junk.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# http://www.bogotobogo.com/python/scikit-learn/scikit_machine_learning_Data_Processing-II-Datasets-Partitioning-Feature-scaling-Feature-Selection-Regularization.php"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.base import clone\n",
    "from itertools import combinations\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class SBS():\n",
    "    def __init__(self, estimator, k_features,\n",
    "        scoring=accuracy_score,\n",
    "        test_size=0.25, random_state=1):\n",
    "        self.scoring = scoring\n",
    "        self.estimator = clone(estimator)\n",
    "        self.k_features = k_features\n",
    "        self.test_size = test_size\n",
    "        self.random_state = random_state\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        X_train, X_test, y_train, y_test = \\\n",
    "            train_test_split(X, y, test_size=self.test_size,\n",
    "        random_state=self.random_state)\n",
    "        dim = X_train.shape[1]\n",
    "        self.indices_ = tuple(range(dim))\n",
    "        self.subsets_ = [self.indices_]\n",
    "        score = self._calc_score(X_train, y_train,\n",
    "        X_test, y_test, self.indices_)\n",
    "        self.scores_ = [score]\n",
    "        while dim > self.k_features:\n",
    "            scores = []\n",
    "            subsets = []\n",
    "            for p in combinations(self.indices_, r=dim-1):\n",
    "                score = self._calc_score(X_train, y_train,\n",
    "                X_test, y_test, p)\n",
    "                scores.append(score)\n",
    "                subsets.append(p)\n",
    "            best = np.argmax(scores)\n",
    "            self.indices_ = subsets[best]\n",
    "            self.subsets_.append(self.indices_)\n",
    "            dim -= 1\n",
    "            self.scores_.append(scores[best])\n",
    "        self.k_score_ = self.scores_[-1]\n",
    "        return self\n",
    "    \n",
    "        def transform(self, X):\n",
    "            return X[:, self.indices_]\n",
    "        \n",
    "        def _calc_score(self, X_train, y_train,\n",
    "                        X_test, y_test, indices):\n",
    "            self.estimator.fit(X_train[:, indices], y_train)\n",
    "            y_pred = self.estimator.predict(X_test[:, indices])\n",
    "            score = self.s\n",
    "            return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_train_std' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-6bd08d89bb83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mknn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msbs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSBS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mknn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0msbs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_std\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'X_train_std' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=2)\n",
    "sbs = SBS(knn, k_features=1)\n",
    "sbs.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Class label</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diluted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.640000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.680000</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.320000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.45</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>1.05</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>14.39</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.45</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>1.02</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.61</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.51</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.050000</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>14.83</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.17</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>1.08</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.27</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.220000</td>\n",
       "      <td>1.01</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>14.10</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.30</td>\n",
       "      <td>18.0</td>\n",
       "      <td>105</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>14.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.32</td>\n",
       "      <td>16.8</td>\n",
       "      <td>95</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.17</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>13.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.41</td>\n",
       "      <td>16.0</td>\n",
       "      <td>89</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>1.15</td>\n",
       "      <td>2.90</td>\n",
       "      <td>1320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>14.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.39</td>\n",
       "      <td>11.4</td>\n",
       "      <td>91</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>14.38</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.38</td>\n",
       "      <td>12.0</td>\n",
       "      <td>102</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.96</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>13.63</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.70</td>\n",
       "      <td>17.2</td>\n",
       "      <td>112</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.46</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>14.30</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.72</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>13.83</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.62</td>\n",
       "      <td>20.0</td>\n",
       "      <td>115</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.72</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>1.13</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>14.19</td>\n",
       "      <td>1.59</td>\n",
       "      <td>2.48</td>\n",
       "      <td>16.5</td>\n",
       "      <td>108</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.93</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.86</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>13.64</td>\n",
       "      <td>3.10</td>\n",
       "      <td>2.56</td>\n",
       "      <td>15.2</td>\n",
