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VeylanSolmira/binary_classifier.ipynb

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binary_classifier.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset\n",
"- [Here](https://s3-us-west-2.amazonaws.com/ga-dat-2015-suneel/datasets/breast-cancer.csv) is the dataset.\n",
"- [Here](https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.names) is a description of the data. Ignore column 0 as it is merely the ID of a patient record."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas\n",
"import statistics\n",
"from sklearn import cross_validation, ensemble, metrics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Read in the data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open(\"C:/Users/ThatVoice/Desktop/Challenge/breast-cancer.csv\") as f:\n",
" patient_data = pandas.read_csv(f, header = None)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>842302</td>\n",
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" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
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" <td>M</td>\n",
" <td>19.690</td>\n",
" <td>21.25</td>\n",
" <td>130.00</td>\n",
" <td>1203.0</td>\n",
" <td>0.10960</td>\n",
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" <td>0.197400</td>\n",
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" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
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" <td>0.42450</td>\n",
" <td>0.45040</td>\n",
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" <th>3</th>\n",
" <td>84348301</td>\n",
" <td>M</td>\n",
" <td>11.420</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.241400</td>\n",
" <td>0.105200</td>\n",
" <td>...</td>\n",
" <td>14.910</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.20980</td>\n",
" <td>0.86630</td>\n",
" <td>0.68690</td>\n",
" <td>0.25750</td>\n",
" <td>0.6638</td>\n",
" <td>0.17300</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>84358402</td>\n",
" <td>M</td>\n",
" <td>20.290</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
" <td>0.10030</td>\n",
" <td>0.13280</td>\n",
" <td>0.198000</td>\n",
" <td>0.104300</td>\n",
" <td>...</td>\n",
" <td>22.540</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.13740</td>\n",
" <td>0.20500</td>\n",
" <td>0.40000</td>\n",
" <td>0.16250</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
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" <th>5</th>\n",
" <td>843786</td>\n",
" <td>M</td>\n",
" <td>12.450</td>\n",
" <td>15.70</td>\n",
" <td>82.57</td>\n",
" <td>477.1</td>\n",
" <td>0.12780</td>\n",
" <td>0.17000</td>\n",
" <td>0.157800</td>\n",
" <td>0.080890</td>\n",
" <td>...</td>\n",
" <td>15.470</td>\n",
" <td>23.75</td>\n",
" <td>103.40</td>\n",
" <td>741.6</td>\n",
" <td>0.17910</td>\n",
" <td>0.52490</td>\n",
" <td>0.53550</td>\n",
" <td>0.17410</td>\n",
" <td>0.3985</td>\n",
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" <th>6</th>\n",
" <td>844359</td>\n",
" <td>M</td>\n",
" <td>18.250</td>\n",
" <td>19.98</td>\n",
" <td>119.60</td>\n",
" <td>1040.0</td>\n",
" <td>0.09463</td>\n",
" <td>0.10900</td>\n",
" <td>0.112700</td>\n",
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" <td>22.880</td>\n",
" <td>27.66</td>\n",
" <td>153.20</td>\n",
" <td>1606.0</td>\n",
" <td>0.14420</td>\n",
" <td>0.25760</td>\n",
" <td>0.37840</td>\n",
" <td>0.19320</td>\n",
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" <th>7</th>\n",
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" <td>M</td>\n",
" <td>13.710</td>\n",
" <td>20.83</td>\n",
" <td>90.20</td>\n",
" <td>577.9</td>\n",
" <td>0.11890</td>\n",
" <td>0.16450</td>\n",
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" <td>...</td>\n",
" <td>17.060</td>\n",
" <td>28.14</td>\n",
" <td>110.60</td>\n",
" <td>897.0</td>\n",
" <td>0.16540</td>\n",
" <td>0.36820</td>\n",
" <td>0.26780</td>\n",
" <td>0.15560</td>\n",
" <td>0.3196</td>\n",
" <td>0.11510</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>844981</td>\n",
" <td>M</td>\n",
" <td>13.000</td>\n",
" <td>21.82</td>\n",
" <td>87.50</td>\n",
" <td>519.8</td>\n",
" <td>0.12730</td>\n",
" <td>0.19320</td>\n",
" <td>0.185900</td>\n",
" <td>0.093530</td>\n",
" <td>...</td>\n",
" <td>15.490</td>\n",
" <td>30.73</td>\n",
" <td>106.20</td>\n",
" <td>739.3</td>\n",
" <td>0.17030</td>\n",
" <td>0.54010</td>\n",
" <td>0.53900</td>\n",
" <td>0.20600</td>\n",
" <td>0.4378</td>\n",
" <td>0.10720</td>\n",
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" <tr>\n",
" <th>9</th>\n",
" <td>84501001</td>\n",
" <td>M</td>\n",
" <td>12.460</td>\n",
" <td>24.04</td>\n",
" <td>83.97</td>\n",
" <td>475.9</td>\n",
" <td>0.11860</td>\n",
" <td>0.23960</td>\n",
" <td>0.227300</td>\n",
" <td>0.085430</td>\n",
" <td>...</td>\n",
" <td>15.090</td>\n",
" <td>40.68</td>\n",
" <td>97.65</td>\n",
" <td>711.4</td>\n",
" <td>0.18530</td>\n",
" <td>1.05800</td>\n",
" <td>1.10500</td>\n",
" <td>0.22100</td>\n",
" <td>0.4366</td>\n",
" <td>0.20750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>845636</td>\n",
" <td>M</td>\n",
" <td>16.020</td>\n",
" <td>23.24</td>\n",
" <td>102.70</td>\n",
" <td>797.8</td>\n",
" <td>0.08206</td>\n",
" <td>0.06669</td>\n",
" <td>0.032990</td>\n",
" <td>0.033230</td>\n",
" <td>...</td>\n",
" <td>19.190</td>\n",
" <td>33.88</td>\n",
" <td>123.80</td>\n",
" <td>1150.0</td>\n",
" <td>0.11810</td>\n",
" <td>0.15510</td>\n",
" <td>0.14590</td>\n",
" <td>0.09975</td>\n",
" <td>0.2948</td>\n",
" <td>0.08452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>84610002</td>\n",
" <td>M</td>\n",
" <td>15.780</td>\n",
" <td>17.89</td>\n",
" <td>103.60</td>\n",
" <td>781.0</td>\n",
" <td>0.09710</td>\n",
" <td>0.12920</td>\n",
" <td>0.099540</td>\n",
" <td>0.066060</td>\n",
" <td>...</td>\n",
" <td>20.420</td>\n",
" <td>27.28</td>\n",
" <td>136.50</td>\n",
" <td>1299.0</td>\n",
" <td>0.13960</td>\n",
" <td>0.56090</td>\n",
" <td>0.39650</td>\n",
" <td>0.18100</td>\n",
" <td>0.3792</td>\n",
" <td>0.10480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>846226</td>\n",
" <td>M</td>\n",
" <td>19.170</td>\n",
" <td>24.80</td>\n",
" <td>132.40</td>\n",
" <td>1123.0</td>\n",
" <td>0.09740</td>\n",
" <td>0.24580</td>\n",
" <td>0.206500</td>\n",
" <td>0.111800</td>\n",
" <td>...</td>\n",
" <td>20.960</td>\n",
" <td>29.94</td>\n",
" <td>151.70</td>\n",
" <td>1332.0</td>\n",
" <td>0.10370</td>\n",
" <td>0.39030</td>\n",
" <td>0.36390</td>\n",
" <td>0.17670</td>\n",
" <td>0.3176</td>\n",
" <td>0.10230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>846381</td>\n",
" <td>M</td>\n",
" <td>15.850</td>\n",
" <td>23.95</td>\n",
" <td>103.70</td>\n",
" <td>782.7</td>\n",
" <td>0.08401</td>\n",
" <td>0.10020</td>\n",
" <td>0.099380</td>\n",
" <td>0.053640</td>\n",
" <td>...</td>\n",
" <td>16.840</td>\n",
" <td>27.66</td>\n",
" <td>112.00</td>\n",
" <td>876.5</td>\n",
" <td>0.11310</td>\n",
" <td>0.19240</td>\n",
" <td>0.23220</td>\n",
" <td>0.11190</td>\n",
" <td>0.2809</td>\n",
" <td>0.06287</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>84667401</td>\n",
" <td>M</td>\n",
" <td>13.730</td>\n",
" <td>22.61</td>\n",
" <td>93.60</td>\n",
" <td>578.3</td>\n",
" <td>0.11310</td>\n",
" <td>0.22930</td>\n",
" <td>0.212800</td>\n",
" <td>0.080250</td>\n",
" <td>...</td>\n",
" <td>15.030</td>\n",
" <td>32.01</td>\n",
" <td>108.80</td>\n",
" <td>697.7</td>\n",
" <td>0.16510</td>\n",
" <td>0.77250</td>\n",
" <td>0.69430</td>\n",
" <td>0.22080</td>\n",
" <td>0.3596</td>\n",
" <td>0.14310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>84799002</td>\n",
" <td>M</td>\n",
" <td>14.540</td>\n",
" <td>27.54</td>\n",
" <td>96.73</td>\n",
" <td>658.8</td>\n",
" <td>0.11390</td>\n",
" <td>0.15950</td>\n",
" <td>0.163900</td>\n",
" <td>0.073640</td>\n",
" <td>...</td>\n",
" <td>17.460</td>\n",
" <td>37.13</td>\n",
" <td>124.10</td>\n",
" <td>943.2</td>\n",
" <td>0.16780</td>\n",
" <td>0.65770</td>\n",
" <td>0.70260</td>\n",
" <td>0.17120</td>\n",
" <td>0.4218</td>\n",
" <td>0.13410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>848406</td>\n",
" <td>M</td>\n",
" <td>14.680</td>\n",
" <td>20.13</td>\n",
" <td>94.74</td>\n",
" <td>684.5</td>\n",
" <td>0.09867</td>\n",
" <td>0.07200</td>\n",
" <td>0.073950</td>\n",
" <td>0.052590</td>\n",
" <td>...</td>\n",
" <td>19.070</td>\n",
" <td>30.88</td>\n",
" <td>123.40</td>\n",
" <td>1138.0</td>\n",
" <td>0.14640</td>\n",
" <td>0.18710</td>\n",
" <td>0.29140</td>\n",
" <td>0.16090</td>\n",
" <td>0.3029</td>\n",
" <td>0.08216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>84862001</td>\n",
" <td>M</td>\n",
" <td>16.130</td>\n",
" <td>20.68</td>\n",
" <td>108.10</td>\n",
" <td>798.8</td>\n",
" <td>0.11700</td>\n",
" <td>0.20220</td>\n",
" <td>0.172200</td>\n",
" <td>0.102800</td>\n",
" <td>...</td>\n",
" <td>20.960</td>\n",
" <td>31.48</td>\n",
" <td>136.80</td>\n",
" <td>1315.0</td>\n",
" <td>0.17890</td>\n",
" <td>0.42330</td>\n",
" <td>0.47840</td>\n",
" <td>0.20730</td>\n",
" <td>0.3706</td>\n",
" <td>0.11420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>849014</td>\n",
" <td>M</td>\n",
" <td>19.810</td>\n",
" <td>22.15</td>\n",
" <td>130.00</td>\n",
" <td>1260.0</td>\n",
" <td>0.09831</td>\n",
" <td>0.10270</td>\n",
" <td>0.147900</td>\n",
" <td>0.094980</td>\n",
" <td>...</td>\n",
" <td>27.320</td>\n",
" <td>30.88</td>\n",
" <td>186.80</td>\n",
" <td>2398.0</td>\n",
" <td>0.15120</td>\n",
" <td>0.31500</td>\n",
" <td>0.53720</td>\n",
" <td>0.23880</td>\n",
" <td>0.2768</td>\n",
" <td>0.07615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>8510426</td>\n",
" <td>B</td>\n",
" <td>13.540</td>\n",
" <td>14.36</td>\n",
" <td>87.46</td>\n",
" <td>566.3</td>\n",
" <td>0.09779</td>\n",
" <td>0.08129</td>\n",
" <td>0.066640</td>\n",
" <td>0.047810</td>\n",
" <td>...</td>\n",
" <td>15.110</td>\n",
" <td>19.26</td>\n",
" <td>99.70</td>\n",
" <td>711.2</td>\n",
" <td>0.14400</td>\n",
" <td>0.17730</td>\n",
" <td>0.23900</td>\n",
" <td>0.12880</td>\n",
