CourseMaterials/GensimWordVectorVisualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gensim word vector visualization of various word vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:01:17.082396Z",
     "start_time": "2019-07-08T04:01:13.955679Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Get the interactive Tools for Matplotlib\n",
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from gensim.test.utils import datapath, get_tmpfile\n",
    "from gensim.models import KeyedVectors\n",
    "from gensim.scripts.glove2word2vec import glove2word2vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For looking at word vectors, I'll use Gensim. We also use it in hw1 for word vectors. Gensim isn't really a deep learning package. It's a package for for word and text similarity modeling, which started with (LDA-style) topic models and grew into SVD and neural word representations. But its efficient and scalable, and quite widely used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our homegrown Stanford offering is GloVe word vectors. Gensim doesn't give them first class support, but allows you to convert a file of GloVe vectors into word2vec format. You can download the GloVe vectors from [the Glove page](https://nlp.stanford.edu/projects/glove/). They're inside [this zip file](https://nlp.stanford.edu/data/glove.6B.zip)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(I use the 100d vectors below as a mix between speed and smallness vs. quality. If you try out the 50d vectors, they basically work for similarity but clearly aren't as good for analogy problems. If you load the 300d vectors, they're even better than the 100d vectors.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:01:17.151147Z",
     "start_time": "2019-07-08T04:01:17.085972Z"
    }
   },
   "outputs": [],
   "source": [
    "temp_path = !pwd\n",
    "glove_path = temp_path[0] + '/../embedding/GloVe/glove.6B.100d.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:01:24.133881Z",
     "start_time": "2019-07-08T04:01:17.154646Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400000, 100)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glove_file = datapath(glove_path)\n",
    "word2vec_glove_file = get_tmpfile(\"glove.6B.100d.word2vec.txt\")\n",
    "glove2word2vec(glove_file, word2vec_glove_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:09.884913Z",
     "start_time": "2019-07-08T04:01:24.137096Z"
    }
   },
   "outputs": [],
   "source": [
    "model = KeyedVectors.load_word2vec_format(word2vec_glove_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:10.750040Z",
     "start_time": "2019-07-08T04:02:09.890630Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('barack', 0.937216579914093),\n",
       " ('bush', 0.927285373210907),\n",
       " ('clinton', 0.8960003852844238),\n",
       " ('mccain', 0.8875633478164673),\n",
       " ('gore', 0.8000321388244629),\n",
       " ('hillary', 0.7933663129806519),\n",
       " ('dole', 0.7851964235305786),\n",
       " ('rodham', 0.751889705657959),\n",
       " ('romney', 0.7488929629325867),\n",
       " ('kerry', 0.7472623586654663)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar('obama')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:10.800197Z",
     "start_time": "2019-07-08T04:02:10.756462Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('coconut', 0.7097253799438477),\n",
       " ('mango', 0.7054824233055115),\n",
       " ('bananas', 0.6887733936309814),\n",
       " ('potato', 0.6629636287689209),\n",
       " ('pineapple', 0.6534532904624939),\n",
       " ('fruit', 0.6519855260848999),\n",
       " ('peanut', 0.6420576572418213),\n",
       " ('pecan', 0.6349173188209534),\n",
       " ('cashew', 0.6294420957565308),\n",
       " ('papaya', 0.6246591210365295)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar('banana')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:10.881166Z",
     "start_time": "2019-07-08T04:02:10.811088Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('keyrates', 0.7173938751220703),\n",
       " ('sungrebe', 0.7119239568710327),\n",
       " ('þórður', 0.7067720890045166),\n",
       " ('zety', 0.7056615352630615),\n",
       " ('23aou94', 0.6959497928619385),\n",
       " ('___________________________________________________________',\n",
       "  0.694915235042572),\n",
       " ('elymians', 0.6945434212684631),\n",
       " ('camarina', 0.6927202939987183),\n",
       " ('ryryryryryry', 0.6905653476715088),\n",
       " ('maurilio', 0.6865653395652771)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar(negative='banana')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:11.033349Z",
     "start_time": "2019-07-08T04:02:10.885976Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "queen: 0.7699\n"
     ]
    }
   ],
   "source": [
    "result = model.most_similar(positive=['woman', 'king'], negative=['man'])\n",
    "print(\"{}: {:.4f}\".format(*result[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:11.051916Z",
     "start_time": "2019-07-08T04:02:11.041260Z"
    }
   },
   "outputs": [],
   "source": [
    "def analogy(x1, x2, y1):\n",
    "    result = model.most_similar(positive=[y1, x2], negative=[x1])\n",
    "    return result[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Analogy](imgs/word2vec-king-queen-composition.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:11.187051Z",
     "start_time": "2019-07-08T04:02:11.069203Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'australian'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogy('japan', 'japanese', 'australia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:11.246122Z",
     "start_time": "2019-07-08T04:02:11.192906Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'champagne'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogy('australia', 'beer', 'france')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:11.292821Z",
     "start_time": "2019-07-08T04:02:11.251583Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'nixon'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogy('obama', 'clinton', 'reagan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:11.367352Z",
     "start_time": "2019-07-08T04:02:11.301466Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'longest'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogy('tall', 'tallest', 'long')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:11.422192Z",
     "start_time": "2019-07-08T04:02:11.370939Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'terrible'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogy('good', 'fantastic', 'bad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:11.462685Z",
     "start_time": "2019-07-08T04:02:11.428334Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cereal\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/gensim/models/keyedvectors.py:895: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
      "  if np.issubdtype(vec.dtype, np.int):\n"
     ]
    }
   ],
   "source": [
    "print(model.doesnt_match(\"breakfast cereal dinner lunch\".split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:11.491259Z",
     "start_time": "2019-07-08T04:02:11.472466Z"
    }
   },
   "outputs": [],
   "source": [
    "def display_pca_scatterplot(model, words=None, sample=0):\n",
    "    if words == None:\n",
    "        if sample > 0:\n",
    "            words = np.random.choice(list(model.vocab.keys()), sample)\n",
    "        else:\n",
    "            words = [ word for word in model.vocab ]\n",
    "        \n",
    "    word_vectors = np.array([model[w] for w in words])\n",
    "\n",
    "    twodim = PCA().fit_transform(word_vectors)[:,:2]\n",
    "    \n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.scatter(twodim[:,0], twodim[:,1], edgecolors='k', c='r')\n",
    "    for word, (x,y) in zip(words, twodim):\n",
    "        plt.text(x+0.05, y+0.05, word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:11.844473Z",
     "start_time": "2019-07-08T04:02:11.497845Z"
    }
   },
   "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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_pca_scatterplot(model, \n",
    "                        ['coffee', 'tea', 'beer', 'wine', 'brandy', 'rum', 'champagne', 'water',\n",
    "                         'spaghetti', 'borscht', 'hamburger', 'pizza', 'falafel', 'sushi', 'meatballs',\n",
    "                         'dog', 'horse', 'cat', 'monkey', 'parrot', 'koala', 'lizard',\n",
    "                         'frog', 'toad', 'monkey', 'ape', 'kangaroo', 'wombat', 'wolf',\n",
    "                         'france', 'germany', 'hungary', 'luxembourg', 'australia', 'fiji', 'china',\n",
    "                         'homework', 'assignment', 'problem', 'exam', 'test', 'class',\n",
    "                         'school', 'college', 'university', 'institute'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T04:02:12.820979Z",
     "start_time": "2019-07-08T04:02:11.849893Z"
    }
   },
   "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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_pca_scatterplot(model, sample=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}