\n",
      "  'entire', 'chemical', 'industry', 'is', 'headed', 'for', 'a', 'record', 'year', 'or', 'close',\n",
      "  'to', 'it', ',\"', 'oreffice', 'said', '.', 'gaf', 'chairman', 'samuel', 'heyman', 'estimated',\n",
      "  'that', 'the', 'u', '.', 's', '.', 'chemical', 'industry', 'would', 'report', 'a', '20', 'pct',\n",
      "  'gain', 'in', 'profits', 'during', '1987', '.', 'last', 'year', ',', 'the', 'domestic',\n",
      "  'industry', 'earned', 'a', 'total', 'of', '13', 'billion', 'dlrs', ',', 'a', '54', 'pct', 'leap',\n",
      "  'from', '1985', '.', 'the', 'turn', 'in', 'the', 'fortunes', 'of', 'the', 'once', '-', 'sickly',\n",
      "  'chemical', 'industry', 'has', 'been', 'brought', 'about', 'by', 'a', 'combination', 'of', 'luck',\n",
      "  'and', 'planning', ',', 'said', 'pace', \"'\", 's', 'john', 'dosher', '.', 'dosher', 'said', 'last',\n",
      "  'year', \"'\", 's', 'fall', 'in', 'oil', 'prices', 'made', 'feedstocks', 'dramatically', 'cheaper',\n",
      "  'and', 'at', 'the', 'same', 'time', 'the', 'american', 'dollar', 'was', 'weakening', 'against',\n",
      "  'foreign', 'currencies', '.', 'that', 'helped', 'boost', 'u', '.', 's', '.', 'chemical',\n",
      "  'exports', '.', 'also', 'helping', 'to', 'bring', 'supply', 'and', 'demand', 'into', 'balance',\n",
      "  'has', 'been', 'the', 'gradual', 'market', 'absorption', 'of', 'the', 'extra', 'chemical',\n",
      "  'manufacturing', 'capacity', 'created', 'by', 'middle', 'eastern', 'oil', 'producers', 'in',\n",
      "  'the', 'early', '1980s', '.', 'finally', ',', 'virtually', 'all', 'major', 'u', '.', 's', '.',\n",
      "  'chemical', 'manufacturers', 'have', 'embarked', 'on', 'an', 'extensive', 'corporate',\n",
      "  'restructuring', 'program', 'to', 'mothball', 'inefficient', 'plants', ',', 'trim', 'the',\n",
      "  'payroll', 'and', 'eliminate', 'unrelated', 'businesses', '.', 'the', 'restructuring', 'touched',\n",
      "  'off', 'a', 'flurry', 'of', 'friendly', 'and', 'hostile', 'takeover', 'attempts', '.', 'gaf', ',',\n",
      "  'which', 'made', 'an', 'unsuccessful', 'attempt', 'in', '1985', 'to', 'acquire', 'union',\n",
      "  'carbide', 'corp', '&', 'lt', ';', 'uk', '>,', 'recently', 'offered', 'three', 'billion', 'dlrs',\n",
      "  'for', 'borg', 'warner', 'corp', '&', 'lt', ';', 'bor', '>,', 'a', 'chicago', 'manufacturer',\n",
      "  'of', 'plastics', 'and', 'chemicals', '.', 'another', 'industry', 'powerhouse', ',', 'w', '.',\n",
      "  'r', '.', 'grace', '&', 'lt', ';', 'gra', '>', 'has', 'divested', 'its', 'retailing', ',',\n",
      "  'restaurant', 'and', 'fertilizer', 'businesses', 'to', 'raise', 'cash', 'for', 'chemical',\n",
      "  'acquisitions', '.', 'but', 'some', 'experts', 'worry', 'that', 'the', 'chemical', 'industry',\n",
      "  'may', 'be', 'headed', 'for', 'trouble', 'if', 'companies', 'continue', 'turning', 'their',\n",
      "  'back', 'on', 'the', 'manufacturing', 'of', 'staple', 'petrochemical', 'commodities', ',', 'such',\n",
      "  'as', 'ethylene', ',', 'in', 'favor', 'of', 'more', 'profitable', 'specialty', 'chemicals',\n",
      "  'that', 'are', 'custom', '-', 'designed', 'for', 'a', 'small', 'group', 'of', 'buyers', '.', '\"',\n",
      "  'companies', 'like', 'dupont', '&', 'lt', ';', 'dd', '>', 'and', 'monsanto', 'co', '&', 'lt', ';',\n",
      "  'mtc', '>', 'spent', 'the', 'past', 'two', 'or', 'three', 'years', 'trying', 'to', 'get', 'out',\n",
      "  'of', 'the', 'commodity', 'chemical', 'business', 'in', 'reaction', 'to', 'how', 'badly', 'the',\n",
      "  'market', 'had', 'deteriorated', ',\"', 'dosher', 'said', '.', '\"', 'but', 'i', 'think', 'they',\n",
      "  'will', 'eventually', 'kill', 'the', 'margins', 'on', 'the', 'profitable', 'chemicals', 'in',\n",
      "  'the', 'niche', 'market', '.\"', 'some', 'top', 'chemical', 'executives', 'share', 'the',\n",
      "  'concern', '.', '\"', 'the', 'challenge', 'for', 'our', 'industry', 'is', 'to', 'keep', 'from',\n",
      "  'getting', 'carried', 'away', 'and', 'repeating', 'past', 'mistakes', ',\"', 'gaf', \"'\", 's',\n",
      "  'heyman', 'cautioned', '.', '\"', 'the', 'shift', 'from', 'commodity', 'chemicals', 'may', 'be',\n",
      "  'ill', '-', 'advised', '.', 'specialty', 'businesses', 'do', 'not', 'stay', 'special', 'long',\n",
      "  '.\"', 'houston', '-', 'based', 'cain', 'chemical', ',', 'created', 'this', 'month', 'by', 'the',\n",
      "  'sterling', 'investment', 'banking', 'group', ',', 'believes', 'it', 'can', 'generate', '700',\n",
      "  'mln', 'dlrs', 'in', 'annual', 'sales', 'by', 'bucking', 'the', 'industry', 'trend', '.',\n",
      "  'chairman', 'gordon', 'cain', ',', 'who', 'previously', 'led', 'a', 'leveraged', 'buyout', 'of',\n",
      "  'dupont', \"'\", 's', 'conoco', 'inc', \"'\", 's', 'chemical', 'business', ',', 'has', 'spent', '1',\n",
      "  '.', '1', 'billion', 'dlrs', 'since', 'january', 'to', 'buy', 'seven', 'petrochemical', 'plants',\n",
      "  'along', 'the', 'texas', 'gulf', 'coast', '.', 'the', 'plants', 'produce', 'only', 'basic',\n",
      "  'commodity', 'petrochemicals', 'that', 'are', 'the', 'building', 'blocks', 'of', 'specialty',\n",
      "  'products', '.', '\"', 'this', 'kind', 'of', 'commodity', 'chemical', 'business', 'will', 'never',\n",
      "  'be', 'a', 'glamorous', ',', 'high', '-', 'margin', 'business', ',\"', 'cain', 'said', ',',\n",
      "  'adding', 'that', 'demand', 'is', 'expected', 'to', 'grow', 'by', 'about', 'three', 'pct',\n",
      "  'annually', '.', 'garo', 'armen', ',', 'an', 'analyst', 'with', 'dean', 'witter', 'reynolds', ',',\n",
      "  'said', 'chemical', 'makers', 'have', 'also', 'benefitted', 'by', 'increasing', 'demand', 'for',\n",
      "  'plastics', 'as', 'prices', 'become', 'more', 'competitive', 'with', 'aluminum', ',', 'wood',\n",
      "  'and', 'steel', 'products', '.', 'armen', 'estimated', 'the', 'upturn', 'in', 'the', 'chemical',\n",
