notebooks/clustering_and_dimensionality_reduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os, sys, codecs\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from random import randint\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download GloVe dataset if it doesn't already exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def download_data():\n",
    "    pass\n",
    "\n",
    "if not os.path.exists('../data/glove/glove.6B.300d.txt'):\n",
    "    print('GloVe data not found, downloading (this could take a while)...')\n",
    "    if os.system('../download_GloVe_data.sh') == 0:\n",
    "        print('Downloaded data successfully.')\n",
    "    else:\n",
    "        print('Download failed, try to download manually using download_GloVe_data.sh script.')\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse the GloVe dataset and build the word embeddings matrix as well as the label list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def build_word_vector_matrix(vector_file, n_words):\n",
    "    '''Read a GloVe array from sys.argv[1] and return its vectors and labels as arrays'''\n",
    "    np_arrays = []\n",
    "    labels_array = []\n",
    "\n",
    "    with codecs.open(vector_file, 'r', 'utf-8') as f:\n",
    "        for i, line in enumerate(f):\n",
    "            sr = line.split()\n",
    "            labels_array.append(sr[0])\n",
    "            np_arrays.append(np.array([float(j) for j in sr[1:]]))\n",
    "            if i == n_words - 1:\n",
    "                return np.array(np_arrays), labels_array\n",
    "            \n",
    "# parse data and create embedding matrix and labels array\n",
    "embeddings, labels = build_word_vector_matrix('../data/glove/glove.6B.300d.txt', 10000)\n",
    "\n",
    "# create dictionaries from labels array for quick lookups\n",
    "id_to_word = dict(zip(range(len(labels)), labels))\n",
    "word_to_id = dict((v,k) for k,v in id_to_word.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply k-means with k clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running K-means clustering algorithm...\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "k = 100 # number of clusters\n",
    "# Create KMeans model using all CPUs\n",
    "kmeans_model = KMeans(init='k-means++', n_clusters=k, n_jobs=-1, n_init=10)\n",
    "print('Running K-means clustering algorithm...')\n",
    "kmeans_model.fit(embeddings)\n",
    "print('Done.')\n",
    "\n",
    "cluster_labels  = kmeans_model.labels_\n",
    "cluster_inertia = kmeans_model.inertia_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create `clusters` list and populate each element with the words from that cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 labels created\n",
      "100 unique labels\n"
     ]
    }
   ],
   "source": [
    "uniq_labels = list(set(cluster_labels))\n",
    "# below expression is a nifty way to create \n",
    "# an array consisting of k empty arrays\n",
    "clusters = [[] for _ in range(len(uniq_labels))]\n",
    "\n",
    "print('{} labels created'.format(len(cluster_labels)))\n",
    "print('{} unique labels'.format(len(clusters)))\n",
    "\n",
    "for i, label in enumerate(cluster_labels):\n",
    "    clusters[int(label)].append(id_to_word[i])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print three random words from each cluster to help and identify the subject of the cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLUSTER INDEX             RANDOM WORDS FROM CLUSTER\n",
      "----------------------------------------------------------\n",
      "#0          transition           repairs         initiated\n",
      "#1                alex             kevin               dan\n",
      "#2                 2.5               4.2               1.3\n",
      "#3                 fcc          immunity             judge\n",
      "#4              remove             plans            enable\n",
      "#5             outside           bedroom               wet\n",
      "#6           directing             drama               pbs\n",
      "#7            africans             ghana          thailand\n",
      "#8                  50                94                kg\n",
      "#9                1993             1950s              1979\n",
      "#10            skilled        enthusiasm        indication\n",
      "#11        constructed             built          removing\n",
      "#12            150,000                 $               300\n",
      "#13               bath         edinburgh         tottenham\n",
      "#14            holiday          blessing          birthday\n",
      "#15              argue              deny      deliberately\n",
      "#16               tide            washed          shelters\n",
      "#17             francs            jumped           dropped\n",
      "#18        proposition        amendments        compliance\n",
      "#19          insurgent           iranian        guerrillas\n",
      "#20              shame          thoughts          grateful\n",
      "#21           guardian      publications             books\n",
      "#22        ambassadors            ivanov           gerhard\n",
      "#23            ranking        contention           gaining\n",
      "#24             hughes            oliver              webb\n",
      "#25             passes         extending        interstate\n",
      "#26          terrorist           illegal         notorious\n",
      "#27          resolving         consensus               wto\n",
      "#28            broader    infrastructure         mechanism\n",
      "#29             bomber        casualties            rocked\n",
      "#30              gauge            errors          accuracy\n",
      "#31          campaigns           hillary         candidacy\n",
      "#32                1-2              wins             venus\n",
      "#33           chechnya          albanian            serbia\n",
      "#34           inducted            skiing         wrestling\n",
      "#35               into             ended              down\n",
      "#36                  i               n't               got\n",
      "#37                6-3               def\n",
      "#38               1995              2003                 5\n",
      "#39            murdoch           largest             &amp;\n",
