notebooks/14_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agenda:\n",
    "\n",
    "1. K-means clustering\n",
    "2. Clustering evaluation\n",
    "3. DBSCAN clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>            Budweiser</td>\n",
       "      <td> 144</td>\n",
       "      <td> 15</td>\n",
       "      <td> 4.7</td>\n",
       "      <td> 0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td>              Schlitz</td>\n",
       "      <td> 151</td>\n",
       "      <td> 19</td>\n",
       "      <td> 4.9</td>\n",
       "      <td> 0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 </th>\n",
       "      <td>            Lowenbrau</td>\n",
       "      <td> 157</td>\n",
       "      <td> 15</td>\n",
       "      <td> 0.9</td>\n",
       "      <td> 0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 </th>\n",
       "      <td>          Kronenbourg</td>\n",
       "      <td> 170</td>\n",
       "      <td>  7</td>\n",
       "      <td> 5.2</td>\n",
       "      <td> 0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 </th>\n",
       "      <td>             Heineken</td>\n",
       "      <td> 152</td>\n",
       "      <td> 11</td>\n",
       "      <td> 5.0</td>\n",
       "      <td> 0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 </th>\n",
       "      <td>        Old_Milwaukee</td>\n",
       "      <td> 145</td>\n",
       "      <td> 23</td>\n",
       "      <td> 4.6</td>\n",
       "      <td> 0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td>           Augsberger</td>\n",
       "      <td> 175</td>\n",
       "      <td> 24</td>\n",
       "      <td> 5.5</td>\n",
       "      <td> 0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td> Srohs_Bohemian_Style</td>\n",
       "      <td> 149</td>\n",
       "      <td> 27</td>\n",
       "      <td> 4.7</td>\n",
       "      <td> 0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td>          Miller_Lite</td>\n",
       "      <td>  99</td>\n",
       "      <td> 10</td>\n",
       "      <td> 4.3</td>\n",
       "      <td> 0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td>      Budweiser_Light</td>\n",
       "      <td> 113</td>\n",
       "      <td>  8</td>\n",
       "      <td> 3.7</td>\n",
       "      <td> 0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>                Coors</td>\n",
       "      <td> 140</td>\n",
       "      <td> 18</td>\n",
       "      <td> 4.6</td>\n",
       "      <td> 0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>          Coors_Light</td>\n",
       "      <td> 102</td>\n",
       "      <td> 15</td>\n",
       "      <td> 4.1</td>\n",
       "      <td> 0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>       Michelob_Light</td>\n",
       "      <td> 135</td>\n",
       "      <td> 11</td>\n",
       "      <td> 4.2</td>\n",
       "      <td> 0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>                Becks</td>\n",
       "      <td> 150</td>\n",
       "      <td> 19</td>\n",
       "      <td> 4.7</td>\n",
       "      <td> 0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>                Kirin</td>\n",
       "      <td> 149</td>\n",
       "      <td>  6</td>\n",
       "      <td> 5.0</td>\n",
       "      <td> 0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>    Pabst_Extra_Light</td>\n",
       "      <td>  68</td>\n",
       "      <td> 15</td>\n",
       "      <td> 2.3</td>\n",
       "      <td> 0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>                Hamms</td>\n",
       "      <td> 139</td>\n",
       "      <td> 19</td>\n",
       "      <td> 4.4</td>\n",
       "      <td> 0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>  Heilemans_Old_Style</td>\n",
       "      <td> 144</td>\n",
       "      <td> 24</td>\n",
       "      <td> 4.9</td>\n",
       "      <td> 0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>  Olympia_Goled_Light</td>\n",
       "      <td>  72</td>\n",
       "      <td>  6</td>\n",
       "      <td> 2.9</td>\n",
       "      <td> 0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>        Schlitz_Light</td>\n",
       "      <td>  97</td>\n",
       "      <td>  7</td>\n",
       "      <td> 4.2</td>\n",
       "      <td> 0.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost\n",
       "0              Budweiser       144      15      4.7  0.43\n",
       "1                Schlitz       151      19      4.9  0.43\n",
       "2              Lowenbrau       157      15      0.9  0.48\n",
       "3            Kronenbourg       170       7      5.2  0.73\n",
       "4               Heineken       152      11      5.0  0.77\n",
       "5          Old_Milwaukee       145      23      4.6  0.28\n",
       "6             Augsberger       175      24      5.5  0.40\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42\n",
       "8            Miller_Lite        99      10      4.3  0.43\n",
       "9        Budweiser_Light       113       8      3.7  0.40\n",
       "10                 Coors       140      18      4.6  0.44\n",
       "11           Coors_Light       102      15      4.1  0.46\n",
       "12        Michelob_Light       135      11      4.2  0.50\n",
       "13                 Becks       150      19      4.7  0.76\n",
       "14                 Kirin       149       6      5.0  0.79\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38\n",
       "16                 Hamms       139      19      4.4  0.43\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46\n",
       "19         Schlitz_Light        97       7      4.2  0.47"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# beer dataset\n",