      "  'business', 'could', 'last', 'as', 'long', 'as', 'four', 'or', 'five', 'years', ',', 'provided',\n",
      "  'the', 'u', '.', 's', '.', 'economy', 'continues', 'its', 'modest', 'rate', 'of', 'growth', '.',\n",
      "  '<END>'],\n",
      " ['<START>', 'turkey', 'calls', 'for', 'dialogue', 'to', 'solve', 'dispute', 'turkey', 'said',\n",
      "  'today', 'its', 'disputes', 'with', 'greece', ',', 'including', 'rights', 'on', 'the',\n",
      "  'continental', 'shelf', 'in', 'the', 'aegean', 'sea', ',', 'should', 'be', 'solved', 'through',\n",
      "  'negotiations', '.', 'a', 'foreign', 'ministry', 'statement', 'said', 'the', 'latest', 'crisis',\n",
      "  'between', 'the', 'two', 'nato', 'members', 'stemmed', 'from', 'the', 'continental', 'shelf',\n",
      "  'dispute', 'and', 'an', 'agreement', 'on', 'this', 'issue', 'would', 'effect', 'the', 'security',\n",
      "  ',', 'economy', 'and', 'other', 'rights', 'of', 'both', 'countries', '.', '\"', 'as', 'the',\n",
      "  'issue', 'is', 'basicly', 'political', ',', 'a', 'solution', 'can', 'only', 'be', 'found', 'by',\n",
      "  'bilateral', 'negotiations', ',\"', 'the', 'statement', 'said', '.', 'greece', 'has', 'repeatedly',\n",
      "  'said', 'the', 'issue', 'was', 'legal', 'and', 'could', 'be', 'solved', 'at', 'the',\n",
      "  'international', 'court', 'of', 'justice', '.', 'the', 'two', 'countries', 'approached', 'armed',\n",
      "  'confrontation', 'last', 'month', 'after', 'greece', 'announced', 'it', 'planned', 'oil',\n",
      "  'exploration', 'work', 'in', 'the', 'aegean', 'and', 'turkey', 'said', 'it', 'would', 'also',\n",
      "  'search', 'for', 'oil', '.', 'a', 'face', '-', 'off', 'was', 'averted', 'when', 'turkey',\n",
      "  'confined', 'its', 'research', 'to', 'territorrial', 'waters', '.', '\"', 'the', 'latest',\n",
      "  'crises', 'created', 'an', 'historic', 'opportunity', 'to', 'solve', 'the', 'disputes', 'between',\n",
      "  'the', 'two', 'countries', ',\"', 'the', 'foreign', 'ministry', 'statement', 'said', '.', 'turkey',\n",
      "  \"'\", 's', 'ambassador', 'in', 'athens', ',', 'nazmi', 'akiman', ',', 'was', 'due', 'to', 'meet',\n",
      "  'prime', 'minister', 'andreas', 'papandreou', 'today', 'for', 'the', 'greek', 'reply', 'to', 'a',\n",
      "  'message', 'sent', 'last', 'week', 'by', 'turkish', 'prime', 'minister', 'turgut', 'ozal', '.',\n",
      "  'the', 'contents', 'of', 'the', 'message', 'were', 'not', 'disclosed', '.', '<END>']]\n"
     ]
    }
   ],
   "source": [
    "reuters_corpus = read_corpus()\n",
    "pprint.pprint(reuters_corpus[:3], compact=True, width=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1.1: Implement `distinct_words` [code] (2 points)\n",
    "\n",
    "Write a method to work out the distinct words (word types) that occur in the corpus. You can do this with `for` loops, but it's more efficient to do it with Python list comprehensions. In particular, [this](https://coderwall.com/p/rcmaea/flatten-a-list-of-lists-in-one-line-in-python) may be useful to flatten a list of lists. If you're not familiar with Python list comprehensions in general, here's [more information](https://python-3-patterns-idioms-test.readthedocs.io/en/latest/Comprehensions.html).\n",
    "\n",
    "You may find it useful to use [Python sets](https://www.w3schools.com/python/python_sets.asp) to remove duplicate words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distinct_words(corpus):\n",
    "    \"\"\" Determine a list of distinct words for the corpus.\n",
    "        Params:\n",
    "            corpus (list of list of strings): corpus of documents\n",
    "        Return:\n",
    "            corpus_words (list of strings): list of distinct words across the corpus, sorted (using python 'sorted' function)\n",
    "            num_corpus_words (integer): number of distinct words across the corpus\n",
    "    \"\"\"\n",
    "    corpus_words = []\n",
    "    num_corpus_words = -1\n",
    "    \n",
    "    # ------------------\n",
    "    # Write your implementation here.\n",
    "    corpus_words = [word for doc in corpus for word in doc]\n",
    "    corpus_words = sorted(set(corpus_words))\n",
    "    num_corpus_words = len(corpus_words)\n",
    "\n",
    "    # ------------------\n",
    "\n",
    "    return corpus_words, num_corpus_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "Passed All Tests!\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# ---------------------\n",
    "# Run this sanity check\n",
    "# Note that this not an exhaustive check for correctness.\n",
    "# ---------------------\n",
    "\n",
    "# Define toy corpus\n",
    "test_corpus = [\"START All that glitters isn't gold END\".split(\" \"), \"START All's well that ends well END\".split(\" \")]\n",
    "test_corpus_words, num_corpus_words = distinct_words(test_corpus)\n",
    "\n",
    "# Correct answers\n",
    "ans_test_corpus_words = sorted(list(set([\"START\", \"All\", \"ends\", \"that\", \"gold\", \"All's\", \"glitters\", \"isn't\", \"well\", \"END\"])))\n",
    "ans_num_corpus_words = len(ans_test_corpus_words)\n",
    "\n",
    "# Test correct number of words\n",
    "assert(num_corpus_words == ans_num_corpus_words), \"Incorrect number of distinct words. Correct: {}. Yours: {}\".format(ans_num_corpus_words, num_corpus_words)\n",
    "\n",
    "# Test correct words\n",
    "assert (test_corpus_words == ans_test_corpus_words), \"Incorrect corpus_words.\\nCorrect: {}\\nYours:   {}\".format(str(ans_test_corpus_words), str(test_corpus_words))\n",
    "\n",
    "# Print Success\n",
    "print (\"-\" * 80)\n",
    "print(\"Passed All Tests!\")\n",
    "print (\"-\" * 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1.2: Implement `compute_co_occurrence_matrix` [code] (3 points)\n",
    "\n",
