ecg-exp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Remove question mark (missing values) in the raw file\n",
    "# and save it in a new file 'arrhythmia.data'.\n",
    "# This needs to be done only once, and all the following experiments can load 'arrhythmia.data' directly\n",
    "with open('arrhythmia.data.orig') as inputfile:\n",
    "    with open('arrhythmia.data', 'w') as outputfile:\n",
    "        for line in inputfile:\n",
    "            outputfile.write(line.replace('?', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# read the data\n",
    "df = pd.read_csv('arrhythmia.data', header=None)\n",
    "y = df.iloc[:, -1]  # The last column is the ground-truth label vector\n",
    "X = df.iloc[:, :-1]  # The first to second-last columns are the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>269</th>\n",
       "      <th>270</th>\n",
       "      <th>271</th>\n",
       "      <th>272</th>\n",
       "      <th>273</th>\n",
       "      <th>274</th>\n",
       "      <th>275</th>\n",
       "      <th>276</th>\n",
       "      <th>277</th>\n",
       "      <th>278</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>80</td>\n",
       "      <td>91</td>\n",
       "      <td>193</td>\n",
       "      <td>371</td>\n",
       "      <td>174</td>\n",
       "      <td>121</td>\n",
       "      <td>-16</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>23.3</td>\n",
       "      <td>49.4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "      <td>174</td>\n",
       "      <td>401</td>\n",
       "      <td>149</td>\n",
       "      <td>39</td>\n",
       "      <td>25</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>20.4</td>\n",
       "      <td>38.8</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>95</td>\n",
       "      <td>138</td>\n",
       "      <td>163</td>\n",
       "      <td>386</td>\n",
       "      <td>185</td>\n",
       "      <td>102</td>\n",
       "      <td>96</td>\n",
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       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>12.3</td>\n",
       "      <td>49.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>94</td>\n",
       "      <td>100</td>\n",
       "      <td>202</td>\n",
       "      <td>380</td>\n",
       "      <td>179</td>\n",
       "      <td>143</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
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       "      <td>61.6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>190</td>\n",
       "      <td>80</td>\n",
       "      <td>88</td>\n",
       "      <td>181</td>\n",
       "      <td>360</td>\n",
       "      <td>177</td>\n",
       "      <td>103</td>\n",
       "      <td>-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>62.8</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>51</td>\n",
       "      <td>100</td>\n",
       "      <td>167</td>\n",
       "      <td>321</td>\n",
       "      <td>174</td>\n",
       "      <td>91</td>\n",
       "      <td>107</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.6</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>13.5</td>\n",
       "      <td>31.1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>52</td>\n",
       "      <td>77</td>\n",
       "      <td>129</td>\n",
       "      <td>377</td>\n",
       "      <td>133</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
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       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>20.5</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>54</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>157</td>\n",
       "      <td>70</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>15.8</td>\n",
       "      <td>19.8</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>56</td>\n",
       "      <td>84</td>\n",
       "      <td>118</td>\n",
       "      <td>354</td>\n",
       "      <td>160</td>\n",
       "      <td>63</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>12.5</td>\n",
       "      <td>30.9</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>167</td>\n",
       "      <td>67</td>\n",
       "      <td>89</td>\n",
       "      <td>130</td>\n",
       "      <td>383</td>\n",
       "      <td>156</td>\n",
       "      <td>73</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>10.8</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>20.1</td>\n",
       "      <td>25.1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>102</td>\n",
       "      <td>135</td>\n",
       "      <td>401</td>\n",
       "      <td>156</td>\n",
       "      <td>83</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>12.3</td>\n",
       "      <td>19.3</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>86</td>\n",
       "      <td>77</td>\n",
       "      <td>143</td>\n",
       "      <td>373</td>\n",
       "      <td>150</td>\n",
       "      <td>65</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>17.2</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>58</td>\n",
       "      <td>78</td>\n",
       "      <td>155</td>\n",
       "      <td>382</td>\n",
       "      <td>163</td>\n",
       "      <td>81</td>\n",
       "      <td>-24</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>73</td>\n",
       "      <td>91</td>\n",
       "      <td>180</td>\n",
       "      <td>355</td>\n",
       "      <td>157</td>\n",
       "      <td>104</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>28.5</td>\n",
       "      <td>48.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "      <td>158</td>\n",
       "      <td>399</td>\n",
       "      <td>163</td>\n",
       "      <td>94</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>39.2</td>\n",
       "      <td>54.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>48</td>\n",
       "      <td>75</td>\n",
       "      <td>132</td>\n",
       "      <td>350</td>\n",
       "      <td>169</td>\n",
       "      <td>65</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>17.2</td>\n",
       "      <td>31.1</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>59</td>\n",
       "      <td>82</td>\n",
       "      <td>145</td>\n",
       "      <td>347</td>\n",
       "      <td>169</td>\n",
       "      <td>61</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>19.5</td>\n",
       "      <td>41.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>58</td>\n",
       "      <td>70</td>\n",
       "      <td>120</td>\n",
       "      <td>353</td>\n",
       "      <td>122</td>\n",
       "      <td>52</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>17.1</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>63</td>\n",
       "      <td>91</td>\n",
       "      <td>154</td>\n",
       "      <td>392</td>\n",
       "      <td>175</td>\n",
       "      <td>83</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>166</td>\n",
       "      <td>72</td>\n",
       "      <td>82</td>\n",
       "      <td>181</td>\n",
       "      <td>399</td>\n",
       "      <td>158</td>\n",
       "      <td>79</td>\n",
       "      <td>-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>58</td>\n",
       "      <td>83</td>\n",
       "      <td>251</td>\n",
       "      <td>383</td>\n",
       "      <td>189</td>\n",
       "      <td>183</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>17.1</td>\n",
       "      <td>54.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>67</td>\n",
       "      <td>90</td>\n",
       "      <td>122</td>\n",
       "      <td>336</td>\n",
       "      <td>177</td>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>8.3</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>11.7</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>75</td>\n",
       "      <td>71</td>\n",
       "      <td>132</td>\n",
       "      <td>364</td>\n",
       "      <td>169</td>\n",
       "      <td>82</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>19.7</td>\n",
       "      <td>34.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>59</td>\n",
       "      <td>75</td>\n",
       "      <td>157</td>\n",
       "      <td>406</td>\n",
       "      <td>143</td>\n",
       "      <td>92</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>18.4</td>\n",
       "      <td>28.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>55</td>\n",
       "      <td>82</td>\n",
       "      <td>140</td>\n",
       "      <td>388</td>\n",
       "      <td>149</td>\n",
       "      <td>82</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>35.3</td>\n",
       "      <td>57.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>80</td>\n",
       "      <td>109</td>\n",
       "      <td>128</td>\n",
       "      <td>382</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>-34</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>20.7</td>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>73</td>\n",
       "      <td>94</td>\n",
       "      <td>186</td>\n",
       "      <td>373</td>\n",
       "      <td>224</td>\n",
       "      <td>125</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>44.0</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>54</td>\n",
       "      <td>95</td>\n",
       "      <td>161</td>\n",
       "      <td>407</td>\n",
       "      <td>168</td>\n",
       "      <td>83</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>25.4</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>164</td>\n",
       "      <td>420</td>\n",
       "      <td>381</td>\n",
       "      <td>99</td>\n",
       "      <td>-8</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>17.2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>83</td>\n",
       "      <td>96</td>\n",
       "      <td>147</td>\n",
       "      <td>400</td>\n",
       "      <td>301</td>\n",
       "      <td>82</td>\n",
       "      <td>-37</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-6.3</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",
       "      <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>422</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>57</td>\n",
       "      <td>83</td>\n",
       "      <td>164</td>\n",
       "      <td>359</td>\n",
       "      <td>154</td>\n",
       "      <td>69</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.1</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.7</td>\n",
       "      <td>56.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>95</td>\n",
       "      <td>94</td>\n",
       "      <td>203</td>\n",
       "      <td>367</td>\n",
       "      <td>171</td>\n",
       "      <td>106</td>\n",
       "      <td>-7</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>119</td>\n",
       "      <td>21</td>\n",
       "      <td>140</td>\n",
       "      <td>157</td>\n",
       "      <td>438</td>\n",
       "      <td>226</td>\n",
       "      <td>81</td>\n",
       "      <td>-40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>36.7</td>\n",
       "      <td>115.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>150</td>\n",
       "      <td>362</td>\n",
       "      <td>177</td>\n",
       "      <td>96</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.1</td>\n",
       "      <td>52.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>53</td>\n",
       "      <td>55</td>\n",
       "      <td>163</td>\n",
       "      <td>340</td>\n",
       "      <td>162</td>\n",
       "      <td>102</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>20.9</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>65</td>\n",
       "      <td>133</td>\n",
       "      <td>148</td>\n",
       "      <td>417</td>\n",
       "      <td>260</td>\n",
       "      <td>92</td>\n",
       "      <td>-158</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>63</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>364</td>\n",
       "      <td>120</td>\n",
       "      <td>90</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>23.7</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>24</td>\n",
       "      <td>77</td>\n",
       "      <td>125</td>\n",
       "      <td>358</td>\n",
       "      <td>159</td>\n",
       "      <td>70</td>\n",
       "      <td>87</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>16.1</td>\n",
       "      <td>49.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>29</td>\n",