    "import pandas as pd\n",
    "url = '../data/beer.txt'\n",
    "beer = pd.read_csv(url, sep=' ')\n",
    "beer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How would you cluster these beers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define X\n",
    "X = beer.drop('name', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happened to y?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: K-means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=3, n_init=10,\n",
       "    n_jobs=1, precompute_distances='auto', random_state=1, tol=0.0001,\n",
       "    verbose=0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# K-means with 3 clusters\n",
    "from sklearn.cluster import KMeans\n",
    "km = KMeans(n_clusters=3, random_state=1)\n",
    "km.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 2, 0, 0, 2, 1], dtype=int32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review the cluster labels\n",
    "km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster\n",
       "0              Budweiser       144      15      4.7  0.43        0\n",
       "1                Schlitz       151      19      4.9  0.43        0\n",
       "2              Lowenbrau       157      15      0.9  0.48        0\n",
       "3            Kronenbourg       170       7      5.2  0.73        0\n",
       "4               Heineken       152      11      5.0  0.77        0\n",
       "5          Old_Milwaukee       145      23      4.6  0.28        0\n",
       "6             Augsberger       175      24      5.5  0.40        0\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        0\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        0\n",
       "16                 Hamms       139      19      4.4  0.43        0\n",
       "10                 Coors       140      18      4.6  0.44        0\n",
       "14                 Kirin       149       6      5.0  0.79        0\n",
       "12        Michelob_Light       135      11      4.2  0.50        0\n",
       "13                 Becks       150      19      4.7  0.76        0\n",
       "9        Budweiser_Light       113       8      3.7  0.40        1\n",
       "8            Miller_Lite        99      10      4.3  0.43        1\n",
       "11           Coors_Light       102      15      4.1  0.46        1\n",
       "19         Schlitz_Light        97       7      4.2  0.47        1\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38        2\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46        2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save the cluster labels and sort by cluster\n",
    "beer['cluster'] = km.labels_\n",
    "beer.sort('cluster')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do the clusters seem to be based on? Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 150.        ,   17.        ,    4.52142857,    0.52071429],\n",
       "       [ 102.75      ,   10.        ,    4.075     ,    0.44      ],\n",
       "       [  70.        ,   10.5       ,    2.6       ,    0.42      ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review the cluster centers\n",
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150.00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.521429</td>\n",
       "      <td>0.520714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102.75</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.075000</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.00</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.420000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         calories  sodium   alcohol      cost\n",
       "cluster                                      \n",
       "0          150.00    17.0  4.521429  0.520714\n",
       "1          102.75    10.0  4.075000  0.440000\n",
       "2           70.00    10.5  2.600000  0.420000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the mean of each feature for each cluster\n",
    "beer.groupby('cluster').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# save the DataFrame of cluster centers\n",
    "centers = beer.groupby('cluster').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "# allow plots to appear in the notebook\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create a \"colors\" array for plotting\n",
    "import numpy as np\n",
    "colors = np.array(['red', 'green', 'blue', 'yellow'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11743da50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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MmzaNgQMH+hKbkoPES4PZ9cjixYu97qZo2kHTb5rSYnILTrQTeW3ya1x04UUAzJkzh/OH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\ncgYWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116bbfcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot of calories versus alcohol, colored by cluster (0=red, 1=green, 2=blue)\n",
    "plt.scatter(beer.calories, beer.alcohol, c=colors[list(beer.cluster)], s=50)\n",
    "\n",
    "# cluster centers, marked by \"+\"\n",
    "plt.scatter(centers.calories, centers.alcohol, linewidths=3, marker='+', s=300, c='black')\n",
    "\n",
    "# add labels\n",
    "plt.xlabel('calories')\n",