    "Write a method that constructs a co-occurrence matrix for a certain window-size $n$ (with a default of 4), considering words $n$ before and $n$ after the word in the center of the window. Here, we start to use `numpy (np)` to represent vectors, matrices, and tensors. If you're not familiar with NumPy, there's a NumPy tutorial in the second half of this cs231n [Python NumPy tutorial](http://cs231n.github.io/python-numpy-tutorial/).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_co_occurrence_matrix(corpus, window_size=4):\n",
    "    \"\"\" Compute co-occurrence matrix for the given corpus and window_size (default of 4).\n",
    "    \n",
    "        Note: Each word in a document should be at the center of a window. Words near edges will have a smaller\n",
    "              number of co-occurring words.\n",
    "              \n",
    "              For example, if we take the document \"START All that glitters is not gold END\" with window size of 4,\n",
    "              \"All\" will co-occur with \"START\", \"that\", \"glitters\", \"is\", and \"not\".\n",
    "    \n",
    "        Params:\n",
    "            corpus (list of list of strings): corpus of documents\n",
    "            window_size (int): size of context window\n",
    "        Return:\n",
    "            M (numpy matrix of shape (number of corpus words, number of corpus words)): \n",
    "                Co-occurence matrix of word counts. \n",
    "                The ordering of the words in the rows/columns should be the same as the ordering of the words given by the distinct_words function.\n",
    "            word2Ind (dict): dictionary that maps word to index (i.e. row/column number) for matrix M.\n",
    "    \"\"\"\n",
    "    words, num_words = distinct_words(corpus)\n",
    "    M = np.zeros((num_words, num_words))\n",
    "    word2Ind = {}\n",
    "    \n",
    "    # ------------------\n",
    "    # Write your implementation here.\n",
    "    for i, word in enumerate(words):\n",
    "        word2Ind[word] = i\n",
    "    \n",
    "    for doc in corpus:\n",
    "        for pos in range(len(doc)):\n",
    "            centerWordInd = word2Ind[doc[pos]]\n",
    "            for i in range(2*window_size):\n",
    "                if i == window_size or pos-window_size+i < 0 or pos-window_size+i >= len(doc):\n",
    "                    continue\n",
    "                contextWordInd = word2Ind[doc[pos-window_size+i]]\n",
    "                M[contextWordInd][centerWordInd] += 1\n",
    "                M[centerWordInd][contextWordInd] += 1\n",
    "\n",
    "    # ------------------\n",
    "\n",
    "    return M, word2Ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "Passed All Tests!\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# ---------------------\n",
    "# Run this sanity check\n",
    "# Note that this is not an exhaustive check for correctness.\n",
    "# ---------------------\n",
    "\n",
    "# Define toy corpus and get student's co-occurrence matrix\n",
    "test_corpus = [\"START All that glitters isn't gold END\".split(\" \"), \"START All's well that ends well END\".split(\" \")]\n",
    "M_test, word2Ind_test = compute_co_occurrence_matrix(test_corpus, window_size=1)\n",
    "\n",
    "# Correct M and word2Ind\n",
    "M_test_ans = np.array( \n",
    "    [[0., 0., 0., 1., 0., 0., 0., 0., 1., 0.,],\n",
    "     [0., 0., 0., 1., 0., 0., 0., 0., 0., 1.,],\n",
    "     [0., 0., 0., 0., 0., 0., 1., 0., 0., 1.,],\n",
    "     [1., 1., 0., 0., 0., 0., 0., 0., 0., 0.,],\n",
    "     [0., 0., 0., 0., 0., 0., 0., 0., 1., 1.,],\n",
    "     [0., 0., 0., 0., 0., 0., 0., 1., 1., 0.,],\n",
    "     [0., 0., 1., 0., 0., 0., 0., 1., 0., 0.,],\n",
    "     [0., 0., 0., 0., 0., 1., 1., 0., 0., 0.,],\n",
    "     [1., 0., 0., 0., 1., 1., 0., 0., 0., 1.,],\n",
    "     [0., 1., 1., 0., 1., 0., 0., 0., 1., 0.,]]\n",
    ")\n",
    "word2Ind_ans = {'All': 0, \"All's\": 1, 'END': 2, 'START': 3, 'ends': 4, 'glitters': 5, 'gold': 6, \"isn't\": 7, 'that': 8, 'well': 9}\n",
    "\n",
    "# Test correct word2Ind\n",
    "assert (word2Ind_ans == word2Ind_test), \"Your word2Ind is incorrect:\\nCorrect: {}\\nYours: {}\".format(word2Ind_ans, word2Ind_test)\n",
    "\n",
    "# Test correct M shape\n",
    "assert (M_test.shape == M_test_ans.shape), \"M matrix has incorrect shape.\\nCorrect: {}\\nYours: {}\".format(M_test.shape, M_test_ans.shape)\n",
    "\n",
    "# Test correct M values\n",
    "for w1 in word2Ind_ans.keys():\n",
    "    idx1 = word2Ind_ans[w1]\n",
    "    for w2 in word2Ind_ans.keys():\n",
    "        idx2 = word2Ind_ans[w2]\n",
    "        student = M_test[idx1, idx2]\n",
    "        correct = M_test_ans[idx1, idx2]\n",
    "        if student != correct:\n",
    "            print(\"Correct M:\")\n",
    "            print(M_test_ans)\n",
    "            print(\"Your M: \")\n",
    "            print(M_test)\n",
    "            raise AssertionError(\"Incorrect count at index ({}, {})=({}, {}) in matrix M. Yours has {} but should have {}.\".format(idx1, idx2, w1, w2, student, correct))\n",
    "\n",
    "# Print Success\n",
    "print (\"-\" * 80)\n",
    "print(\"Passed All Tests!\")\n",
    "print (\"-\" * 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1.3: Implement `reduce_to_k_dim` [code] (1 point)\n",
    "\n",
    "Construct a method that performs dimensionality reduction on the matrix to produce k-dimensional embeddings. Use SVD to take the top k components and produce a new matrix of k-dimensional embeddings. \n",
    "\n",
    "**Note:** All of numpy, scipy, and scikit-learn (`sklearn`) provide *some* implementation of SVD, but only scipy and sklearn provide an implementation of Truncated SVD, and only sklearn provides an efficient randomized algorithm for calculating large-scale Truncated SVD. So please use [sklearn.decomposition.TruncatedSVD](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_to_k_dim(M, k=2):\n",