       "      <td>123</td>\n",
       "      <td>145</td>\n",
       "      <td>361</td>\n",
       "      <td>221</td>\n",
       "      <td>80</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>19.6</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>25.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>56</td>\n",
       "      <td>79</td>\n",
       "      <td>145</td>\n",
       "      <td>381</td>\n",
       "      <td>173</td>\n",
       "      <td>101</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>20.4</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>42</td>\n",
       "      <td>88</td>\n",
       "      <td>123</td>\n",
       "      <td>362</td>\n",
       "      <td>228</td>\n",
       "      <td>81</td>\n",
       "      <td>-18</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>5.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>84.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>167</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>149</td>\n",
       "      <td>290</td>\n",
       "      <td>128</td>\n",
       "      <td>93</td>\n",
       "      <td>-67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>-8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>178</td>\n",
       "      <td>65</td>\n",
       "      <td>88</td>\n",
       "      <td>155</td>\n",
       "      <td>360</td>\n",
       "      <td>163</td>\n",
       "      <td>71</td>\n",
       "      <td>-22</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>62</td>\n",
       "      <td>79</td>\n",
       "      <td>155</td>\n",
       "      <td>367</td>\n",
       "      <td>153</td>\n",
       "      <td>95</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>24.1</td>\n",
       "      <td>33.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>57</td>\n",
       "      <td>77</td>\n",
       "      <td>144</td>\n",
       "      <td>340</td>\n",
       "      <td>148</td>\n",
       "      <td>82</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>155</td>\n",
       "      <td>63</td>\n",
       "      <td>87</td>\n",
       "      <td>142</td>\n",
       "      <td>391</td>\n",
       "      <td>137</td>\n",
       "      <td>88</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>25.6</td>\n",
       "      <td>43.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>82</td>\n",
       "      <td>88</td>\n",
       "      <td>146</td>\n",
       "      <td>357</td>\n",
       "      <td>179</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>13.5</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>30.1</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>66</td>\n",
       "      <td>94</td>\n",
       "      <td>170</td>\n",
       "      <td>383</td>\n",
       "      <td>152</td>\n",
       "      <td>115</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>21.5</td>\n",
       "      <td>33.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>153</td>\n",
       "      <td>389</td>\n",
       "      <td>172</td>\n",
       "      <td>89</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>16.6</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>24.9</td>\n",
       "      <td>41.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>50</td>\n",
       "      <td>74</td>\n",
       "      <td>143</td>\n",
       "      <td>374</td>\n",
       "      <td>146</td>\n",
       "      <td>75</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>40.1</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>50</td>\n",
       "      <td>85</td>\n",
       "      <td>143</td>\n",
       "      <td>363</td>\n",
       "      <td>146</td>\n",
       "      <td>84</td>\n",
       "      <td>-40</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>75</td>\n",
       "      <td>88</td>\n",
       "      <td>157</td>\n",
       "      <td>384</td>\n",
       "      <td>132</td>\n",
       "      <td>112</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>10.5</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>63</td>\n",
       "      <td>81</td>\n",
       "      <td>143</td>\n",
       "      <td>325</td>\n",
       "      <td>218</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>14.1</td>\n",
       "      <td>37.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>75</td>\n",
       "      <td>91</td>\n",
       "      <td>134</td>\n",
       "      <td>376</td>\n",
       "      <td>160</td>\n",
       "      <td>83</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>57</td>\n",
       "      <td>81</td>\n",
       "      <td>151</td>\n",
       "      <td>363</td>\n",
       "      <td>166</td>\n",
       "      <td>80</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>17.6</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>199</td>\n",
       "      <td>382</td>\n",
       "      <td>154</td>\n",
       "      <td>117</td>\n",
       "      <td>-37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-4.4</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>137</td>\n",
       "      <td>361</td>\n",
       "      <td>201</td>\n",
       "      <td>73</td>\n",
       "      <td>86</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>38.0</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>68</td>\n",
       "      <td>108</td>\n",
       "      <td>176</td>\n",
       "      <td>365</td>\n",
       "      <td>194</td>\n",
       "      <td>116</td>\n",
       "      <td>-85</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>-28.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-44.2</td>\n",
       "      <td>-33.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>155</td>\n",
       "      <td>55</td>\n",
       "      <td>93</td>\n",
       "      <td>106</td>\n",
       "      <td>386</td>\n",
       "      <td>218</td>\n",
       "      <td>63</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>46.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>127</td>\n",
       "      <td>364</td>\n",
       "      <td>138</td>\n",
       "      <td>78</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>21.3</td>\n",
       "      <td>32.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>452 rows × 279 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>269</th>\n",
       "      <th>270</th>\n",
       "      <th>271</th>\n",
       "      <th>272</th>\n",
       "      <th>273</th>\n",
       "      <th>274</th>\n",
       "      <th>275</th>\n",
       "      <th>276</th>\n",
       "      <th>277</th>\n",
       "      <th>278</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>80</td>\n",
       "      <td>91</td>\n",
       "      <td>193</td>\n",
       "      <td>371</td>\n",
       "      <td>174</td>\n",
       "      <td>121</td>\n",
       "      <td>-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>23.3</td>\n",
       "      <td>49.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "      <td>174</td>\n",
       "      <td>401</td>\n",
       "      <td>149</td>\n",
       "      <td>39</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>20.4</td>\n",
       "      <td>38.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>95</td>\n",
       "      <td>138</td>\n",
       "      <td>163</td>\n",
       "      <td>386</td>\n",
       "      <td>185</td>\n",
       "      <td>102</td>\n",
       "      <td>96</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>12.3</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>94</td>\n",
       "      <td>100</td>\n",
       "      <td>202</td>\n",
       "      <td>380</td>\n",
       "      <td>179</td>\n",
       "      <td>143</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>34.6</td>\n",
       "      <td>61.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>80</td>\n",
       "      <td>88</td>\n",
       "      <td>181</td>\n",
       "      <td>360</td>\n",
       "      <td>177</td>\n",
       "      <td>103</td>\n",
       "      <td>-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>62.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>51</td>\n",
       "      <td>100</td>\n",
       "      <td>167</td>\n",
       "      <td>321</td>\n",
       "      <td>174</td>\n",
       "      <td>91</td>\n",
       "      <td>107</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>12.2</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>13.5</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>52</td>\n",
       "      <td>77</td>\n",
       "      <td>129</td>\n",
       "      <td>377</td>\n",
       "      <td>133</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>54</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>157</td>\n",
       "      <td>70</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>15.8</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>56</td>\n",
       "      <td>84</td>\n",
       "      <td>118</td>\n",
       "      <td>354</td>\n",
       "      <td>160</td>\n",
       "      <td>63</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>12.5</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>167</td>\n",
       "      <td>67</td>\n",
       "      <td>89</td>\n",
       "      <td>130</td>\n",
       "      <td>383</td>\n",
       "      <td>156</td>\n",
       "      <td>73</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>10.8</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>20.1</td>\n",
       "      <td>25.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>102</td>\n",
       "      <td>135</td>\n",
       "      <td>401</td>\n",
       "      <td>156</td>\n",
       "      <td>83</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>12.3</td>\n",
       "      <td>19.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>86</td>\n",
       "      <td>77</td>\n",
       "      <td>143</td>\n",
       "      <td>373</td>\n",
       "      <td>150</td>\n",
       "      <td>65</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>58</td>\n",
       "      <td>78</td>\n",
       "      <td>155</td>\n",
       "      <td>382</td>\n",
       "      <td>163</td>\n",
       "      <td>81</td>\n",
       "      <td>-24</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>73</td>\n",
       "      <td>91</td>\n",
       "      <td>180</td>\n",
       "      <td>355</td>\n",
       "      <td>157</td>\n",
       "      <td>104</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>28.5</td>\n",
       "      <td>48.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "      <td>158</td>\n",
       "      <td>399</td>\n",
       "      <td>163</td>\n",
       "      <td>94</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>39.2</td>\n",
       "      <td>54.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>48</td>\n",
       "      <td>75</td>\n",
       "      <td>132</td>\n",
       "      <td>350</td>\n",
       "      <td>169</td>\n",
       "      <td>65</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>17.2</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>59</td>\n",
       "      <td>82</td>\n",
       "      <td>145</td>\n",
       "      <td>347</td>\n",
       "      <td>169</td>\n",
       "      <td>61</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>19.5</td>\n",
       "      <td>41.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>58</td>\n",
       "      <td>70</td>\n",
       "      <td>120</td>\n",
       "      <td>353</td>\n",
       "      <td>122</td>\n",
       "      <td>52</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>17.1</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>63</td>\n",
       "      <td>91</td>\n",
       "      <td>154</td>\n",
       "      <td>392</td>\n",
       "      <td>175</td>\n",
       "      <td>83</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>166</td>\n",
       "      <td>72</td>\n",
       "      <td>82</td>\n",
       "      <td>181</td>\n",
       "      <td>399</td>\n",
       "      <td>158</td>\n",
       "      <td>79</td>\n",
       "      <td>-12</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>58</td>\n",
       "      <td>83</td>\n",
       "      <td>251</td>\n",
       "      <td>383</td>\n",
       "      <td>189</td>\n",
       "      <td>183</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>9.1</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>17.1</td>\n",
       "      <td>54.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>67</td>\n",