    "plt.ylabel('alcohol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1174d6b90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x117a781d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x117b69ed0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x117bd8710>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x117d5e690>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x117bf9150>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x117e4c710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x117ed0590>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118030ed0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1180b3e50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x117de3ed0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118193150>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x118303f90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118373690>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1183f8510>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11855d550>]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rge9qrdtcyCmEEEIIMSK5cq9Mxzd8u1+jMpVSvkAA8A6wGLACdq11q1Lq98BW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\nsDMigsOV7hlPDldWsiM8XPt2q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V3RLf6rMXVI2qt8Lg6StKeyVukoYCmpwH64pPMl7S1p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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11740e4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot matrix (0=red, 1=green, 2=blue)\n",
    "pd.scatter_matrix(X, c=colors[list(beer.cluster)], figsize=(10,10), s=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repeat with scaled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# center and scale the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=3, n_init=10,\n",
       "    n_jobs=1, precompute_distances='auto', random_state=1, tol=0.0001,\n",
       "    verbose=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# K-means with 3 clusters on scaled data\n",
    "km = KMeans(n_clusters=3, random_state=1)\n",
    "km.fit(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster\n",
       "0              Budweiser       144      15      4.7  0.43        0\n",
       "1                Schlitz       151      19      4.9  0.43        0\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        0\n",
       "16                 Hamms       139      19      4.4  0.43        0\n",
       "5          Old_Milwaukee       145      23      4.6  0.28        0\n",
       "6             Augsberger       175      24      5.5  0.40        0\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        0\n",
       "10                 Coors       140      18      4.6  0.44        0\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38        1\n",
       "12        Michelob_Light       135      11      4.2  0.50        1\n",
       "11           Coors_Light       102      15      4.1  0.46        1\n",
       "9        Budweiser_Light       113       8      3.7  0.40        1\n",
       "8            Miller_Lite        99      10      4.3  0.43        1\n",
       "2              Lowenbrau       157      15      0.9  0.48        1\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46        1\n",
       "19         Schlitz_Light        97       7      4.2  0.47        1\n",
       "13                 Becks       150      19      4.7  0.76        2\n",
       "14                 Kirin       149       6      5.0  0.79        2\n",
       "4               Heineken       152      11      5.0  0.77        2\n",
       "3            Kronenbourg       170       7      5.2  0.73        2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save the cluster labels and sort by cluster\n",
    "beer['cluster'] = km.labels_\n",
    "beer.sort('cluster')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the \"characteristics\" of each cluster?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148.375</td>\n",
       "      <td>21.125</td>\n",
       "      <td>4.7875</td>\n",
       "      <td>0.4075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>105.375</td>\n",
       "      <td>10.875</td>\n",
       "      <td>3.3250</td>\n",
       "      <td>0.4475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>155.250</td>\n",
       "      <td>10.750</td>\n",
       "      <td>4.9750</td>\n",
       "      <td>0.7625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         calories  sodium  alcohol    cost\n",
       "cluster                                   \n",
       "0         148.375  21.125   4.7875  0.4075\n",
       "1         105.375  10.875   3.3250  0.4475\n",
       "2         155.250  10.750   4.9750  0.7625"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review the cluster centers\n",
    "beer.groupby('cluster').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1187ba690>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118f163d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x118f98a50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119815590>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x119897690>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1198fc1d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119a80390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119a1d610>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x119c711d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119cf5050>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119d56710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119dd9690>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1189cff90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119e64cd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119ee7b50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119f58250>]], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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o6L9QXp6H1ZpBU9NBJk1ysWTJEsxmMy6XHvDfGynMxKigVD42WwZWazJ1dfuJiKhk3Lgb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h5cRd++3+eLL57mmmsKdBJV5VcsaTEzxmwDGkRkLdBkjNksIk+5nn4Y+FhE1gNrjDFVVuSo3FNZ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\nJEmb8H/QxauBV6B0oQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11895ed50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot matrix of new cluster assignments (0=red, 1=green, 2=blue)\n",