    "    \"\"\" Reduce a co-occurence count matrix of dimensionality (num_corpus_words, num_corpus_words)\n",
    "        to a matrix of dimensionality (num_corpus_words, k) using the following SVD function from Scikit-Learn:\n",
    "            - http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html\n",
    "    \n",
    "        Params:\n",
    "            M (numpy matrix of shape (number of corpus words, number of corpus words)): co-occurence matrix of word counts\n",
    "            k (int): embedding size of each word after dimension reduction\n",
    "        Return:\n",
    "            M_reduced (numpy matrix of shape (number of corpus words, k)): matrix of k-dimensioal word embeddings.\n",
    "                    In terms of the SVD from math class, this actually returns U * S\n",
    "    \"\"\"    \n",
    "    n_iters = 10     # Use this parameter in your call to `TruncatedSVD`\n",
    "    M_reduced = None\n",
    "    print(\"Running Truncated SVD over %i words...\" % (M.shape[0]))\n",
    "    \n",
    "    # ------------------\n",
    "    # Write your implementation here.\n",
    "    truncSVD = TruncatedSVD(k)\n",
    "    M_reduced = truncSVD.fit_transform(M)\n",
    "\n",
    "    # ------------------\n",
    "\n",
    "    print(\"Done.\")\n",
    "    return M_reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Truncated SVD over 10 words...\n",
      "Done.\n",
      "--------------------------------------------------------------------------------\n",
      "Passed All Tests!\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# ---------------------\n",
    "# Run this sanity check\n",
    "# Note that this not an exhaustive check for correctness \n",
    "# In fact we only check that your M_reduced has the right dimensions.\n",
    "# ---------------------\n",
    "\n",
    "# Define toy corpus and run student code\n",
    "test_corpus = [\"START All that glitters isn't gold END\".split(\" \"), \"START All's well that ends well END\".split(\" \")]\n",
    "M_test, word2Ind_test = compute_co_occurrence_matrix(test_corpus, window_size=1)\n",
    "M_test_reduced = reduce_to_k_dim(M_test, k=2)\n",
    "\n",
    "# Test proper dimensions\n",
    "assert (M_test_reduced.shape[0] == 10), \"M_reduced has {} rows; should have {}\".format(M_test_reduced.shape[0], 10)\n",
    "assert (M_test_reduced.shape[1] == 2), \"M_reduced has {} columns; should have {}\".format(M_test_reduced.shape[1], 2)\n",
    "\n",
    "# Print Success\n",
    "print (\"-\" * 80)\n",
    "print(\"Passed All Tests!\")\n",
    "print (\"-\" * 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1.4: Implement `plot_embeddings` [code] (1 point)\n",
    "\n",
    "Here you will write a function to plot a set of 2D vectors in 2D space. For graphs, we will use Matplotlib (`plt`).\n",
    "\n",
    "For this example, you may find it useful to adapt [this code](https://www.pythonmembers.club/2018/05/08/matplotlib-scatter-plot-annotate-set-text-at-label-each-point/). In the future, a good way to make a plot is to look at [the Matplotlib gallery](https://matplotlib.org/gallery/index.html), find a plot that looks somewhat like what you want, and adapt the code they give."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_embeddings(M_reduced, word2Ind, words):\n",
    "    \"\"\" Plot in a scatterplot the embeddings of the words specified in the list \"words\".\n",
    "        NOTE: do not plot all the words listed in M_reduced / word2Ind.\n",
    "        Include a label next to each point.\n",
    "        \n",
    "        Params:\n",
    "            M_reduced (numpy matrix of shape (number of unique words in the corpus , k)): matrix of k-dimensioal word embeddings\n",
    "            word2Ind (dict): dictionary that maps word to indices for matrix M\n",
    "            words (list of strings): words whose embeddings we want to visualize\n",
    "    \"\"\"\n",
    "\n",
    "    # ------------------\n",
    "    # Write your implementation here.\n",
    "    plt.figure(figsize=(6,6))\n",
    "    for word in words:\n",
    "        x = M_reduced[word2Ind[word]][0]\n",
    "        y = M_reduced[word2Ind[word]][1]\n",
    "        plt.scatter(x, y, marker='x', color='red')\n",
    "        plt.text(x+0.003, y+0.003, word, fontsize=9)\n",
    "    plt.show()\n",
    "\n",
    "    # ------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "Outputted Plot:\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# ---------------------\n",
    "# Run this sanity check\n",
    "# Note that this not an exhaustive check for correctness.\n",
    "# The plot produced should look like the \"test solution plot\" depicted below. \n",
    "# ---------------------\n",
    "\n",
    "print (\"-\" * 80)\n",
    "print (\"Outputted Plot:\")\n",
    "\n",
    "M_reduced_plot_test = np.array([[1, 1], [-1, -1], [1, -1], [-1, 1], [0, 0]])\n",
    "word2Ind_plot_test = {'test1': 0, 'test2': 1, 'test3': 2, 'test4': 3, 'test5': 4}\n",
    "words = ['test1', 'test2', 'test3', 'test4', 'test5']\n",
    "plot_embeddings(M_reduced_plot_test, word2Ind_plot_test, words)\n",
    "\n",
    "print (\"-\" * 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red>**Test Plot Solution**</font>\n",
    "<br>\n",
    "<img src=\"imgs/test_plot.png\" width=40% style=\"float: left;\"> </img>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1.5: Co-Occurrence Plot Analysis [written] (3 points)\n",
    "\n",
    "Now we will put together all the parts you have written! We will compute the co-occurrence matrix with fixed window of 4, over the Reuters \"crude\" corpus. Then we will use TruncatedSVD to compute 2-dimensional embeddings of each word. TruncatedSVD returns U\\*S, so we normalize the returned vectors, so that all the vectors will appear around the unit circle (therefore closeness is directional closeness). **Note**: The line of code below that does the normalizing uses the NumPy concept of *broadcasting*. If you don't know about broadcasting, check out\n",