       "      <td>90</td>\n",
       "      <td>122</td>\n",
       "      <td>336</td>\n",
       "      <td>177</td>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>8.3</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>11.7</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>75</td>\n",
       "      <td>71</td>\n",
       "      <td>132</td>\n",
       "      <td>364</td>\n",
       "      <td>169</td>\n",
       "      <td>82</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>19.7</td>\n",
       "      <td>34.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>59</td>\n",
       "      <td>75</td>\n",
       "      <td>157</td>\n",
       "      <td>406</td>\n",
       "      <td>143</td>\n",
       "      <td>92</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>18.4</td>\n",
       "      <td>28.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>55</td>\n",
       "      <td>82</td>\n",
       "      <td>140</td>\n",
       "      <td>388</td>\n",
       "      <td>149</td>\n",
       "      <td>82</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>35.3</td>\n",
       "      <td>57.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>80</td>\n",
       "      <td>109</td>\n",
       "      <td>128</td>\n",
       "      <td>382</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>-34</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>20.7</td>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>73</td>\n",
       "      <td>94</td>\n",
       "      <td>186</td>\n",
       "      <td>373</td>\n",
       "      <td>224</td>\n",
       "      <td>125</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>44.0</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>54</td>\n",
       "      <td>95</td>\n",
       "      <td>161</td>\n",
       "      <td>407</td>\n",
       "      <td>168</td>\n",
       "      <td>83</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>25.4</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>164</td>\n",
       "      <td>420</td>\n",
       "      <td>381</td>\n",
       "      <td>99</td>\n",
       "      <td>-8</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>17.2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>83</td>\n",
       "      <td>96</td>\n",
       "      <td>147</td>\n",
       "      <td>400</td>\n",
       "      <td>301</td>\n",
       "      <td>82</td>\n",
       "      <td>-37</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-6.3</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",
       "      <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>422</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>57</td>\n",
       "      <td>83</td>\n",
       "      <td>164</td>\n",
       "      <td>359</td>\n",
       "      <td>154</td>\n",
       "      <td>69</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.1</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.7</td>\n",
       "      <td>56.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>95</td>\n",
       "      <td>94</td>\n",
       "      <td>203</td>\n",
       "      <td>367</td>\n",
       "      <td>171</td>\n",
       "      <td>106</td>\n",
       "      <td>-7</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>119</td>\n",
       "      <td>21</td>\n",
       "      <td>140</td>\n",
       "      <td>157</td>\n",
       "      <td>438</td>\n",
       "      <td>226</td>\n",
       "      <td>81</td>\n",
       "      <td>-40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>36.7</td>\n",
       "      <td>115.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>150</td>\n",
       "      <td>362</td>\n",
       "      <td>177</td>\n",
       "      <td>96</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.1</td>\n",
       "      <td>52.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>53</td>\n",
       "      <td>55</td>\n",
       "      <td>163</td>\n",
       "      <td>340</td>\n",
       "      <td>162</td>\n",
       "      <td>102</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>20.9</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>65</td>\n",
       "      <td>133</td>\n",
       "      <td>148</td>\n",
       "      <td>417</td>\n",
       "      <td>260</td>\n",
       "      <td>92</td>\n",
       "      <td>-158</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>63</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>364</td>\n",
       "      <td>120</td>\n",
       "      <td>90</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>23.7</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>24</td>\n",
       "      <td>77</td>\n",
       "      <td>125</td>\n",
       "      <td>358</td>\n",
       "      <td>159</td>\n",
       "      <td>70</td>\n",
       "      <td>87</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>16.1</td>\n",
       "      <td>49.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>29</td>\n",
       "      <td>123</td>\n",
       "      <td>145</td>\n",
       "      <td>361</td>\n",
       "      <td>221</td>\n",
       "      <td>80</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>19.6</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>25.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>56</td>\n",
       "      <td>79</td>\n",
       "      <td>145</td>\n",
       "      <td>381</td>\n",
       "      <td>173</td>\n",
       "      <td>101</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>20.4</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>42</td>\n",
       "      <td>88</td>\n",
       "      <td>123</td>\n",
       "      <td>362</td>\n",
       "      <td>228</td>\n",
       "      <td>81</td>\n",
       "      <td>-18</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>5.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>84.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>167</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>149</td>\n",
       "      <td>290</td>\n",
       "      <td>128</td>\n",
       "      <td>93</td>\n",
       "      <td>-67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>-8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>178</td>\n",
       "      <td>65</td>\n",
       "      <td>88</td>\n",
       "      <td>155</td>\n",
       "      <td>360</td>\n",
       "      <td>163</td>\n",
       "      <td>71</td>\n",
       "      <td>-22</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>62</td>\n",
       "      <td>79</td>\n",
       "      <td>155</td>\n",
       "      <td>367</td>\n",
       "      <td>153</td>\n",
       "      <td>95</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>24.1</td>\n",
       "      <td>33.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>57</td>\n",
       "      <td>77</td>\n",
       "      <td>144</td>\n",
       "      <td>340</td>\n",
       "      <td>148</td>\n",
       "      <td>82</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>155</td>\n",
       "      <td>63</td>\n",
       "      <td>87</td>\n",
       "      <td>142</td>\n",
       "      <td>391</td>\n",
       "      <td>137</td>\n",
       "      <td>88</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>25.6</td>\n",
       "      <td>43.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>82</td>\n",
       "      <td>88</td>\n",
       "      <td>146</td>\n",
       "      <td>357</td>\n",
       "      <td>179</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>13.5</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>30.1</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>66</td>\n",
       "      <td>94</td>\n",
       "      <td>170</td>\n",
       "      <td>383</td>\n",
       "      <td>152</td>\n",
       "      <td>115</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>21.5</td>\n",
       "      <td>33.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>153</td>\n",
       "      <td>389</td>\n",
       "      <td>172</td>\n",
       "      <td>89</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>16.6</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>24.9</td>\n",
       "      <td>41.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>50</td>\n",
       "      <td>74</td>\n",
       "      <td>143</td>\n",
       "      <td>374</td>\n",
       "      <td>146</td>\n",
       "      <td>75</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>40.1</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>50</td>\n",
       "      <td>85</td>\n",
       "      <td>143</td>\n",
       "      <td>363</td>\n",
       "      <td>146</td>\n",
       "      <td>84</td>\n",
       "      <td>-40</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>75</td>\n",
       "      <td>88</td>\n",
       "      <td>157</td>\n",
       "      <td>384</td>\n",
       "      <td>132</td>\n",
       "      <td>112</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>10.5</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>63</td>\n",
       "      <td>81</td>\n",
       "      <td>143</td>\n",
       "      <td>325</td>\n",
       "      <td>218</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>14.1</td>\n",
       "      <td>37.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>75</td>\n",
       "      <td>91</td>\n",
       "      <td>134</td>\n",
       "      <td>376</td>\n",
       "      <td>160</td>\n",
       "      <td>83</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>57</td>\n",
       "      <td>81</td>\n",
       "      <td>151</td>\n",
       "      <td>363</td>\n",
       "      <td>166</td>\n",
       "      <td>80</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>17.6</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>199</td>\n",
       "      <td>382</td>\n",
       "      <td>154</td>\n",
       "      <td>117</td>\n",
       "      <td>-37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-4.4</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>137</td>\n",
       "      <td>361</td>\n",
       "      <td>201</td>\n",
       "      <td>73</td>\n",
       "      <td>86</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>38.0</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>68</td>\n",
       "      <td>108</td>\n",
       "      <td>176</td>\n",
       "      <td>365</td>\n",
       "      <td>194</td>\n",
       "      <td>116</td>\n",
       "      <td>-85</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>-28.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-44.2</td>\n",
       "      <td>-33.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>155</td>\n",
       "      <td>55</td>\n",
       "      <td>93</td>\n",
       "      <td>106</td>\n",
       "      <td>386</td>\n",
       "      <td>218</td>\n",
       "      <td>63</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>46.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>127</td>\n",
       "      <td>364</td>\n",
       "      <td>138</td>\n",
       "      <td>78</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>21.3</td>\n",
       "      <td>32.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>452 rows × 279 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       8\n",
       "1       6\n",
       "2      10\n",
       "3       1\n",
       "4       7\n",
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       "6       1\n",
       "7       1\n",
       "8       1\n",
       "9      10\n",
       "10      3\n",
       "11      1\n",
       "12     10\n",
       "13      6\n",
       "14      1\n",
       "15      1\n",
       "16     10\n",
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       "18      1\n",
       "19      1\n",
       "20      1\n",
       "21      1\n",
       "22      1\n",
       "23      1\n",
       "24      1\n",
       "25     16\n",
       "26     14\n",
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       "28      2\n",
       "29      2\n",
       "       ..\n",
       "422     1\n",
       "423     1\n",
       "424     9\n",
       "425     1\n",
       "426     1\n",
       "427    10\n",
       "428     1\n",
       "429    16\n",
       "430    10\n",
       "431     6\n",
       "432    10\n",
       "433     3\n",
       "434     1\n",
       "435     1\n",
       "436     1\n",
       "437     1\n",
       "438     1\n",
       "439     1\n",
       "440     1\n",
       "441     1\n",
