Notebook_2_Embed_Words.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary Size: 147700\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import math\n",
    "import random\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.models.rnn import rnn  \n",
    "from tensorflow.models.rnn.rnn_cell import BasicLSTMCell, LSTMCell    \n",
    "import collections\n",
    "import re\n",
    "import cPickle as pickle\n",
    "\n",
    "urlFinder = re.compile('\\w+:\\/{2}[\\d\\w-]+(\\.[\\d\\w-]+)*(?:(?:\\/[^\\s/]*))*')\n",
    "atNameFinder = re.compile(r'@([A-Za-z0-9_]+)')\n",
    "atNameCounter = 0\n",
    "\n",
    "exclude_punc = set([\n",
    "        \"!\",\n",
    "        \"?\",\n",
    "        \".\",\n",
    "        \",\",\n",
    "        \":\",\n",
    "        \";\",\n",
    "        \"'\",\n",
    "        \"\\\"\",\n",
    "        \"“\",\n",
    "        \"’\",\n",
    "        \"-\"\n",
    "])\n",
    "\n",
    "\n",
    "words = []\n",
    "\n",
    "with open('sentences.csv', 'r') as f:\n",
    "    reader = csv.reader(f.read().splitlines())\n",
    "    for row in reader:\n",
    "        if len(row) != 0:\n",
    "            for word in row[0] \\\n",
    "                .strip() \\\n",
    "                .replace(\"&amp;\", \"\") \\\n",
    "                .replace(\"&gt;\",\"\") \\\n",
    "                .replace(\"&lt;\", \"\") \\\n",
    "                .lower().split():\n",
    "\n",
    "                if urlFinder.match(word):\n",
    "                    words.append(\"<URL/>\")\n",
    "                elif atNameFinder.search(word):\n",
    "                    words.append(\"<AT_NAME/>\")\n",
    "                else:\n",
    "                    ''.join([i if ord(i) < 128 else '' for i in text])\n",
    "                    word = ''.join(ch for ch in word if ch not in exclude_punc)\n",
    "                    word.strip()\n",
    "                    if word != \"\":\n",
    "                        words.append(word)\n",
    "\n",
    "    \n",
    "#words = filter(None, (' '.join(bag)).split())\n",
    "vocabulary_size = int(round(len(set(words)),-2))-100\n",
    "\n",
    "print(\"Vocabulary Size: %s\" % vocabulary_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Most common words (+UNK)', [['UNK', 108], ('<AT_NAME/>', 707202), ('the', 184706), ('you', 181925), ('to', 144481)])\n",
      "('Sample data', [1, 6863, 4, 4780, 4781, 3390, 91, 10, 2058, 169])\n"
     ]
    }
   ],
   "source": [
    "count = [['UNK', -1]]\n",
    "count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\n",
    "dictionary = dict()\n",
    "for word, _ in count:\n",
    "    dictionary[word] = len(dictionary)\n",
    "data = list()\n",
    "unk_count = 0\n",
    "for word in words:\n",
    "    if word in dictionary:\n",
    "        index = dictionary[word]\n",
    "    else:\n",
    "        index = 0  # dictionary['UNK']\n",
    "        unk_count += 1\n",
    "    data.append(index)\n",
    "count[0][1] = unk_count\n",
    "reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n",
    "\n",
    "del words  # Hint to reduce memory.\n",
    "print('Most common words (+UNK)', count[:5])\n",
    "print('Sample data', data[:10])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3390, '->', 1)\n",
      "('prosecute', '->', '<AT_NAME/>')\n",
      "(3390, '->', 10)\n",
      "('prosecute', '->', 'for')\n",
      "(3390, '->', 91)\n",
      "('prosecute', '->', 'them')\n",
      "(3390, '->', 4)\n",
      "('prosecute', '->', 'to')\n",
      "(3390, '->', 169)\n",
      "('prosecute', '->', 'against')\n",
      "(3390, '->', 6863)\n",
      "('prosecute', '->', 'pledges')\n",
      "(3390, '->', 2058)\n",
      "('prosecute', '->', 'crimes')\n",
      "(3390, '->', 4781)\n",
      "('prosecute', '->', '#plannedparenthood')\n",
      "(3390, '->', 2)\n",
      "('prosecute', '->', 'the')\n",
      "(3390, '->', 4780)\n",
      "('prosecute', '->', 'defund')\n"
     ]
    }
   ],
   "source": [
    "data_index = 0\n",
    "\n",
    "def generate_batch(batch_size, num_skips, skip_window):\n",
    "    global data_index\n",
    "    assert batch_size % num_skips == 0\n",
    "    assert num_skips <= 2 * skip_window\n",
    "    batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "    span = 2 * skip_window + 1 # [ skip_window target skip_window ]\n",
    "    buffer = collections.deque(maxlen=span)\n",
    "    for _ in range(span):\n",
    "        buffer.append(data[data_index])\n",
    "        data_index = (data_index + 1) % len(data)\n",
    "    for i in range(batch_size // num_skips):\n",
    "        target = skip_window  # target label at the center of the buffer\n",
    "        targets_to_avoid = [ skip_window ]\n",
    "    for j in range(num_skips):\n",
    "        while target in targets_to_avoid:\n",
    "            target = random.randint(0, span - 1)\n",
    "        targets_to_avoid.append(target)\n",