    "[Computation on Arrays: Broadcasting by Jake VanderPlas](https://jakevdp.github.io/PythonDataScienceHandbook/02.05-computation-on-arrays-broadcasting.html).\n",
    "\n",
    "Run the below cell to produce the plot. It'll probably take a few seconds to run. What clusters together in 2-dimensional embedding space? What doesn't cluster together that you might think should have?  **Note:** \"bpd\" stands for \"barrels per day\" and is a commonly used abbreviation in crude oil topic articles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Truncated SVD over 8185 words...\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------------\n",
    "# Run This Cell to Produce Your Plot\n",
    "# ------------------------------\n",
    "reuters_corpus = read_corpus()\n",
    "M_co_occurrence, word2Ind_co_occurrence = compute_co_occurrence_matrix(reuters_corpus)\n",
    "M_reduced_co_occurrence = reduce_to_k_dim(M_co_occurrence, k=2)\n",
    "\n",
    "# Rescale (normalize) the rows to make them each of unit-length\n",
    "M_lengths = np.linalg.norm(M_reduced_co_occurrence, axis=1)\n",
    "M_normalized = M_reduced_co_occurrence / M_lengths[:, np.newaxis] # broadcasting\n",
    "\n",
    "words = ['barrels', 'bpd', 'ecuador', 'energy', 'industry', 'kuwait', 'oil', 'output', 'petroleum', 'venezuela']\n",
    "plot_embeddings(M_normalized, word2Ind_co_occurrence, words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=\"red\">Write your answer here.</font>\n",
    "There're three clusters that we can see:\n",
    "\n",
    "* petroleum, industry\n",
    "* energy, oil\n",
    "* ecuador, venezuela, kuwait\n",
    "\n",
    "\n",
    "bpd and barrels and output should cluster together, but they have less co-occurance in given datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Prediction-Based Word Vectors (15 points)\n",
    "\n",
    "As discussed in class, more recently prediction-based word vectors have come into fashion, e.g. word2vec. Here, we shall explore the embeddings produced by word2vec. Please revisit the class notes and lecture slides for more details on the word2vec algorithm. If you're feeling adventurous, challenge yourself and try reading the [original paper](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf).\n",
    "\n",
    "Then run the following cells to load the word2vec vectors into memory. **Note**: This might take several minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_word2vec():\n",
    "    \"\"\" Load Word2Vec Vectors\n",
    "        Return:\n",
    "            wv_from_bin: All 3 million embeddings, each lengh 300\n",
    "    \"\"\"\n",
    "    import gensim.downloader as api\n",
    "    wv_from_bin = api.load(\"word2vec-google-news-300\")\n",
    "    vocab = list(wv_from_bin.vocab.keys())\n",
    "    print(\"Loaded vocab size %i\" % len(vocab))\n",
    "    return wv_from_bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\smart_open\\smart_open_lib.py:398: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
      "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded vocab size 3000000\n"
     ]
    }
   ],
   "source": [
    "# -----------------------------------\n",
    "# Run Cell to Load Word Vectors\n",
    "# Note: This may take several minutes\n",
    "# -----------------------------------\n",
    "wv_from_bin = load_word2vec()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note: If you are receiving out of memory issues on your local machine, try closing other applications to free more memory on your device. You may want to try restarting your machine so that you can free up extra memory. Then immediately run the jupyter notebook and see if you can load the word vectors properly. If you still have problems with loading the embeddings onto your local machine after this, please follow the Piazza instructions, as how to run remotely on Stanford Farmshare machines.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reducing dimensionality of Word2Vec Word Embeddings\n",
    "Let's directly compare the word2vec embeddings to those of the co-occurrence matrix. Run the following cells to:\n",
    "\n",
    "1. Put the 3 million word2vec vectors into a matrix M\n",
    "2. Run reduce_to_k_dim (your Truncated SVD function) to reduce the vectors from 300-dimensional to 2-dimensional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_matrix_of_vectors(wv_from_bin, required_words=['barrels', 'bpd', 'ecuador', 'energy', 'industry', 'kuwait', 'oil', 'output', 'petroleum', 'venezuela']):\n",
    "    \"\"\" Put the word2vec vectors into a matrix M.\n",
    "        Param:\n",
    "            wv_from_bin: KeyedVectors object; the 3 million word2vec vectors loaded from file\n",
    "        Return:\n",
    "            M: numpy matrix shape (num words, 300) containing the vectors\n",
    "            word2Ind: dictionary mapping each word to its row number in M\n",
    "    \"\"\"\n",
    "    import random\n",
    "    words = list(wv_from_bin.vocab.keys())\n",
    "    print(\"Shuffling words ...\")\n",
    "    random.shuffle(words)\n",
    "    words = words[:10000]\n",
    "    print(\"Putting %i words into word2Ind and matrix M...\" % len(words))\n",
    "    word2Ind = {}\n",
    "    M = []\n",
    "    curInd = 0\n",
    "    for w in words:\n",
    "        try:\n",
    "            M.append(wv_from_bin.word_vec(w))\n",
    "            word2Ind[w] = curInd\n",
    "            curInd += 1\n",
    "        except KeyError:\n",
    "            continue\n",
    "    for w in required_words:\n",
    "        try:\n",
    "            M.append(wv_from_bin.word_vec(w))\n",
    "            word2Ind[w] = curInd\n",
    "            curInd += 1\n",