    "pd.scatter_matrix(X, c=colors[list(beer.cluster)], figsize=(10,10), s=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you notice any cluster assignments that seem a bit odd? How might we explain those?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Clustering evaluation\n",
    "\n",
    "The [Silhouette Coefficient](http://scikit-learn.org/stable/modules/clustering.html#silhouette-coefficient) is a common metric for evaluating clustering \"performance\" in situations when the \"true\" cluster assignments are not known.\n",
    "\n",
    "A Silhouette Coefficient is calculated for **each observation**:\n",
    "\n",
    "$$SC = \\frac{b-a} {max(a, b)}$$\n",
    "\n",
    "- a = mean distance to all other points in **its cluster**\n",
    "- b = mean distance to all other points in **the next nearest cluster**\n",
    "\n",
    "It ranges from -1 (worst) to 1 (best). A **global score** is calculated by taking the mean score for all observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45777415910909475"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate SC for K=3\n",
    "from sklearn import metrics\n",
    "metrics.silhouette_score(X_scaled, km.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# calculate SC for K=2 through K=19\n",
    "k_range = range(2, 20)\n",
    "scores = []\n",
    "for k in k_range:\n",
    "    km = KMeans(n_clusters=k, random_state=1)\n",
    "    km.fit(X_scaled)\n",
    "    scores.append(metrics.silhouette_score(X_scaled, km.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a18b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the results\n",
    "plt.plot(k_range, scores)\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Silhouette Coefficient')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster\n",
       "0              Budweiser       144      15      4.7  0.43        0\n",
       "1                Schlitz       151      19      4.9  0.43        0\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        0\n",
       "16                 Hamms       139      19      4.4  0.43        0\n",
       "5          Old_Milwaukee       145      23      4.6  0.28        0\n",
       "6             Augsberger       175      24      5.5  0.40        0\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        0\n",
       "10                 Coors       140      18      4.6  0.44        0\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38        1\n",
       "12        Michelob_Light       135      11      4.2  0.50        1\n",
       "11           Coors_Light       102      15      4.1  0.46        1\n",
       "9        Budweiser_Light       113       8      3.7  0.40        1\n",
       "8            Miller_Lite        99      10      4.3  0.43        1\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46        1\n",
       "19         Schlitz_Light        97       7      4.2  0.47        1\n",
       "13                 Becks       150      19      4.7  0.76        2\n",
       "14                 Kirin       149       6      5.0  0.79        2\n",
       "4               Heineken       152      11      5.0  0.77        2\n",
       "3            Kronenbourg       170       7      5.2  0.73        2\n",
       "2              Lowenbrau       157      15      0.9  0.48        3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# K-means with 4 clusters on scaled data\n",
    "km = KMeans(n_clusters=4, random_state=1)\n",
    "km.fit(X_scaled)\n",
    "beer['cluster'] = km.labels_\n",
    "beer.sort('cluster')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: DBSCAN clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(algorithm='auto', eps=1, leaf_size=30, metric='euclidean',\n",
       "    min_samples=3, p=None, random_state=None)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DBSCAN with eps=1 and min_samples=3\n",
    "from sklearn.cluster import DBSCAN\n",
    "db = DBSCAN(eps=1, min_samples=3)\n",
    "db.fit(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  0, -1,  1,  1, -1, -1,  0,  2,  2,  0,  2,  0, -1,  1, -1,  0,\n",
       "        0, -1,  2])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review the cluster labels\n",
    "db.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowenbrau</td>\n",
       "      <td>157</td>\n",
       "      <td>15</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.48</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Old_Milwaukee</td>\n",
       "      <td>145</td>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsberger</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Olympia_Goled_Light</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.46</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Becks</td>\n",
       "      <td>150</td>\n",
       "      <td>19</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pabst_Extra_Light</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Budweiser</td>\n",
       "      <td>144</td>\n",
       "      <td>15</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schlitz</td>\n",
       "      <td>151</td>\n",
       "      <td>19</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Srohs_Bohemian_Style</td>\n",
       "      <td>149</td>\n",
       "      <td>27</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Heilemans_Old_Style</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coors</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hamms</td>\n",
       "      <td>139</td>\n",