    "        batch[i * num_skips + j] = buffer[skip_window]\n",
    "        labels[i * num_skips + j, 0] = buffer[target]\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "    return batch, labels\n",
    "\n",
    "batch, labels = generate_batch(batch_size=10, num_skips=10, skip_window=5)\n",
    "\n",
    "for i in range(10):\n",
    "    print(batch[i], '->', labels[i, 0])\n",
    "    print(reverse_dictionary[batch[i]], '->', reverse_dictionary[labels[i, 0]])\n",
    "\n",
    "\n",
    "# In[4]:\n",
    "\n",
    "batch_size = 20\n",
    "embedding_size = 128  # Dimension of the embedding vector.\n",
    "skip_window = 10       # How many words to consider left and right.\n",
    "num_skips = 20         # How many times to reuse an input to generate a label.\n",
    "# We pick a random validation set to sample nearest neighbors. Here we limit the\n",
    "# validation samples to the words that have a low numeric ID, which by\n",
    "# construction are also the most frequent.\n",
    "valid_size = 16     # Random set of words to evaluate similarity on.\n",
    "valid_window = 100  # Only pick dev samples in the head of the distribution.\n",
    "valid_examples = np.array(random.sample(np.arange(valid_window), valid_size))\n",
    "num_sampled = 64    # Number of negative examples to sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    # Input da 4ta.\n",
    "    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n",
    "    # Ops and variables pinned to the CPU because of missing GPU implementation\n",
    "    with tf.device('/cpu:0'):\n",
    "        # Look up embeddings for inputs.\n",
    "        embeddings = tf.Variable(\n",
    "            tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "        embed = tf.nn.embedding_lookup(embeddings, train_inputs)\n",
    "        # Construct the variables for the NCE loss\n",
    "        with tf.name_scope(\"nce_weights\") as scope:\n",
    "            nce_weights = tf.Variable(\n",
    "                tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                                    stddev=1.0 / math.sqrt(embedding_size)))\n",
    "        nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "        nce_biases_hist = tf.histogram_summary(\"nce_biases\", nce_biases)\n",
    "        \n",
    "    # Compute the average NCE loss for the batch.\n",
    "    # tf.nce_loss automatically draws a new sample of the negative labels each\n",
    "    # time we evaluate the loss.\n",
    "    with tf.name_scope(\"loss\") as scope:\n",
    "        loss = tf.reduce_mean(\n",
    "            tf.nn.nce_loss(nce_weights, nce_biases, embed, train_labels,\n",
    "                         num_sampled, vocabulary_size))\n",
    "    # Construct the SGD optimizer using a learning rate of 1.0.\n",
    "    with tf.name_scope(\"train\") as scope:\n",
    "        optimizer = tf.train.GradientDescentOptimizer(0.25).minimize(loss)\n",
    "        \n",
    "    # Compute the cosine similarity between minibatch examples and all embeddings.\n",
    "    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n",
    "    normalized_embeddings = embeddings / norm\n",
    "    valid_embeddings = tf.nn.embedding_lookup(\n",
    "        normalized_embeddings, valid_dataset)\n",
    "    similarity = tf.matmul(\n",
    "    valid_embeddings, normalized_embeddings, transpose_b=True)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "('Average loss at step ', 0, ': ', 350.19332885742188)\n",
      "Nearest to just: emblemtico, alikeembarrassingu, intelligencekeep, debatedo, viper, officelet, 693141812555431937, faultsprophets,\n",
      "('Average loss at step ', 2000, ': ', 215.8384572277069)\n",
      "('Average loss at step ', 4000, ': ', 149.01597768211366)\n",
      "Nearest to just: lib, bible, watch, tampon, sound, mcconnell, barker, cpm,\n",
      "('Average loss at step ', 6000, ': ', 121.80322549247742)\n",
      "('Average loss at step ', 8000, ': ', 107.62388191413879)\n",
      "('Average loss at step ', 10000, ': ', 90.075384081840511)\n",
      "Nearest to just: regimes, naw, comments, mclain, citizens, affect, #dumpthetrump, taxdollars,\n",
      "('Average loss at step ', 12000, ': ', 79.627757375240321)\n",
      "('Average loss at step ', 14000, ': ', 68.503273129940027)\n",
      "Nearest to just: feel, affect, barely, this, regimes, king, excuse, interpret,\n",
      "('Average loss at step ', 16000, ': ', 61.513241620779034)\n",
      "('Average loss at step ', 18000, ': ', 58.774893156528471)\n",
      "('Average loss at step ', 20000, ': ', 54.366528251647949)\n",
      "Nearest to just: itty, the, is, apologize, arena, affect, wrinkle, floating,\n",
      "('Average loss at step ', 22000, ': ', 48.428345870256422)\n",
      "('Average loss at step ', 24000, ': ', 45.031994832992552)\n",
      "Nearest to just: and, of, loses, corn, anywayyour, will, feelingswarp, beta,\n",
      "('Average loss at step ', 26000, ': ', 44.325009624242782)\n",