       "      <td>116</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.66</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>3.36</td>\n",
       "      <td>845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>14.06</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.28</td>\n",
       "      <td>16.0</td>\n",
       "      <td>126</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.10</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3.71</td>\n",
       "      <td>780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>12.93</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.65</td>\n",
       "      <td>18.6</td>\n",
       "      <td>102</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.52</td>\n",
       "      <td>770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>13.71</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.36</td>\n",
       "      <td>16.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.69</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.11</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>12.85</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2.52</td>\n",
       "      <td>17.8</td>\n",
       "      <td>95</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.37</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>3.930000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3.63</td>\n",
       "      <td>1015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>13.50</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.61</td>\n",
       "      <td>20.0</td>\n",
       "      <td>96</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.66</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>1.12</td>\n",
       "      <td>3.82</td>\n",
       "      <td>845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>13.05</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.22</td>\n",
       "      <td>25.0</td>\n",
       "      <td>124</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>1.13</td>\n",
       "      <td>3.20</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>13.39</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.62</td>\n",
       "      <td>16.1</td>\n",
       "      <td>93</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.45</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.22</td>\n",
       "      <td>1195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>13.30</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.14</td>\n",
       "      <td>17.0</td>\n",
       "      <td>94</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.35</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2.77</td>\n",
       "      <td>1285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>13.87</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.80</td>\n",
       "      <td>19.4</td>\n",
       "      <td>107</td>\n",
       "      <td>2.95</td>\n",
       "      <td>2.97</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.76</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.40</td>\n",
       "      <td>915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>14.02</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.21</td>\n",
       "      <td>16.0</td>\n",
       "      <td>96</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.33</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>3</td>\n",
       "      <td>13.32</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.5</td>\n",
       "      <td>92</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.420000</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.62</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>3</td>\n",
       "      <td>13.08</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.36</td>\n",
       "      <td>21.5</td>\n",
       "      <td>113</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.14</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.33</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>3</td>\n",
       "      <td>13.50</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.62</td>\n",
       "      <td>24.0</td>\n",
       "      <td>123</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.30</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>3</td>\n",
       "      <td>12.79</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2.48</td>\n",
       "      <td>22.0</td>\n",
       "      <td>112</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.26</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.47</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3</td>\n",
       "      <td>13.11</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>116</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.56</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.33</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>3</td>\n",
       "      <td>13.23</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.28</td>\n",
       "      <td>18.5</td>\n",
       "      <td>98</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.87</td>\n",
       "      <td>10.520000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.51</td>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>3</td>\n",
       "      <td>12.58</td>\n",
       "      <td>1.29</td>\n",
       "      <td>2.10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>103</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.40</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.55</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>5.19</td>\n",
       "      <td>2.32</td>\n",
       "      <td>22.0</td>\n",
       "      <td>93</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.55</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>3</td>\n",
       "      <td>13.84</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.38</td>\n",
       "      <td>19.5</td>\n",
       "      <td>89</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.56</td>\n",
       "      <td>9.010000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.64</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>3</td>\n",
       "      <td>12.45</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.64</td>\n",
       "      <td>27.0</td>\n",
       "      <td>97</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.14</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.73</td>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>3</td>\n",