       "442     1\n",
       "443    10\n",
       "444     1\n",
       "445     1\n",
       "446     1\n",
       "447     1\n",
       "448    10\n",
       "449     2\n",
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       "451     1\n",
       "Name: 279, dtype: int64"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y"
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "pandas.core.series.Series"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "type(y)"
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f687458bf28>,\n",
       "  <matplotlib.axis.XTick at 0x7f68745b4cf8>,\n",
       "  <matplotlib.axis.XTick at 0x7f68745630b8>,\n",
       "  <matplotlib.axis.XTick at 0x7f687c7164e0>,\n",
       "  <matplotlib.axis.XTick at 0x7f687451e5f8>,\n",
       "  <matplotlib.axis.XTick at 0x7f6874523048>,\n",
       "  <matplotlib.axis.XTick at 0x7f6874523a58>,\n",
       "  <matplotlib.axis.XTick at 0x7f68745254a8>,\n",
       "  <matplotlib.axis.XTick at 0x7f6874525eb8>,\n",
       "  <matplotlib.axis.XTick at 0x7f6874529908>,\n",
       "  <matplotlib.axis.XTick at 0x7f687452c358>,\n",
       "  <matplotlib.axis.XTick at 0x7f687452cd68>,\n",
       "  <matplotlib.axis.XTick at 0x7f68745317b8>,\n",
       "  <matplotlib.axis.XTick at 0x7f6874536208>,\n",
       "  <matplotlib.axis.XTick at 0x7f6874536c18>,\n",
       "  <matplotlib.axis.XTick at 0x7f6874538668>],\n",
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     "metadata": {},
     "output_type": "execute_result"
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     "data": {
      "image/png": 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Zrjnol2z4NXpT4D3A1kCSQETE01yt5iBJmwPHUCWA84DTbN/fcmxTxZLmoIiIPjUaHSTp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f68ab83e6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# plot class distribution\n",
    "x = np.arange(1,17)\n",
    "h, bins = np.histogram(y, 16)\n",
    "plt.bar(x-0.4, h)\n",
    "plt.xlabel('Class Labels')\n",
    "plt.ylabel('Number of Instances')\n",
    "plt.xticks(x)\n",
    "# We can see that the class distribution is unbalanced with the normal type being the major.\n",
    "# Class 11, 12 and 13 are missing in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "4        0\n",
       "5        0\n",
       "6        0\n",
       "7        0\n",
       "8        0\n",
       "9        0\n",
       "10       8\n",
       "11      22\n",
       "12       1\n",
       "13     376\n",
       "14       1\n",
       "15       0\n",
       "16       0\n",
       "17       0\n",
       "18       0\n",
       "19       0\n",
       "20       0\n",
       "21       0\n",
       "22       0\n",
       "23       0\n",
       "24       0\n",
       "25       0\n",
       "26       0\n",
       "27       0\n",
       "28       0\n",
       "29       0\n",
       "      ... \n",
       "250      0\n",
       "251      0\n",
       "252      0\n",
       "253      0\n",
       "254      0\n",
       "255      0\n",
       "256      0\n",
       "257      0\n",
       "258      0\n",
       "259      0\n",
       "260      0\n",
       "261      0\n",
       "262      0\n",
       "263      0\n",
       "264      0\n",
       "265      0\n",
       "266      0\n",
       "267      0\n",
       "268      0\n",
       "269      0\n",
       "270      0\n",
       "271      0\n",
       "272      0\n",
       "273      0\n",
       "274      0\n",
       "275      0\n",
       "276      0\n",
       "277      0\n",
       "278      0\n",
       "279      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the missing values in every column\n",
    "# The missing values are encoded as NaN (float, np.nan).\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "408"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the number of missing values\n",
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  75. ,    0. ,  190. , ...,    2.9,   23.3,   49.4],\n",
       "       [  56. ,    1. ,  165. , ...,    2.1,   20.4,   38.8],\n",
       "       [  54. ,    0. ,  172. , ...,    3.4,   12.3,   49. ],\n",
       "       ..., \n",
       "       [  36. ,    0. ,  166. , ...,    1. ,  -44.2,  -33.2],\n",
       "       [  32. ,    1. ,  155. , ...,    2.4,   25. ,   46.6],\n",
       "       [  78. ,    1. ,  160. , ...,    1.6,   21.3,   32.8]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# impute the missing data in the dataset\n",
    "from sklearn.preprocessing import Imputer\n",
    "# For the missing feature values, we impute it by using the feature's mean value,\n",
    "# as this tend to improve performance (Jerez, 2010)\n",
    "imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
    "X = imp.fit_transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(452, 279)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.73443926, -1.1075202 ,  0.64132669, ...,  1.17773685,\n",
       "         0.29460309,  1.07867028],\n",
       "       [ 0.57931213,  0.90291807, -0.03199781, ...,  0.61612582,\n",
       "         0.0796127 ,  0.50487408],\n",
       "       [ 0.4577198 , -1.1075202 ,  0.15653305, ...,  1.52874375,\n",
       "        -0.52087767,  1.0570176 ],\n",
       "       ..., \n",
       "       [-0.63661117, -1.1075202 , -0.00506483, ..., -0.15608935,\n",
       "        -4.7094834 , -3.3926096 ],\n",
       "       [-0.87979583,  0.90291807, -0.30132761, ...,  0.82672996,\n",
       "         0.42063193,  0.92710147],\n",
       "       [ 1.91682776,  0.90291807, -0.16666271, ...,  0.26511892,\n",
       "         0.14633386,  0.18008377]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalizing the dataset\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# splitting the dataset to training and validation datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(316, 279)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(136, 279)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(316,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(136,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGB Train Score: 1.0\n",
      "XGB Test Score: 0.75\n"
     ]
    }
   ],
   "source": [
    "import xgboost\n",
    "xgb = xgboost.XGBClassifier(objective=\"multi:softprob\", nthread=-1)\n",
    "xgb.fit(X_train, y_train)\n",
    "y_train_xgb = xgb.predict(X_train)\n",
    "y_pred_xgb = xgb.predict(X_test)\n",
    "print('XGB Train Score:', np.mean(y_train == y_train_xgb))\n",
    "print('XGB Test Score:', np.mean(y_test == y_pred_xgb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGB Train Score: 1.00\n",
      "XGB Test Score: 0.75\n"
     ]
    }
   ],
   "source": [
    "print('XGB Train Score: {:.2f}'.format(xgb.score(X_train, y_train))) # R^2 score: mean accuracy\n",
    "print('XGB Test Score: {:.2f}'.format(xgb.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGB Accuracy: 0.74 (+/- 0.10)\n"
     ]
    }
   ],
   "source": [
    "# 10-fold cross validation for XGB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(xgb, X, y, cv=10)\n",
    "print(\"XGB Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.73076923,  0.75      ,  0.62      ,  0.75      ,  0.73913043,\n",
       "        0.7804878 ,  0.82926829,  0.73170732,  0.7804878 ,  0.725     ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GBRT Train Score: 1.00\n",
      "GBRT Test Score: 0.77\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbrt = GradientBoostingClassifier(random_state=0)\n",
    "gbrt.fit(X_train, y_train)\n",
    "y_train_gbrt = gbrt.predict(X_train)\n",
    "y_pred_gbrt = gbrt.predict(X_test)\n",
    "print('GBRT Train Score: {:.2f}'.format(gbrt.score(X_train, y_train))) # R^2 score: mean accuracy\n",
    "print('GBRT Test Score: {:.2f}'.format(gbrt.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GBRT Accuracy: 0.73 (+/- 0.15)\n"
     ]
    }
   ],
   "source": [
    "# 10-fold cross validation for GBRT\n",
    "scores = cross_val_score(gbrt, X, y, cv=10)\n",
    "print(\"GBRT Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.71153846,  0.73076923,  0.56      ,  0.70833333,  0.7173913 ,\n",
       "        0.80487805,  0.82926829,  0.65853659,  0.75609756,  0.8       ])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Train Score: 0.98\n",
      "Random Forest Test Score: 0.71\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest = RandomForestClassifier(n_jobs=-1, random_state=0)\n",
    "forest.fit(X_train, y_train)\n",
    "y_train_forest = forest.predict(X_train)\n",
    "y_pred_forest = forest.predict(X_test)\n",
    "print('Random Forest Train Score: {:.2f}'.format(forest.score(X_train, y_train)))\n",
    "print('Random Forest Test Score: {:.2f}'.format(forest.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Score: 0.99\n",
      "Logistic Regression Test Score: 0.71\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "y_train_lr = lr.predict(X_train)\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "print('Logistic Regression Train Score: {:.2f}'.format(lr.score(X_train, y_train)))\n",
    "print('Logistic Regression Test Score: {:.2f}'.format(lr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Regularization on LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Score: 0.99\n",
      "Logistic Regression Test Score: 0.71\n"
     ]
    }
   ],
   "source": [
    "# Tuning the regulation on Logistic Regression Model\n",
    "# C controls the strength of regularization, smaller value, stronger regularization\n",
    "lr = LogisticRegression(C=0.8) # C's default value is 1.0\n",
    "lr.fit(X_train, y_train)\n",
    "y_train_lr = lr.predict(X_train)\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "print('Logistic Regression Train Score: {:.2f}'.format(lr.score(X_train, y_train)))\n",
    "print('Logistic Regression Test Score: {:.2f}'.format(lr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Score: 0.97\n",
      "Logistic Regression Test Score: 0.73\n"
     ]
    }
   ],
   "source": [
    "# Tuning the regulation on Logistic Regression Model\n",
    "# C controls the strength of regularization, smaller value, stronger regularization\n",
    "lr = LogisticRegression(C=0.5) # C's default value is 1.0\n",
    "lr.fit(X_train, y_train)\n",
    "y_train_lr = lr.predict(X_train)\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "print('Logistic Regression Train Score: {:.2f}'.format(lr.score(X_train, y_train)))\n",
    "print('Logistic Regression Test Score: {:.2f}'.format(lr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Score: 0.92\n",
      "Logistic Regression Test Score: 0.77\n"
     ]
    }
   ],
   "source": [
    "# Tuning the regulation on Logistic Regression Model\n",
    "# C controls the strength of regularization, smaller value, stronger regularization\n",
    "lr = LogisticRegression(C=0.1) # C's default value is 1.0\n",
    "lr.fit(X_train, y_train)\n",
    "y_train_lr = lr.predict(X_train)\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "print('Logistic Regression Train Score: {:.2f}'.format(lr.score(X_train, y_train)))\n",
    "print('Logistic Regression Test Score: {:.2f}'.format(lr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Score: 0.89\n",
      "Logistic Regression Test Score: 0.79\n"
     ]
    }
   ],
   "source": [
    "# Tuning the regulation on Logistic Regression Model\n",
    "# C controls the strength of regularization, smaller value, stronger regularization\n",
    "lr = LogisticRegression(C=0.05) # C's default value is 1.0\n",
    "lr.fit(X_train, y_train)\n",
    "y_train_lr = lr.predict(X_train)\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "print('Logistic Regression Train Score: {:.2f}'.format(lr.score(X_train, y_train)))\n",
    "print('Logistic Regression Test Score: {:.2f}'.format(lr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Score: 0.83\n",
      "Logistic Regression Test Score: 0.77\n"
     ]
    }
   ],
   "source": [
    "# Tuning the regulation on Logistic Regression Model\n",
    "# C controls the strength of regularization, smaller value, stronger regularization\n",
    "lr = LogisticRegression(C=0.01) # C's default value is 1.0\n",
    "lr.fit(X_train, y_train)\n",
    "y_train_lr = lr.predict(X_train)\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "print('Logistic Regression Train Score: {:.2f}'.format(lr.score(X_train, y_train)))\n",
    "print('Logistic Regression Test Score: {:.2f}'.format(lr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Score: 0.87\n",