    "        except KeyError:\n",
    "            continue\n",
    "    M = np.stack(M)\n",
    "    print(\"Done.\")\n",
    "    return M, word2Ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shuffling words ...\n",
      "Putting 10000 words into word2Ind and matrix M...\n",
      "Done.\n",
      "Running Truncated SVD over 10010 words...\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# -----------------------------------------------------------------\n",
    "# Run Cell to Reduce 300-Dimensinal Word Embeddings to k Dimensions\n",
    "# Note: This may take several minutes\n",
    "# -----------------------------------------------------------------\n",
    "M, word2Ind = get_matrix_of_vectors(wv_from_bin)\n",
    "M_reduced = reduce_to_k_dim(M, k=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2.1: Word2Vec Plot Analysis [written] (4 points)\n",
    "\n",
    "Run the cell below to plot the 2D word2vec embeddings for `['barrels', 'bpd', 'ecuador', 'energy', 'industry', 'kuwait', 'oil', 'output', 'petroleum', 'venezuela']`.\n",
    "\n",
    "What clusters together in 2-dimensional embedding space? What doesn't cluster together that you might think should have? How is the plot different from the one generated earlier from the co-occurrence matrix?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "words = ['barrels', 'bpd', 'ecuador', 'energy', 'industry', 'kuwait', 'oil', 'output', 'petroleum', 'venezuela']\n",
    "plot_embeddings(M_reduced, word2Ind, words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=\"red\">Write your answer here.</font>\n",
    "According to distance, there are two clusters:\n",
    "\n",
    "* energy, industry\n",
    "* oil, petroleum\n",
    "\n",
    "These word embeddings are more separated compared to the co-occurence word embeddings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cosine Similarity\n",
    "Now that we have word vectors, we need a way to quantify the similarity between individual words, according to these vectors. One such metric is cosine-similarity. We will be using this to find words that are \"close\" and \"far\" from one another.\n",
    "\n",
    "We can think of n-dimensional vectors as points in n-dimensional space. If we take this perspective L1 and L2 Distances help quantify the amount of space \"we must travel\" to get between these two points. Another approach is to examine the angle between two vectors. From trigonometry we know that:\n",
    "\n",
    "<img src=\"imgs/inner_product.png\" width=20% style=\"float: center;\"></img>\n",
    "\n",
    "Instead of computing the actual angle, we can leave the similarity in terms of $similarity = cos(\\Theta)$. Formally the [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity) $s$ between two vectors $p$ and $q$ is defined as:\n",
    "\n",
    "$$s = \\frac{p \\cdot q}{||p|| ||q||}, \\textrm{ where } s \\in [-1, 1] $$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2.2: Polysemous Words (2 points) [code + written] \n",
    "Find a [polysemous](https://en.wikipedia.org/wiki/Polysemy) word (for example, \"leaves\" or \"scoop\") such that the top-10 most similar words (according to cosine similarity) contains related words from *both* meanings. For example, \"leaves\" has both \"vanishes\" and \"stalks\" in the top 10, and \"scoop\" has both \"handed_waffle_cone\" and \"lowdown\". You will probably need to try several polysemous words before you find one. Please state the polysemous word you discover and the multiple meanings that occur in the top 10. Why do you think many of the polysemous words you tried didn't work?\n",
    "\n",
    "**Note**: You should use the `wv_from_bin.most_similar(word)` function to get the top 10 similar words. This function ranks all other words in the vocabulary with respect to their cosine similarity to the given word. For further assistance please check the __[GenSim documentation](https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.FastTextKeyedVectors.most_similar)__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('weapons', 0.5423978567123413),\n",
       " ('stimulates_innate_immunity', 0.5276414752006531),\n",
       " ('Copacabana_frantically', 0.5087413787841797),\n",
       " ('armaments', 0.4976296126842499),\n",
       " ('doctors_revived_Coltyn', 0.48752516508102417),\n",
       " ('arm', 0.48622867465019226),\n",
       " ('Copacabana_frantically_waving_flags', 0.47894033789634705),\n",
       " ('Arms', 0.47419047355651855),\n",
       " ('cradling_squirming_infant', 0.4572298228740692),\n",
       " ('bowled_shouldering', 0.4543934464454651)]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ------------------\n",
    "# Write your polysemous word exploration code here.\n",
    "\n",
    "wv_from_bin.most_similar(\"arms\")\n",
    "\n",
    "# ------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=\"red\">Write your answer here.</font>\n",
    "I used the word \"arms\" which shows 2 meanings:\n",
    "1. limb\n",
    "2. armaments(weapons)\n",
    "\n",
    "A reason why many polysemous words don't display different meanings is that these word vectors are built upon google news data in which more often than not words tend to take on the same meanings. Other reasons could be that the top 10 similar words sometimes include different forms of the same word(plural), a completely contrasting word(man to woman) or chunked phrases(Copacabana_frantically_waving_flags) which did not get aptly tokenized for some reason."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2.3: Synonyms & Antonyms (2 points) [code + written] \n",
    "\n",
    "When considering Cosine Similarity, it's often more convenient to think of Cosine Distance, which is simply 1 - Cosine Similarity.\n",
    "\n",
    "Find three words (w1,w2,w3) where w1 and w2 are synonyms and w1 and w3 are antonyms, but Cosine Distance(w1,w3) < Cosine Distance(w1,w2). For example, w1=\"happy\" is closer to w3=\"sad\" than to w2=\"cheerful\". \n",