      "('Average loss at step ', 28000, ': ', 40.674263622999192)\n",
      "('Average loss at step ', 30000, ': ', 38.089861671686172)\n",
      "Nearest to just: trump, are, be, what, we, dont, dignity, <AT_NAME/>,\n",
      "('Average loss at step ', 32000, ': ', 36.394718993425371)\n",
      "('Average loss at step ', 34000, ': ', 35.787602216720579)\n",
      "Nearest to just: <AT_NAME/>, trump, dont, do, the, go, to, fox,\n",
      "('Average loss at step ', 36000, ': ', 32.57766688680649)\n",
      "('Average loss at step ', 38000, ': ', 31.000357847452165)\n",
      "('Average loss at step ', 40000, ': ', 30.507840244770051)\n",
      "Nearest to just: it, you, go, trump, has, up, do, debate,\n",
      "('Average loss at step ', 42000, ': ', 30.113049228191375)\n",
      "('Average loss at step ', 44000, ': ', 30.056077422857285)\n",
      "Nearest to just: is, with, do, you, i, your, a, if,\n",
      "('Average loss at step ', 46000, ': ', 28.685249381303787)\n",
      "('Average loss at step ', 48000, ': ', 26.118268079757691)\n",
      "('Average loss at step ', 50000, ': ', 25.912107474327087)\n",
      "Nearest to just: are, <AT_NAME/>, the, but, at, that, for, be,\n",
      "('Average loss at step ', 52000, ': ', 27.936884975910186)\n",
      "('Average loss at step ', 54000, ': ', 25.957169585466385)\n",
      "Nearest to just: are, you, to, it, trump, be, about, as,\n",
      "('Average loss at step ', 56000, ': ', 23.93789682507515)\n",
      "('Average loss at step ', 58000, ': ', 22.906729511737822)\n",
      "('Average loss at step ', 60000, ': ', 24.943025908946989)\n",
      "Nearest to just: and, i, why, be, about, you, it, trump,\n",
      "('Average loss at step ', 62000, ': ', 22.883154880046845)\n",
      "('Average loss at step ', 64000, ': ', 24.586600219249725)\n",
      "Nearest to just: about, <URL/>, and, the, debate, what, be, in,\n",
      "('Average loss at step ', 66000, ': ', 23.859514656066896)\n",
      "('Average loss at step ', 68000, ': ', 20.79074923312664)\n",
      "('Average loss at step ', 70000, ': ', 21.275065330028532)\n",
      "Nearest to just: u, no, but, to, for, be, and, i,\n",
      "('Average loss at step ', 72000, ': ', 20.66986272740364)\n",
      "('Average loss at step ', 74000, ': ', 23.193401330351829)\n",
      "Nearest to just: but, what, that, a, no, u, by, to,\n",
      "('Average loss at step ', 76000, ': ', 21.683933151602744)\n",
      "('Average loss at step ', 78000, ': ', 20.674814225435256)\n",
      "('Average loss at step ', 80000, ': ', 20.208865152239799)\n",
      "Nearest to just: but, <AT_NAME/>, no, dont, be, is, a, have,\n",
      "('Average loss at step ', 82000, ': ', 19.355070816755294)\n",
      "('Average loss at step ', 84000, ': ', 22.284987780332564)\n",
      "Nearest to just: <AT_NAME/>, but, <URL/>, the, dont, trump, and, no,\n",
      "('Average loss at step ', 86000, ': ', 21.231469596624375)\n",
      "('Average loss at step ', 88000, ': ', 20.954731869339945)\n",
      "('Average loss at step ', 90000, ': ', 21.003308974027632)\n",
      "Nearest to just: the, and, what, <AT_NAME/>, u, make, trump, to,\n",
      "('Average loss at step ', 92000, ': ', 20.063574535489082)\n",
      "('Average loss at step ', 94000, ': ', 21.016299239695073)\n",
      "Nearest to just: <AT_NAME/>, and, u, this, that, for, fox, the,\n",
      "('Average loss at step ', 96000, ': ', 18.472585219860076)\n",
      "('Average loss at step ', 98000, ': ', 21.034833451509474)\n",
      "('Average loss at step ', 100000, ': ', 19.964479461431502)\n",
      "Nearest to just: this, and, at, <AT_NAME/>, if, that, of, will,\n"
     ]
    }
   ],
   "source": [
    "# Step 5: Begin training.\n",
    "num_steps = 100001\n",
    "with tf.Session(graph=graph) as session:\n",
    "    # We must initialize all variables before we use them.\n",
    "    merged = tf.merge_all_summaries()\n",
    "    writer = tf.train.SummaryWriter(\"/tmp/tensor_logs/expiriment_1\", session.graph_def)\n",
    "    \n",
    "    #Adds an op to initialize all variables\n",
    "    init_op = tf.initialize_all_variables()\n",
    "    \n",
    "    # Begins running the init opp\n",
    "    init_op.run()\n",
    "    \n",
    "    print(\"Initialized\")\n",
    "    average_loss = 0\n",
    "    for step in xrange(num_steps):\n",
    "        batch_inputs, batch_labels = generate_batch(\n",
    "            batch_size, num_skips, skip_window)\n",
    "        feed_dict = {train_inputs : batch_inputs, train_labels : batch_labels}\n",
    "        # We perform one update step by evaluating the optimizer op (including it\n",
    "        # in the list of returned values for session.run()\n",
    "        summary_str, _, loss_val = session.run([merged, optimizer, loss], feed_dict=feed_dict)\n",
    "        writer.add_summary(summary_str, step)\n",
    "        average_loss += loss_val\n",
    "        if step % 2000 == 0:\n",