" <td>0.2977</td>\n",
" <td>0.07259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>8510653</td>\n",
" <td>B</td>\n",
" <td>13.080</td>\n",
" <td>15.71</td>\n",
" <td>85.63</td>\n",
" <td>520.0</td>\n",
" <td>0.10750</td>\n",
" <td>0.12700</td>\n",
" <td>0.045680</td>\n",
" <td>0.031100</td>\n",
" <td>...</td>\n",
" <td>14.500</td>\n",
" <td>20.49</td>\n",
" <td>96.09</td>\n",
" <td>630.5</td>\n",
" <td>0.13120</td>\n",
" <td>0.27760</td>\n",
" <td>0.18900</td>\n",
" <td>0.07283</td>\n",
" <td>0.3184</td>\n",
" <td>0.08183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>8510824</td>\n",
" <td>B</td>\n",
" <td>9.504</td>\n",
" <td>12.44</td>\n",
" <td>60.34</td>\n",
" <td>273.9</td>\n",
" <td>0.10240</td>\n",
" <td>0.06492</td>\n",
" <td>0.029560</td>\n",
" <td>0.020760</td>\n",
" <td>...</td>\n",
" <td>10.230</td>\n",
" <td>15.66</td>\n",
" <td>65.13</td>\n",
" <td>314.9</td>\n",
" <td>0.13240</td>\n",
" <td>0.11480</td>\n",
" <td>0.08867</td>\n",
" <td>0.06227</td>\n",
" <td>0.2450</td>\n",
" <td>0.07773</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>8511133</td>\n",
" <td>M</td>\n",
" <td>15.340</td>\n",
" <td>14.26</td>\n",
" <td>102.50</td>\n",
" <td>704.4</td>\n",
" <td>0.10730</td>\n",
" <td>0.21350</td>\n",
" <td>0.207700</td>\n",
" <td>0.097560</td>\n",
" <td>...</td>\n",
" <td>18.070</td>\n",
" <td>19.08</td>\n",
" <td>125.10</td>\n",
" <td>980.9</td>\n",
" <td>0.13900</td>\n",
" <td>0.59540</td>\n",
" <td>0.63050</td>\n",
" <td>0.23930</td>\n",
" <td>0.4667</td>\n",
" <td>0.09946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>851509</td>\n",
" <td>M</td>\n",
" <td>21.160</td>\n",
" <td>23.04</td>\n",
" <td>137.20</td>\n",
" <td>1404.0</td>\n",
" <td>0.09428</td>\n",
" <td>0.10220</td>\n",
" <td>0.109700</td>\n",
" <td>0.086320</td>\n",
" <td>...</td>\n",
" <td>29.170</td>\n",
" <td>35.59</td>\n",
" <td>188.00</td>\n",
" <td>2615.0</td>\n",
" <td>0.14010</td>\n",
" <td>0.26000</td>\n",
" <td>0.31550</td>\n",
" <td>0.20090</td>\n",
" <td>0.2822</td>\n",
" <td>0.07526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>852552</td>\n",
" <td>M</td>\n",
" <td>16.650</td>\n",
" <td>21.38</td>\n",
" <td>110.00</td>\n",
" <td>904.6</td>\n",
" <td>0.11210</td>\n",
" <td>0.14570</td>\n",
" <td>0.152500</td>\n",
" <td>0.091700</td>\n",
" <td>...</td>\n",
" <td>26.460</td>\n",
" <td>31.56</td>\n",
" <td>177.00</td>\n",
" <td>2215.0</td>\n",
" <td>0.18050</td>\n",
" <td>0.35780</td>\n",
" <td>0.46950</td>\n",
" <td>0.20950</td>\n",
" <td>0.3613</td>\n",
" <td>0.09564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>852631</td>\n",
" <td>M</td>\n",
" <td>17.140</td>\n",
" <td>16.40</td>\n",
" <td>116.00</td>\n",
" <td>912.7</td>\n",
" <td>0.11860</td>\n",
" <td>0.22760</td>\n",
" <td>0.222900</td>\n",
" <td>0.140100</td>\n",
" <td>...</td>\n",
" <td>22.250</td>\n",
" <td>21.40</td>\n",
" <td>152.40</td>\n",
" <td>1461.0</td>\n",
" <td>0.15450</td>\n",
" <td>0.39490</td>\n",
" <td>0.38530</td>\n",
" <td>0.25500</td>\n",
" <td>0.4066</td>\n",
" <td>0.10590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>852763</td>\n",
" <td>M</td>\n",
" <td>14.580</td>\n",
" <td>21.53</td>\n",
" <td>97.41</td>\n",
" <td>644.8</td>\n",
" <td>0.10540</td>\n",
" <td>0.18680</td>\n",
" <td>0.142500</td>\n",
" <td>0.087830</td>\n",
" <td>...</td>\n",
" <td>17.620</td>\n",
" <td>33.21</td>\n",
" <td>122.40</td>\n",
" <td>896.9</td>\n",
" <td>0.15250</td>\n",
" <td>0.66430</td>\n",
" <td>0.55390</td>\n",
" <td>0.27010</td>\n",
" <td>0.4264</td>\n",
" <td>0.12750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>852781</td>\n",
" <td>M</td>\n",
" <td>18.610</td>\n",
" <td>20.25</td>\n",
" <td>122.10</td>\n",
" <td>1094.0</td>\n",
" <td>0.09440</td>\n",
" <td>0.10660</td>\n",
" <td>0.149000</td>\n",
" <td>0.077310</td>\n",
" <td>...</td>\n",
" <td>21.310</td>\n",
" <td>27.26</td>\n",
" <td>139.90</td>\n",
" <td>1403.0</td>\n",
" <td>0.13380</td>\n",
" <td>0.21170</td>\n",
" <td>0.34460</td>\n",
" <td>0.14900</td>\n",
" <td>0.2341</td>\n",
" <td>0.07421</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>852973</td>\n",
" <td>M</td>\n",
" <td>15.300</td>\n",
" <td>25.27</td>\n",
" <td>102.40</td>\n",
" <td>732.4</td>\n",
" <td>0.10820</td>\n",
" <td>0.16970</td>\n",
" <td>0.168300</td>\n",
" <td>0.087510</td>\n",
" <td>...</td>\n",
" <td>20.270</td>\n",
" <td>36.71</td>\n",
" <td>149.30</td>\n",
" <td>1269.0</td>\n",
" <td>0.16410</td>\n",
" <td>0.61100</td>\n",
" <td>0.63350</td>\n",
" <td>0.20240</td>\n",
" <td>0.4027</td>\n",
" <td>0.09876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>853201</td>\n",
" <td>M</td>\n",
" <td>17.570</td>\n",
" <td>15.05</td>\n",
" <td>115.00</td>\n",
" <td>955.1</td>\n",
" <td>0.09847</td>\n",
" <td>0.11570</td>\n",
" <td>0.098750</td>\n",
" <td>0.079530</td>\n",
" <td>...</td>\n",
" <td>20.010</td>\n",
" <td>19.52</td>\n",
" <td>134.90</td>\n",
" <td>1227.0</td>\n",
" <td>0.12550</td>\n",
" <td>0.28120</td>\n",
" <td>0.24890</td>\n",
" <td>0.14560</td>\n",
" <td>0.2756</td>\n",
" <td>0.07919</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>539</th>\n",
" <td>921362</td>\n",
" <td>B</td>\n",
" <td>7.691</td>\n",
" <td>25.44</td>\n",
" <td>48.34</td>\n",
" <td>170.4</td>\n",
" <td>0.08668</td>\n",
" <td>0.11990</td>\n",
" <td>0.092520</td>\n",
" <td>0.013640</td>\n",
" <td>...</td>\n",
" <td>8.678</td>\n",
" <td>31.89</td>\n",
" <td>54.49</td>\n",
" <td>223.6</td>\n",
" <td>0.15960</td>\n",
" <td>0.30640</td>\n",
" <td>0.33930</td>\n",
" <td>0.05000</td>\n",
" <td>0.2790</td>\n",
" <td>0.10660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>540</th>\n",
" <td>921385</td>\n",
" <td>B</td>\n",
" <td>11.540</td>\n",
" <td>14.44</td>\n",
" <td>74.65</td>\n",
" <td>402.9</td>\n",
" <td>0.09984</td>\n",
" <td>0.11200</td>\n",
" <td>0.067370</td>\n",
" <td>0.025940</td>\n",
" <td>...</td>\n",
" <td>12.260</td>\n",
" <td>19.68</td>\n",
" <td>78.78</td>\n",
" <td>457.8</td>\n",
" <td>0.13450</td>\n",
" <td>0.21180</td>\n",
" <td>0.17970</td>\n",
" <td>0.06918</td>\n",
" <td>0.2329</td>\n",
" <td>0.08134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>541</th>\n",
" <td>921386</td>\n",
" <td>B</td>\n",
" <td>14.470</td>\n",
" <td>24.99</td>\n",
" <td>95.81</td>\n",
" <td>656.4</td>\n",
" <td>0.08837</td>\n",
" <td>0.12300</td>\n",
" <td>0.100900</td>\n",
" <td>0.038900</td>\n",
" <td>...</td>\n",
" <td>16.220</td>\n",
" <td>31.73</td>\n",
" <td>113.50</td>\n",
" <td>808.9</td>\n",
" <td>0.13400</td>\n",
" <td>0.42020</td>\n",
" <td>0.40400</td>\n",
" <td>0.12050</td>\n",
" <td>0.3187</td>\n",
" <td>0.10230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>542</th>\n",
" <td>921644</td>\n",
" <td>B</td>\n",
" <td>14.740</td>\n",
" <td>25.42</td>\n",
" <td>94.70</td>\n",
" <td>668.6</td>\n",
" <td>0.08275</td>\n",
" <td>0.07214</td>\n",
" <td>0.041050</td>\n",
" <td>0.030270</td>\n",
" <td>...</td>\n",
" <td>16.510</td>\n",
" <td>32.29</td>\n",
" <td>107.40</td>\n",
" <td>826.4</td>\n",
" <td>0.10600</td>\n",
" <td>0.13760</td>\n",
" <td>0.16110</td>\n",
" <td>0.10950</td>\n",
" <td>0.2722</td>\n",
" <td>0.06956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>543</th>\n",
" <td>922296</td>\n",
" <td>B</td>\n",
" <td>13.210</td>\n",
" <td>28.06</td>\n",
" <td>84.88</td>\n",
" <td>538.4</td>\n",
" <td>0.08671</td>\n",
" <td>0.06877</td>\n",
" <td>0.029870</td>\n",
" <td>0.032750</td>\n",
" <td>...</td>\n",
" <td>14.370</td>\n",
" <td>37.17</td>\n",
" <td>92.48</td>\n",
" <td>629.6</td>\n",
" <td>0.10720</td>\n",
" <td>0.13810</td>\n",
" <td>0.10620</td>\n",
" <td>0.07958</td>\n",
" <td>0.2473</td>\n",
" <td>0.06443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>544</th>\n",
" <td>922297</td>\n",
" <td>B</td>\n",
" <td>13.870</td>\n",
" <td>20.70</td>\n",
" <td>89.77</td>\n",
" <td>584.8</td>\n",
" <td>0.09578</td>\n",
" <td>0.10180</td>\n",
" <td>0.036880</td>\n",
" <td>0.023690</td>\n",
" <td>...</td>\n",
" <td>15.050</td>\n",
" <td>24.75</td>\n",
" <td>99.17</td>\n",
" <td>688.6</td>\n",
" <td>0.12640</td>\n",
" <td>0.20370</td>\n",
" <td>0.13770</td>\n",
" <td>0.06845</td>\n",
" <td>0.2249</td>\n",
" <td>0.08492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>545</th>\n",
" <td>922576</td>\n",
" <td>B</td>\n",
" <td>13.620</td>\n",
" <td>23.23</td>\n",
" <td>87.19</td>\n",
" <td>573.2</td>\n",
" <td>0.09246</td>\n",
" <td>0.06747</td>\n",
" <td>0.029740</td>\n",
" <td>0.024430</td>\n",
" <td>...</td>\n",
" <td>15.350</td>\n",
" <td>29.09</td>\n",
" <td>97.58</td>\n",
" <td>729.8</td>\n",
" <td>0.12160</td>\n",
" <td>0.15170</td>\n",
" <td>0.10490</td>\n",
" <td>0.07174</td>\n",
" <td>0.2642</td>\n",
" <td>0.06953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>546</th>\n",
" <td>922577</td>\n",
" <td>B</td>\n",
" <td>10.320</td>\n",
" <td>16.35</td>\n",
" <td>65.31</td>\n",
" <td>324.9</td>\n",
" <td>0.09434</td>\n",
" <td>0.04994</td>\n",
" <td>0.010120</td>\n",
" <td>0.005495</td>\n",
" <td>...</td>\n",
" <td>11.250</td>\n",
" <td>21.77</td>\n",
" <td>71.12</td>\n",
" <td>384.9</td>\n",
" <td>0.12850</td>\n",
" <td>0.08842</td>\n",
" <td>0.04384</td>\n",
" <td>0.02381</td>\n",
" <td>0.2681</td>\n",
" <td>0.07399</td>\n",
" </tr>\n",
" <tr>\n",
" <th>547</th>\n",
" <td>922840</td>\n",
" <td>B</td>\n",
" <td>10.260</td>\n",
" <td>16.58</td>\n",
" <td>65.85</td>\n",
" <td>320.8</td>\n",
" <td>0.08877</td>\n",
" <td>0.08066</td>\n",
" <td>0.043580</td>\n",
" <td>0.024380</td>\n",
" <td>...</td>\n",
" <td>10.830</td>\n",
" <td>22.04</td>\n",
" <td>71.08</td>\n",
" <td>357.4</td>\n",
" <td>0.14610</td>\n",
" <td>0.22460</td>\n",
" <td>0.17830</td>\n",
" <td>0.08333</td>\n",
" <td>0.2691</td>\n",
" <td>0.09479</td>\n",
" </tr>\n",
" <tr>\n",
" <th>548</th>\n",
" <td>923169</td>\n",
" <td>B</td>\n",
" <td>9.683</td>\n",
" <td>19.34</td>\n",
" <td>61.05</td>\n",
" <td>285.7</td>\n",
" <td>0.08491</td>\n",
" <td>0.05030</td>\n",
" <td>0.023370</td>\n",
" <td>0.009615</td>\n",
" <td>...</td>\n",
" <td>10.930</td>\n",
