K-Means, PCA, and Dendrogram on the Animals with Attributes Dataset.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\"> K-Means, PCA, and Dendrogram on the Animals with Attributes Dataset </h1><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>About the dataset:</b> This is a small dataset that has information on about 50 animals. The animals are listed in classes.txt. For each animal, the information consists of values for 85 features: does the animal have a tail, is it slow, does it have tusks, etc. The details of the features are in the predicates.txt. The full data consists of a 50 x 85 matrix of real values, in predicate-matrix-continuous.txt. There is also a binarized version of this data, in predicate-matrix-binary.txt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import scale\n",
    "from pylab import rcParams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minor Bash Scripting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2016-03-28 03:19:57--  http://attributes.kyb.tuebingen.mpg.de/AwA-base.tar.bz2\n",
      "Resolving attributes.kyb.tuebingen.mpg.de (attributes.kyb.tuebingen.mpg.de)... 192.124.27.50\n",
      "Connecting to attributes.kyb.tuebingen.mpg.de (attributes.kyb.tuebingen.mpg.de)|192.124.27.50|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1062822 (1.0M) [application/x-bzip2]\n",
      "Saving to: ‘data.tar.bz2’\n",
      "\n",
      "data.tar.bz2        100%[=====================>]   1.01M   316KB/s   in 3.3s   \n",
      "\n",
      "2016-03-28 03:20:02 (316 KB/s) - ‘data.tar.bz2’ saved [1062822/1062822]\n",
      "\n",
      "\u001b[34mFeatures\u001b[m\u001b[m                        predicate-matrix-continuous.txt\n",
      "README-attributes.txt           predicate-matrix.png\n",
      "classes.txt                     predicates.txt\n",
      "lampert-cvpr2009.pdf            testclasses.txt\n",
      "predicate-matrix-binary.txt     trainclasses.txt\n"
     ]
    }
   ],
   "source": [
    "!wget -O data.tar.bz2 http://attributes.kyb.tuebingen.mpg.de/AwA-base.tar.bz2\n",
    "    \n",
    "# -C changes to the specified directory before unpacking (or packing). \n",
    "# --strip-components removes the specified number of directories from the filenames stored in the archive.\n",
    "\n",
    "!mkdir data && tar xf data.tar.bz2 -C data --strip-components 1\n",
    "\n",
    "# Looking at various text files and removing old zipped file\n",
    "!ls data && rm data.tar.bz2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 align=\"center\">Loading the real-valued array, and also the animal names into Python. </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 85)\n"
     ]
    }
   ],
   "source": [
    "samples_features = pd.read_fwf(\"data/predicate-matrix-continuous.txt\", header=None).values\n",
    "print samples_features.shape\n",
    "# 50 is the number of samples n (number of animals)\n",
    "# 85 is the number of features m (number of features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['antelope', 'grizzly+bear', 'killer+whale', 'beaver', 'dalmatian',\n",
       "       'persian+cat', 'horse', 'german+shepherd', 'blue+whale',\n",
       "       'siamese+cat', 'skunk', 'mole', 'tiger', 'hippopotamus', 'leopard',\n",
       "       'moose', 'spider+monkey', 'humpback+whale', 'elephant', 'gorilla',\n",
       "       'ox', 'fox', 'sheep', 'seal', 'chimpanzee', 'hamster', 'squirrel',\n",
       "       'rhinoceros', 'rabbit', 'bat', 'giraffe', 'wolf', 'chihuahua',\n",
       "       'rat', 'weasel', 'otter', 'buffalo', 'zebra', 'giant+panda', 'deer',\n",
       "       'bobcat', 'pig', 'lion', 'mouse', 'polar+bear', 'collie', 'walrus',\n",
       "       'raccoon', 'cow', 'dolphin'], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes=pd.read_fwf(\"data/classes.txt\", header=None)[1].values\n",
    "classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to make the real_value array data (samples_features) clearer, I put it into a pandas dataframe. Please notice how all the animals differ from each other. For example, notice how the dalmation has the column spots at 100 and the other dogs have values around 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>black</th>\n",
       "      <th>white</th>\n",
       "      <th>blue</th>\n",
       "      <th>brown</th>\n",
       "      <th>gray</th>\n",
       "      <th>orange</th>\n",
       "      <th>red</th>\n",
       "      <th>yellow</th>\n",
       "      <th>patches</th>\n",
       "      <th>spots</th>\n",
       "      <th>...</th>\n",
       "      <th>water</th>\n",
       "      <th>tree</th>\n",
       "      <th>cave</th>\n",
       "      <th>fierce</th>\n",
       "      <th>timid</th>\n",