       "      <td>14.34</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.70</td>\n",
       "      <td>25.0</td>\n",
       "      <td>98</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.70</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.96</td>\n",
       "      <td>660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>3</td>\n",
       "      <td>13.48</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.64</td>\n",
       "      <td>22.5</td>\n",
       "      <td>89</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2.29</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.78</td>\n",
       "      <td>620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>3</td>\n",
       "      <td>12.36</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.04</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.58</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>3</td>\n",
       "      <td>13.69</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.54</td>\n",
       "      <td>20.0</td>\n",
       "      <td>107</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>5.880000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.82</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>3</td>\n",
       "      <td>12.85</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2.58</td>\n",
       "      <td>22.0</td>\n",
       "      <td>106</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.96</td>\n",
       "      <td>5.580000</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.11</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>3</td>\n",
       "      <td>12.96</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.35</td>\n",
       "      <td>18.5</td>\n",
       "      <td>106</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.94</td>\n",
       "      <td>5.280000</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.75</td>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>3</td>\n",
       "      <td>13.78</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.30</td>\n",
       "      <td>22.0</td>\n",
       "      <td>90</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.03</td>\n",
       "      <td>9.580000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.68</td>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>3</td>\n",
       "      <td>13.73</td>\n",
       "      <td>4.36</td>\n",
       "      <td>2.26</td>\n",
       "      <td>22.5</td>\n",
       "      <td>88</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.15</td>\n",
       "      <td>6.620000</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.75</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>3</td>\n",
       "      <td>13.45</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.60</td>\n",
       "      <td>23.0</td>\n",
       "      <td>111</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.46</td>\n",
       "      <td>10.680000</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.56</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>3</td>\n",
       "      <td>12.82</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.30</td>\n",
       "      <td>19.5</td>\n",
       "      <td>88</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.97</td>\n",
       "      <td>10.260000</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.75</td>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>3</td>\n",
       "      <td>13.58</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.69</td>\n",
       "      <td>24.5</td>\n",
       "      <td>105</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.54</td>\n",
       "      <td>8.660000</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.80</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>3</td>\n",
       "      <td>13.40</td>\n",
       "      <td>4.60</td>\n",
       "      <td>2.86</td>\n",
       "      <td>25.0</td>\n",
       "      <td>112</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.11</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.92</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>3</td>\n",
       "      <td>12.20</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.32</td>\n",
       "      <td>19.0</td>\n",
       "      <td>96</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.73</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.83</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>3</td>\n",
       "      <td>12.77</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.28</td>\n",
       "      <td>19.5</td>\n",
       "      <td>86</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.64</td>\n",
       "      <td>9.899999</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.63</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>3</td>\n",
       "      <td>14.16</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.48</td>\n",
       "      <td>20.0</td>\n",
       "      <td>91</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.24</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.71</td>\n",
       "      <td>660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Class label  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "0              1    14.23        1.71  2.43               15.6        127   \n",
       "1              1    13.20        1.78  2.14               11.2        100   \n",
       "2              1    13.16        2.36  2.67               18.6        101   \n",
       "3              1    14.37        1.95  2.50               16.8        113   \n",
       "4              1    13.24        2.59  2.87               21.0        118   \n",
       "5              1    14.20        1.76  2.45               15.2        112   \n",
       "6              1    14.39        1.87  2.45               14.6         96   \n",
       "7              1    14.06        2.15  2.61               17.6        121   \n",
       "8              1    14.83        1.64  2.17               14.0         97   \n",
       "9              1    13.86        1.35  2.27               16.0         98   \n",
       "10             1    14.10        2.16  2.30               18.0        105   \n",
       "11             1    14.12        1.48  2.32               16.8         95   \n",
       "12             1    13.75        1.73  2.41               16.0         89   \n",
       "13             1    14.75        1.73  2.39               11.4         91   \n",
       "14             1    14.38        1.87  2.38               12.0        102   \n",