" <td>25.59</td>\n",
" <td>69.10</td>\n",
" <td>364.2</td>\n",
" <td>0.11990</td>\n",
" <td>0.09546</td>\n",
" <td>0.09350</td>\n",
" <td>0.03846</td>\n",
" <td>0.2552</td>\n",
" <td>0.07920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>549</th>\n",
" <td>923465</td>\n",
" <td>B</td>\n",
" <td>10.820</td>\n",
" <td>24.21</td>\n",
" <td>68.89</td>\n",
" <td>361.6</td>\n",
" <td>0.08192</td>\n",
" <td>0.06602</td>\n",
" <td>0.015480</td>\n",
" <td>0.008160</td>\n",
" <td>...</td>\n",
" <td>13.030</td>\n",
" <td>31.45</td>\n",
" <td>83.90</td>\n",
" <td>505.6</td>\n",
" <td>0.12040</td>\n",
" <td>0.16330</td>\n",
" <td>0.06194</td>\n",
" <td>0.03264</td>\n",
" <td>0.3059</td>\n",
" <td>0.07626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>550</th>\n",
" <td>923748</td>\n",
" <td>B</td>\n",
" <td>10.860</td>\n",
" <td>21.48</td>\n",
" <td>68.51</td>\n",
" <td>360.5</td>\n",
" <td>0.07431</td>\n",
" <td>0.04227</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>11.660</td>\n",
" <td>24.77</td>\n",
" <td>74.08</td>\n",
" <td>412.3</td>\n",
" <td>0.10010</td>\n",
" <td>0.07348</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.2458</td>\n",
" <td>0.06592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>551</th>\n",
" <td>923780</td>\n",
" <td>B</td>\n",
" <td>11.130</td>\n",
" <td>22.44</td>\n",
" <td>71.49</td>\n",
" <td>378.4</td>\n",
" <td>0.09566</td>\n",
" <td>0.08194</td>\n",
" <td>0.048240</td>\n",
" <td>0.022570</td>\n",
" <td>...</td>\n",
" <td>12.020</td>\n",
" <td>28.26</td>\n",
" <td>77.80</td>\n",
" <td>436.6</td>\n",
" <td>0.10870</td>\n",
" <td>0.17820</td>\n",
" <td>0.15640</td>\n",
" <td>0.06413</td>\n",
" <td>0.3169</td>\n",
" <td>0.08032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>552</th>\n",
" <td>924084</td>\n",
" <td>B</td>\n",
" <td>12.770</td>\n",
" <td>29.43</td>\n",
" <td>81.35</td>\n",
" <td>507.9</td>\n",
" <td>0.08276</td>\n",
" <td>0.04234</td>\n",
" <td>0.019970</td>\n",
" <td>0.014990</td>\n",
" <td>...</td>\n",
" <td>13.870</td>\n",
" <td>36.00</td>\n",
" <td>88.10</td>\n",
" <td>594.7</td>\n",
" <td>0.12340</td>\n",
" <td>0.10640</td>\n",
" <td>0.08653</td>\n",
" <td>0.06498</td>\n",
" <td>0.2407</td>\n",
" <td>0.06484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>553</th>\n",
" <td>924342</td>\n",
" <td>B</td>\n",
" <td>9.333</td>\n",
" <td>21.94</td>\n",
" <td>59.01</td>\n",
" <td>264.0</td>\n",
" <td>0.09240</td>\n",
" <td>0.05605</td>\n",
" <td>0.039960</td>\n",
" <td>0.012820</td>\n",
" <td>...</td>\n",
" <td>9.845</td>\n",
" <td>25.05</td>\n",
" <td>62.86</td>\n",
" <td>295.8</td>\n",
" <td>0.11030</td>\n",
" <td>0.08298</td>\n",
" <td>0.07993</td>\n",
" <td>0.02564</td>\n",
" <td>0.2435</td>\n",
" <td>0.07393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>554</th>\n",
" <td>924632</td>\n",
" <td>B</td>\n",
" <td>12.880</td>\n",
" <td>28.92</td>\n",
" <td>82.50</td>\n",
" <td>514.3</td>\n",
" <td>0.08123</td>\n",
" <td>0.05824</td>\n",
" <td>0.061950</td>\n",
" <td>0.023430</td>\n",
" <td>...</td>\n",
" <td>13.890</td>\n",
" <td>35.74</td>\n",
" <td>88.84</td>\n",
" <td>595.7</td>\n",
" <td>0.12270</td>\n",
" <td>0.16200</td>\n",
" <td>0.24390</td>\n",
" <td>0.06493</td>\n",
" <td>0.2372</td>\n",
" <td>0.07242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>555</th>\n",
" <td>924934</td>\n",
" <td>B</td>\n",
" <td>10.290</td>\n",
" <td>27.61</td>\n",
" <td>65.67</td>\n",
" <td>321.4</td>\n",
" <td>0.09030</td>\n",
" <td>0.07658</td>\n",
" <td>0.059990</td>\n",
" <td>0.027380</td>\n",
" <td>...</td>\n",
" <td>10.840</td>\n",
" <td>34.91</td>\n",
" <td>69.57</td>\n",
" <td>357.6</td>\n",
" <td>0.13840</td>\n",
" <td>0.17100</td>\n",
" <td>0.20000</td>\n",
" <td>0.09127</td>\n",
" <td>0.2226</td>\n",
" <td>0.08283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>556</th>\n",
" <td>924964</td>\n",
" <td>B</td>\n",
" <td>10.160</td>\n",
" <td>19.59</td>\n",
" <td>64.73</td>\n",
" <td>311.7</td>\n",
" <td>0.10030</td>\n",
" <td>0.07504</td>\n",
" <td>0.005025</td>\n",
" <td>0.011160</td>\n",
" <td>...</td>\n",
" <td>10.650</td>\n",
" <td>22.88</td>\n",
" <td>67.88</td>\n",
" <td>347.3</td>\n",
" <td>0.12650</td>\n",
" <td>0.12000</td>\n",
" <td>0.01005</td>\n",
" <td>0.02232</td>\n",
" <td>0.2262</td>\n",
" <td>0.06742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>557</th>\n",
" <td>925236</td>\n",
" <td>B</td>\n",
" <td>9.423</td>\n",
" <td>27.88</td>\n",
" <td>59.26</td>\n",
" <td>271.3</td>\n",
" <td>0.08123</td>\n",
" <td>0.04971</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>10.490</td>\n",
" <td>34.24</td>\n",
" <td>66.50</td>\n",
" <td>330.6</td>\n",
" <td>0.10730</td>\n",
" <td>0.07158</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.2475</td>\n",
" <td>0.06969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>558</th>\n",
" <td>925277</td>\n",
" <td>B</td>\n",
" <td>14.590</td>\n",
" <td>22.68</td>\n",
" <td>96.39</td>\n",
" <td>657.1</td>\n",
" <td>0.08473</td>\n",
" <td>0.13300</td>\n",
" <td>0.102900</td>\n",
" <td>0.037360</td>\n",
" <td>...</td>\n",
" <td>15.480</td>\n",
" <td>27.27</td>\n",
" <td>105.90</td>\n",
" <td>733.5</td>\n",
" <td>0.10260</td>\n",
" <td>0.31710</td>\n",
" <td>0.36620</td>\n",
" <td>0.11050</td>\n",
" <td>0.2258</td>\n",
" <td>0.08004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>559</th>\n",
" <td>925291</td>\n",
" <td>B</td>\n",
" <td>11.510</td>\n",
" <td>23.93</td>\n",
" <td>74.52</td>\n",
" <td>403.5</td>\n",
" <td>0.09261</td>\n",
" <td>0.10210</td>\n",
" <td>0.111200</td>\n",
" <td>0.041050</td>\n",
" <td>...</td>\n",
" <td>12.480</td>\n",
" <td>37.16</td>\n",
" <td>82.28</td>\n",
" <td>474.2</td>\n",
" <td>0.12980</td>\n",
" <td>0.25170</td>\n",
" <td>0.36300</td>\n",
" <td>0.09653</td>\n",
" <td>0.2112</td>\n",
" <td>0.08732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>560</th>\n",
" <td>925292</td>\n",
" <td>B</td>\n",
" <td>14.050</td>\n",
" <td>27.15</td>\n",
" <td>91.38</td>\n",
" <td>600.4</td>\n",
" <td>0.09929</td>\n",
" <td>0.11260</td>\n",
" <td>0.044620</td>\n",
" <td>0.043040</td>\n",
" <td>...</td>\n",
" <td>15.300</td>\n",
" <td>33.17</td>\n",
" <td>100.20</td>\n",
" <td>706.7</td>\n",
" <td>0.12410</td>\n",
" <td>0.22640</td>\n",
" <td>0.13260</td>\n",
" <td>0.10480</td>\n",
" <td>0.2250</td>\n",
" <td>0.08321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>925311</td>\n",
" <td>B</td>\n",
" <td>11.200</td>\n",
" <td>29.37</td>\n",
" <td>70.67</td>\n",
" <td>386.0</td>\n",
" <td>0.07449</td>\n",
" <td>0.03558</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>11.920</td>\n",
" <td>38.30</td>\n",
" <td>75.19</td>\n",
" <td>439.6</td>\n",
" <td>0.09267</td>\n",
" <td>0.05494</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.1566</td>\n",
" <td>0.05905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>562</th>\n",
" <td>925622</td>\n",
" <td>M</td>\n",
" <td>15.220</td>\n",
" <td>30.62</td>\n",
" <td>103.40</td>\n",
" <td>716.9</td>\n",
" <td>0.10480</td>\n",
" <td>0.20870</td>\n",
" <td>0.255000</td>\n",
" <td>0.094290</td>\n",
" <td>...</td>\n",
" <td>17.520</td>\n",
" <td>42.79</td>\n",
" <td>128.70</td>\n",
" <td>915.0</td>\n",
" <td>0.14170</td>\n",
" <td>0.79170</td>\n",
" <td>1.17000</td>\n",
" <td>0.23560</td>\n",
" <td>0.4089</td>\n",
" <td>0.14090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>563</th>\n",
" <td>926125</td>\n",
" <td>M</td>\n",
" <td>20.920</td>\n",
" <td>25.09</td>\n",
" <td>143.00</td>\n",
" <td>1347.0</td>\n",
" <td>0.10990</td>\n",
" <td>0.22360</td>\n",
" <td>0.317400</td>\n",
" <td>0.147400</td>\n",
" <td>...</td>\n",
" <td>24.290</td>\n",
" <td>29.41</td>\n",
" <td>179.10</td>\n",
" <td>1819.0</td>\n",
" <td>0.14070</td>\n",
" <td>0.41860</td>\n",
" <td>0.65990</td>\n",
" <td>0.25420</td>\n",
" <td>0.2929</td>\n",
" <td>0.09873</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>926424</td>\n",
" <td>M</td>\n",
" <td>21.560</td>\n",
" <td>22.39</td>\n",
" <td>142.00</td>\n",
" <td>1479.0</td>\n",
" <td>0.11100</td>\n",
" <td>0.11590</td>\n",
" <td>0.243900</td>\n",
" <td>0.138900</td>\n",
" <td>...</td>\n",
" <td>25.450</td>\n",
" <td>26.40</td>\n",
" <td>166.10</td>\n",
" <td>2027.0</td>\n",
" <td>0.14100</td>\n",
" <td>0.21130</td>\n",
" <td>0.41070</td>\n",
" <td>0.22160</td>\n",
" <td>0.2060</td>\n",
" <td>0.07115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>926682</td>\n",
" <td>M</td>\n",
" <td>20.130</td>\n",
" <td>28.25</td>\n",
" <td>131.20</td>\n",
" <td>1261.0</td>\n",
" <td>0.09780</td>\n",
" <td>0.10340</td>\n",
" <td>0.144000</td>\n",
" <td>0.097910</td>\n",
" <td>...</td>\n",
" <td>23.690</td>\n",
" <td>38.25</td>\n",
" <td>155.00</td>\n",
" <td>1731.0</td>\n",
" <td>0.11660</td>\n",
" <td>0.19220</td>\n",
" <td>0.32150</td>\n",
" <td>0.16280</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>926954</td>\n",
" <td>M</td>\n",
" <td>16.600</td>\n",
" <td>28.08</td>\n",
" <td>108.30</td>\n",
" <td>858.1</td>\n",
" <td>0.08455</td>\n",
" <td>0.10230</td>\n",
" <td>0.092510</td>\n",
" <td>0.053020</td>\n",
" <td>...</td>\n",
" <td>18.980</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
" <td>1124.0</td>\n",
" <td>0.11390</td>\n",
" <td>0.30940</td>\n",
" <td>0.34030</td>\n",
" <td>0.14180</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>927241</td>\n",
" <td>M</td>\n",
" <td>20.600</td>\n",
" <td>29.33</td>\n",
" <td>140.10</td>\n",
" <td>1265.0</td>\n",
" <td>0.11780</td>\n",
" <td>0.27700</td>\n",
" <td>0.351400</td>\n",
" <td>0.152000</td>\n",
" <td>...</td>\n",
" <td>25.740</td>\n",
" <td>39.42</td>\n",
" <td>184.60</td>\n",
" <td>1821.0</td>\n",
" <td>0.16500</td>\n",
" <td>0.86810</td>\n",
" <td>0.93870</td>\n",
" <td>0.26500</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>92751</td>\n",
" <td>B</td>\n",
" <td>7.760</td>\n",
" <td>24.54</td>\n",
" <td>47.92</td>\n",
" <td>181.0</td>\n",
" <td>0.05263</td>\n",
" <td>0.04362</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>9.456</td>\n",
" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
" <td>0.06444</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>569 rows × 32 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 \\\n",
"0 842302 M 17.990 10.38 122.80 1001.0 0.11840 0.27760 0.300100 \n",
"1 842517 M 20.570 17.77 132.90 1326.0 0.08474 0.07864 0.086900 \n",
"2 84300903 M 19.690 21.25 130.00 1203.0 0.10960 0.15990 0.197400 \n",
"3 84348301 M 11.420 20.38 77.58 386.1 0.14250 0.28390 0.241400 \n",
"4 84358402 M 20.290 14.34 135.10 1297.0 0.10030 0.13280 0.198000 \n",
"5 843786 M 12.450 15.70 82.57 477.1 0.12780 0.17000 0.157800 \n",
"6 844359 M 18.250 19.98 119.60 1040.0 0.09463 0.10900 0.112700 \n",
"7 84458202 M 13.710 20.83 90.20 577.9 0.11890 0.16450 0.093660 \n",
"8 844981 M 13.000 21.82 87.50 519.8 0.12730 0.19320 0.185900 \n",
"9 84501001 M 12.460 24.04 83.97 475.9 0.11860 0.23960 0.227300 \n",
"10 845636 M 16.020 23.24 102.70 797.8 0.08206 0.06669 0.032990 \n",
"11 84610002 M 15.780 17.89 103.60 781.0 0.09710 0.12920 0.099540 \n",
"12 846226 M 19.170 24.80 132.40 1123.0 0.09740 0.24580 0.206500 \n",
"13 846381 M 15.850 23.95 103.70 782.7 0.08401 0.10020 0.099380 \n",
"14 84667401 M 13.730 22.61 93.60 578.3 0.11310 0.22930 0.212800 \n",
"15 84799002 M 14.540 27.54 96.73 658.8 0.11390 0.15950 0.163900 \n",
"16 848406 M 14.680 20.13 94.74 684.5 0.09867 0.07200 0.073950 \n",
"17 84862001 M 16.130 20.68 108.10 798.8 0.11700 0.20220 0.172200 \n",
"18 849014 M 19.810 22.15 130.00 1260.0 0.09831 0.10270 0.147900 \n",
"19 8510426 B 13.540 14.36 87.46 566.3 0.09779 0.08129 0.066640 \n",
"20 8510653 B 13.080 15.71 85.63 520.0 0.10750 0.12700 0.045680 \n",
"21 8510824 B 9.504 12.44 60.34 273.9 0.10240 0.06492 0.029560 \n",
"22 8511133 M 15.340 14.26 102.50 704.4 0.10730 0.21350 0.207700 \n",
"23 851509 M 21.160 23.04 137.20 1404.0 0.09428 0.10220 0.109700 \n",
"24 852552 M 16.650 21.38 110.00 904.6 0.11210 0.14570 0.152500 \n",
"25 852631 M 17.140 16.40 116.00 912.7 0.11860 0.22760 0.222900 \n",
"26 852763 M 14.580 21.53 97.41 644.8 0.10540 0.18680 0.142500 \n",
"27 852781 M 18.610 20.25 122.10 1094.0 0.09440 0.10660 0.149000 \n",
"28 852973 M 15.300 25.27 102.40 732.4 0.10820 0.16970 0.168300 \n",
"29 853201 M 17.570 15.05 115.00 955.1 0.09847 0.11570 0.098750 \n",
".. ... .. ... ... ... ... ... ... ... \n",
"539 921362 B 7.691 25.44 48.34 170.4 0.08668 0.11990 0.092520 \n",
"540 921385 B 11.540 14.44 74.65 402.9 0.09984 0.11200 0.067370 \n",
"541 921386 B 14.470 24.99 95.81 656.4 0.08837 0.12300 0.100900 \n",
"542 921644 B 14.740 25.42 94.70 668.6 0.08275 0.07214 0.041050 \n",
"543 922296 B 13.210 28.06 84.88 538.4 0.08671 0.06877 0.029870 \n",
"544 922297 B 13.870 20.70 89.77 584.8 0.09578 0.10180 0.036880 \n",
"545 922576 B 13.620 23.23 87.19 573.2 0.09246 0.06747 0.029740 \n",
"546 922577 B 10.320 16.35 65.31 324.9 0.09434 0.04994 0.010120 \n",
"547 922840 B 10.260 16.58 65.85 320.8 0.08877 0.08066 0.043580 \n",
"548 923169 B 9.683 19.34 61.05 285.7 0.08491 0.05030 0.023370 \n",
"549 923465 B 10.820 24.21 68.89 361.6 0.08192 0.06602 0.015480 \n",
"550 923748 B 10.860 21.48 68.51 360.5 0.07431 0.04227 0.000000 \n",
"551 923780 B 11.130 22.44 71.49 378.4 0.09566 0.08194 0.048240 \n",
"552 924084 B 12.770 29.43 81.35 507.9 0.08276 0.04234 0.019970 \n",
"553 924342 B 9.333 21.94 59.01 264.0 0.09240 0.05605 0.039960 \n",
"554 924632 B 12.880 28.92 82.50 514.3 0.08123 0.05824 0.061950 \n",
"555 924934 B 10.290 27.61 65.67 321.4 0.09030 0.07658 0.059990 \n",
"556 924964 B 10.160 19.59 64.73 311.7 0.10030 0.07504 0.005025 \n",
"557 925236 B 9.423 27.88 59.26 271.3 0.08123 0.04971 0.000000 \n",
"558 925277 B 14.590 22.68 96.39 657.1 0.08473 0.13300 0.102900 \n",
"559 925291 B 11.510 23.93 74.52 403.5 0.09261 0.10210 0.111200 \n",
"560 925292 B 14.050 27.15 91.38 600.4 0.09929 0.11260 0.044620 \n",
"561 925311 B 11.200 29.37 70.67 386.0 0.07449 0.03558 0.000000 \n",
"562 925622 M 15.220 30.62 103.40 716.9 0.10480 0.20870 0.255000 \n",
"563 926125 M 20.920 25.09 143.00 1347.0 0.10990 0.22360 0.317400 \n",
"564 926424 M 21.560 22.39 142.00 1479.0 0.11100 0.11590 0.243900 \n",
"565 926682 M 20.130 28.25 131.20 1261.0 0.09780 0.10340 0.144000 \n",
"566 926954 M 16.600 28.08 108.30 858.1 0.08455 0.10230 0.092510 \n",
"567 927241 M 20.600 29.33 140.10 1265.0 0.11780 0.27700 0.351400 \n",
"568 92751 B 7.760 24.54 47.92 181.0 0.05263 0.04362 0.000000 \n",
"\n",
" 9 ... 22 23 24 25 26 27 \\\n",
"0 0.147100 ... 25.380 17.33 184.60 2019.0 0.16220 0.66560 \n",
"1 0.070170 ... 24.990 23.41 158.80 1956.0 0.12380 0.18660 \n",
"2 0.127900 ... 23.570 25.53 152.50 1709.0 0.14440 0.42450 \n",
"3 0.105200 ... 14.910 26.50 98.87 567.7 0.20980 0.86630 \n",
"4 0.104300 ... 22.540 16.67 152.20 1575.0 0.13740 0.20500 \n",
"5 0.080890 ... 15.470 23.75 103.40 741.6 0.17910 0.52490 \n",
"6 0.074000 ... 22.880 27.66 153.20 1606.0 0.14420 0.25760 \n",
"7 0.059850 ... 17.060 28.14 110.60 897.0 0.16540 0.36820 \n",
"8 0.093530 ... 15.490 30.73 106.20 739.3 0.17030 0.54010 \n",
"9 0.085430 ... 15.090 40.68 97.65 711.4 0.18530 1.05800 \n",
"10 0.033230 ... 19.190 33.88 123.80 1150.0 0.11810 0.15510 \n",
"11 0.066060 ... 20.420 27.28 136.50 1299.0 0.13960 0.56090 \n",
"12 0.111800 ... 20.960 29.94 151.70 1332.0 0.10370 0.39030 \n",
"13 0.053640 ... 16.840 27.66 112.00 876.5 0.11310 0.19240 \n",
"14 0.080250 ... 15.030 32.01 108.80 697.7 0.16510 0.77250 \n",
"15 0.073640 ... 17.460 37.13 124.10 943.2 0.16780 0.65770 \n",
"16 0.052590 ... 19.070 30.88 123.40 1138.0 0.14640 0.18710 \n",
"17 0.102800 ... 20.960 31.48 136.80 1315.0 0.17890 0.42330 \n",
"18 0.094980 ... 27.320 30.88 186.80 2398.0 0.15120 0.31500 \n",
"19 0.047810 ... 15.110 19.26 99.70 711.2 0.14400 0.17730 \n",
"20 0.031100 ... 14.500 20.49 96.09 630.5 0.13120 0.27760 \n",
"21 0.020760 ... 10.230 15.66 65.13 314.9 0.13240 0.11480 \n",
"22 0.097560 ... 18.070 19.08 125.10 980.9 0.13900 0.59540 \n",
"23 0.086320 ... 29.170 35.59 188.00 2615.0 0.14010 0.26000 \n",
"24 0.091700 ... 26.460 31.56 177.00 2215.0 0.18050 0.35780 \n",
"25 0.140100 ... 22.250 21.40 152.40 1461.0 0.15450 0.39490 \n",
"26 0.087830 ... 17.620 33.21 122.40 896.9 0.15250 0.66430 \n",
"27 0.077310 ... 21.310 27.26 139.90 1403.0 0.13380 0.21170 \n",
"28 0.087510 ... 20.270 36.71 149.30 1269.0 0.16410 0.61100 \n",
"29 0.079530 ... 20.010 19.52 134.90 1227.0 0.12550 0.28120 \n",
".. ... ... ... ... ... ... ... ... \n",
"539 0.013640 ... 8.678 31.89 54.49 223.6 0.15960 0.30640 \n",
"540 0.025940 ... 12.260 19.68 78.78 457.8 0.13450 0.21180 \n",
"541 0.038900 ... 16.220 31.73 113.50 808.9 0.13400 0.42020 \n",
"542 0.030270 ... 16.510 32.29 107.40 826.4 0.10600 0.13760 \n",
"543 0.032750 ... 14.370 37.17 92.48 629.6 0.10720 0.13810 \n",
"544 0.023690 ... 15.050 24.75 99.17 688.6 0.12640 0.20370 \n",
"545 0.024430 ... 15.350 29.09 97.58 729.8 0.12160 0.15170 \n",
"546 0.005495 ... 11.250 21.77 71.12 384.9 0.12850 0.08842 \n",
"547 0.024380 ... 10.830 22.04 71.08 357.4 0.14610 0.22460 \n",
"548 0.009615 ... 10.930 25.59 69.10 364.2 0.11990 0.09546 \n",
"549 0.008160 ... 13.030 31.45 83.90 505.6 0.12040 0.16330 \n",
"550 0.000000 ... 11.660 24.77 74.08 412.3 0.10010 0.07348 \n",
"551 0.022570 ... 12.020 28.26 77.80 436.6 0.10870 0.17820 \n",
"552 0.014990 ... 13.870 36.00 88.10 594.7 0.12340 0.10640 \n",
"553 0.012820 ... 9.845 25.05 62.86 295.8 0.11030 0.08298 \n",
"554 0.023430 ... 13.890 35.74 88.84 595.7 0.12270 0.16200 \n",
"555 0.027380 ... 10.840 34.91 69.57 357.6 0.13840 0.17100 \n",
"556 0.011160 ... 10.650 22.88 67.88 347.3 0.12650 0.12000 \n",
"557 0.000000 ... 10.490 34.24 66.50 330.6 0.10730 0.07158 \n",
"558 0.037360 ... 15.480 27.27 105.90 733.5 0.10260 0.31710 \n",
"559 0.041050 ... 12.480 37.16 82.28 474.2 0.12980 0.25170 \n",
"560 0.043040 ... 15.300 33.17 100.20 706.7 0.12410 0.22640 \n",
"561 0.000000 ... 11.920 38.30 75.19 439.6 0.09267 0.05494 \n",
"562 0.094290 ... 17.520 42.79 128.70 915.0 0.14170 0.79170 \n",
"563 0.147400 ... 24.290 29.41 179.10 1819.0 0.14070 0.41860 \n",
"564 0.138900 ... 25.450 26.40 166.10 2027.0 0.14100 0.21130 \n",
"565 0.097910 ... 23.690 38.25 155.00 1731.0 0.11660 0.19220 \n",
"566 0.053020 ... 18.980 34.12 126.70 1124.0 0.11390 0.30940 \n",
"567 0.152000 ... 25.740 39.42 184.60 1821.0 0.16500 0.86810 \n",
"568 0.000000 ... 9.456 30.37 59.16 268.6 0.08996 0.06444 \n",
"\n",
" 28 29 30 31 \n",
"0 0.71190 0.26540 0.4601 0.11890 \n",
"1 0.24160 0.18600 0.2750 0.08902 \n",
"2 0.45040 0.24300 0.3613 0.08758 \n",
"3 0.68690 0.25750 0.6638 0.17300 \n",
"4 0.40000 0.16250 0.2364 0.07678 \n",
"5 0.53550 0.17410 0.3985 0.12440 \n",
"6 0.37840 0.19320 0.3063 0.08368 \n",
"7 0.26780 0.15560 0.3196 0.11510 \n",
"8 0.53900 0.20600 0.4378 0.10720 \n",
"9 1.10500 0.22100 0.4366 0.20750 \n",
"10 0.14590 0.09975 0.2948 0.08452 \n",
"11 0.39650 0.18100 0.3792 0.10480 \n",
"12 0.36390 0.17670 0.3176 0.10230 \n",
"13 0.23220 0.11190 0.2809 0.06287 \n",
"14 0.69430 0.22080 0.3596 0.14310 \n",
"15 0.70260 0.17120 0.4218 0.13410 \n",
"16 0.29140 0.16090 0.3029 0.08216 \n",
"17 0.47840 0.20730 0.3706 0.11420 \n",
"18 0.53720 0.23880 0.2768 0.07615 \n",
"19 0.23900 0.12880 0.2977 0.07259 \n",
"20 0.18900 0.07283 0.3184 0.08183 \n",
"21 0.08867 0.06227 0.2450 0.07773 \n",
"22 0.63050 0.23930 0.4667 0.09946 \n",
"23 0.31550 0.20090 0.2822 0.07526 \n",
"24 0.46950 0.20950 0.3613 0.09564 \n",
"25 0.38530 0.25500 0.4066 0.10590 \n",
"26 0.55390 0.27010 0.4264 0.12750 \n",
"27 0.34460 0.14900 0.2341 0.07421 \n",
"28 0.63350 0.20240 0.4027 0.09876 \n",
"29 0.24890 0.14560 0.2756 0.07919 \n",
".. ... ... ... ... \n",
"539 0.33930 0.05000 0.2790 0.10660 \n",
"540 0.17970 0.06918 0.2329 0.08134 \n",
"541 0.40400 0.12050 0.3187 0.10230 \n",
"542 0.16110 0.10950 0.2722 0.06956 \n",
"543 0.10620 0.07958 0.2473 0.06443 \n",
"544 0.13770 0.06845 0.2249 0.08492 \n",
"545 0.10490 0.07174 0.2642 0.06953 \n",
"546 0.04384 0.02381 0.2681 0.07399 \n",
"547 0.17830 0.08333 0.2691 0.09479 \n",
"548 0.09350 0.03846 0.2552 0.07920 \n",
"549 0.06194 0.03264 0.3059 0.07626 \n",