    "\n",
    "Once you have found your example, please give a possible explanation for why this counter-intuitive result may have happened.\n",
    "\n",
    "You should use the the `wv_from_bin.distance(w1, w2)` function here in order to compute the cosine distance between two words. Please see the __[GenSim documentation](https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.FastTextKeyedVectors.distance)__ for further assistance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Synonyms man, dude have cosine distance: 0.5276602220064749\n",
      "Antonyms man, woman have cosine distance: 0.23359877690046482\n"
     ]
    }
   ],
   "source": [
    "# ------------------\n",
    "# Write your synonym & antonym exploration code here.\n",
    "\n",
    "w1 = \"man\"\n",
    "w2 = \"dude\"\n",
    "w3 = \"woman\"\n",
    "w1_w2_dist = wv_from_bin.distance(w1, w2)\n",
    "w1_w3_dist = wv_from_bin.distance(w1, w3)\n",
    "\n",
    "print(\"Synonyms {}, {} have cosine distance: {}\".format(w1, w2, w1_w2_dist))\n",
    "print(\"Antonyms {}, {} have cosine distance: {}\".format(w1, w3, w1_w3_dist))\n",
    "\n",
    "# ------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=\"red\">Write your answer here.</font>\n",
    "* Some words that are antonyms, but they are usually used in the same context, so their distance is lower than the synonyms.\n",
    "* Even where two words do seem to have more or less identical meanings, they will often be used in different contexts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solving Analogies with Word Vectors\n",
    "Word2Vec vectors have been shown to *sometimes* exhibit the ability to solve analogies. \n",
    "\n",
    "As an example, for the analogy \"man : king :: woman : x\", what is x?\n",
    "\n",
    "In the cell below, we show you how to use word vectors to find x. The `most_similar` function finds words that are most similar to the words in the `positive` list and most dissimilar from the words in the `negative` list. The answer to the analogy will be the word ranked most similar (largest numerical value).\n",
    "\n",
    "**Note:** Further Documentation on the `most_similar` function can be found within the __[GenSim documentation](https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.FastTextKeyedVectors.most_similar)__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('queen', 0.7118192911148071),\n",
      " ('monarch', 0.6189674139022827),\n",
      " ('princess', 0.5902431607246399),\n",
      " ('crown_prince', 0.5499460697174072),\n",
      " ('prince', 0.5377321243286133),\n",
      " ('kings', 0.5236844420433044),\n",
      " ('Queen_Consort', 0.5235945582389832),\n",
      " ('queens', 0.5181134343147278),\n",
      " ('sultan', 0.5098593235015869),\n",
      " ('monarchy', 0.5087411999702454)]\n"
     ]
    }
   ],
   "source": [
    "# Run this cell to answer the analogy -- man : king :: woman : x\n",
    "pprint.pprint(wv_from_bin.most_similar(positive=['woman', 'king'], negative=['man']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2.4: Finding Analogies [code + written]  (2 Points)\n",
    "Find an example of analogy that holds according to these vectors (i.e. the intended word is ranked top). In your solution please state the full analogy in the form x:y :: a:b. If you believe the analogy is complicated, explain why the analogy holds in one or two sentences.\n",
    "\n",
    "**Note**: You may have to try many analogies to find one that works!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('adobe_photoshop', 0.5694045424461365),\n",
      " ('windows_xp', 0.5655165910720825),\n",
      " ('buy_adobe_cs5', 0.5485445261001587),\n",
      " ('quickbooks', 0.5461218357086182),\n",
      " ('unix', 0.5448261499404907),\n",
      " ('macintosh', 0.5436373949050903),\n",
      " ('cs4', 0.5313177108764648),\n",
      " ('autocad', 0.5302920341491699),\n",
      " ('comprar_windows', 0.5286654233932495),\n",
      " ('os', 0.5267903804779053)]\n"
     ]
    }
   ],
   "source": [
    "# ------------------\n",
    "# Write your analogy exploration code here.\n",
    "\n",
    "pprint.pprint(wv_from_bin.most_similar(positive=[\"microsoft\", \"adobe\"], negative=[\"microsoft_office\"]))\n",
    "\n",
    "# ------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=\"red\">Write your answer here.</font>\n",
    "I used microsoft:microsoft_office :: adobe:adobe_photoshop\n",
    "In embedding space, each word meaning is embedded as a point, and each direction also contains a specific meaning, some direction may have the meaning \"is capital of (country)\", or \"is product of (company)\".\n",
    "My analogy could have been complicated but since this is news data, I imagine there would have been atleast a few articles talking about each company and their product."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2.5: Incorrect Analogy [code + written] (1 point)\n",
    "Find an example of analogy that does *not* hold according to these vectors. In your solution, state the intended analogy in the form x:y :: a:b, and state the (incorrect) value of b according to the word vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('pakistan', 0.5850905179977417),\n",
      " ('usa', 0.559581995010376),\n",
      " ('america', 0.5376701354980469),\n",
      " ('sri_lanka', 0.5092425346374512),\n",
      " ('chennai', 0.5061986446380615),\n",
      " ('europe', 0.5057437419891357),\n",
      " ('nigeria', 0.5057042837142944),\n",
      " ('spain', 0.5053790211677551),\n",
      " ('australia', 0.5049945116043091),\n",
      " ('africa', 0.5045742392539978)]\n"
     ]
    }
   ],
   "source": [
    "# ------------------\n",
    "# Write your incorrect analogy exploration code here.\n",
    "\n",