       "      <td>19</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Michelob_Light</td>\n",
       "      <td>135</td>\n",
       "      <td>11</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kronenbourg</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Heineken</td>\n",
       "      <td>152</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kirin</td>\n",
       "      <td>149</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Budweiser_Light</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miller_Lite</td>\n",
       "      <td>99</td>\n",
       "      <td>10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Coors_Light</td>\n",
       "      <td>102</td>\n",
       "      <td>15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Schlitz_Light</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  calories  sodium  alcohol  cost  cluster\n",
       "2              Lowenbrau       157      15      0.9  0.48       -1\n",
       "5          Old_Milwaukee       145      23      4.6  0.28       -1\n",
       "6             Augsberger       175      24      5.5  0.40       -1\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46       -1\n",
       "13                 Becks       150      19      4.7  0.76       -1\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38       -1\n",
       "0              Budweiser       144      15      4.7  0.43        0\n",
       "1                Schlitz       151      19      4.9  0.43        0\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42        0\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43        0\n",
       "10                 Coors       140      18      4.6  0.44        0\n",
       "16                 Hamms       139      19      4.4  0.43        0\n",
       "12        Michelob_Light       135      11      4.2  0.50        0\n",
       "3            Kronenbourg       170       7      5.2  0.73        1\n",
       "4               Heineken       152      11      5.0  0.77        1\n",
       "14                 Kirin       149       6      5.0  0.79        1\n",
       "9        Budweiser_Light       113       8      3.7  0.40        2\n",
       "8            Miller_Lite        99      10      4.3  0.43        2\n",
       "11           Coors_Light       102      15      4.1  0.46        2\n",
       "19         Schlitz_Light        97       7      4.2  0.47        2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save the cluster labels and sort by cluster\n",
    "beer['cluster'] = db.labels_\n",
    "beer.sort('cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>sodium</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>127.833333</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.483333</td>\n",
       "      <td>0.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>143.142857</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.628571</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.066667</td>\n",
       "      <td>0.763333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102.750000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.075000</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           calories  sodium   alcohol      cost\n",
       "cluster                                        \n",
       "-1       127.833333    17.0  3.483333  0.460000\n",
       " 0       143.142857    19.0  4.628571  0.440000\n",
       " 1       157.000000     8.0  5.066667  0.763333\n",
       " 2       102.750000    10.0  4.075000  0.440000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review the cluster centers\n",
    "beer.groupby('cluster').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x118ec8650>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11afb7490>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a89c110>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b09bd90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11b11df50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b18d390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b32c310>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b37af90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11b407f10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b48ad90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b4f9f90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b67be10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a8d61d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11aa01c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a8aad10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b739a50>]], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BlFIewA+Bv325TAghhBBC9K8ejcrUWncCP+DilzGFEEIIIUQ/cGaC2U+A+QMV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\n7IPIhRBCOLvQ0FDuufEe3t/yPipEoZoUM4fNZMKECf0Wg1N2/u9MzJ5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IAAAg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\nJEmSJOkj5MIsSZIkSZKkj/B/I/0s2XSTdxIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aa0bcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot matrix of DBSCAN cluster assignments (0=red, 1=green, 2=blue, -1=yellow)\n",
    "pd.scatter_matrix(X, c=colors[list(beer.cluster)], figsize=(10,10), s=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23754067391520689"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Silhousette Coefficient also works on DBSCAN\n",
    "metrics.silhouette_score(X_scaled, db.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# calculate SC for K=2 through K=19\n",
    "eps_range = range(1, 10)\n",
    "scores = []\n",
    "for k in eps_range:\n",
    "    db = DBSCAN(eps=k, min_samples=3)\n",