"550 0.00000 0.00000 0.2458 0.06592 \n",
"551 0.15640 0.06413 0.3169 0.08032 \n",
"552 0.08653 0.06498 0.2407 0.06484 \n",
"553 0.07993 0.02564 0.2435 0.07393 \n",
"554 0.24390 0.06493 0.2372 0.07242 \n",
"555 0.20000 0.09127 0.2226 0.08283 \n",
"556 0.01005 0.02232 0.2262 0.06742 \n",
"557 0.00000 0.00000 0.2475 0.06969 \n",
"558 0.36620 0.11050 0.2258 0.08004 \n",
"559 0.36300 0.09653 0.2112 0.08732 \n",
"560 0.13260 0.10480 0.2250 0.08321 \n",
"561 0.00000 0.00000 0.1566 0.05905 \n",
"562 1.17000 0.23560 0.4089 0.14090 \n",
"563 0.65990 0.25420 0.2929 0.09873 \n",
"564 0.41070 0.22160 0.2060 0.07115 \n",
"565 0.32150 0.16280 0.2572 0.06637 \n",
"566 0.34030 0.14180 0.2218 0.07820 \n",
"567 0.93870 0.26500 0.4087 0.12400 \n",
"568 0.00000 0.00000 0.2871 0.07039 \n",
"\n",
"[569 rows x 32 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"patient_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Separate the data into feature and target."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = patient_data.drop(1, axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Y = patient_data[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Create and evaluate using cross_val_score and 5 folds.\n",
"- What is the mean accuracy?\n",
"- What is the standard deviation of accuracy?"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#for the model I'll select a random forest, RFs tend to have predictive accuracy\n",
"params = {'n_estimators':500,\n",
" #'n_jobs':3,\n",
" #'max_features':50,\n",
" 'criterion':'gini',\n",
" 'min_samples_split':4,\n",
" 'max_depth':20,\n",
" 'min_samples_leaf':4}\n",
"clf = ensemble.RandomForestClassifier(**params)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"scores = cross_validation.cross_val_score(clf, X, Y, cv = 5, scoring = 'accuracy')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"scores = [ 0.92173913 0.94782609 0.98230088 0.97345133 0.96460177]\n",
"scores mean accuracy= 0.957983839938\n",
"scores standard deviation = 0.0214066240102\n"
]
}
],
"source": [
"print \"scores =\", scores\n",
"print \"scores mean accuracy=\", scores.mean()\n",
"print \"scores standard deviation =\", scores.std()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Get a classification report to identify type 1, type 2 errors.\n",
"- Use train_test_split to run your model once, with a test size of 0.33\n",
"- Make predictions on the test set\n",
"- Compare the predictions to the answers to determine the classification report"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xtrain, xtest, ytrain, ytest = cross_validation.train_test_split(X, Y, test_size=0.33, random_state=0, stratify = Y)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"clf_33 = clf.fit(xtrain, ytrain)\n",
"y_pred = clf_33.predict(xtest)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" B 0.974 0.949 0.961 118\n",
" M 0.918 0.957 0.937 70\n",
"\n",
"avg / total 0.953 0.952 0.952 188\n",
"\n"
]
}
],
"source": [
"report = metrics.classification_report(ytest, y_pred, digits=3)\n",
"print report\n",
"#type 1 error, or false positives, are those instances identified as positive that are in fact negative, i.e. 1 - precision.\n",
"#type 2 error, or false negative, are those instances identified as negative that are in fact nepositive, i.e. 1 - recall\n",
"#Benign (support = 118): Type 1 error rate = 0.026; 3 misidentifications\n",
"# Type 2 error rate = 0.051; 6 missed instances\n",
"#Malignant (support = 70): Type 1 error rate = 0.082; 6 misidentifications\n",
"# Type 2 error rate = 0.043; 3 missed instances"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Scale the data and see if that improves the score."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#for scaling, I'll map the values of each column to the interval [0,1]. Alternatively, we could have done a normalization, i.e. \n",
"#mean = 0, standard deviation/variance = 1\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"scaler = MinMaxScaler()\n",
"\n",
"X_scaled = pandas.DataFrame(scaler.fit_transform(X), columns=X.columns)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th>0</th>\n",
" <th>2</th>\n",
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" <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>10</th>\n",
" <th>...</th>\n",
" <th>22</th>\n",
" <th>23</th>\n",
" <th>24</th>\n",
" <th>25</th>\n",
" <th>26</th>\n",
" <th>27</th>\n",
" <th>28</th>\n",
" <th>29</th>\n",
" <th>30</th>\n",
" <th>31</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000915</td>\n",
" <td>0.521037</td>\n",
" <td>0.022658</td>\n",
" <td>0.545989</td>\n",
" <td>0.363733</td>\n",
" <td>0.593753</td>\n",
" <td>0.792037</td>\n",
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" <td>0.000915</td>\n",
" <td>0.643144</td>\n",
" <td>0.272574</td>\n",
" <td>0.615783</td>\n",
" <td>0.501591</td>\n",
" <td>0.289880</td>\n",
" <td>0.181768</td>\n",
" <td>0.203608</td>\n",
" <td>0.348757</td>\n",
" <td>0.379798</td>\n",
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" <td>0.606901</td>\n",
" <td>0.303571</td>\n",
" <td>0.539818</td>\n",
" <td>0.435214</td>\n",
" <td>0.347553</td>\n",
" <td>0.154563</td>\n",
" <td>0.192971</td>\n",
" <td>0.639175</td>\n",
" <td>0.233590</td>\n",
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" <td>0.092495</td>\n",
" <td>0.601496</td>\n",
" <td>0.390260</td>\n",
" <td>0.595743</td>\n",
" <td>0.449417</td>\n",
" <td>0.514309</td>\n",
" <td>0.431017</td>\n",
" <td>0.462512</td>\n",
" <td>0.635686</td>\n",
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" <td>0.556386</td>\n",
" <td>0.360075</td>\n",
" <td>0.508442</td>\n",
" <td>0.374508</td>\n",
" <td>0.483590</td>\n",
" <td>0.385375</td>\n",
" <td>0.359744</td>\n",
" <td>0.835052</td>\n",
" <td>0.403706</td>\n",
" <td>0.213433</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>0.092547</td>\n",
" <td>0.210090</td>\n",
" <td>0.360839</td>\n",
" <td>0.233501</td>\n",
" <td>0.102906</td>\n",
" <td>0.811321</td>\n",
" <td>0.811361</td>\n",
" <td>0.565604</td>\n",
" <td>0.522863</td>\n",
" <td>0.776263</td>\n",
" <td>...</td>\n",
" <td>0.248310</td>\n",
" <td>0.385928</td>\n",
" <td>0.241347</td>\n",
" <td>0.094008</td>\n",
" <td>0.915472</td>\n",
" <td>0.814012</td>\n",
" <td>0.548642</td>\n",
" <td>0.884880</td>\n",
" <td>1.000000</td>\n",
" <td>0.773711</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>0.092559</td>\n",
" <td>0.629893</td>\n",
" <td>0.156578</td>\n",
" <td>0.630986</td>\n",
" <td>0.489290</td>\n",
" <td>0.430351</td>\n",
" <td>0.347893</td>\n",
" <td>0.463918</td>\n",
" <td>0.518390</td>\n",
" <td>0.378283</td>\n",
" <td>...</td>\n",
" <td>0.519744</td>\n",
" <td>0.123934</td>\n",
" <td>0.506948</td>\n",
" <td>0.341575</td>\n",
" <td>0.437364</td>\n",
" <td>0.172415</td>\n",
" <td>0.319489</td>\n",
" <td>0.558419</td>\n",
" <td>0.157500</td>\n",
" <td>0.142595</td>\n",
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" <th>5</th>\n",
" <td>0.000916</td>\n",
" <td>0.258839</td>\n",
" <td>0.202570</td>\n",
" <td>0.267984</td>\n",
" <td>0.141506</td>\n",
" <td>0.678613</td>\n",
" <td>0.461996</td>\n",
" <td>0.369728</td>\n",
" <td>0.402038</td>\n",
" <td>0.518687</td>\n",
" <td>...</td>\n",
" <td>0.268232</td>\n",
" <td>0.312633</td>\n",
" <td>0.263908</td>\n",
" <td>0.136748</td>\n",
" <td>0.712739</td>\n",
" <td>0.482784</td>\n",
" <td>0.427716</td>\n",
" <td>0.598282</td>\n",
" <td>0.477035</td>\n",
" <td>0.454939</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>0.000917</td>\n",
" <td>0.533343</td>\n",
" <td>0.347311</td>\n",
" <td>0.523875</td>\n",
" <td>0.380276</td>\n",
" <td>0.379164</td>\n",
" <td>0.274891</td>\n",
" <td>0.264058</td>\n",
" <td>0.367793</td>\n",
" <td>0.370707</td>\n",
" <td>...</td>\n",
" <td>0.531839</td>\n",
" <td>0.416844</td>\n",
" <td>0.511928</td>\n",
" <td>0.349194</td>\n",
" <td>0.482269</td>\n",
" <td>0.223448</td>\n",
" <td>0.302236</td>\n",
" <td>0.663918</td>\n",
" <td>0.295289</td>\n",
" <td>0.187853</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.092668</td>\n",
" <td>0.318472</td>\n",
" <td>0.376057</td>\n",
" <td>0.320710</td>\n",
" <td>0.184263</td>\n",
" <td>0.598267</td>\n",
" <td>0.445126</td>\n",
" <td>0.219447</td>\n",
" <td>0.297465</td>\n",
" <td>0.573737</td>\n",
" <td>...</td>\n",
" <td>0.324795</td>\n",
" <td>0.429638</td>\n",
" <td>0.299766</td>\n",
" <td>0.174941</td>\n",
" <td>0.622268</td>\n",
" <td>0.330753</td>\n",
" <td>0.213898</td>\n",
" <td>0.534708</td>\n",
" <td>0.321506</td>\n",
" <td>0.393939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.000918</td>\n",
" <td>0.284869</td>\n",
" <td>0.409537</td>\n",
" <td>0.302052</td>\n",
" <td>0.159618</td>\n",
" <td>0.674099</td>\n",
" <td>0.533157</td>\n",
" <td>0.435567</td>\n",
" <td>0.464861</td>\n",
" <td>0.651515</td>\n",
" <td>...</td>\n",
" <td>0.268943</td>\n",
" <td>0.498667</td>\n",
" <td>0.277852</td>\n",
" <td>0.136183</td>\n",
" <td>0.654626</td>\n",
" <td>0.497531</td>\n",
" <td>0.430511</td>\n",
" <td>0.707904</td>\n",
" <td>0.554504</td>\n",
" <td>0.342123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.092715</td>\n",
" <td>0.259312</td>\n",
" <td>0.484613</td>\n",
" <td>0.277659</td>\n",
" <td>0.140997</td>\n",
" <td>0.595558</td>\n",
" <td>0.675480</td>\n",
" <td>0.532568</td>\n",
" <td>0.424602</td>\n",
" <td>0.489899</td>\n",
" <td>...</td>\n",
" <td>0.254714</td>\n",
" <td>0.763859</td>\n",
" <td>0.235271</td>\n",
" <td>0.129326</td>\n",
" <td>0.753682</td>\n",
" <td>1.000000</td>\n",
" <td>0.882588</td>\n",
" <td>0.759450</td>\n",
" <td>0.552139</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.000918</td>\n",
" <td>0.427801</td>\n",
" <td>0.457558</td>\n",
" <td>0.407090</td>\n",
" <td>0.277540</td>\n",
" <td>0.265686</td>\n",
" <td>0.145114</td>\n",
" <td>0.077296</td>\n",
" <td>0.165159</td>\n",
" <td>0.236364</td>\n",
" <td>...</td>\n",
" <td>0.400569</td>\n",
" <td>0.582623</td>\n",
" <td>0.365506</td>\n",
" <td>0.237122</td>\n",
" <td>0.309912</td>\n",