       "      <th>smart</th>\n",
       "      <th>group</th>\n",
       "      <th>solitary</th>\n",
       "      <th>nestspot</th>\n",
       "      <th>domestic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>german+shepherd</th>\n",
       "      <td>43.54</td>\n",
       "      <td>15.88</td>\n",
       "      <td>5</td>\n",
       "      <td>54.16</td>\n",
       "      <td>26.82</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.38</td>\n",
       "      <td>48.78</td>\n",
       "      <td>11.59</td>\n",
       "      <td>...</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>57.44</td>\n",
       "      <td>10.00</td>\n",
       "      <td>57.53</td>\n",
       "      <td>12.50</td>\n",
       "      <td>35.11</td>\n",
       "      <td>16.53</td>\n",
       "      <td>68.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collie</th>\n",
       "      <td>10.13</td>\n",
       "      <td>41.37</td>\n",
       "      <td>0</td>\n",
       "      <td>47.27</td>\n",
       "      <td>3.75</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>9.09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>43.09</td>\n",
       "      <td>42.17</td>\n",
       "      <td>0.62</td>\n",
       "      <td>45.99</td>\n",
       "      <td>18.57</td>\n",
       "      <td>79.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dalmatian</th>\n",
       "      <td>69.58</td>\n",
       "      <td>73.33</td>\n",
       "      <td>0</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.08</td>\n",
       "      <td>100.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.38</td>\n",
       "      <td>31.67</td>\n",
       "      <td>53.26</td>\n",
       "      <td>24.44</td>\n",
       "      <td>29.38</td>\n",
       "      <td>11.25</td>\n",
       "      <td>72.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 black  white  blue  brown   gray  orange  red  yellow  \\\n",
       "german+shepherd  43.54  15.88     5  54.16  26.82    3.12  2.5    0.38   \n",
       "collie           10.13  41.37     0  47.27   3.75    8.00  0.5    0.00   \n",
       "dalmatian        69.58  73.33     0   6.39   0.00    0.00  0.0    0.00   \n",
       "\n",
       "                 patches   spots    ...     water  tree  cave  fierce  timid  \\\n",
       "german+shepherd    48.78   11.59    ...      3.75  0.00   2.5   57.44  10.00   \n",
       "collie             37.00    9.09    ...      0.00  0.00   0.0    5.25  43.09   \n",
       "dalmatian          37.08  100.00    ...      1.25  6.25   0.0    9.38  31.67   \n",
       "\n",
       "                 smart  group  solitary  nestspot  domestic  \n",
       "german+shepherd  57.53  12.50     35.11     16.53     68.55  \n",
       "collie           42.17   0.62     45.99     18.57     79.11  \n",
       "dalmatian        53.26  24.44     29.38     11.25     72.71  \n",
       "\n",
       "[3 rows x 85 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names=pd.read_fwf(\"data/predicates.txt\", header=None)[1].values\n",
    "classes_features = pd.DataFrame(data = samples_features, columns = feature_names)\n",
    "classes_features.index = classes\n",
    "classes_features.loc[['german+shepherd', 'collie', 'dalmatian']]\n",
    "#classes_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 align=\"center\">k-means grouping of the animals into an arbitrary number (10) of clusters </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: ['skunk', 'mole', 'hamster', 'rabbit', 'giant+panda', 'mouse'],\n",
       " 1: ['antelope',\n",
       "  'horse',\n",
       "  'moose',\n",
       "  'ox',\n",
       "  'sheep',\n",
       "  'giraffe',\n",
       "  'zebra',\n",
       "  'deer',\n",
       "  'cow'],\n",
       " 2: ['killer+whale', 'seal', 'dolphin'],\n",
       " 3: ['grizzly+bear',\n",
       "  'german+shepherd',\n",
       "  'siamese+cat',\n",
       "  'bat',\n",
       "  'rat',\n",
       "  'weasel',\n",
       "  'lion',\n",
       "  'raccoon'],\n",
       " 4: ['elephant', 'rhinoceros', 'buffalo', 'pig'],\n",
       " 5: ['beaver', 'otter', 'polar+bear'],\n",
       " 6: ['tiger', 'leopard', 'fox', 'wolf', 'bobcat'],\n",
       " 7: ['dalmatian',\n",
       "  'persian+cat',\n",
       "  'spider+monkey',\n",
       "  'gorilla',\n",
       "  'chimpanzee',\n",
       "  'squirrel',\n",
       "  'chihuahua',\n",
       "  'collie'],\n",
       " 8: ['blue+whale', 'humpback+whale', 'walrus'],\n",
       " 9: ['hippopotamus']}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the results on PCA-reduced data\n",
    "reduced_data = PCA(n_components=2).fit_transform(samples_features)\n",
    "kmeans = KMeans(init='k-means++', n_clusters=10, n_init=10)\n",
