notebooks/19_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>\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 = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/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": 2,
   "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": 3,
   "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": 3,
     "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": 4,
   "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])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review the cluster labels\n",
    "km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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": 5,
     "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": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review the cluster centers\n",
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "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": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save the DataFrame of cluster centers\n",
    "centers = beer.groupby('cluster').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a \"colors\" array for plotting\n",
    "import numpy as np\n",
    "colors = np.array(['red', 'green', 'blue', 'yellow'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x18bce240>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EUXIQEZE4Sg4iIhJHyUFEROIoOYiISBwlBxERiaPkICIicZQcREQkTijJwcyG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\nvpopxTBv/p8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18aeb668>"
      ]
     },
     "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[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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000018BCEFD0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000018EE5908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000018F06518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000019023F28>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000019046A58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000019168EB8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000019185908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000019326D68>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000191755F8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000194A8F60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000194D0B70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000195F3748>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000001979B278>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000197C06D8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000198A2128>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000198C7668>]], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hRG/S0dss/dDoS7TWP3rGTSmVBCwHhgChQDrwGm1D+U4At2qtnUqpm2m7XFoJ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\nx8djS0lhX5v7vW4tK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PaOUxZ2RZlSSimlVGek5wWUUkoppUKAFmVKKaWUUiFAizKllDoO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\nyKAspZRSSqkf+D/lGp9GM8SwPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18aebf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot matrix (0=red, 1=green, 2=blue)\n",
    "pd.scatter_matrix(X, c=colors[beer.cluster], figsize=(10,10), s=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repeat with scaled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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": 14,
     "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": 15,
   "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>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": 15,
     "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": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review the cluster centers\n",
    "beer.groupby('cluster').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001A2FA9E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001A540C50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001A723710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001A74B518>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000001A72F470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001A8CF860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001A9B96A0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001A9D4EF0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000001AB3BF60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001AB4FCF8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001B4248D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001B58BDA0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000001B5A2FD0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001B7508D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001B7794E0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001B8586A0>]], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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WrbkoFUqfPk386Ed3+NwcXjExMdQFB3OusZFYqxWAo9XVJE2danAy0VnTr5xNzvFXOV1b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NeiTPlEfn4+n366ARFhxIgM8vI2sW1bGTExNubNm8acOTMteRajMYYdO3awatUmwsPtzJgxkays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\ng4FPE6ET1XRGg+4q4MaysZ8gafem3m0fI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fa9828>"
      ]
     },
     "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[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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4577741591090948"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kevin\\Anaconda\\lib\\site-packages\\numpy\\core\\_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    }
   ],
   "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": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sW7kSatbMbIRWry7ksssK2DHm1WIbNsB777msoa+95t6n1q3dAtVddolXW4qK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\nBG5ZRi4nMwWfk6X+nldGI3M3MB23Bic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      "text/plain": [
       "<matplotlib.figure.Figure at 0x190166d8>"
      ]
     },
     "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": 21,
   "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>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": 21,
     "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": 22,
   "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": 22,
     "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": 23,
   "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], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review the cluster labels\n",
    "db.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>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": 24,
     "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": 25,
   "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</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</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</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</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  3.483333  0.460000\n",
       " 0       143.142857      19  4.628571  0.440000\n",
       " 1       157.000000       8  5.066667  0.763333\n",
       " 2       102.750000      10  4.075000  0.440000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review the cluster centers\n",
    "beer.groupby('cluster').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001C074320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001C19C828>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001EDA2D30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001EDC8E48>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000001EEEC208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001EF0F978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001EF1D748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001F092F98>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000001F1FF4A8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001F21E2E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001F303710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001F46B6A0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000001F48B668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001F5F8780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001F495208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001F73D748>]], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qrU+7Hn8RrbU2D0i68+eSI2UGkiNlwl9JWxHukPYieqq/OvpnATVnPRZCCCGE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aL/ieiI/sFnw8YNPPPXZwjPDMcYQ1NeE3dfczcjho8gPDzc0sd0GGOY9cAsogdE43K6cOQ7uH/2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\n1tcHDlQTH5+K3X56ja5WFxO+Y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gUMkzSgTNEPeKikXrxie4bteWWr/Z0yZkUJFmxwik5v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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18accac8>"
      ]
     },
     "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[beer.cluster], figsize=(10,10), s=100)"
   ]
  }
 ],
 "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": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}