" <td>0.124002</td>\n",
" <td>0.116534</td>\n",
" <td>0.342784</td>\n",
" <td>0.272620</td>\n",
" <td>0.193362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.092835</td>\n",
" <td>0.416442</td>\n",
" <td>0.276632</td>\n",
" <td>0.413309</td>\n",
" <td>0.270414</td>\n",
" <td>0.401462</td>\n",
" <td>0.336850</td>\n",
" <td>0.233224</td>\n",
" <td>0.328330</td>\n",
" <td>0.394949</td>\n",
" <td>...</td>\n",
" <td>0.444326</td>\n",
" <td>0.406716</td>\n",
" <td>0.428756</td>\n",
" <td>0.273742</td>\n",
" <td>0.451892</td>\n",
" <td>0.517711</td>\n",
" <td>0.316693</td>\n",
" <td>0.621993</td>\n",
" <td>0.438991</td>\n",
" <td>0.326381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.000919</td>\n",
" <td>0.576885</td>\n",
" <td>0.510315</td>\n",
" <td>0.612328</td>\n",
" <td>0.415483</td>\n",
" <td>0.404171</td>\n",
" <td>0.694497</td>\n",
" <td>0.483833</td>\n",
" <td>0.555666</td>\n",
" <td>0.675253</td>\n",
" <td>...</td>\n",
" <td>0.463536</td>\n",
" <td>0.477612</td>\n",
" <td>0.504457</td>\n",
" <td>0.281852</td>\n",
" <td>0.214819</td>\n",
" <td>0.352194</td>\n",
" <td>0.290655</td>\n",
" <td>0.607216</td>\n",
" <td>0.317564</td>\n",
" <td>0.309983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.000919</td>\n",
" <td>0.419755</td>\n",
" <td>0.481569</td>\n",
" <td>0.414000</td>\n",
" <td>0.271135</td>\n",
" <td>0.283290</td>\n",
" <td>0.247899</td>\n",
" <td>0.232849</td>\n",
" <td>0.266600</td>\n",
" <td>0.397475</td>\n",
" <td>...</td>\n",
" <td>0.316969</td>\n",
" <td>0.416844</td>\n",
" <td>0.306738</td>\n",
" <td>0.169903</td>\n",
" <td>0.276894</td>\n",
" <td>0.160191</td>\n",
" <td>0.185463</td>\n",
" <td>0.384536</td>\n",
" <td>0.245220</td>\n",
" <td>0.051358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.092898</td>\n",
" <td>0.319419</td>\n",
" <td>0.436253</td>\n",
" <td>0.344206</td>\n",
" <td>0.184433</td>\n",
" <td>0.545906</td>\n",
" <td>0.643887</td>\n",
" <td>0.498594</td>\n",
" <td>0.398857</td>\n",
" <td>0.509596</td>\n",
" <td>...</td>\n",
" <td>0.252579</td>\n",
" <td>0.532783</td>\n",
" <td>0.290801</td>\n",
" <td>0.125959</td>\n",
" <td>0.620287</td>\n",
" <td>0.723006</td>\n",
" <td>0.554553</td>\n",
" <td>0.758763</td>\n",
" <td>0.400355</td>\n",
" <td>0.577594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.093042</td>\n",
" <td>0.357755</td>\n",
" <td>0.602976</td>\n",
" <td>0.365835</td>\n",
" <td>0.218579</td>\n",
" <td>0.553128</td>\n",
" <td>0.429790</td>\n",
" <td>0.384021</td>\n",
" <td>0.366004</td>\n",
" <td>0.627778</td>\n",
" <td>...</td>\n",
" <td>0.339025</td>\n",
" <td>0.669243</td>\n",
" <td>0.367000</td>\n",
" <td>0.186296</td>\n",
" <td>0.638117</td>\n",
" <td>0.611627</td>\n",
" <td>0.561182</td>\n",
" <td>0.588316</td>\n",
" <td>0.522965</td>\n",
" <td>0.518562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.000921</td>\n",
" <td>0.364381</td>\n",
" <td>0.352384</td>\n",
" <td>0.352083</td>\n",
" <td>0.229480</td>\n",
" <td>0.415636</td>\n",
" <td>0.161401</td>\n",
" <td>0.173266</td>\n",
" <td>0.261382</td>\n",
" <td>0.265657</td>\n",
" <td>...</td>\n",
" <td>0.396300</td>\n",
" <td>0.502665</td>\n",
" <td>0.363514</td>\n",
" <td>0.234172</td>\n",
" <td>0.496797</td>\n",
" <td>0.155048</td>\n",
" <td>0.232748</td>\n",
" <td>0.552921</td>\n",
" <td>0.288587</td>\n",
" <td>0.177883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.093111</td>\n",
" <td>0.433007</td>\n",
" <td>0.370984</td>\n",
" <td>0.444406</td>\n",
" <td>0.277964</td>\n",
" <td>0.581114</td>\n",
" <td>0.560763</td>\n",
" <td>0.403468</td>\n",
" <td>0.510934</td>\n",
" <td>0.557576</td>\n",
" <td>...</td>\n",
" <td>0.463536</td>\n",
" <td>0.518657</td>\n",
" <td>0.430251</td>\n",
" <td>0.277674</td>\n",
" <td>0.711418</td>\n",
" <td>0.384211</td>\n",
" <td>0.382109</td>\n",
" <td>0.712371</td>\n",
" <td>0.422038</td>\n",
" <td>0.388036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.000922</td>\n",
" <td>0.607175</td>\n",
" <td>0.420697</td>\n",
" <td>0.595743</td>\n",
" <td>0.473595</td>\n",
" <td>0.412386</td>\n",
" <td>0.255567</td>\n",
" <td>0.346532</td>\n",
" <td>0.472068</td>\n",
" <td>0.263636</td>\n",
" <td>...</td>\n",
" <td>0.689790</td>\n",
" <td>0.502665</td>\n",
" <td>0.679267</td>\n",
" <td>0.543846</td>\n",
" <td>0.528495</td>\n",
" <td>0.279138</td>\n",
" <td>0.429073</td>\n",
" <td>0.820619</td>\n",
" <td>0.237138</td>\n",
" <td>0.138463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.009329</td>\n",
" <td>0.310426</td>\n",
" <td>0.157254</td>\n",
" <td>0.301776</td>\n",
" <td>0.179343</td>\n",
" <td>0.407692</td>\n",
" <td>0.189896</td>\n",
" <td>0.156139</td>\n",
" <td>0.237624</td>\n",
" <td>0.416667</td>\n",
" <td>...</td>\n",
" <td>0.255425</td>\n",
" <td>0.192964</td>\n",
" <td>0.245480</td>\n",
" <td>0.129276</td>\n",
" <td>0.480948</td>\n",
" <td>0.145540</td>\n",
" <td>0.190895</td>\n",
" <td>0.442612</td>\n",
" <td>0.278336</td>\n",
" <td>0.115112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.009329</td>\n",
" <td>0.288655</td>\n",
" <td>0.202908</td>\n",
" <td>0.289130</td>\n",
" <td>0.159703</td>\n",
" <td>0.495351</td>\n",
" <td>0.330102</td>\n",
" <td>0.107029</td>\n",
" <td>0.154573</td>\n",
" <td>0.458081</td>\n",
" <td>...</td>\n",
" <td>0.233725</td>\n",
" <td>0.225746</td>\n",
" <td>0.227501</td>\n",
" <td>0.109443</td>\n",
" <td>0.396421</td>\n",
" <td>0.242852</td>\n",
" <td>0.150958</td>\n",
" <td>0.250275</td>\n",
" <td>0.319141</td>\n",
" <td>0.175718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.009330</td>\n",
" <td>0.119409</td>\n",
" <td>0.092323</td>\n",
" <td>0.114367</td>\n",
" <td>0.055313</td>\n",
" <td>0.449309</td>\n",
" <td>0.139685</td>\n",
" <td>0.069260</td>\n",
" <td>0.103181</td>\n",
" <td>0.381313</td>\n",
" <td>...</td>\n",
" <td>0.081821</td>\n",
" <td>0.097015</td>\n",
" <td>0.073310</td>\n",
" <td>0.031877</td>\n",
" <td>0.404345</td>\n",
" <td>0.084903</td>\n",
" <td>0.070823</td>\n",
" <td>0.213986</td>\n",
" <td>0.174453</td>\n",
" <td>0.148826</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.009330</td>\n",
" <td>0.395617</td>\n",
" <td>0.153872</td>\n",
" <td>0.405708</td>\n",
" <td>0.237922</td>\n",
" <td>0.493545</td>\n",
" <td>0.595424</td>\n",
" <td>0.486645</td>\n",
" <td>0.484891</td>\n",
" <td>0.737879</td>\n",
" <td>...</td>\n",
" <td>0.360726</td>\n",
" <td>0.188166</td>\n",
" <td>0.371981</td>\n",
" <td>0.195561</td>\n",
" <td>0.447930</td>\n",
" <td>0.551183</td>\n",
" <td>0.503594</td>\n",
" <td>0.822337</td>\n",
" <td>0.611473</td>\n",
" <td>0.291355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.000925</td>\n",
" <td>0.671068</td>\n",
" <td>0.450795</td>\n",
" <td>0.645498</td>\n",
" <td>0.534677</td>\n",
" <td>0.376004</td>\n",
" <td>0.254033</td>\n",
" <td>0.257029</td>\n",
" <td>0.429026</td>\n",
" <td>0.358081</td>\n",
" <td>...</td>\n",
" <td>0.755603</td>\n",
" <td>0.628198</td>\n",
" <td>0.685243</td>\n",
" <td>0.597179</td>\n",
" <td>0.455194</td>\n",
" <td>0.225776</td>\n",
" <td>0.251997</td>\n",
" <td>0.690378</td>\n",
" <td>0.247782</td>\n",
" <td>0.132625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.000926</td>\n",
" <td>0.457617</td>\n",
" <td>0.394657</td>\n",
" <td>0.457536</td>\n",
" <td>0.322842</td>\n",
" <td>0.536878</td>\n",
" <td>0.387461</td>\n",
" <td>0.357310</td>\n",
" <td>0.455765</td>\n",
" <td>0.472222</td>\n",
" <td>...</td>\n",
" <td>0.659196</td>\n",
" <td>0.520789</td>\n",
" <td>0.630460</td>\n",
" <td>0.498869</td>\n",
" <td>0.721984</td>\n",
" <td>0.320662</td>\n",
" <td>0.375000</td>\n",
" <td>0.719931</td>\n",
" <td>0.403706</td>\n",
" <td>0.266299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.000926</td>\n",
" <td>0.480808</td>\n",
" <td>0.226243</td>\n",
" <td>0.498998</td>\n",
" <td>0.326278</td>\n",
" <td>0.595558</td>\n",
" <td>0.638672</td>\n",
" <td>0.522259</td>\n",
" <td>0.696322</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>0.509427</td>\n",
" <td>0.250000</td>\n",
" <td>0.507944</td>\n",
" <td>0.313557</td>\n",
" <td>0.550287</td>\n",
" <td>0.356657</td>\n",
" <td>0.307748</td>\n",
" <td>0.876289</td>\n",
" <td>0.493002</td>\n",
" <td>0.333596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.000926</td>\n",
" <td>0.359648</td>\n",
" <td>0.399729</td>\n",
" <td>0.370534</td>\n",
" <td>0.212641</td>\n",
" <td>0.476393</td>\n",
" <td>0.513527</td>\n",
" <td>0.333880</td>\n",
" <td>0.436531</td>\n",
" <td>0.602020</td>\n",
" <td>...</td>\n",
" <td>0.344717</td>\n",
" <td>0.564765</td>\n",
" <td>0.358534</td>\n",
" <td>0.174916</td>\n",
" <td>0.537080</td>\n",
" <td>0.618030</td>\n",
" <td>0.442412</td>\n",
" <td>0.928179</td>\n",
" <td>0.532032</td>\n",
" <td>0.475272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.000926</td>\n",
" <td>0.550381</td>\n",
" <td>0.356442</td>\n",
" <td>0.541151</td>\n",
" <td>0.403181</td>\n",
" <td>0.377088</td>\n",
" <td>0.267530</td>\n",
" <td>0.349110</td>\n",
" <td>0.384245</td>\n",
" <td>0.321717</td>\n",
" <td>...</td>\n",
" <td>0.475987</td>\n",
" <td>0.406183</td>\n",
" <td>0.445690</td>\n",
" <td>0.299302</td>\n",
" <td>0.413590</td>\n",
" <td>0.178916</td>\n",
" <td>0.275240</td>\n",
" <td>0.512027</td>\n",
" <td>0.152967</td>\n",
" <td>0.125738</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.000926</td>\n",
" <td>0.393724</td>\n",
" <td>0.526209</td>\n",
" <td>0.405017</td>\n",
" <td>0.249799</td>\n",
" <td>0.501670</td>\n",
" <td>0.461076</td>\n",
" <td>0.394330</td>\n",
" <td>0.434940</td>\n",
" <td>0.437374</td>\n",
" <td>...</td>\n",
" <td>0.438990</td>\n",
" <td>0.658049</td>\n",
" <td>0.492505</td>\n",
" <td>0.266368</td>\n",
" <td>0.613683</td>\n",
" <td>0.566318</td>\n",
" <td>0.505990</td>\n",
" <td>0.695533</td>\n",
" <td>0.485314</td>\n",
" <td>0.286764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.000927</td>\n",
" <td>0.501160</td>\n",
" <td>0.180588</td>\n",
" <td>0.492088</td>\n",
" <td>0.344263</td>\n",
" <td>0.413830</td>\n",
" <td>0.295442</td>\n",
" <td>0.231373</td>\n",