    "kmeans.fit(reduced_data, classes)\n",
    "\n",
    "grouping = {i:[] for i in xrange(0,10)} #dictionary comprehension\n",
    "\n",
    "for i,animal in enumerate(classes):\n",
    "    grouping[kmeans.labels_[i]].append(animal)\n",
    "grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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layt3qnq1iByIrZgsiFWuVTHF1U6q+mzFJC+i3CTpXMxhYhGvVHhQsn5xym4H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SXinlGJ+Ah1YRuxKlgnKWURChiJ2J/lsQYEMEV0DVoTIFQFEMYQfCw9TDSwO9BASxoZf0lCR1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\ny+KHUgVoDwT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ba9ba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step size of the mesh. Decrease to increase the quality\n",
    "h = .1     # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
    "\n",
    "# Plot the decision boundary.\n",
    "x_min, x_max = reduced_data[:, 0].min() - 10, reduced_data[:, 0].max() + 10\n",
    "y_min, y_max = reduced_data[:, 1].min() - 10, reduced_data[:, 1].max() + 10\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1, figsize=(20, 20))\n",
    "plt.clf()\n",
    "plt.imshow(Z, interpolation='nearest',\n",
    "           extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n",
    "           cmap=plt.cm.Paired,\n",
    "            origin='lower')\n",
    "\n",
    "# Plot the centroids as a white o\n",
    "centroids = kmeans.cluster_centers_\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "            marker='o', s=10, linewidths=1,\n",
    "            color='w', zorder=10)\n",
    "\n",
    "# Plot the Animals\n",
    "for i,animal in enumerate(classes):\n",
    "    # colors is just an array of tuples containing RGB values\n",
    "    colors = [(1,.55,0),(1,.3,.2),(0,0, .5), (.545, .322, .176), (0,0, 0), (.3,.7, .7), (.9,.9, .1), (0,.7, .1), (0,1, 1), (0,0, 1)]\n",
    "    plt.plot(reduced_data[i, 0], reduced_data[i, 1], color = colors[kmeans.labels_[i]], marker = '.',  markersize=17)\n",
    "    if animal == 'dalmatian':\n",
    "        plt.annotate('  ' + animal, xy = (reduced_data[i,0], reduced_data[i, 1]), xytext = (reduced_data[i,0], reduced_data[i, 1]), fontsize = 10)\n",
    "    elif animal == 'german+shepherd':\n",
    "        plt.annotate('  ' + animal, xy = (reduced_data[i,0], reduced_data[i, 1]), xytext = (reduced_data[i,0], reduced_data[i, 1]-5), fontsize = 10)\n",
    "    elif animal == 'sheep' or animal == 'dolphin' :\n",
    "        plt.annotate('  ' + animal, xy = (reduced_data[i,0], reduced_data[i, 1]), xytext = (reduced_data[i,0], reduced_data[i, 1]+3), fontsize = 10)\n",
    "    elif animal == 'siamese+cat':\n",
    "        plt.annotate('  ' + animal, xy = (reduced_data[i,0], reduced_data[i, 1]), xytext = (reduced_data[i,0] - 8, reduced_data[i, 1]+3), fontsize = 10) \n",
    "    else: \n",
    "        plt.annotate('  ' + animal, xy = (reduced_data[i,0], reduced_data[i, 1]), xytext = (reduced_data[i,0], reduced_data[i, 1]-5), fontsize = 10)\n",
    "        \n",
    "plt.title('K-means Clustering of Animals with Attributes Dataset\\n # of clusters = 10 ', fontsize = 40)\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.savefig('images/animals_attributes.png') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the clustering, it is clear that the groups that are clustered together make some intuitive sense. Water dwelling creatures are grouped close together as well as other creatures such as the various types of dogs. However, more work needs to be done to group the animals better (change algorithm, increase number of clusters etc). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = \"images/Worksheet7/4.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 align=\"center\">Hierarchical Clustering of the Data </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5bpLlzfVfOQHv+ah86L8kSZIkSVJ/2AM4uqrenOQrwJGjjBmqqr2SvA74BPCy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\n5wd26vqfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d1f6b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cluster_link array (contains the hierarchical clustering information)\n",
    "cluster_link = linkage(samples_features, method='ward');\n",
    "\n",
    "dendrogram(cluster_link, orientation=\"right\", labels=classes)\n",
    "rcParams['figure.figsize']=[20,15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 align=\"center\"> The dendrogram seems more sensible to me since it makes more intuitive sense. The grouping of polar and grizzly bears together plus the other hierarchical relationships makes this an intriguing option </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}