      "#40          searching         revealing          document\n",
      "#41        connecticut            county           midwest\n",
      "#42            drummer          ensemble            rhythm\n",
      "#43                pro        government         prominent\n",
      "#44               corn               hot          drinking\n",
      "#45           aviation            pilots            runway\n",
      "#46             emerge              fate          provoked\n",
      "#47              goods              sold              sell\n",
      "#48                ann             maria             diane\n",
      "#49              eagle              nest             ducks\n",
      "#50            skipper            umpire            bowler\n",
      "#51         succession              nazi           decades\n",
      "#52           edmonton           orleans          columbus\n",
      "#53            gardens        plantation         relocated\n",
      "#54     superintendent           timothy            samuel\n",
      "#55         registered           specify        membership\n",
      "#56            catcher            astros           clemens\n",
      "#57          northwest        population       territories\n",
      "#58            mubarak             hamid         malaysian\n",
      "#59              boots             shoes           flowers\n",
      "#60            surname           derived           peoples\n",
      "#61               wrap             sheet              fold\n",
      "#62             inland           springs          mountain\n",
      "#63              bench         averaging            backup\n",
      "#64       accidentally           slammed            struck\n",
      "#65               farc            garcia                en\n",
      "#66                gdp           surplus            profit\n",
      "#67       partnerships             youth        organizing\n",
      "#68           squadron          division\n",
      "#69              lifts       attribution                nn\n",
      "#70           medicaid        additional               aid\n",
      "#71               auto              fiat             honda\n",
      "#72          treatment            cancer\n",
      "#73       imprisonment        committing          criminal\n",
      "#74              sachs           finance           merrill\n",
      "#75              moral          creative    interpretation\n",
      "#76           relative         sometimes           example\n",
      "#77                kim              shin               cpc\n",
      "#78            turnout        signatures         governing\n",
      "#79              busch              race              laps\n",
      "#80            bangkok         embassies         frankfurt\n",
      "#81           attended           academy        georgetown\n",
      "#82          caribbean             ferry           pirates\n",
      "#83         suspension        withdrawal        inspectors\n",
      "#84              buses           tourist          airports\n",
      "#85             detect        navigation              grid\n",
      "#86            ukraine            baltic           hungary\n",
      "#87               20th              10th\n",
      "#88            textile         petroleum         exporting\n",
      "#89                 ab               bah               boo\n",
      "#90            wealthy              boys              ages\n",
      "#91          persuaded            issued         embattled\n",
      "#92          australia              fiji          canberra\n",
      "#93         bundesliga         marseille           hamburg\n",
      "#94            strokes         mickelson            stroke\n",
      "#95           centered             fewer            formed\n",
      "#96                 mr           retired             count\n",
      "#97          microsoft             files               aol\n",
      "#98            priests          catholic         monastery\n",
      "#99                  –              1936              1924\n"
     ]
    }
   ],
   "source": [
    "# print three words from each cluster to get an idea \n",
    "# of what what that cluster might represent\n",
    "print('CLUSTER INDEX             RANDOM WORDS FROM CLUSTER')\n",
    "print('----------------------------------------------------------')\n",
    "for i, cluster in enumerate(clusters):\n",
    "    indexes = list(set([randint(0, len(cluster)) - 1 for _ in range(0, 3)]))\n",
    "    words = [cluster[index] for index in indexes]\n",
    "    print('#{}'.format(str(i).ljust(3)), *list(map(lambda w: w.rjust(17), words)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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Nko5TPZJiyp0Rslq1aowaNYrU1FRiYmJo1qxZheUMHz6cIUOG0Lx5c5o3bx4aJ9awYUOm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Kpk+fzvz585k2bRq+vr7Y7XYAAscvwzGz2LKo8cS/MWXKcv6XH7kj6f+xefNm5s6dy4QJE3jr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b2c1b4a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fit_and_plot_pca(cluster):\n",
    "    \n",
    "    # extract the embeddings of only the words in the cluster\n",
    "    indicies = [word_to_id[word] for word in cluster]\n",
    "    cluster_embeddings = [embeddings[index] for index in indicies]\n",
    "\n",
    "    pca = PCA(n_components=2)\n",
    "    pca.fit(cluster_embeddings)\n",
    "    reduced_dim = pca.transform(cluster_embeddings)\n",
    "    # print(pca.explained_variance_ratio_)\n",
    "\n",
    "    xs = reduced_dim[:, 0]\n",
    "    ys = reduced_dim[:, 1]\n",
    "    fig = plt.figure(figsize=(15, 10))\n",
    "    plt.scatter(xs, ys, marker = 'o')\n",
    "    for i, word in enumerate(cluster):\n",
    "         plt.annotate(\n",
    "             word,\n",
    "             xy = (xs[i], ys[i]), xytext = (3, 3),\n",
    "             textcoords = 'offset points', ha = 'left', va = 'top')\n",
    "    \n",
    "cluster = clusters[0] # select an index from the list above to visualize with PC\n",
    "fit_and_plot_pca(cluster)\n",
    "## Uncomment below to plot all k clusters\n",
    "## WARNING: This can use a LOT of memory\n",
    "# for i, cluster in enumerate(clusters):\n",
    "#    fit_and_plot_pca(cluster)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}