    "            if step > 0:\n",
    "                average_loss /= 2000\n",
    "            # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "            print(\"Average loss at step \", step, \": \", average_loss)\n",
    "            average_loss = 0\n",
    "        # Note that this is expensive (~20% slowdown if computed every 500 steps)\n",
    "        if step % 5000 == 0:\n",
    "            sim = similarity.eval()\n",
    "            for i in xrange(valid_size):\n",
    "                valid_word = reverse_dictionary[valid_examples[i]]\n",
    "                top_k = 8 # number of nearest neighbors\n",
    "                nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
    "                log_str = \"Nearest to %s:\" % valid_word\n",
    "            for k in xrange(top_k):\n",
    "                close_word = reverse_dictionary[nearest[k]]\n",
    "                log_str = \"%s %s,\" % (log_str, close_word)\n",
    "            print(log_str)\n",
    "\n",
    "    final_embeddings = normalized_embeddings.eval()\n",
    "    \n",
    "    # save final embeddings for Expiriment #2\n",
    "    with open(\"word_embeddings.pkl\", \"wb\") as f:\n",
    "        pickle.dump(final_embeddings, f)\n",
    "    with open(\"word_mappings.pkl\", \"wb\") as f:\n",
    "        pickle.dump(reverse_dictionary, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "\n$$\n^\nExpected end of text (at char 0), (line:1, col:1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-476cf7525cfb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[0mlow_dim_embs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtsne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfinal_embeddings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mplot_only\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mreverse_dictionary\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_only\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m \u001b[0mplot_with_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlow_dim_embs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36m_get_layout\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    359\u001b[0m                 w, h, d = renderer.get_text_width_height_descent(clean_line,\n\u001b[0;32m    360\u001b[0m                                                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 361\u001b[1;33m                                                         ismath=ismath)\n\u001b[0m\u001b[0;32m    362\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    363\u001b[0m                 \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[1;34m(self, s, prop, ismath)\u001b[0m\n\u001b[0;32m    232\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mismath\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    233\u001b[0m             \u001b[0mox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescent\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfonts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mused_characters\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 234\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmathtext_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    235\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescent\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    236\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/mathtext.pyc\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self, s, dpi, prop)\u001b[0m\n\u001b[0;32m   3118\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mParser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3120\u001b[1;33m         \u001b[0mbox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfont_output\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3121\u001b[0m         \u001b[0mfont_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_canvas_size\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdepth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3122\u001b[0m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfont_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_results\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/mathtext.pyc\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self, s, fonts_object, fontsize, dpi)\u001b[0m\n\u001b[0;32m   2372\u001b[0m                         \u001b[0merr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2373\u001b[0m                         \u001b[1;34m\" \"\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumn\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"^\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2374\u001b[1;33m                         six.text_type(err)]))\n\u001b[0m\u001b[0;32m   2375\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state_stack\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2376\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_em_width_cache\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: \n$$\n^\nExpected end of text (at char 0), (line:1, col:1)"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "\n$$\n^\nExpected end of text (at char 0), (line:1, col:1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mpost_execute\u001b[1;34m()\u001b[0m\n\u001b[0;32m    145\u001b[0m             \u001b[1;32mdef\u001b[0m \u001b[0mpost_execute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    146\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m                     \u001b[0mdraw_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    148\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m             \u001b[1;31m# IPython >= 2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/_pylab_helpers.pyc\u001b[0m in \u001b[0;36mdraw_all\u001b[1;34m(cls, force)\u001b[0m\n\u001b[0;32m    148\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mf_mgr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mf_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 150\u001b[1;33m                 \u001b[0mf_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m \u001b[0matexit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGcf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdestroy_all\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mdraw_idle\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2024\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2025\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2026\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2027\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2028\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_cursor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36m_get_layout\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    359\u001b[0m                 w, h, d = renderer.get_text_width_height_descent(clean_line,\n\u001b[0;32m    360\u001b[0m                                                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 361\u001b[1;33m                                                         ismath=ismath)\n\u001b[0m\u001b[0;32m    362\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    363\u001b[0m                 \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[1;34m(self, s, prop, ismath)\u001b[0m\n\u001b[0;32m    232\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mismath\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    233\u001b[0m             \u001b[0mox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescent\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfonts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mused_characters\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 234\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmathtext_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    235\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescent\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    236\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/mathtext.pyc\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self, s, dpi, prop)\u001b[0m\n\u001b[0;32m   3118\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mParser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3120\u001b[1;33m         \u001b[0mbox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfont_output\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3121\u001b[0m         \u001b[0mfont_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_canvas_size\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdepth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3122\u001b[0m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfont_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_results\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/mathtext.pyc\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self, s, fonts_object, fontsize, dpi)\u001b[0m\n\u001b[0;32m   2372\u001b[0m                         \u001b[0merr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2373\u001b[0m                         \u001b[1;34m\" \"\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumn\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"^\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2374\u001b[1;33m                         six.text_type(err)]))\n\u001b[0m\u001b[0;32m   2375\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state_stack\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2376\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_em_width_cache\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: \n$$\n^\nExpected end of text (at char 0), (line:1, col:1)"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "\n$$\n^\nExpected end of text (at char 0), (line:1, col:1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    337\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    338\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 