NB/Ex12.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/beer.txt',delimiter = ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Beer</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>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>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",
       "    <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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Beer  Calories  Sodium  Alcohol  Cost\n",
       "15     Pabst_Extra_Light        68      15      2.3  0.38\n",
       "18   Olympia_Goled_Light        72       6      2.9  0.46\n",
       "19         Schlitz_Light        97       7      4.2  0.47\n",
       "8            Miller_Lite        99      10      4.3  0.43\n",
       "11           Coors_Light       102      15      4.1  0.46\n",
       "9        Budweiser_Light       113       8      3.7  0.40\n",
       "12        Michelob_Light       135      11      4.2  0.50\n",
       "16                 Hamms       139      19      4.4  0.43\n",
       "10                 Coors       140      18      4.6  0.44\n",
       "17   Heilemans_Old_Style       144      24      4.9  0.43\n",
       "0              Budweiser       144      15      4.7  0.43\n",
       "5          Old_Milwaukee       145      23      4.6  0.28\n",
       "7   Srohs_Bohemian_Style       149      27      4.7  0.42\n",
       "14                 Kirin       149       6      5.0  0.79\n",
       "13                 Becks       150      19      4.7  0.76\n",
       "1                Schlitz       151      19      4.9  0.43\n",
       "4               Heineken       152      11      5.0  0.77\n",
       "2              Lowenbrau       157      15      0.9  0.48\n",
       "3            Kronenbourg       170       7      5.2  0.73\n",
       "6             Augsberger       175      24      5.5  0.40"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort('Calories')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 11)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "8Y3FuxIi1UNLkYqIiESUWuIiIiIRpRAXERGJKIW4iIhIRCnERUREIkohLiIiElEKcRERkYhSiIuI\n",
       "iESUQlxERCSi/j9CNGbRrc5g8gAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a9c2f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import cluster\n",
    "from __future__ import division\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "beer = df.iloc[:, 1:]\n",
    "krange = range(1, int(beer.shape[0] / 2))\n",
    "sum_squares = [cluster.KMeans(n_clusters=k).fit(beer).inertia_ for k in krange]\n",
    "\n",
    "variance_explained = [1.0 - (s / sum(sum_squares)) for s in sum_squares]\n",
    "plt.figure()\n",
    "plt.plot(range(1, 10), variance_explained)\n",
    "plt.ylabel('% of explained variance')\n",
    "plt.xlabel('clusters K')\n",
    "plt.xlim([1, 75])\n",
    "plt.xlim([1, 11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10b397d90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a0600d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## -- 3 Clusters --\n",
    "clusters_3 = cluster.KMeans(n_clusters=3).fit(beer)\n",
    "fig, ax = plt.subplots(2, 2, figsize=[9, 4])\n",
    "fig.suptitle('K Cluster Targets')\n",
    "ax[0][0].scatter(beer.iloc[:, 0], beer.iloc[:, 1], c=clusters_3.predict(beer))\n",
    "ax[0][1].scatter(beer.iloc[:, 0], beer.iloc[:, 2], c=clusters_3.predict(beer))\n",
    "ax[1][0].scatter(beer.iloc[:, 0], beer.iloc[:, 3], c=clusters_3.predict(beer))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "# fit a classification tree with max_depth=2 on all data\n",
    "treeclf = tree.DecisionTreeClassifier(max_depth=2, random_state=1)\n",
    "treeclf.fit(beer, clusters_3.predict(beer))\n",
    "\n",
    "# create a Graphviz file\n",
    "with open(\"beer_clusters.dot\", 'wb') as f:\n",
    "    f = tree.export_graphviz(treeclf, out_file=f, feature_names=list(beer.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='beers.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}