    "    db.fit(X_scaled)\n",
    "    if len(set(db.labels_)) > 1:\n",
    "        scores.append(metrics.silhouette_score(X_scaled, db.labels_))\n",
    "    else:\n",
    "        scores.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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at5+krWq9qKShkqZLmidpYjSFcbV9+0u6U9JsSR9LmixpkSH4o/0mRuecJumo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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bb55590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the results\n",
    "plt.plot(eps_range, scores)\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Silhouette Coefficient')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "l = np.array([1]*1000+[10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "315.88026297725895"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.622776601683796"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(10000 - np.mean(l)) / np.std(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/Users/sinanozdemir/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:583: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/Users/sinanozdemir/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/Users/sinanozdemir/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:646: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ -0.03162278,  -0.03162278,  -0.03162278, ...,  -0.03162278,\n",
       "        -0.03162278,  31.6227766 ])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit_transform(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  topic modelling for news"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "# adapted in http://alexperrier.github.io/jekyll/update/2015/09/16/segmentation_twitter_timelines_lda_vs_lsa.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "categories = [\n",
    "    'alt.atheism',\n",
    "    'talk.religion.misc',\n",
    "    'comp.graphics',\n",
    "    'sci.space',\n",
    "]\n",
    "# grab only four topics of articles\n",
    "dataset = fetch_20newsgroups(subset='all', categories=categories,\n",
    "                             shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'From: healta@saturn.wwc.edu (Tammy R Healy)\\nSubject: Re: who are we to judge, Bobby?\\nLines: 38\\nOrganization: Walla Walla College\\nLines: 38\\n\\nIn article <1993Apr14.213356.22176@ultb.isc.rit.edu> snm6394@ultb.isc.rit.edu (S.N. Mozumder ) writes:\\n>From: snm6394@ultb.isc.rit.edu (S.N. Mozumder )\\n>Subject: Re: who are we to judge, Bobby?\\n>Date: Wed, 14 Apr 1993 21:33:56 GMT\\n>In article <healta.56.734556346@saturn.wwc.edu> healta@saturn.wwc.edu (TAMMY R HEALY) writes:\\n>>Bobby,\\n>>\\n>>I would like to take the liberty to quote from a Christian writer named \\n>>Ellen G. White.  I hope that what she said will help you to edit your \\n>>remarks in this group in the future.\\n>>\\n>>\"Do not set yourself as a standard.  Do not make your opinions, your views \\n>>of duty, your interpretations of scripture, a criterion for others and in \\n>>your heart condemn them if they do not come up to your ideal.\"\\n>>                         Thoughts Fromthe Mount of Blessing p. 124\\n>>\\n>>I hope quoting this doesn\\'t make the atheists gag, but I think Ellen White \\n>>put it better than I could.\\n>> \\n>>Tammy\\n>\\n>Point?\\n>\\n>Peace,\\n>\\n>Bobby Mozumder\\n>\\nMy point is that you set up your views as the only way to believe.  Saying \\nthat all eveil in this world is caused by atheism is ridiculous and \\ncounterproductive to dialogue in this newsgroups.  I see in your posts a \\nspirit of condemnation of the atheists in this newsgroup bacause they don\\'\\nt believe exactly as you do.  If you\\'re here to try to convert the atheists \\nhere, you\\'re failing miserably.  Who wants to be in position of constantly \\ndefending themselves agaist insulting attacks, like you seem to like to do?!\\nI\\'m sorry you\\'re so blind that you didn\\'t get the messgae in the quote, \\neveryone else has seemed to.\\n\\nTammy\\n'"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = dataset.target  # actual targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism', 'comp.graphics', 'sci.space', 'talk.religion.misc']"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.target_names  # target names, the above text is about atheism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.preprocessing import Normalizer\n",
    "# use a normalizer module to normalize data for KMeans which is affected by scale\n",
    "\n",
    "vect = TfidfVectorizer(stop_words='english')  # vectorize the data\n",
    "normal = Normalizer(copy=False)               # normalize the vectorized data\n",
    "km = KMeans(n_clusters=4, random_state=1)     # cluster\n",
    "\n",
    "feature_preprocessing = Pipeline(steps=(('vect', vect), ('normal', normal)))\n",
    "pipe = Pipeline(steps=(('preprocess', feature_preprocessing), ('cluster', km)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('preprocess', Pipeline(steps=(('vect', TfidfVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',\n",
       "        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), norm=u'l2', pre..., n_init=10,\n",
       "    n_jobs=1, precompute_distances='auto', random_state=1, tol=0.0001,\n",
       "    verbose=0))))"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(dataset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predicted_topics = pipe.steps[1][1].labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism',\n",
       " 'comp.graphics',\n",
       " 'comp.graphics',\n",
       " 'talk.religion.misc',\n",