       "15             1    13.63        1.81  2.70               17.2        112   \n",
       "16             1    14.30        1.92  2.72               20.0        120   \n",
       "17             1    13.83        1.57  2.62               20.0        115   \n",
       "18             1    14.19        1.59  2.48               16.5        108   \n",
       "19             1    13.64        3.10  2.56               15.2        116   \n",
       "20             1    14.06        1.63  2.28               16.0        126   \n",
       "21             1    12.93        3.80  2.65               18.6        102   \n",
       "22             1    13.71        1.86  2.36               16.6        101   \n",
       "23             1    12.85        1.60  2.52               17.8         95   \n",
       "24             1    13.50        1.81  2.61               20.0         96   \n",
       "25             1    13.05        2.05  3.22               25.0        124   \n",
       "26             1    13.39        1.77  2.62               16.1         93   \n",
       "27             1    13.30        1.72  2.14               17.0         94   \n",
       "28             1    13.87        1.90  2.80               19.4        107   \n",
       "29             1    14.02        1.68  2.21               16.0         96   \n",
       "..           ...      ...         ...   ...                ...        ...   \n",
       "148            3    13.32        3.24  2.38               21.5         92   \n",
       "149            3    13.08        3.90  2.36               21.5        113   \n",
       "150            3    13.50        3.12  2.62               24.0        123   \n",
       "151            3    12.79        2.67  2.48               22.0        112   \n",
       "152            3    13.11        1.90  2.75               25.5        116   \n",
       "153            3    13.23        3.30  2.28               18.5         98   \n",
       "154            3    12.58        1.29  2.10               20.0        103   \n",
       "155            3    13.17        5.19  2.32               22.0         93   \n",
       "156            3    13.84        4.12  2.38               19.5         89   \n",
       "157            3    12.45        3.03  2.64               27.0         97   \n",
       "158            3    14.34        1.68  2.70               25.0         98   \n",
       "159            3    13.48        1.67  2.64               22.5         89   \n",
       "160            3    12.36        3.83  2.38               21.0         88   \n",
       "161            3    13.69        3.26  2.54               20.0        107   \n",
       "162            3    12.85        3.27  2.58               22.0        106   \n",
       "163            3    12.96        3.45  2.35               18.5        106   \n",
       "164            3    13.78        2.76  2.30               22.0         90   \n",
       "165            3    13.73        4.36  2.26               22.5         88   \n",
       "166            3    13.45        3.70  2.60               23.0        111   \n",
       "167            3    12.82        3.37  2.30               19.5         88   \n",
       "168            3    13.58        2.58  2.69               24.5        105   \n",
       "169            3    13.40        4.60  2.86               25.0        112   \n",
       "170            3    12.20        3.03  2.32               19.0         96   \n",
       "171            3    12.77        2.39  2.28               19.5         86   \n",
       "172            3    14.16        2.51  2.48               20.0         91   \n",
       "173            3    13.71        5.65  2.45               20.5         95   \n",
       "174            3    13.40        3.91  2.48               23.0        102   \n",
       "175            3    13.27        4.28  2.26               20.0        120   \n",
       "176            3    13.17        2.59  2.37               20.0        120   \n",
       "177            3    14.13        4.10  2.74               24.5         96   \n",
       "\n",
       "     Total phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0             2.80        3.06                  0.28             2.29   \n",
       "1             2.65        2.76                  0.26             1.28   \n",
       "2             2.80        3.24                  0.30             2.81   \n",
       "3             3.85        3.49                  0.24             2.18   \n",
       "4             2.80        2.69                  0.39             1.82   \n",
       "5             3.27        3.39                  0.34             1.97   \n",
       "6             2.50        2.52                  0.30             1.98   \n",
       "7             2.60        2.51                  0.31             1.25   \n",
       "8             2.80        2.98                  0.29             1.98   \n",
       "9             2.98        3.15                  0.22             1.85   \n",
       "10            2.95        3.32                  0.22             2.38   \n",
       "11            2.20        2.43                  0.26             1.57   \n",
       "12            2.60        2.76                  0.29             1.81   \n",
       "13            3.10        3.69                  0.43             2.81   \n",
       "14            3.30        3.64                  0.29             2.96   \n",
       "15            2.85        2.91                  0.30             1.46   \n",
       "16            2.80        3.14                  0.33             1.97   \n",
       "17            2.95        3.40                  0.40             1.72   \n",
       "18            3.30        3.93                  0.32             1.86   \n",
       "19            2.70        3.03                  0.17             1.66   \n",
       "20            3.00        3.17                  0.24             2.10   \n",
       "21            2.41        2.41                  0.25             1.98   \n",
       "22            2.61        2.88                  0.27             1.69   \n",
       "23            2.48        2.37                  0.26             1.46   \n",
       "24            2.53        2.61                  0.28             1.66   \n",
       "25            2.63        2.68                  0.47             1.92   \n",
       "26            2.85        2.94                  0.34             1.45   \n",