339\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    340\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    341\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_safe_get_formatter_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    224\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    225\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 226\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    227\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    228\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 117\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    118\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2178\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2179\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2180\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2181\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2182\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m         \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    747\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    748\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0m_wrap_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 749\u001b[1;33m             \u001b[0mbbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    750\u001b[0m             \u001b[0mtrans\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    751\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36m_get_layout\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    359\u001b[0m                 w, h, d = renderer.get_text_width_height_descent(clean_line,\n\u001b[0;32m    360\u001b[0m                                                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 361\u001b[1;33m                                                         ismath=ismath)\n\u001b[0m\u001b[0;32m    362\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    363\u001b[0m                 \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/mathtext.pyc\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self, s, dpi, prop)\u001b[0m\n\u001b[0;32m   3118\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mParser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3120\u001b[1;33m         \u001b[0mbox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfont_output\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3121\u001b[0m         \u001b[0mfont_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_canvas_size\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdepth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3122\u001b[0m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfont_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_results\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/matplotlib/mathtext.pyc\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self, s, fonts_object, fontsize, dpi)\u001b[0m\n\u001b[0;32m   2372\u001b[0m                         \u001b[0merr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2373\u001b[0m                         \u001b[1;34m\" \"\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumn\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"^\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2374\u001b[1;33m                         six.text_type(err)]))\n\u001b[0m\u001b[0;32m   2375\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state_stack\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2376\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_em_width_cache\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: \n$$\n^\nExpected end of text (at char 0), (line:1, col:1)"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4e3bdc5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys  \n",
    "\n",
    "reload(sys)  \n",
    "sys.setdefaultencoding('utf8')\n",
    "\n",
    "# Step 6: Visualize the embeddings.\n",
    "\n",
    "def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):\n",
    "    assert low_dim_embs.shape[0] >= len(labels), \"More labels than embeddings\"\n",
    "    plt.figure(figsize=(18, 18))  #in inches\n",
    "    for i, label in enumerate(labels):\n",
    "        x, y = low_dim_embs[i,:]\n",
    "        plt.scatter(x, y)\n",
    "        plt.annotate(label,\n",
    "                     xy=(x, y),\n",
    "                     xytext=(5, 2),\n",
    "                     textcoords='offset points',\n",
    "                     ha='right',\n",
    "                     va='bottom')\n",
    "    plt.savefig(filename)\n",
    "\n",
    "get_ipython().magic(u'matplotlib inline')\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
    "plot_only = 10000\n",
    "low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only,:])\n",
    "labels = [reverse_dictionary[i] for i in xrange(plot_only)]\n",
    "plot_with_labels(low_dim_embs, labels)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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