      "Logistic Regression Test Score: 0.77\n"
     ]
    }
   ],
   "source": [
    "# Tuning the regulation on Logistic Regression Model\n",
    "# C controls the strength of regularization, smaller value, stronger regularization\n",
    "lr = LogisticRegression(C=0.03) # C's default value is 1.0\n",
    "lr.fit(X_train, y_train)\n",
    "y_train_lr = lr.predict(X_train)\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "print('Logistic Regression Train Score: {:.2f}'.format(lr.score(X_train, y_train)))\n",
    "print('Logistic Regression Test Score: {:.2f}'.format(lr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Score: 0.90\n",
      "Logistic Regression Test Score: 0.78\n"
     ]
    }
   ],
   "source": [
    "# Tuning the regulation on Logistic Regression Model\n",
    "# C controls the strength of regularization, smaller value, stronger regularization\n",
    "lr = LogisticRegression(C=0.08) # C's default value is 1.0\n",
    "lr.fit(X_train, y_train)\n",
    "y_train_lr = lr.predict(X_train)\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "print('Logistic Regression Train Score: {:.2f}'.format(lr.score(X_train, y_train)))\n",
    "print('Logistic Regression Test Score: {:.2f}'.format(lr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Score: 0.90\n",
      "Logistic Regression Test Score: 0.78\n"
     ]
    }
   ],
   "source": [
    "# Tuning the regulation on Logistic Regression Model\n",
    "# C controls the strength of regularization, smaller value, stronger regularization\n",
    "lr = LogisticRegression(C=0.07) # C's default value is 1.0\n",
    "lr.fit(X_train, y_train)\n",
    "y_train_lr = lr.predict(X_train)\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "print('Logistic Regression Train Score: {:.2f}'.format(lr.score(X_train, y_train)))\n",
    "print('Logistic Regression Test Score: {:.2f}'.format(lr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Score: 0.88\n",
      "Logistic Regression Test Score: 0.79\n"
     ]
    }
   ],
   "source": [
    "# Tuning the regulation on Logistic Regression Model\n",
    "# C controls the strength of regularization, smaller value, stronger regularization\n",
    "lr = LogisticRegression(C=0.04) # C's default value is 1.0\n",
    "lr.fit(X_train, y_train)\n",
    "y_train_lr = lr.predict(X_train)\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "print('Logistic Regression Train Score: {:.2f}'.format(lr.score(X_train, y_train)))\n",
    "print('Logistic Regression Test Score: {:.2f}'.format(lr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb1c0366b70>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qzaxvmHVFRCS3IgsMMysA/giUAp8HJpvZ2PRl3P0Gdx/n7uOAqUC5u28Ks67s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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1e47356d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "C = [0.01, 0.03, 0.04, 0.05, 0.07, 0.08, 0.1, 0.5, 0.8, 1.0]\n",
    "train = [0.83, 0.87, 0.88, 0.89, 0.9, 0.9, 0.92, 0.97, 0.99, 0.99]\n",
    "test = [0.77, 0.77, 0.79, 0.79, 0.78, 0.78, 0.77, 0.73, 0.71, 0.71]\n",
    "line1, = plt.plot(C, train, label=\"train\")\n",
    "line2, = plt.plot(C, test, label=\"test\")\n",
    "plt.legend(loc='right')\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Ensemble VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble Voting Train Score: 1.00\n",
      "Ensemble Voting Test Score: 0.78\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "eclf = VotingClassifier(estimators=[('xgboost', xgb), ('gbrt', gbrt), ('forest', forest),\n",
    "                                    ('logistic regression', lr)],\n",
    "                        voting='soft',\n",
    "                        weights=None) # weights's default value is None: uses uniform weights\n",
    "eclf = eclf.fit(X_train, y_train)\n",
    "y_train_ensemble = eclf.predict(X_train)\n",
    "y_pred_ensemble = eclf.predict(X_test)\n",
    "print('Ensemble Voting Train Score: {:.2f}'.format(eclf.score(X_train, y_train)))\n",
    "print('Ensemble Voting Test Score: {:.2f}'.format(eclf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 10-Fold Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 0.72 (+/- 0.10)\n"
     ]
    }
   ],
   "source": [
    "# 10-fold cross validation for Random Forest\n",
    "scores = cross_val_score(forest, X, y, cv=10)\n",
    "print(\"Random Forest Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 0.72 (+/- 0.13)\n"
     ]
    }
   ],
   "source": [
    "# 10-fold cross validation for Logistic Regression\n",
    "scores = cross_val_score(lr, X, y, cv=10)\n",
    "print(\"Logistic Regression Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble Voting Accuracy: 0.75 (+/- 0.15)\n"
     ]
    }
   ],
   "source": [
    "# 10-fold cross validation for Ensemble Voting\n",
    "scores = cross_val_score(eclf, X, y, cv=10)\n",
    "print(\"Ensemble Voting Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Grid Search and Hyperparameter Tuning\n",
    "\n",
    "For XGBoost, we focus on 3 parameters:\n",
    "1. reg_lambda (L2 regularization)\n",
    "2. subsampling ratio to control the variance\n",
    "3. reg_alpha (L1 regularization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n",
      "Fitting 3 folds for each of 27 candidates, totalling 81 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=3.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] subsample=0.8, reg_alpha=0.6, reg_lambda=0.05 ...................\n",
      "[CV] subsample=0.8, reg_alpha=0.6, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.8, reg_alpha=0.6, reg_lambda=0.05, total=  24.1s\n",
      "[CV] subsample=0.8, reg_alpha=0.6, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.8, reg_alpha=0.6, reg_lambda=0.05, total=  25.9s\n",
      "[CV] subsample=0.9, reg_alpha=0.6, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.9, reg_alpha=0.6, reg_lambda=0.05, total=  32.4s\n",
      "[CV] subsample=0.9, reg_alpha=0.6, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.8, reg_alpha=0.6, reg_lambda=0.05, total=  34.3s\n",
      "[CV] subsample=0.9, reg_alpha=0.6, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.9, reg_alpha=0.6, reg_lambda=0.05, total=  44.0s\n",
      "[CV] subsample=1.0, reg_alpha=0.6, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.9, reg_alpha=0.6, reg_lambda=0.05, total=  45.8s\n",
      "[CV] subsample=1.0, reg_alpha=0.6, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=1.0, reg_alpha=0.6, reg_lambda=0.05, total=   9.5s\n",
      "[CV] subsample=1.0, reg_alpha=0.6, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=1.0, reg_alpha=0.6, reg_lambda=0.05, total=   8.4s\n",
      "[CV] subsample=0.8, reg_alpha=0.6, reg_lambda=0.5 ....................\n",
      "[CV] .... subsample=1.0, reg_alpha=0.6, reg_lambda=0.05, total=  25.4s\n",
      "[CV] subsample=0.8, reg_alpha=0.6, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.6, reg_lambda=0.5, total=  25.9s\n",
      "[CV] subsample=0.8, reg_alpha=0.6, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.6, reg_lambda=0.5, total=  48.8s\n",
      "[CV] subsample=0.9, reg_alpha=0.6, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.6, reg_lambda=0.5, total=  50.6s\n",
      "[CV] subsample=0.9, reg_alpha=0.6, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.6, reg_lambda=0.5, total=  24.0s\n",
      "[CV] subsample=0.9, reg_alpha=0.6, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.6, reg_lambda=0.5, total=  26.2s\n",
      "[CV] subsample=1.0, reg_alpha=0.6, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.6, reg_lambda=0.5, total=  37.5s\n",
      "[CV] subsample=1.0, reg_alpha=0.6, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.6, reg_lambda=0.5, total=  36.4s\n",
      "[CV] subsample=1.0, reg_alpha=0.6, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.6, reg_lambda=0.5, total=  27.3s\n",
      "[CV] subsample=0.8, reg_alpha=0.6, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.6, reg_lambda=0.5, total=  25.3s\n",
      "[CV] subsample=0.8, reg_alpha=0.6, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.6, reg_lambda=0.7, total=  31.7s\n",
      "[CV] subsample=0.8, reg_alpha=0.6, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.6, reg_lambda=0.7, total=  35.5s\n",
      "[CV] subsample=0.9, reg_alpha=0.6, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.6, reg_lambda=0.7, total=  41.4s\n",
      "[CV] subsample=0.9, reg_alpha=0.6, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.6, reg_lambda=0.7, total=  40.9s\n",
      "[CV] subsample=0.9, reg_alpha=0.6, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.6, reg_lambda=0.7, total=  24.4s\n",
      "[CV] subsample=1.0, reg_alpha=0.6, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.6, reg_lambda=0.7, total=  24.4s\n",
      "[CV] subsample=1.0, reg_alpha=0.6, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.6, reg_lambda=0.7, total=  34.6s\n",
      "[CV] subsample=1.0, reg_alpha=0.6, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.6, reg_lambda=0.7, total=  37.3s\n",
      "[CV] subsample=0.8, reg_alpha=0.7, reg_lambda=0.05 ...................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.6, reg_lambda=0.7, total=  52.7s\n",
      "[CV] subsample=0.8, reg_alpha=0.7, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.8, reg_alpha=0.7, reg_lambda=0.05, total=  46.0s\n",
      "[CV] subsample=0.8, reg_alpha=0.7, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.8, reg_alpha=0.7, reg_lambda=0.05, total=  29.3s\n",
      "[CV] subsample=0.9, reg_alpha=0.7, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.8, reg_alpha=0.7, reg_lambda=0.05, total=  29.1s\n",
      "[CV] subsample=0.9, reg_alpha=0.7, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.9, reg_alpha=0.7, reg_lambda=0.05, total=  14.5s\n",
      "[CV] subsample=0.9, reg_alpha=0.7, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.9, reg_alpha=0.7, reg_lambda=0.05, total=  13.7s\n",
      "[CV] subsample=1.0, reg_alpha=0.7, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=1.0, reg_alpha=0.7, reg_lambda=0.05, total=  19.3s\n",
      "[CV] subsample=1.0, reg_alpha=0.7, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.9, reg_alpha=0.7, reg_lambda=0.05, total=  19.5s\n",
      "[CV] subsample=1.0, reg_alpha=0.7, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=1.0, reg_alpha=0.7, reg_lambda=0.05, total=  10.3s\n",
      "[CV] subsample=0.8, reg_alpha=0.7, reg_lambda=0.5 ....................\n",
      "[CV] .... subsample=1.0, reg_alpha=0.7, reg_lambda=0.05, total=  10.4s\n",
      "[CV] subsample=0.8, reg_alpha=0.7, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.7, reg_lambda=0.5, total=  37.4s\n",
      "[CV] subsample=0.8, reg_alpha=0.7, reg_lambda=0.5 ....................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:  9.5min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..... subsample=0.8, reg_alpha=0.7, reg_lambda=0.5, total=  39.4s\n",
      "[CV] subsample=0.9, reg_alpha=0.7, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.7, reg_lambda=0.5, total=  35.6s\n",
      "[CV] subsample=0.9, reg_alpha=0.7, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.7, reg_lambda=0.5, total=  33.8s\n",
      "[CV] subsample=0.9, reg_alpha=0.7, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.7, reg_lambda=0.5, total=  22.4s\n",
      "[CV] subsample=1.0, reg_alpha=0.7, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.7, reg_lambda=0.5, total=  24.5s\n",
      "[CV] subsample=1.0, reg_alpha=0.7, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.7, reg_lambda=0.5, total=  16.6s\n",
      "[CV] subsample=1.0, reg_alpha=0.7, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.7, reg_lambda=0.5, total=  18.3s\n",
      "[CV] subsample=0.8, reg_alpha=0.7, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.7, reg_lambda=0.5, total=  17.8s\n",
      "[CV] subsample=0.8, reg_alpha=0.7, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.7, reg_lambda=0.7, total=  14.2s\n",
      "[CV] subsample=0.8, reg_alpha=0.7, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.7, reg_lambda=0.7, total=  14.5s\n",
      "[CV] subsample=0.9, reg_alpha=0.7, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.7, reg_lambda=0.7, total=  30.9s\n",
      "[CV] subsample=0.9, reg_alpha=0.7, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.7, reg_lambda=0.7, total=  48.3s\n",