       "27            2.40        2.19                  0.27             1.35   \n",
       "28            2.95        2.97                  0.37             1.76   \n",
       "29            2.65        2.33                  0.26             1.98   \n",
       "..             ...         ...                   ...              ...   \n",
       "148           1.93        0.76                  0.45             1.25   \n",
       "149           1.41        1.39                  0.34             1.14   \n",
       "150           1.40        1.57                  0.22             1.25   \n",
       "151           1.48        1.36                  0.24             1.26   \n",
       "152           2.20        1.28                  0.26             1.56   \n",
       "153           1.80        0.83                  0.61             1.87   \n",
       "154           1.48        0.58                  0.53             1.40   \n",
       "155           1.74        0.63                  0.61             1.55   \n",
       "156           1.80        0.83                  0.48             1.56   \n",
       "157           1.90        0.58                  0.63             1.14   \n",
       "158           2.80        1.31                  0.53             2.70   \n",
       "159           2.60        1.10                  0.52             2.29   \n",
       "160           2.30        0.92                  0.50             1.04   \n",
       "161           1.83        0.56                  0.50             0.80   \n",
       "162           1.65        0.60                  0.60             0.96   \n",
       "163           1.39        0.70                  0.40             0.94   \n",
       "164           1.35        0.68                  0.41             1.03   \n",
       "165           1.28        0.47                  0.52             1.15   \n",
       "166           1.70        0.92                  0.43             1.46   \n",
       "167           1.48        0.66                  0.40             0.97   \n",
       "168           1.55        0.84                  0.39             1.54   \n",
       "169           1.98        0.96                  0.27             1.11   \n",
       "170           1.25        0.49                  0.40             0.73   \n",
       "171           1.39        0.51                  0.48             0.64   \n",
       "172           1.68        0.70                  0.44             1.24   \n",
       "173           1.68        0.61                  0.52             1.06   \n",
       "174           1.80        0.75                  0.43             1.41   \n",
       "175           1.59        0.69                  0.43             1.35   \n",
       "176           1.65        0.68                  0.53             1.46   \n",
       "177           2.05        0.76                  0.56             1.35   \n",
       "\n",
       "     Color intensity   Hue  OD280/OD315 of diluted wines  Proline  \n",
       "0           5.640000  1.04                          3.92     1065  \n",
       "1           4.380000  1.05                          3.40     1050  \n",
       "2           5.680000  1.03                          3.17     1185  \n",
       "3           7.800000  0.86                          3.45     1480  \n",
       "4           4.320000  1.04                          2.93      735  \n",
       "5           6.750000  1.05                          2.85     1450  \n",
       "6           5.250000  1.02                          3.58     1290  \n",
       "7           5.050000  1.06                          3.58     1295  \n",
       "8           5.200000  1.08                          2.85     1045  \n",
       "9           7.220000  1.01                          3.55     1045  \n",
       "10          5.750000  1.25                          3.17     1510  \n",
       "11          5.000000  1.17                          2.82     1280  \n",
       "12          5.600000  1.15                          2.90     1320  \n",
       "13          5.400000  1.25                          2.73     1150  \n",
       "14          7.500000  1.20                          3.00     1547  \n",
       "15          7.300000  1.28                          2.88     1310  \n",
       "16          6.200000  1.07                          2.65     1280  \n",
       "17          6.600000  1.13                          2.57     1130  \n",
       "18          8.700000  1.23                          2.82     1680  \n",
       "19          5.100000  0.96                          3.36      845  \n",
       "20          5.650000  1.09                          3.71      780  \n",
       "21          4.500000  1.03                          3.52      770  \n",
       "22          3.800000  1.11                          4.00     1035  \n",
       "23          3.930000  1.09                          3.63     1015  \n",
       "24          3.520000  1.12                          3.82      845  \n",
       "25          3.580000  1.13                          3.20      830  \n",
       "26          4.800000  0.92                          3.22     1195  \n",
       "27          3.950000  1.02                          2.77     1285  \n",
       "28          4.500000  1.25                          3.40      915  \n",
       "29          4.700000  1.04                          3.59     1035  \n",
       "..               ...   ...                           ...      ...  \n",
       "148         8.420000  0.55                          1.62      650  \n",
       "149         9.400000  0.57                          1.33      550  \n",
       "150         8.600000  0.59                          1.30      500  \n",
       "151        10.800000  0.48                          1.47      480  \n",
       "152         7.100000  0.61                          1.33      425  \n",
       "153        10.520000  0.56                          1.51      675  \n",
       "154         7.600000  0.58                          1.55      640  \n",
       "155         7.900000  0.60                          1.48      725  \n",
       "156         9.010000  0.57                          1.64      480  \n",
       "157         7.500000  0.67                          1.73      880  \n",
       "158        13.000000  0.57                          1.96      660  \n",
       "159        11.750000  0.57                          1.78      620  \n",
       "160         7.650000  0.56                          1.58      520  \n",