" <td>0.395278</td>\n",
" <td>0.342929</td>\n",
" <td>...</td>\n",
" <td>0.429740</td>\n",
" <td>0.199893</td>\n",
" <td>0.420788</td>\n",
" <td>0.256046</td>\n",
" <td>0.358780</td>\n",
" <td>0.246345</td>\n",
" <td>0.198802</td>\n",
" <td>0.500344</td>\n",
" <td>0.234772</td>\n",
" <td>0.158402</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>539</th>\n",
" <td>0.001002</td>\n",
" <td>0.033603</td>\n",
" <td>0.531958</td>\n",
" <td>0.031442</td>\n",
" <td>0.011410</td>\n",
" <td>0.307394</td>\n",
" <td>0.308325</td>\n",
" <td>0.216776</td>\n",
" <td>0.067793</td>\n",
" <td>0.493434</td>\n",
" <td>...</td>\n",
" <td>0.026610</td>\n",
" <td>0.529584</td>\n",
" <td>0.020320</td>\n",
" <td>0.009438</td>\n",
" <td>0.583966</td>\n",
" <td>0.270794</td>\n",
" <td>0.271006</td>\n",
" <td>0.171821</td>\n",
" <td>0.241474</td>\n",
" <td>0.338187</td>\n",
" </tr>\n",
" <tr>\n",
" <th>540</th>\n",
" <td>0.001002</td>\n",
" <td>0.215770</td>\n",
" <td>0.159959</td>\n",
" <td>0.213254</td>\n",
" <td>0.110032</td>\n",
" <td>0.426198</td>\n",
" <td>0.284093</td>\n",
" <td>0.157849</td>\n",
" <td>0.128926</td>\n",
" <td>0.382828</td>\n",
" <td>...</td>\n",
" <td>0.154038</td>\n",
" <td>0.204158</td>\n",
" <td>0.141292</td>\n",
" <td>0.066998</td>\n",
" <td>0.418213</td>\n",
" <td>0.179013</td>\n",
" <td>0.143530</td>\n",
" <td>0.237732</td>\n",
" <td>0.150601</td>\n",
" <td>0.172504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>541</th>\n",
" <td>0.001002</td>\n",
" <td>0.354442</td>\n",
" <td>0.516740</td>\n",
" <td>0.359478</td>\n",
" <td>0.217561</td>\n",
" <td>0.322651</td>\n",
" <td>0.317833</td>\n",
" <td>0.236410</td>\n",
" <td>0.193340</td>\n",
" <td>0.410101</td>\n",
" <td>...</td>\n",
" <td>0.294913</td>\n",
" <td>0.525320</td>\n",
" <td>0.314209</td>\n",
" <td>0.153288</td>\n",
" <td>0.414911</td>\n",
" <td>0.381203</td>\n",
" <td>0.322684</td>\n",
" <td>0.414089</td>\n",
" <td>0.319732</td>\n",
" <td>0.309983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>542</th>\n",
" <td>0.001002</td>\n",
" <td>0.367220</td>\n",
" <td>0.531282</td>\n",
" <td>0.351807</td>\n",
" <td>0.222736</td>\n",
" <td>0.271915</td>\n",
" <td>0.161831</td>\n",
" <td>0.096181</td>\n",
" <td>0.150447</td>\n",
" <td>0.393939</td>\n",
" <td>...</td>\n",
" <td>0.305229</td>\n",
" <td>0.540245</td>\n",
" <td>0.283829</td>\n",
" <td>0.157589</td>\n",
" <td>0.230007</td>\n",
" <td>0.107023</td>\n",
" <td>0.128674</td>\n",
" <td>0.376289</td>\n",
" <td>0.228070</td>\n",
" <td>0.095238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>543</th>\n",
" <td>0.001003</td>\n",
" <td>0.294808</td>\n",
" <td>0.620561</td>\n",
" <td>0.283947</td>\n",
" <td>0.167508</td>\n",
" <td>0.307665</td>\n",
" <td>0.151494</td>\n",
" <td>0.069986</td>\n",
" <td>0.162773</td>\n",
" <td>0.286869</td>\n",
" <td>...</td>\n",
" <td>0.229100</td>\n",
" <td>0.670309</td>\n",
" <td>0.209522</td>\n",
" <td>0.109221</td>\n",
" <td>0.237932</td>\n",
" <td>0.107508</td>\n",
" <td>0.084824</td>\n",
" <td>0.273471</td>\n",
" <td>0.178987</td>\n",
" <td>0.061590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>544</th>\n",
" <td>0.001003</td>\n",
" <td>0.326045</td>\n",
" <td>0.371660</td>\n",
" <td>0.317739</td>\n",
" <td>0.187190</td>\n",
" <td>0.389546</td>\n",
" <td>0.252807</td>\n",
" <td>0.086410</td>\n",
" <td>0.117744</td>\n",
" <td>0.282828</td>\n",
" <td>...</td>\n",
" <td>0.253291</td>\n",
" <td>0.339286</td>\n",
" <td>0.242841</td>\n",
" <td>0.123722</td>\n",
" <td>0.364723</td>\n",
" <td>0.171154</td>\n",
" <td>0.109984</td>\n",
" <td>0.235223</td>\n",
" <td>0.134831</td>\n",
" <td>0.195986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>545</th>\n",
" <td>0.001003</td>\n",
" <td>0.314213</td>\n",
" <td>0.457220</td>\n",
" <td>0.299910</td>\n",
" <td>0.182269</td>\n",
" <td>0.359574</td>\n",
" <td>0.147506</td>\n",
" <td>0.069681</td>\n",
" <td>0.121421</td>\n",
" <td>0.305051</td>\n",
" <td>...</td>\n",
" <td>0.263963</td>\n",
" <td>0.454957</td>\n",
" <td>0.234922</td>\n",
" <td>0.133848</td>\n",
" <td>0.333025</td>\n",
" <td>0.120703</td>\n",
" <td>0.083786</td>\n",
" <td>0.246529</td>\n",
" <td>0.212300</td>\n",
" <td>0.095041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>546</th>\n",
" <td>0.001003</td>\n",
" <td>0.158029</td>\n",
" <td>0.224552</td>\n",
" <td>0.148711</td>\n",
" <td>0.076946</td>\n",
" <td>0.376546</td>\n",
" <td>0.093737</td>\n",
" <td>0.023711</td>\n",
" <td>0.027311</td>\n",
" <td>0.416667</td>\n",
" <td>...</td>\n",
" <td>0.118107</td>\n",
" <td>0.259861</td>\n",
" <td>0.103143</td>\n",
" <td>0.049081</td>\n",
" <td>0.378591</td>\n",
" <td>0.059309</td>\n",
" <td>0.035016</td>\n",
" <td>0.081821</td>\n",
" <td>0.219988</td>\n",
" <td>0.124295</td>\n",
" </tr>\n",
" <tr>\n",
" <th>547</th>\n",
" <td>0.001003</td>\n",
" <td>0.155190</td>\n",
" <td>0.232330</td>\n",
" <td>0.152443</td>\n",
" <td>0.075207</td>\n",
" <td>0.326262</td>\n",
" <td>0.187964</td>\n",
" <td>0.102109</td>\n",
" <td>0.121173</td>\n",
" <td>0.307576</td>\n",
" <td>...</td>\n",
" <td>0.103166</td>\n",
" <td>0.267058</td>\n",
" <td>0.102943</td>\n",
" <td>0.042322</td>\n",
" <td>0.494816</td>\n",
" <td>0.191431</td>\n",
" <td>0.142412</td>\n",
" <td>0.286357</td>\n",
" <td>0.221959</td>\n",
" <td>0.260724</td>\n",
" </tr>\n",
" <tr>\n",
" <th>548</th>\n",
" <td>0.001003</td>\n",
" <td>0.127881</td>\n",
" <td>0.325668</td>\n",
" <td>0.119273</td>\n",
" <td>0.060318</td>\n",
" <td>0.291415</td>\n",
" <td>0.094841</td>\n",
" <td>0.054756</td>\n",
" <td>0.047788</td>\n",
" <td>0.262626</td>\n",
" <td>...</td>\n",
" <td>0.106724</td>\n",
" <td>0.361674</td>\n",
" <td>0.093082</td>\n",
" <td>0.043993</td>\n",
" <td>0.321799</td>\n",
" <td>0.066139</td>\n",
" <td>0.074681</td>\n",
" <td>0.132165</td>\n",
" <td>0.194559</td>\n",
" <td>0.158468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>549</th>\n",
" <td>0.001004</td>\n",
" <td>0.181693</td>\n",
" <td>0.490362</td>\n",
" <td>0.173450</td>\n",
" <td>0.092513</td>\n",
" <td>0.264422</td>\n",
" <td>0.143059</td>\n",
" <td>0.036270</td>\n",
" <td>0.040557</td>\n",
" <td>0.462626</td>\n",
" <td>...</td>\n",
" <td>0.181430</td>\n",
" <td>0.517857</td>\n",
" <td>0.166791</td>\n",
" <td>0.078746</td>\n",
" <td>0.325101</td>\n",
" <td>0.131958</td>\n",
" <td>0.049473</td>\n",
" <td>0.112165</td>\n",
" <td>0.294500</td>\n",
" <td>0.139184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>550</th>\n",
" <td>0.001004</td>\n",
" <td>0.183587</td>\n",
" <td>0.398039</td>\n",
" <td>0.170824</td>\n",
" <td>0.092047</td>\n",
" <td>0.195721</td>\n",
" <td>0.070210</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.303535</td>\n",
" <td>...</td>\n",
" <td>0.132693</td>\n",
" <td>0.339819</td>\n",
" <td>0.117884</td>\n",
" <td>0.055815</td>\n",
" <td>0.191045</td>\n",
" <td>0.044814</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.176030</td>\n",
" <td>0.071363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>551</th>\n",
" <td>0.001004</td>\n",
" <td>0.196365</td>\n",
" <td>0.430504</td>\n",
" <td>0.191417</td>\n",
" <td>0.099639</td>\n",
" <td>0.388463</td>\n",
" <td>0.191890</td>\n",
" <td>0.113027</td>\n",
" <td>0.112177</td>\n",
" <td>0.489899</td>\n",
" <td>...</td>\n",
" <td>0.145500</td>\n",
" <td>0.432836</td>\n",
" <td>0.136411</td>\n",
" <td>0.061787</td>\n",
" <td>0.247837</td>\n",
" <td>0.146414</td>\n",
" <td>0.124920</td>\n",
" <td>0.220378</td>\n",
" <td>0.316184</td>\n",
" <td>0.165814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>552</th>\n",
" <td>0.001005</td>\n",
" <td>0.273984</td>\n",
" <td>0.666892</td>\n",
" <td>0.259554</td>\n",
" <td>0.154571</td>\n",
" <td>0.272005</td>\n",
" <td>0.070425</td>\n",
" <td>0.046790</td>\n",
" <td>0.074503</td>\n",
" <td>0.241919</td>\n",
" <td>...</td>\n",
" <td>0.211313</td>\n",
" <td>0.639126</td>\n",
" <td>0.187709</td>\n",
" <td>0.100644</td>\n",
" <td>0.344912</td>\n",
" <td>0.076753</td>\n",
" <td>0.069113</td>\n",
" <td>0.223299</td>\n",
" <td>0.165977</td>\n",
" <td>0.064279</td>\n",
" </tr>\n",
" <tr>\n",
" <th>553</th>\n",
" <td>0.001005</td>\n",
" <td>0.111316</td>\n",
" <td>0.413595</td>\n",
" <td>0.105176</td>\n",
" <td>0.051113</td>\n",
" <td>0.359032</td>\n",
" <td>0.112478</td>\n",
" <td>0.093627</td>\n",
" <td>0.063718</td>\n",
" <td>0.319192</td>\n",
" <td>...</td>\n",
" <td>0.068125</td>\n",
" <td>0.347281</td>\n",
" <td>0.062005</td>\n",
" <td>0.027182</td>\n",
" <td>0.258403</td>\n",
" <td>0.054031</td>\n",
" <td>0.063842</td>\n",
" <td>0.088110</td>\n",
" <td>0.171496</td>\n",
" <td>0.123901</td>\n",
" </tr>\n",
" <tr>\n",
" <th>554</th>\n",
" <td>0.001005</td>\n",
" <td>0.279190</td>\n",
" <td>0.649645</td>\n",
" <td>0.267501</td>\n",
" <td>0.157285</td>\n",
" <td>0.258193</td>\n",
" <td>0.119195</td>\n",
" <td>0.145150</td>\n",
" <td>0.116451</td>\n",
" <td>0.255556</td>\n",
" <td>...</td>\n",
" <td>0.212024</td>\n",
" <td>0.632196</td>\n",
" <td>0.191394</td>\n",
" <td>0.100890</td>\n",
" <td>0.340289</td>\n",
" <td>0.130696</td>\n",
" <td>0.194808</td>\n",
" <td>0.223127</td>\n",
" <td>0.159077</td>\n",
" <td>0.113997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>555</th>\n",
" <td>0.001005</td>\n",
" <td>0.156609</td>\n",
" <td>0.605343</td>\n",
" <td>0.151199</td>\n",
" <td>0.075461</td>\n",
" <td>0.340074</td>\n",
" <td>0.175449</td>\n",
" <td>0.140558</td>\n",
" <td>0.136083</td>\n",
" <td>0.269192</td>\n",
" <td>...</td>\n",
" <td>0.103522</td>\n",
" <td>0.610075</td>\n",
" <td>0.095423</td>\n",
" <td>0.042371</td>\n",
" <td>0.443968</td>\n",
" <td>0.139428</td>\n",
" <td>0.159744</td>\n",
" <td>0.313643</td>\n",
" <td>0.130298</td>\n",
" <td>0.182277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>556</th>\n",
" <td>0.001005</td>\n",
" <td>0.150457</td>\n",
" <td>0.334122</td>\n",
" <td>0.144703</td>\n",
" <td>0.071347</td>\n",
" <td>0.430351</td>\n",
" <td>0.170726</td>\n",
" <td>0.011774</td>\n",
" <td>0.055467</td>\n",
" <td>0.369192</td>\n",
" <td>...</td>\n",