      "[CV] subsample=0.9, reg_alpha=0.7, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.7, reg_lambda=0.7, total=  45.7s\n",
      "[CV] subsample=1.0, reg_alpha=0.7, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.7, reg_lambda=0.7, total=  51.6s\n",
      "[CV] subsample=1.0, reg_alpha=0.7, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.7, reg_lambda=0.7, total=  50.3s\n",
      "[CV] subsample=1.0, reg_alpha=0.7, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.7, reg_lambda=0.7, total=  45.9s\n",
      "[CV] subsample=0.8, reg_alpha=0.8, reg_lambda=0.05 ...................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.7, reg_lambda=0.7, total=  37.2s\n",
      "[CV] subsample=0.8, reg_alpha=0.8, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.8, reg_alpha=0.8, reg_lambda=0.05, total=  21.2s\n",
      "[CV] subsample=0.8, reg_alpha=0.8, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.8, reg_alpha=0.8, reg_lambda=0.05, total=  25.0s\n",
      "[CV] subsample=0.9, reg_alpha=0.8, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.8, reg_alpha=0.8, reg_lambda=0.05, total=  52.5s\n",
      "[CV] subsample=0.9, reg_alpha=0.8, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.9, reg_alpha=0.8, reg_lambda=0.05, total=  55.4s\n",
      "[CV] subsample=0.9, reg_alpha=0.8, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.9, reg_alpha=0.8, reg_lambda=0.05, total=  22.9s\n",
      "[CV] subsample=1.0, reg_alpha=0.8, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=0.9, reg_alpha=0.8, reg_lambda=0.05, total=  21.0s\n",
      "[CV] subsample=1.0, reg_alpha=0.8, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=1.0, reg_alpha=0.8, reg_lambda=0.05, total=  24.4s\n",
      "[CV] subsample=1.0, reg_alpha=0.8, reg_lambda=0.05 ...................\n",
      "[CV] .... subsample=1.0, reg_alpha=0.8, reg_lambda=0.05, total=  24.1s\n",
      "[CV] subsample=0.8, reg_alpha=0.8, reg_lambda=0.5 ....................\n",
      "[CV] .... subsample=1.0, reg_alpha=0.8, reg_lambda=0.05, total=  31.1s\n",
      "[CV] subsample=0.8, reg_alpha=0.8, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.8, reg_lambda=0.5, total=  37.6s\n",
      "[CV] subsample=0.8, reg_alpha=0.8, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.8, reg_lambda=0.5, total=  34.3s\n",
      "[CV] subsample=0.9, reg_alpha=0.8, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.8, reg_lambda=0.5, total=  23.8s\n",
      "[CV] subsample=0.9, reg_alpha=0.8, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.8, reg_lambda=0.5, total=  10.5s\n",
      "[CV] subsample=0.9, reg_alpha=0.8, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.8, reg_lambda=0.5, total=  29.1s\n",
      "[CV] subsample=1.0, reg_alpha=0.8, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.8, reg_lambda=0.5, total=  48.3s\n",
      "[CV] subsample=1.0, reg_alpha=0.8, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.8, reg_lambda=0.5, total=  33.2s\n",
      "[CV] subsample=1.0, reg_alpha=0.8, reg_lambda=0.5 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.8, reg_lambda=0.5, total=  33.0s\n",
      "[CV] subsample=0.8, reg_alpha=0.8, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.8, reg_lambda=0.5, total=  35.3s\n",
      "[CV] subsample=0.8, reg_alpha=0.8, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.8, reg_lambda=0.7, total=  47.4s\n",
      "[CV] subsample=0.8, reg_alpha=0.8, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.8, reg_lambda=0.7, total=  40.0s\n",
      "[CV] subsample=0.9, reg_alpha=0.8, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.8, reg_alpha=0.8, reg_lambda=0.7, total=  16.2s\n",
      "[CV] subsample=0.9, reg_alpha=0.8, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.8, reg_lambda=0.7, total=  18.7s\n",
      "[CV] subsample=0.9, reg_alpha=0.8, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.8, reg_lambda=0.7, total=  27.9s\n",
      "[CV] subsample=1.0, reg_alpha=0.8, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=0.9, reg_alpha=0.8, reg_lambda=0.7, total=  26.1s\n",
      "[CV] subsample=1.0, reg_alpha=0.8, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.8, reg_lambda=0.7, total=  21.1s\n",
      "[CV] subsample=1.0, reg_alpha=0.8, reg_lambda=0.7 ....................\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.8, reg_lambda=0.7, total=  29.1s\n",
      "[CV] ..... subsample=1.0, reg_alpha=0.8, reg_lambda=0.7, total=  12.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  81 out of  81 | elapsed: 20.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#Load the classifier\n",
    "import xgboost as xgb\n",
    "model = xgb.XGBClassifier(objective=\"multi:softprob\", nthread=-1)\n",
    "\n",
    "#Set up a new classifier that will do a grid search\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "clf = GridSearchCV(\n",
    "    model,\n",
    "    {\n",
    "        'subsample': [0.8,0.9,1.0],\n",
    "        'reg_lambda': [0.05,0.5,0.7],\n",
    "        'reg_alpha': [0.6,0.7,0.8],\n",
    "    },\n",
    "    verbose=2,\n",
    "    n_jobs=-1,\n",
    ")\n",
    "print(\"Done\")\n",
    "#Fit the classifier to the training data, which will randomly search for hyperparameters\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "print(\"DONE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGB Train Score: 1.00\n",
      "XGB Test Score: 0.77\n"
     ]
    }
   ],
   "source": [
    "print('XGB Train Score: {:.2f}'.format(clf.score(X_train, y_train)))\n",
    "print('XGB Test Score: {:.2f}'.format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,\n",
       "       objective='multi:softprob', reg_alpha=0.7, reg_lambda=0.05,\n",
       "       scale_pos_weight=1, seed=0, silent=True, subsample=0.9)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'reg_alpha': 0.7, 'reg_lambda': 0.05, 'subsample': 0.9}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n",
      "Fitting 3 folds for each of 19 candidates, totalling 57 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=3.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] C=0.01 ..........................................................\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] ........................................... C=0.01, total=   0.1s\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] ........................................... C=0.01, total=   0.2s\n",
      "[CV] C=0.02 ..........................................................\n",
      "[CV] ........................................... C=0.01, total=   0.2s\n",
      "[CV] ........................................... C=0.02, total=   0.1s\n",
      "[CV] C=0.02 ..........................................................\n",
      "[CV] C=0.03 ..........................................................\n",
      "[CV] ........................................... C=0.03, total=   0.1s\n",
      "[CV] C=0.03 ..........................................................\n",
      "[CV] ........................................... C=0.02, total=   0.3s\n",
      "[CV] C=0.02 ..........................................................\n",
      "[CV] ........................................... C=0.03, total=   0.2s\n",
      "[CV] C=0.03 ..........................................................\n",
      "[CV] ........................................... C=0.03, total=   0.2s\n",
      "[CV] C=0.04 ..........................................................\n",
      "[CV] ........................................... C=0.02, total=   0.3s\n",
      "[CV] C=0.04 ..........................................................\n",
      "[CV] ........................................... C=0.04, total=   0.2s\n",
      "[CV] C=0.04 ..........................................................\n",
      "[CV] ........................................... C=0.04, total=   0.3s\n",
      "[CV] C=0.05 ..........................................................\n",
      "[CV] ........................................... C=0.05, total=   0.2s\n",
      "[CV] C=0.05 ..........................................................\n",
      "[CV] ........................................... C=0.04, total=   0.4s\n",
      "[CV] C=0.05 ..........................................................\n",
      "[CV] ........................................... C=0.05, total=   0.2s\n",
      "[CV] C=0.06 ..........................................................\n",
      "[CV] ........................................... C=0.06, total=   0.2s\n",
      "[CV] C=0.06 ..........................................................\n",
      "[CV] ........................................... C=0.05, total=   0.4s\n",
      "[CV] C=0.06 ..........................................................\n",
      "[CV] ........................................... C=0.06, total=   0.3s\n",
      "[CV] C=0.07 ..........................................................\n",
      "[CV] ........................................... C=0.06, total=   0.3s\n",
      "[CV] C=0.07 ..........................................................\n",
      "[CV] ........................................... C=0.07, total=   0.2s\n",
      "[CV] C=0.07 ..........................................................\n",
      "[CV] ........................................... C=0.07, total=   0.2s\n",
      "[CV] C=0.08 ..........................................................\n",
      "[CV] ........................................... C=0.07, total=   0.4s\n",
      "[CV] C=0.08 ..........................................................\n",
      "[CV] ........................................... C=0.08, total=   0.3s\n",
      "[CV] C=0.08 ..........................................................\n",
      "[CV] ........................................... C=0.08, total=   0.3s\n",
      "[CV] C=0.09 ..........................................................\n",
      "[CV] ........................................... C=0.08, total=   0.3s\n",
      "[CV] C=0.09 ..........................................................\n",
      "[CV] ........................................... C=0.09, total=   0.3s\n",
      "[CV] C=0.09 ..........................................................\n",
      "[CV] ........................................... C=0.09, total=   0.4s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] ........................................... C=0.09, total=   0.2s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] ............................................ C=0.1, total=   0.2s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] ............................................ C=0.1, total=   0.3s\n",
      "[CV] C=0.2 ...........................................................\n",
      "[CV] ............................................ C=0.1, total=   0.2s\n",
      "[CV] C=0.2 ...........................................................\n",
      "[CV] ............................................ C=0.2, total=   0.3s\n",
      "[CV] C=0.2 ...........................................................\n",
      "[CV] ............................................ C=0.2, total=   0.3s\n",
      "[CV] C=0.3 ...........................................................\n",
      "[CV] ............................................ C=0.3, total=   0.2s\n",
      "[CV] C=0.3 ...........................................................\n",
      "[CV] ............................................ C=0.2, total=   0.4s\n",
      "[CV] C=0.4 ...........................................................\n",
      "[CV] ............................................ C=0.3, total=   0.4s\n",
      "[CV] C=0.3 ...........................................................\n",
      "[CV] ............................................ C=0.4, total=   0.4s\n",
      "[CV] C=0.4 ...........................................................\n",
      "[CV] ............................................ C=0.3, total=   0.3s\n",
      "[CV] C=0.4 ...........................................................\n",
      "[CV] ............................................ C=0.4, total=   0.4s\n",
      "[CV] C=0.5 ...........................................................\n",
      "[CV] ............................................ C=0.4, total=   0.3s\n",
      "[CV] C=0.5 ...........................................................\n",
      "[CV] ............................................ C=0.5, total=   0.3s\n",