       "161         5.880000  0.96                          1.82      680  \n",
       "162         5.580000  0.87                          2.11      570  \n",
       "163         5.280000  0.68                          1.75      675  \n",
       "164         9.580000  0.70                          1.68      615  \n",
       "165         6.620000  0.78                          1.75      520  \n",
       "166        10.680000  0.85                          1.56      695  \n",
       "167        10.260000  0.72                          1.75      685  \n",
       "168         8.660000  0.74                          1.80      750  \n",
       "169         8.500000  0.67                          1.92      630  \n",
       "170         5.500000  0.66                          1.83      510  \n",
       "171         9.899999  0.57                          1.63      470  \n",
       "172         9.700000  0.62                          1.71      660  \n",
       "173         7.700000  0.64                          1.74      740  \n",
       "174         7.300000  0.70                          1.56      750  \n",
       "175        10.200000  0.59                          1.56      835  \n",
       "176         9.300000  0.60                          1.62      840  \n",
       "177         9.200000  0.61                          1.60      560  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wine.iloc[:, 0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "        train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 124)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test), len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mms = MinMaxScaler()\n",
    "X_train_norm = mms.fit_transform(X_train)\n",
    "X_test_norm = mms.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.72043011,  0.20378151,  0.53763441,  0.30927835,  0.33695652,\n",
       "        0.54316547,  0.73700306,  0.25      ,  0.40189873,  0.24068768,\n",
       "        0.48717949,  1.        ,  0.5854251 ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_norm[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.72849462,  0.16386555,  0.47849462,  0.29896907,  0.52173913,\n",
       "        0.53956835,  0.74311927,  0.13461538,  0.37974684,  0.4364852 ,\n",
       "        0.32478632,  0.70695971,  0.60566802])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_norm[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.72043011,  0.20378151,  0.53763441,  0.30927835,  0.33695652,\n",
       "        0.54316547,  0.73700306,  0.25      ,  0.40189873,  0.24068768,\n",
       "        0.48717949,  1.        ,  0.5854251 ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_norm[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "stdsc = StandardScaler()\n",
    "X_train_std = stdsc.fit_transform(X_train)\n",
    "X_test_std = stdsc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.91083058, -0.46259897, -0.01142613, -0.82067872,  0.06241693,\n",
       "        0.58820446,  0.93565436, -0.7619138 ,  0.13007174, -0.51238741,\n",
       "        0.65706596,  1.94354495,  0.93700997])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_std[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7456ea2f50>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JeDAe04I9rGWyrchYhwEJOma9+DSlh+hC8Gn6HmJXRaYONUO33T3VlmDZQ4gRoxldT0e1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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7456ea29d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx = np.arange(len(X_train_std))\n",
    "yy1 = X_train_norm[:,1]\n",
    "yy2 = X_train_std[:,1]\n",
    "scatter(xx, yy1, color='b')\n",
    "scatter(xx, yy2, color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LogisticRegression(penalty='l1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Training accuracy:', 0.9838709677419355)\n",
      "('Test accuracy:', 0.98148148148148151)\n"
     ]
    }
   ],
   "source": [
    "logi_regr = LogisticRegression(penalty='l1', C=0.1)\n",
    "logi_regr.fit(X_train_std, y_train)\n",
    "print('Training accuracy:', logi_regr.score(X_train_std, y_train))\n",
    "print('Test accuracy:', logi_regr.score(X_test_std, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.38379513, -0.15807356, -0.70043322])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logi_regr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.28030554,  0.        ,  0.        , -0.0279115 ,  0.        ,\n",
       "         0.        ,  0.71013196,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  1.23597244],\n",
       "       [-0.6439924 , -0.0688216 , -0.05721304,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        , -0.92681413,\n",
       "         0.06018603,  0.        , -0.37106159],\n",
       "       [ 0.        ,  0.0614675 ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        , -0.63553239,  0.        ,  0.        ,  0.49786892,\n",
       "        -0.35841512, -0.57184628,  0.        ]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logi_regr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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T1kmUUuTn5zsCWf1WP3YPICgoiMTERCZNmsTtt99OdHR0s9Dl5+cnEzWIbpWW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7458956590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = figure()\n",
    "ax = subplot(111)\n",
    "colors = ['blue', 'green', 'red', 'cyan',\n",
    "'magenta', 'yellow', 'black',\n",
    "'pink', 'lightgreen', 'lightblue',\n",
    "'gray', 'indigo', 'orange']\n",
    "weights, params = [], []\n",
    "for c in np.arange(-4, 6):\n",
    "    lr = LogisticRegression(penalty='l1',\n",
    "            C=10**c, random_state=0)\n",
    "    lr.fit(X_train_std, y_train)\n",
    "    weights.append(lr.coef_[1])\n",
    "    params.append(10**c)\n",
    "weights = np.array(weights)\n",
    "for column, color in zip(range(weights.shape[1]), colors):\n",
    "    plot(params, weights[:, column],\n",
    "        label=df_wine.columns[column+1], color=color)\n",
    "axhline(0, color='black', linestyle='--', linewidth=3)\n",
    "xlim([10**(-5), 10**5])\n",
    "ylabel('weight coefficient')\n",
    "xlabel('C')\n",
    "xscale('log')\n",
    "legend(loc='upper left')\n",
    "legend(loc='upper center',\n",
    "bbox_to_anchor=(1.38, 1.03),\n",
    "ncol=1, fancybox=True)\n",
    "show()"
   ]
  },
  {
   "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}