       " 'comp.graphics']"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[dataset.target_names[p] for p in labels][:5]  # the kmeans labels as words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'text':dataset.data, 'cluster':predicted_topics, 'actual': [dataset.target_names[p] for p in labels]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual</th>\n",
       "      <th>cluster</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alt.atheism</td>\n",
       "      <td>2</td>\n",
       "      <td>From: healta@saturn.wwc.edu (Tammy R Healy)\\nS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>comp.graphics</td>\n",
       "      <td>0</td>\n",
       "      <td>From: jk87377@lehtori.cc.tut.fi (Kouhia Juhana...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>comp.graphics</td>\n",
       "      <td>0</td>\n",
       "      <td>Subject: PHIGS User Group Conference\\nFrom: ha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>talk.religion.misc</td>\n",
       "      <td>2</td>\n",
       "      <td>From: joslin@pogo.isp.pitt.edu (David Joslin)\\...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>comp.graphics</td>\n",
       "      <td>0</td>\n",
       "      <td>From: adaptive@cs.nps.navy.mil (zyda res acct)...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               actual  cluster  \\\n",
       "0         alt.atheism        2   \n",
       "1       comp.graphics        0   \n",
       "2       comp.graphics        0   \n",
       "3  talk.religion.misc        2   \n",
       "4       comp.graphics        0   \n",
       "\n",
       "                                                text  \n",
       "0  From: healta@saturn.wwc.edu (Tammy R Healy)\\nS...  \n",
       "1  From: jk87377@lehtori.cc.tut.fi (Kouhia Juhana...  \n",
       "2  Subject: PHIGS User Group Conference\\nFrom: ha...  \n",
       "3  From: joslin@pogo.isp.pitt.edu (David Joslin)\\...  \n",
       "4  From: adaptive@cs.nps.navy.mil (zyda res acct)...  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a readable dataframe\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 sci.space             981\n",
      "comp.graphics         967\n",
      "talk.religion.misc     98\n",
      "alt.atheism            39\n",
      "Name: actual, dtype: int64\n",
      "[(u'edu', 151.63509563077946), (u'com', 90.216278296903027), (u'space', 80.419778405466076), (u'subject', 70.247054094339632), (u'lines', 69.702810840447825), (u'organization', 68.572782997429101), (u'university', 60.657433099907635), (u'writes', 57.332729898666514), (u'posting', 56.328153056409299), (u'host', 54.311826413323004)]\n",
      "\n",
      "\n",
      "1 talk.religion.misc    70\n",
      "alt.atheism           37\n",
      "Name: actual, dtype: int64\n",
      "[(u'sandvik', 11.493184744306847), (u'com', 8.4194395112546587), (u'kent', 7.2687834960860602), (u'apple', 6.9149944738576314), (u'edu', 6.078429458367995), (u'newton', 5.8113837266689794), (u'god', 5.0984586698095988), (u'jesus', 4.247570931776754), (u'article', 4.0865421128808137), (u'people', 3.9007828500661419)]\n",
      "\n",
      "\n",
      "2 alt.atheism           588\n",
      "talk.religion.misc    448\n",
      "sci.space               6\n",
      "comp.graphics           6\n",
      "Name: actual, dtype: int64\n",
      "[(u'edu', 73.426800721001115), (u'god', 55.149527324567543), (u'com', 48.041379295431568), (u'people', 35.416694386331415), (u'writes', 35.185295427397094), (u'don', 31.229658116625075), (u'article', 30.99643734984318), (u'subject', 29.002645223435628), (u'lines', 28.369206688988552), (u'organization', 28.132860991794864)]\n",
      "\n",
      "\n",
      "3 alt.atheism           135\n",
      "talk.religion.misc     12\n",
      "Name: actual, dtype: int64\n",
      "[(u'keith', 11.880793117251176), (u'edu', 11.83506393675351), (u'sgi', 11.700387001080728), (u'livesey', 10.252973406090817), (u'com', 9.8729790202839354), (u'caltech', 8.4308400618133561), (u'jon', 7.9420025289046405), (u'solntze', 7.6662300248428004), (u'wpd', 7.6662300248428004), (u'morality', 6.6283313243372906)]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "smaller_tfidf = TfidfVectorizer(stop_words='english', max_features=1000)\n",
    "for cluster in range(4):\n",
    "    print cluster, df[df.cluster==cluster].actual.value_counts()\n",
    "    matrix = smaller_tfidf.fit_transform(df[df.cluster==cluster].text)\n",
    "    freqs = [(word, matrix.getcol(idx).sum()) for word, idx in smaller_tfidf.vocabulary_.items()]\n",
    "    #sort from largest to smallest\n",
    "    print [k for k in sorted (freqs, key = lambda x: -x[1])[:10]]\n",
    "    print\n",
    "    print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Coefficient: 0.006\n"
     ]
    }
   ],
   "source": [
    "# output the Silhouette score, oof\n",
    "\n",
    "print(\"Silhouette Coefficient: %0.3f\"\n",
    "      % metrics.silhouette_score(feature_preprocessing.fit_transform(dataset.data), pipe.steps[1][1].labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# LSA -- Latent Semantic Analysis\n",
    "\n",
    "# LSA is a vector-based dimension reduction technique \n",
    "# that allows to project a large feature space to a smaller one\n",
    "\n",
    "# It is a tfidf vectorizer that is then put through an svd and finally normalized\n",
    "# before being put through a kmeans clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clustering sparse data with KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=4, n_init=10,\n",
      "    n_jobs=1, precompute_distances='auto', random_state=1, tol=0.0001,\n",
      "    verbose=0)\n",
      "Silhouette Coefficient: 0.006\n",
      "9.86332201958 seconds\n"
     ]
    }
   ],
   "source": [