      "[CV] C=0.6 ...........................................................\n",
      "[CV] ............................................ C=0.5, total=   0.4s\n",
      "[CV] C=0.5 ...........................................................\n",
      "[CV] ............................................ C=0.6, total=   0.3s\n",
      "[CV] C=0.6 ...........................................................\n",
      "[CV] ............................................ C=0.5, total=   0.5s\n",
      "[CV] C=0.6 ...........................................................\n",
      "[CV] ............................................ C=0.6, total=   0.4s\n",
      "[CV] C=0.7 ...........................................................\n",
      "[CV] ............................................ C=0.6, total=   0.4s\n",
      "[CV] C=0.7 ...........................................................\n",
      "[CV] ............................................ C=0.7, total=   0.3s\n",
      "[CV] C=0.7 ...........................................................\n",
      "[CV] ............................................ C=0.7, total=   0.5s\n",
      "[CV] C=0.8 ...........................................................\n",
      "[CV] ............................................ C=0.7, total=   0.4s\n",
      "[CV] C=0.8 ...........................................................\n",
      "[CV] ............................................ C=0.8, total=   0.4s\n",
      "[CV] C=0.8 ...........................................................\n",
      "[CV] ............................................ C=0.8, total=   0.4s\n",
      "[CV] C=0.9 ...........................................................\n",
      "[CV] ............................................ C=0.8, total=   0.4s\n",
      "[CV] C=0.9 ...........................................................\n",
      "[CV] ............................................ C=0.9, total=   0.4s\n",
      "[CV] C=1.0 ...........................................................\n",
      "[CV] ............................................ C=0.9, total=   0.3s\n",
      "[CV] C=0.9 ...........................................................\n",
      "[CV] ............................................ C=1.0, total=   0.3s\n",
      "[CV] C=1.0 ...........................................................\n",
      "[CV] ............................................ C=0.9, total=   0.3s\n",
      "[CV] C=1.0 ...........................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  54 out of  57 | elapsed:    9.1s remaining:    0.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............................................ C=1.0, total=   0.4s\n",
      "[CV] ............................................ C=1.0, total=   0.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  57 out of  57 | elapsed:    9.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "source": [
    "#Load the classifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "\n",
    "#Set up a new classifier that will do a grid search\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "clf = GridSearchCV(\n",
    "    model,\n",
    "    {\n",
    "        'C': [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "    },\n",
    "    verbose=2,\n",
    "    n_jobs=-1\n",
    ")\n",
    "print(\"Done\")\n",
    "#Fit the classifier to the training data, which will randomly search for hyperparameters\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "print(\"DONE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression Train Score: 0.87\n",
      "LogisticRegression Test Score: 0.77\n"
     ]
    }
   ],
   "source": [
    "print('LogisticRegression Train Score: {:.2f}'.format(clf.score(X_train, y_train)))\n",
    "print('LogisticRegression Test Score: {:.2f}'.format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.03}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[mean: 0.65190, std: 0.04685, params: {'C': 0.01},\n",
       " mean: 0.67089, std: 0.03144, params: {'C': 0.02},\n",
       " mean: 0.67405, std: 0.02770, params: {'C': 0.03},\n",
       " mean: 0.66772, std: 0.02779, params: {'C': 0.04},\n",
       " mean: 0.66456, std: 0.03185, params: {'C': 0.05},\n",
       " mean: 0.65823, std: 0.02791, params: {'C': 0.06},\n",
       " mean: 0.66139, std: 0.02856, params: {'C': 0.07},\n",
       " mean: 0.65823, std: 0.03547, params: {'C': 0.08},\n",
       " mean: 0.65190, std: 0.03607, params: {'C': 0.09},\n",
       " mean: 0.64873, std: 0.03559, params: {'C': 0.1},\n",
       " mean: 0.63924, std: 0.02816, params: {'C': 0.2},\n",
       " mean: 0.63291, std: 0.02450, params: {'C': 0.3},\n",
       " mean: 0.62975, std: 0.02073, params: {'C': 0.4},\n",
       " mean: 0.62342, std: 0.01715, params: {'C': 0.5},\n",
       " mean: 0.62025, std: 0.01330, params: {'C': 0.6},\n",
       " mean: 0.62025, std: 0.01330, params: {'C': 0.7},\n",
       " mean: 0.61392, std: 0.01633, params: {'C': 0.8},\n",
       " mean: 0.61076, std: 0.01862, params: {'C': 0.9},\n",
       " mean: 0.60759, std: 0.01438, params: {'C': 1.0}]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 0.1628472 ,  0.24622067,  0.15866995,  0.28946972,  0.23289839,\n",
       "         0.26916718,  0.27529677,  0.26934648,  0.27132312,  0.2537516 ,\n",
       "         0.32405893,  0.280068  ,  0.33458773,  0.37996538,  0.36021797,\n",
       "         0.37131357,  0.3777221 ,  0.31535602,  0.35831642]),\n",
       " 'mean_score_time': array([ 0.00762653,  0.00594918,  0.00761588,  0.00099063,  0.00712856,\n",
       "         0.00401092,  0.01214623,  0.00261482,  0.0123566 ,  0.00479945,\n",
       "         0.00912865,  0.00307663,  0.01119677,  0.0092291 ,  0.0116992 ,\n",
       "         0.00589252,  0.00747681,  0.00178401,  0.00498954]),\n",
       " 'mean_test_score': array([ 0.65189873,  0.67088608,  0.67405063,  0.66772152,  0.66455696,\n",
       "         0.65822785,  0.66139241,  0.65822785,  0.65189873,  0.64873418,\n",
       "         0.63924051,  0.63291139,  0.62974684,  0.62341772,  0.62025316,\n",
       "         0.62025316,  0.61392405,  0.61075949,  0.60759494]),\n",
       " 'mean_train_score': array([ 0.85584793,  0.88116513,  0.89376209,  0.90340919,  0.90967021,\n",
       "         0.91121342,  0.92071069,  0.9270158 ,  0.93338256,  0.94139826,\n",
       "         0.97147737,  0.97784413,  0.97938734,  0.98740304,  0.99373898,\n",
       "         0.99373898,  0.9968254 ,  0.9984127 ,  1.        ]),\n",
       " 'param_C': masked_array(data = [0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.2 0.3 0.4 0.5 0.6 0.7\n",
       "  0.8 0.9 1.0],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False False False False False False],\n",
       "        fill_value = ?),\n",
       " 'params': ({'C': 0.01},\n",
       "  {'C': 0.02},\n",
       "  {'C': 0.03},\n",
       "  {'C': 0.04},\n",
       "  {'C': 0.05},\n",
       "  {'C': 0.06},\n",
       "  {'C': 0.07},\n",
       "  {'C': 0.08},\n",
       "  {'C': 0.09},\n",
       "  {'C': 0.1},\n",
       "  {'C': 0.2},\n",
       "  {'C': 0.3},\n",
       "  {'C': 0.4},\n",
       "  {'C': 0.5},\n",
       "  {'C': 0.6},\n",
       "  {'C': 0.7},\n",
       "  {'C': 0.8},\n",
       "  {'C': 0.9},\n",
       "  {'C': 1.0}),\n",
       " 'rank_test_score': array([ 8,  2,  1,  3,  4,  6,  5,  6,  8, 10, 11, 12, 13, 14, 15, 15, 17,\n",
       "        18, 19], dtype=int32),\n",
       " 'split0_test_score': array([ 0.70909091,  0.70909091,  0.70909091,  0.7       ,  0.7       ,\n",
       "         0.69090909,  0.69090909,  0.7       ,  0.69090909,  0.69090909,\n",
       "         0.67272727,  0.66363636,  0.65454545,  0.64545455,  0.63636364,\n",
       "         0.63636364,  0.63636364,  0.63636364,  0.62727273]),\n",
       " 'split0_train_score': array([ 0.84466019,  0.87378641,  0.87864078,  0.89805825,  0.89805825,\n",
       "         0.89805825,  0.90776699,  0.90776699,  0.91747573,  0.9368932 ,\n",
       "         0.96601942,  0.97572816,  0.97572816,  0.99514563,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ]),\n",
       " 'split1_test_score': array([ 0.59433962,  0.63207547,  0.64150943,  0.63207547,  0.62264151,\n",
       "         0.62264151,  0.62264151,  0.61320755,  0.60377358,  0.60377358,\n",
       "         0.60377358,  0.60377358,  0.60377358,  0.60377358,  0.60377358,\n",
       "         0.60377358,  0.60377358,  0.59433962,  0.59433962]),\n",
       " 'split1_train_score': array([ 0.85714286,  0.87619048,  0.8952381 ,  0.9047619 ,  0.91428571,\n",
       "         0.91428571,  0.92380952,  0.93809524,  0.94285714,  0.94285714,\n",
       "         0.97619048,  0.98095238,  0.98095238,  0.98095238,  0.99047619,\n",
       "         0.99047619,  0.99047619,  0.9952381 ,  1.        ]),\n",
       " 'split2_test_score': array([ 0.65,  0.67,  0.67,  0.67,  0.67,  0.66,  0.67,  0.66,  0.66,\n",
       "         0.65,  0.64,  0.63,  0.63,  0.62,  0.62,  0.62,  0.6 ,  0.6 ,  0.6 ]),\n",
       " 'split2_train_score': array([ 0.86574074,  0.89351852,  0.90740741,  0.90740741,  0.91666667,\n",
       "         0.9212963 ,  0.93055556,  0.93518519,  0.93981481,  0.94444444,\n",
       "         0.97222222,  0.97685185,  0.98148148,  0.98611111,  0.99074074,\n",
       "         0.99074074,  1.        ,  1.        ,  1.        ]),\n",
       " 'std_fit_time': array([ 0.03784834,  0.07422488,  0.01327922,  0.08977326,  0.08913516,\n",
       "         0.06766009,  0.10093087,  0.01806099,  0.05408811,  0.03533033,\n",
       "         0.07022378,  0.08375383,  0.03086719,  0.0839008 ,  0.05436489,\n",
       "         0.09538025,  0.0294487 ,  0.06507145,  0.04173077]),\n",
       " 'std_score_time': array([  1.94474732e-03,   1.40361835e-03,   1.88638060e-03,\n",
       "          3.13058938e-04,   3.09965476e-03,   2.35330820e-03,\n",
       "          3.95519192e-03,   1.69525253e-03,   5.72239355e-03,\n",
       "          6.86994033e-05,   6.03032045e-03,   3.38670404e-03,\n",
       "          3.06965135e-03,   6.20830210e-03,   9.92696386e-03,\n",
       "          6.49719895e-03,   9.57127951e-03,   1.51501074e-03,\n",
       "          3.51554467e-03]),\n",
       " 'std_test_score': array([ 0.04744577,  0.03183718,  0.02806793,  0.02811685,  0.03218703,\n",
       "         0.02824162,  0.02881729,  0.03589269,  0.03643362,  0.03602452,\n",
       "         0.02850413,  0.02482123,  0.02098537,  0.01738352,  0.01347099,\n",
       "         0.01347099,  0.01646805,  0.01884887,  0.01455963]),\n",
       " 'std_train_score': array([ 0.00865467,  0.00879013,  0.01179021,  0.00393481,  0.00826823,\n",
       "         0.00973245,  0.00955795,  0.01366272,  0.0113162 ,  0.0032508 ,\n",
       "         0.00418559,  0.00224523,  0.00259643,  0.00586594,  0.00442853,\n",
       "         0.00442853,  0.00448957,  0.00224478,  0.        ])}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.cv_results_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Learning Curves for Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fae1f5a1cf8>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H+5ycjnUlIj2T6XADiP/vm/TeNkuWLGl/XF5e3if3UyqcO42Ru7eTX7eF/Pq/k7drD/n1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fae5a0d8c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from common_load import load_data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from plot_learning_curve import plot_learning_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "X, y = load_data()\n",
    "\n",
    "lr_a = LogisticRegression() # C=1.0\n",
    "lr_b = LogisticRegression(C=0.1)\n",
    "lr_c = LogisticRegression(C=0.03)\n",
    "plot_learning_curve(lr_a, X, y, 'C=1.0')\n",
    "plot_learning_curve(lr_b, X, y, 'C=0.1')\n",
    "plot_learning_curve(lr_c, X, y, 'C=0.03')\n",
    "plt.legend(loc=(0, 1.00), ncol=3, fontsize=11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Ensemble VotingClassifier built upon tuned classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble Voting Train Score: 1.00\n",
      "Ensemble Voting Test Score: 0.78\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "import xgboost\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "xgb = xgboost.XGBClassifier(objective=\"multi:softprob\", nthread=-1, subsample=0.9, reg_lambda=0.05, reg_alpha=0.7)\n",
    "gbrt = GradientBoostingClassifier(random_state=0)\n",