    "pprint.pprint(wv_from_bin.most_similar(positive=[\"england\", \"india\"], negative=[\"english\"]))\n",
    "\n",
    "# ------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=\"red\">Write your answer here.</font>\n",
    "Expected ->\n",
    "england:english :: india:hindi\n",
    "\n",
    "Actual ->\n",
    "england:english :: india:pakistan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2.6: Guided Analysis of Bias in Word Vectors [written] (1 point)\n",
    "\n",
    "It's important to be cognizant of the biases (gender, race, sexual orientation etc.) implicit to our word embeddings.\n",
    "\n",
    "Run the cell below, to examine (a) which terms are most similar to \"woman\" and \"boss\" and most dissimilar to \"man\", and (b) which terms are most similar to \"man\" and \"boss\" and most dissimilar to \"woman\". What do you find in the top 10?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('bosses', 0.5522644519805908),\n",
      " ('manageress', 0.49151360988616943),\n",
      " ('exec', 0.45940813422203064),\n",
      " ('Manageress', 0.45598435401916504),\n",
      " ('receptionist', 0.4474116563796997),\n",
      " ('Jane_Danson', 0.44480544328689575),\n",
      " ('Fiz_Jennie_McAlpine', 0.44275766611099243),\n",
      " ('Coronation_Street_actress', 0.44275566935539246),\n",
      " ('supremo', 0.4409853219985962),\n",
      " ('coworker', 0.43986251950263977)]\n",
      "\n",
      "[('supremo', 0.6097398400306702),\n",
      " ('MOTHERWELL_boss', 0.5489562153816223),\n",
      " ('CARETAKER_boss', 0.5375303626060486),\n",
      " ('Bully_Wee_boss', 0.5333974361419678),\n",
      " ('YEOVIL_Town_boss', 0.5321705341339111),\n",
      " ('head_honcho', 0.5281980037689209),\n",
      " ('manager_Stan_Ternent', 0.525971531867981),\n",
      " ('Viv_Busby', 0.5256162881851196),\n",
      " ('striker_Gabby_Agbonlahor', 0.5250812768936157),\n",
      " ('BARNSLEY_boss', 0.5238943099975586)]\n"
     ]
    }
   ],
   "source": [
    "# Run this cell\n",
    "# Here `positive` indicates the list of words to be similar to and `negative` indicates the list of words to be\n",
    "# most dissimilar from.\n",
    "pprint.pprint(wv_from_bin.most_similar(positive=['woman', 'boss'], negative=['man']))\n",
    "print()\n",
    "pprint.pprint(wv_from_bin.most_similar(positive=['man', 'boss'], negative=['woman']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=\"red\">Write your answer here.</font>\n",
    "man:boss::woman:? => bosses\n",
    "\n",
    "woman:boss::man:? => supremo\n",
    "\n",
    "This shows our data has a gender bias!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2.7: Independent Analysis of Bias in Word Vectors [code + written]  (2 points)\n",
    "\n",
    "Use the `most_similar` function to find another case where some bias is exhibited by the vectors. Please briefly explain the example of bias that you discover."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('waved', 0.6684030890464783),\n",
      " ('Waving', 0.6393646001815796),\n",
      " ('clutching', 0.5857482552528381),\n",
      " ('frantically_waving', 0.5829086303710938),\n",
      " ('waving_flags', 0.5562124252319336),\n",
      " ('clapping', 0.5321706533432007),\n",
      " ('brandishing', 0.523698091506958),\n",
      " ('twirling', 0.5229986906051636),\n",
      " ('draped', 0.5198728442192078),\n",
      " ('flapping', 0.5180848240852356)]\n",
      "\n",
      "[('waved', 0.6639034748077393),\n",
      " ('Waving', 0.5812463760375977),\n",
      " ('frantically_waving', 0.5594050884246826),\n",
      " ('frantically_waved', 0.5502710342407227),\n",
      " ('clapping', 0.548320472240448),\n",
      " ('shouted', 0.5427951216697693),\n",
      " ('waggled', 0.5404790639877319),\n",
      " ('yelled', 0.5302841663360596),\n",
      " ('brandishing', 0.5293518304824829),\n",
      " ('Cars_honked', 0.5256839394569397)]\n"
     ]
    }
   ],
   "source": [
    "# ------------------\n",
    "# Write your bias exploration code here.\n",
    "\n",
    "pprint.pprint(wv_from_bin.most_similar(positive=[\"white\", \"waving\"], negative=[\"black\"]))\n",
    "print()\n",
    "pprint.pprint(wv_from_bin.most_similar(positive=[\"black\",\"waving\"], negative=[\"white\"]))\n",
    "\n",
    "# ------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=\"red\">Write your answer here.</font>\n",
    "In top 10 of black:waving :: white: ?, we don't see any negative meaning words. In other hands, many negative meaning words such as shouted, yelled, Cars_honked appear in white:waving :: black: ?, so there must be an implicit racial bias in term of word embeddings in our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2.8: Thinking About Bias [written] (1 point)\n",
    "\n",
    "What might be the cause of these biases in the word vectors?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color=\"red\">Write your answer here.</font>\n",
    "The biases come from the implicit biases of the data no matter how impartial a source claims to be. It merely reflects many biases in our society such as gender, race, ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color=\"blue\"> Submission Instructions</font>\n",
    "\n",
    "1. Click the Save button at the top of the Jupyter Notebook.\n",
    "2. Please make sure to have entered your SUNET ID above.\n",
    "3. Select Cell -> All Output -> Clear. This will clear all the outputs from all cells (but will keep the content of ll cells). \n",
    "4. Select Cell -> Run All. This will run all the cells in order, and will take several minutes.\n",
    "5. Once you've rerun everything, select File -> Download as -> PDF via LaTeX\n",
    "6. Look at the PDF file and make sure all your solutions are there, displayed correctly. The PDF is the only thing your graders will see!\n",
    "7. Submit your PDF on Gradescope."
   ]
  }
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