    "from time import time\n",
    "\n",
    "vect = TfidfVectorizer(stop_words='english')\n",
    "# svd = TruncatedSVD(n_components=5)\n",
    "lsa = make_pipeline(vect, Normalizer(copy=False))\n",
    "\n",
    "X_tfidfd = lsa.fit_transform(dataset.data)\n",
    "t0 = time()\n",
    "\n",
    "km = KMeans(n_clusters=4, random_state=1)\n",
    "\n",
    "print(\"Clustering sparse data with %s\" % km)\n",
    "km.fit(X_tfidfd)\n",
    "print(\"Silhouette Coefficient: %0.3f\"\n",
    "      % metrics.silhouette_score(X_tfidfd, km.labels_))\n",
    "print (time()-t0), 'seconds'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "# we will be using SVD as our dimension reduction technique and a normalizer to normalize data (for KMeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vect = TfidfVectorizer(stop_words='english')\n",
    "svd = TruncatedSVD(n_components=5)\n",
    "lsa = make_pipeline(vect, svd, Normalizer(copy=False))\n",
    "\n",
    "X_lsa = lsa.fit_transform(dataset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3387, 5)"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_lsa.shape  # fewer columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clustering sparse data with KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=4, n_init=10,\n",
      "    n_jobs=1, precompute_distances='auto', random_state=1, tol=0.0001,\n",
      "    verbose=0)\n",
      "Silhouette Coefficient: 0.383\n",
      "2.26282095909 seconds\n"
     ]
    }
   ],
   "source": [
    "t0 = time()\n",
    "km = KMeans(n_clusters=4, random_state=1)\n",
    "\n",
    "print(\"Clustering sparse data with %s\" % km)\n",
    "km.fit(X_lsa)\n",
    "print(\"Silhouette Coefficient: %0.3f\"\n",
    "      % metrics.silhouette_score(X_lsa, km.labels_))\n",
    "print (time()-t0), 'seconds'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 2, ..., 1, 2, 2], dtype=int32)"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 2, 1, 1])"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "km_to_actual = {3:0, 2:1, 1:2, 0:3} #  my guess as how the labels realte to the actual targets\n",
    "km_actual = np.array([km_to_actual[k] for k in km.labels_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 2, 1, 1])"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km_actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69560082669028644"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(km_actual, labels) # simulated accuracy of our topic modelling, not bad!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clustering sparse data with 5 components\n",
      "Silhouette Coefficient: 0.395\n",
      "Clustering sparse data with 10 components\n",
      "Silhouette Coefficient: 0.285\n",
      "Clustering sparse data with 25 components\n",
      "Silhouette Coefficient: 0.128\n",
      "Clustering sparse data with 50 components\n",
      "Silhouette Coefficient: 0.065\n",
      "Clustering sparse data with 100 components\n",
      "Silhouette Coefficient: 0.045\n"
     ]
    }
   ],
   "source": [
    "# try a bunch of components, look at the scores below\n",
    "\n",
    "for n_components in (5, 10, 25, 50, 100):\n",
    "    vect = TfidfVectorizer(stop_words='english')\n",
    "    svd = TruncatedSVD(n_components=n_components)\n",
    "    lsa = make_pipeline(vect, svd, Normalizer(copy=False))\n",
    "\n",
    "    X_lsa = lsa.fit_transform(dataset.data)\n",
    "    km = KMeans(n_clusters=4, random_state=1)\n",
    "\n",
    "    print(\"Clustering sparse data with %s components\" % n_components)\n",
    "    km.fit(X_lsa)\n",
    "    print(\"Silhouette Coefficient: %0.3f\"\n",
    "          % metrics.silhouette_score(X_lsa, km.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'From: healta@saturn.wwc.edu (Tammy R Healy)\\nSubject: Re: who are we to judge, Bobby?\\nLines: 38\\nOrganization: Walla Walla College\\nLines: 38\\n\\nIn article <1993Apr14.213356.22176@ultb.isc.rit.edu> snm6394@ultb.isc.rit.edu (S.N. Mozumder ) writes:\\n>From: snm6394@ultb.isc.rit.edu (S.N. Mozumder )\\n>Subject: Re: who are we to judge, Bobby?\\n>Date: Wed, 14 Apr 1993 21:33:56 GMT\\n>In article <healta.56.734556346@saturn.wwc.edu> healta@saturn.wwc.edu (TAMMY R HEALY) writes:\\n>>Bobby,\\n>>\\n>>I would like to take the liberty to quote from a Christian writer named \\n>>Ellen G. White.  I hope that what she said will help you to edit your \\n>>remarks in this group in the future.\\n>>\\n>>\"Do not set yourself as a standard.  Do not make your opinions, your views \\n>>of duty, your interpretations of scripture, a criterion for others and in \\n>>your heart condemn them if they do not come up to your ideal.\"\\n>>                         Thoughts Fromthe Mount of Blessing p. 124\\n>>\\n>>I hope quoting this doesn\\'t make the atheists gag, but I think Ellen White \\n>>put it better than I could.\\n>> \\n>>Tammy\\n>\\n>Point?\\n>\\n>Peace,\\n>\\n>Bobby Mozumder\\n>\\nMy point is that you set up your views as the only way to believe.  Saying \\nthat all eveil in this world is caused by atheism is ridiculous and \\ncounterproductive to dialogue in this newsgroups.  I see in your posts a \\nspirit of condemnation of the atheists in this newsgroup bacause they don\\'\\nt believe exactly as you do.  If you\\'re here to try to convert the atheists \\nhere, you\\'re failing miserably.  Who wants to be in position of constantly \\ndefending themselves agaist insulting attacks, like you seem to like to do?!\\nI\\'m sorry you\\'re so blind that you didn\\'t get the messgae in the quote, \\neveryone else has seemed to.\\n\\nTammy\\n'"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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