    "forest = RandomForestClassifier(n_jobs=-1, random_state=0)\n",
    "lr = LogisticRegression(C=0.03)\n",
    "\n",
    "eclf = VotingClassifier(estimators=[('xgboost', xgb), ('gbrt', gbrt), ('forest', forest),\n",
    "                                    ('logistic regression', lr)],\n",
    "                        voting='soft',\n",
    "                        weights=None) # weights's default value is None: uses uniform weights\n",
    "eclf = eclf.fit(X_train, y_train)\n",
    "y_train_ensemble = eclf.predict(X_train)\n",
    "y_pred_ensemble = eclf.predict(X_test)\n",
    "print('Ensemble Voting Train Score: {:.2f}'.format(eclf.score(X_train, y_train)))\n",
    "print('Ensemble Voting Test Score: {:.2f}'.format(eclf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 10-fold Cross Validation for Tuned Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Accuracy: 0.75 (+/- 0.11)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GBRT Accuracy: 0.73 (+/- 0.15)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 0.72 (+/- 0.13)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 0.72 (+/- 0.10)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/model_selection/_split.py:581: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of groups for any class cannot be less than n_splits=10.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble Voting Accuracy: 0.76 (+/- 0.13)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(xgb, X, y, cv=10)\n",
    "print(\"XGBoost Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
    "\n",
    "scores = cross_val_score(gbrt, X, y, cv=10)\n",
    "print(\"GBRT Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
    "\n",
    "scores = cross_val_score(lr, X, y, cv=10)\n",
    "print(\"Logistic Regression Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
    "\n",
    "scores = cross_val_score(forest, X, y, cv=10)\n",
    "print(\"Random Forest Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
    "\n",
    "scores = cross_val_score(eclf, X, y, cv=10)\n",
    "print(\"Ensemble Voting Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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FjcJ1rsuyLhlZbNKWc0uZXm6rJlMzW06waBgcHRpCmMxXgTROD63iRuE612VZ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\ntaSLY9xnL+DXkX2SgDWj+7k9On+Tmb0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3387696d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C5W4pvasrlxgHbEKYPl+fXrm/Xte5W0qJ8wkT/JaUtA/B1ft3zV5Y0krx53zA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\n/w8QAbDaeaPotAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2ff5f5198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4m0wcySYTe57Vzj1hVmmStNM0vghcamYLgYWS/k54Vq8r4A0zs8elXt1kss0i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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2ff57acf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0O5+ua4cuH82U8GJh6HRCf5lXgQlm9lhFmsOA/Sk/8LzZzL6detwsbimJEywP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\nwqTP89RVNHe+fNTql7JJXEo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2fcb3b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Z+nXmPQW4WtJ8gl3DR6pLanF6aBc3Cte5Ls+67LRbk7bAOYQ5a1sTJvkfCjxT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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2fcae29b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J/2Jmt0u6A7gDmNJEvpsBb5rZDIB43rmEysI9g8kmVcncLaX7uoC7rIw0XTvc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\nMdFmciBebgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2fca11eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mtgHwCrBX+WBXDndLcV1f0xWRal57q8vLQA67tC11SzGzLUrSvADs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2ff5b50b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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10i2lhiHtSGCqxUnCZtZBWOw7sqDcRsC6ku6W9Likn1ZqU8UQLGk74G+EHtia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\npRxdPAYJkzTXT1KfpDVimy9LUh7YGPj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2fcad82e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ucKSZXVL2vdE055MiulG4znVF1jVuHnC8fSlV2sP18yF5xcrQWYTxMi8CB5rZ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\nr4vn6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2fcac9860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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K1OMQUa+rxCD3AOPIurBwUdwvXOe6ZurqoIBteJ1yS1kVuIDw7S8Ange+a2YPJNLU5ZZS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\n3+ABz3GcvsEDnuM4fYMHPMdx+ob/D4a/tZQ6fMi6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2fc0b1518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from common_load import load_data\n",
    "import xgboost\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from plot_confusion_matrix import gen_confusion_matrix_figure\n",
    "\n",
    "\n",
    "X, y = load_data()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n",
    "\n",
    "xgb = xgboost.XGBClassifier(objective=\"multi:softprob\", nthread=-1,\n",
    "                            reg_alpha=0.7, reg_lambda=0.05, subsample=0.9)\n",
    "gbrt = GradientBoostingClassifier(random_state=0)\n",
    "forest = RandomForestClassifier(n_jobs=-1, random_state=0)\n",
    "lr = LogisticRegression(C=0.03)\n",
    "eclf = VotingClassifier(estimators=[('xgboost', xgb), ('gbrt', gbrt), ('forest', forest),\n",
    "                                    ('logistic regression', lr)],\n",
    "                        voting='soft',\n",
    "                        weights=None)\n",
    "\n",
    "classifier_list = [xgb, gbrt, forest, lr, eclf]\n",
    "\n",
    "for clf in classifier_list:\n",
    "    y_pred = clf.fit(X_train, y_train).predict(X_test)\n",
    "    y_train_pred = clf.fit(X_train, y_train).predict(X_train)\n",
    "\n",
    "    # Compute confusion matrix\n",
    "    cnf_test = confusion_matrix(y_test, y_pred, labels=range(1, 17))\n",
    "    cnf_train = confusion_matrix(y_train, y_train_pred, labels=range(1, 17))\n",
    "\n",
    "    # Plot confusion matrix\n",
    "    gen_confusion_matrix_figure(cnf_test, clf.__class__.__name__ + \"_test\")\n",
    "    gen_confusion_matrix_figure(cnf_train, clf.__class__.__name__ + \"_train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBClassifier\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.80      0.97      0.88        73\n",
      "          2       0.67      0.55      0.60        11\n",
      "          3       0.86      0.75      0.80         8\n",
      "          4       0.50      1.00      0.67         1\n",
      "          5       0.50      0.50      0.50         6\n",
      "          6       0.67      0.86      0.75         7\n",
      "          7       0.00      0.00      0.00         1\n",
      "          9       1.00      0.75      0.86         4\n",
      "         10       0.90      0.56      0.69        16\n",
      "         14       0.00      0.00      0.00         2\n",
      "         15       0.00      0.00      0.00         0\n",
      "         16       0.00      0.00      0.00         7\n",
      "\n",
      "avg / total       0.73      0.77      0.74       136\n",
      "\n",
      "GradientBoostingClassifier\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.80      0.96      0.87        73\n",
      "          2       0.64      0.64      0.64        11\n",
      "          3       0.75      0.75      0.75         8\n",
      "          4       0.50      1.00      0.67         1\n",
      "          5       0.75      0.50      0.60         6\n",
      "          6       0.83      0.71      0.77         7\n",
      "          7       0.00      0.00      0.00         1\n",
      "          9       1.00      0.75      0.86         4\n",
      "         10       0.83      0.62      0.71        16\n",
      "         14       0.00      0.00      0.00         2\n",
      "         15       0.00      0.00      0.00         0\n",
      "         16       0.00      0.00      0.00         7\n",
      "\n",
      "avg / total       0.73      0.77      0.74       136\n",
      "\n",
      "RandomForestClassifier\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.69      0.99      0.81        73\n",
      "          2       0.57      0.36      0.44        11\n",
      "          3       1.00      0.88      0.93         8\n",
      "          4       1.00      1.00      1.00         1\n",
      "          5       0.00      0.00      0.00         6\n",
      "          6       0.50      0.14      0.22         7\n",
      "          7       0.00      0.00      0.00         1\n",
      "          9       1.00      0.75      0.86         4\n",
      "         10       0.90      0.56      0.69        16\n",
      "         14       0.00      0.00      0.00         2\n",
      "         16       0.00      0.00      0.00         7\n",
      "\n",
      "avg / total       0.64      0.71      0.65       136\n",
      "\n",
      "LogisticRegression\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.81      0.90      0.86        73\n",
      "          2       0.64      0.64      0.64        11\n",
      "          3       0.67      1.00      0.80         8\n",
      "          4       0.33      1.00      0.50         1\n",
      "          5       0.00      0.00      0.00         6\n",
      "          6       0.60      0.43      0.50         7\n",
      "          7       0.00      0.00      0.00         1\n",
      "          9       1.00      1.00      1.00         4\n",
      "         10       1.00      0.88      0.93        16\n",
      "         14       1.00      0.50      0.67         2\n",
      "         16       0.25      0.14      0.18         7\n",
      "\n",
      "avg / total       0.74      0.77      0.75       136\n",
      "\n",
      "VotingClassifier\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.79      0.97      0.87        73\n",
      "          2       0.78      0.64      0.70        11\n",
      "          3       0.88      0.88      0.88         8\n",
      "          4       0.50      1.00      0.67         1\n",
      "          5       0.60      0.50      0.55         6\n",
      "          6       0.71      0.71      0.71         7\n",
      "          7       0.00      0.00      0.00         1\n",
      "          9       1.00      0.75      0.86         4\n",
      "         10       0.82      0.56      0.67        16\n",
      "         14       0.00      0.00      0.00         2\n",
      "         15       0.00      0.00      0.00         0\n",
      "         16       0.00      0.00      0.00         7\n",
      "\n",
      "avg / total       0.73      0.78      0.75       136\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "for clf in classifier_list:\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_true, y_pred = y_test, clf.predict(X_test)\n",
    "    print(clf.__class__.__name__)\n",
    "    print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Learning Curves for All Tuned Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8/Pwgl8vh7e2N6dOno06dOmbbxVwb1ahRA3v27MHcuXMhl8vh6uqKefPmWWxbzr8Xfd9O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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fae1e0e1898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from common_load import load_data\n",
    "import xgboost\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from plot_learning_curve import plot_learning_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "X, y = load_data()\n",
    "\n",
    "xgb = xgboost.XGBClassifier(objective=\"multi:softprob\", nthread=-1,\n",
    "                            reg_alpha=0.7, reg_lambda=0.05, subsample=0.9)\n",
    "gbrt = GradientBoostingClassifier(random_state=0)\n",
    "forest = RandomForestClassifier(n_jobs=-1, random_state=0)\n",
    "lr = LogisticRegression(C=0.03)\n",
    "eclf = VotingClassifier(estimators=[('xgboost', xgb), ('gbrt', gbrt), ('forest', forest),\n",
    "                                    ('logistic regression', lr)],\n",
    "                        voting='soft',\n",
    "                        weights=None)\n",
    "\n",
    "classifier_list = [xgb, gbrt, forest, lr, eclf]\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "for clf in classifier_list:\n",
    "    plot_learning_curve(clf, X, y, clf.__class__.__name__)\n",
    "    plt.legend(loc=(0, 1.00), ncol=2, fontsize=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}