" <td>0.096763</td>\n",
" <td>0.289446</td>\n",
" <td>0.087006</td>\n",
" <td>0.039840</td>\n",
" <td>0.365383</td>\n",
" <td>0.089948</td>\n",
" <td>0.008027</td>\n",
" <td>0.076701</td>\n",
" <td>0.137394</td>\n",
" <td>0.081202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>557</th>\n",
" <td>0.001006</td>\n",
" <td>0.115576</td>\n",
" <td>0.614474</td>\n",
" <td>0.106903</td>\n",
" <td>0.054210</td>\n",
" <td>0.258193</td>\n",
" <td>0.093031</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.344444</td>\n",
" <td>...</td>\n",
" <td>0.091071</td>\n",
" <td>0.592217</td>\n",
" <td>0.080133</td>\n",
" <td>0.035735</td>\n",
" <td>0.238592</td>\n",
" <td>0.042970</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.179381</td>\n",
" <td>0.096091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>558</th>\n",
" <td>0.001006</td>\n",
" <td>0.360121</td>\n",
" <td>0.438620</td>\n",
" <td>0.363486</td>\n",
" <td>0.217858</td>\n",
" <td>0.289790</td>\n",
" <td>0.348506</td>\n",
" <td>0.241097</td>\n",
" <td>0.185686</td>\n",
" <td>0.198990</td>\n",
" <td>...</td>\n",
" <td>0.268588</td>\n",
" <td>0.406450</td>\n",
" <td>0.276358</td>\n",
" <td>0.134757</td>\n",
" <td>0.207555</td>\n",
" <td>0.281175</td>\n",
" <td>0.292492</td>\n",
" <td>0.379725</td>\n",
" <td>0.136606</td>\n",
" <td>0.163977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>559</th>\n",
" <td>0.001006</td>\n",
" <td>0.214350</td>\n",
" <td>0.480893</td>\n",
" <td>0.212356</td>\n",
" <td>0.110286</td>\n",
" <td>0.360928</td>\n",
" <td>0.253727</td>\n",
" <td>0.260544</td>\n",
" <td>0.204026</td>\n",
" <td>0.165657</td>\n",
" <td>...</td>\n",
" <td>0.161864</td>\n",
" <td>0.670043</td>\n",
" <td>0.158723</td>\n",
" <td>0.071028</td>\n",
" <td>0.387176</td>\n",
" <td>0.217724</td>\n",
" <td>0.289936</td>\n",
" <td>0.331718</td>\n",
" <td>0.107826</td>\n",
" <td>0.211728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>560</th>\n",
" <td>0.001006</td>\n",
" <td>0.334564</td>\n",
" <td>0.589787</td>\n",
" <td>0.328865</td>\n",
" <td>0.193807</td>\n",
" <td>0.421233</td>\n",
" <td>0.285933</td>\n",
" <td>0.104545</td>\n",
" <td>0.213917</td>\n",
" <td>0.240909</td>\n",
" <td>...</td>\n",
" <td>0.262184</td>\n",
" <td>0.563699</td>\n",
" <td>0.247971</td>\n",
" <td>0.128170</td>\n",
" <td>0.349534</td>\n",
" <td>0.193178</td>\n",
" <td>0.105911</td>\n",
" <td>0.360137</td>\n",
" <td>0.135029</td>\n",
" <td>0.184770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>0.001006</td>\n",
" <td>0.199678</td>\n",
" <td>0.664863</td>\n",
" <td>0.185751</td>\n",
" <td>0.102863</td>\n",
" <td>0.197346</td>\n",
" <td>0.049690</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.141942</td>\n",
" <td>0.700426</td>\n",
" <td>0.123413</td>\n",
" <td>0.062525</td>\n",
" <td>0.141980</td>\n",
" <td>0.026826</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000197</td>\n",
" <td>0.026302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>562</th>\n",
" <td>0.001006</td>\n",
" <td>0.389938</td>\n",
" <td>0.707136</td>\n",
" <td>0.411927</td>\n",
" <td>0.243224</td>\n",
" <td>0.470976</td>\n",
" <td>0.580701</td>\n",
" <td>0.597470</td>\n",
" <td>0.468638</td>\n",
" <td>0.539394</td>\n",
" <td>...</td>\n",
" <td>0.341160</td>\n",
" <td>0.820096</td>\n",
" <td>0.389910</td>\n",
" <td>0.179365</td>\n",
" <td>0.465760</td>\n",
" <td>0.741634</td>\n",
" <td>0.934505</td>\n",
" <td>0.809622</td>\n",
" <td>0.497536</td>\n",
" <td>0.563164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>563</th>\n",
" <td>0.001007</td>\n",
" <td>0.659709</td>\n",
" <td>0.520122</td>\n",
" <td>0.685578</td>\n",
" <td>0.510498</td>\n",
" <td>0.517017</td>\n",
" <td>0.626403</td>\n",
" <td>0.743674</td>\n",
" <td>0.732604</td>\n",
" <td>0.550000</td>\n",
" <td>...</td>\n",
" <td>0.581999</td>\n",
" <td>0.463486</td>\n",
" <td>0.640918</td>\n",
" <td>0.401543</td>\n",
" <td>0.459156</td>\n",
" <td>0.379651</td>\n",
" <td>0.527077</td>\n",
" <td>0.873540</td>\n",
" <td>0.268874</td>\n",
" <td>0.286567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>0.001007</td>\n",
" <td>0.690000</td>\n",
" <td>0.428813</td>\n",
" <td>0.678668</td>\n",
" <td>0.566490</td>\n",
" <td>0.526948</td>\n",
" <td>0.296055</td>\n",
" <td>0.571462</td>\n",
" <td>0.690358</td>\n",
" <td>0.336364</td>\n",
" <td>...</td>\n",
" <td>0.623266</td>\n",
" <td>0.383262</td>\n",
" <td>0.576174</td>\n",
" <td>0.452664</td>\n",
" <td>0.461137</td>\n",
" <td>0.178527</td>\n",
" <td>0.328035</td>\n",
" <td>0.761512</td>\n",
" <td>0.097575</td>\n",
" <td>0.105667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>0.001007</td>\n",
" <td>0.622320</td>\n",
" <td>0.626987</td>\n",
" <td>0.604036</td>\n",
" <td>0.474019</td>\n",
" <td>0.407782</td>\n",
" <td>0.257714</td>\n",
" <td>0.337395</td>\n",
" <td>0.486630</td>\n",
" <td>0.349495</td>\n",
" <td>...</td>\n",
" <td>0.560655</td>\n",
" <td>0.699094</td>\n",
" <td>0.520892</td>\n",
" <td>0.379915</td>\n",
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" <td>0.256789</td>\n",
" <td>0.559450</td>\n",
" <td>0.198502</td>\n",
" <td>0.074315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>0.001008</td>\n",
" <td>0.455251</td>\n",
" <td>0.621238</td>\n",
" <td>0.445788</td>\n",
" <td>0.303118</td>\n",
" <td>0.288165</td>\n",
" <td>0.254340</td>\n",
" <td>0.216753</td>\n",
" <td>0.263519</td>\n",
" <td>0.267677</td>\n",
" <td>...</td>\n",
" <td>0.393099</td>\n",
" <td>0.589019</td>\n",
" <td>0.379949</td>\n",
" <td>0.230731</td>\n",
" <td>0.282177</td>\n",
" <td>0.273705</td>\n",
" <td>0.271805</td>\n",
" <td>0.487285</td>\n",
" <td>0.128721</td>\n",
" <td>0.151909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>0.001008</td>\n",
" <td>0.644564</td>\n",
" <td>0.663510</td>\n",
" <td>0.665538</td>\n",
" <td>0.475716</td>\n",
" <td>0.588336</td>\n",
" <td>0.790197</td>\n",
" <td>0.823336</td>\n",
" <td>0.755467</td>\n",
" <td>0.675253</td>\n",
" <td>...</td>\n",
" <td>0.633582</td>\n",
" <td>0.730277</td>\n",
" <td>0.668310</td>\n",
" <td>0.402035</td>\n",
" <td>0.619626</td>\n",
" <td>0.815758</td>\n",
" <td>0.749760</td>\n",
" <td>0.910653</td>\n",
" <td>0.497142</td>\n",
" <td>0.452315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>0.000092</td>\n",
" <td>0.036869</td>\n",
" <td>0.501522</td>\n",
" <td>0.028540</td>\n",
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" <td>0.100682</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>569 rows × 31 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 2 3 4 5 6 7 \\\n",
"0 0.000915 0.521037 0.022658 0.545989 0.363733 0.593753 0.792037 \n",
"1 0.000915 0.643144 0.272574 0.615783 0.501591 0.289880 0.181768 \n",
"2 0.092495 0.601496 0.390260 0.595743 0.449417 0.514309 0.431017 \n",
"3 0.092547 0.210090 0.360839 0.233501 0.102906 0.811321 0.811361 \n",
"4 0.092559 0.629893 0.156578 0.630986 0.489290 0.430351 0.347893 \n",
"5 0.000916 0.258839 0.202570 0.267984 0.141506 0.678613 0.461996 \n",
"6 0.000917 0.533343 0.347311 0.523875 0.380276 0.379164 0.274891 \n",
"7 0.092668 0.318472 0.376057 0.320710 0.184263 0.598267 0.445126 \n",
"8 0.000918 0.284869 0.409537 0.302052 0.159618 0.674099 0.533157 \n",
"9 0.092715 0.259312 0.484613 0.277659 0.140997 0.595558 0.675480 \n",
"10 0.000918 0.427801 0.457558 0.407090 0.277540 0.265686 0.145114 \n",
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"13 0.000919 0.419755 0.481569 0.414000 0.271135 0.283290 0.247899 \n",
"14 0.092898 0.319419 0.436253 0.344206 0.184433 0.545906 0.643887 \n",
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".. ... ... ... ... ... ... ... \n",
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"\n",
" 8 9 10 ... 22 23 24 \\\n",
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"1 0.203608 0.348757 0.379798 ... 0.606901 0.303571 0.539818 \n",
"2 0.462512 0.635686 0.509596 ... 0.556386 0.360075 0.508442 \n",
"3 0.565604 0.522863 0.776263 ... 0.248310 0.385928 0.241347 \n",
"4 0.463918 0.518390 0.378283 ... 0.519744 0.123934 0.506948 \n",
"5 0.369728 0.402038 0.518687 ... 0.268232 0.312633 0.263908 \n",
"6 0.264058 0.367793 0.370707 ... 0.531839 0.416844 0.511928 \n",
"7 0.219447 0.297465 0.573737 ... 0.324795 0.429638 0.299766 \n",
"8 0.435567 0.464861 0.651515 ... 0.268943 0.498667 0.277852 \n",
"9 0.532568 0.424602 0.489899 ... 0.254714 0.763859 0.235271 \n",
"10 0.077296 0.165159 0.236364 ... 0.400569 0.582623 0.365506 \n",
"11 0.233224 0.328330 0.394949 ... 0.444326 0.406716 0.428756 \n",
"12 0.483833 0.555666 0.675253 ... 0.463536 0.477612 0.504457 \n",
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"15 0.384021 0.366004 0.627778 ... 0.339025 0.669243 0.367000 \n",
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"27 0.349110 0.384245 0.321717 ... 0.475987 0.406183 0.445690 \n",
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"29 0.231373 0.395278 0.342929 ... 0.429740 0.199893 0.420788 \n",
".. ... ... ... ... ... ... ... \n",
"539 0.216776 0.067793 0.493434 ... 0.026610 0.529584 0.020320 \n",
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"541 0.236410 0.193340 0.410101 ... 0.294913 0.525320 0.314209 \n",
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"545 0.069681 0.121421 0.305051 ... 0.263963 0.454957 0.234922 \n",
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"547 0.102109 0.121173 0.307576 ... 0.103166 0.267058 0.102943 \n",
"548 0.054756 0.047788 0.262626 ... 0.106724 0.361674 0.093082 \n",
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"\n",
" 25 26 27 28 29 30 31 \n",
"0 0.450698 0.601136 0.619292 0.568610 0.912027 0.598462 0.418864 \n",
"1 0.435214 0.347553 0.154563 0.192971 0.639175 0.233590 0.222878 \n",
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"9 0.129326 0.753682 1.000000 0.882588 0.759450 0.552139 1.000000 \n",
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".. ... ... ... ... ... ... ... \n",
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"566 0.230731 0.282177 0.273705 0.271805 0.487285 0.128721 0.151909 \n",
"567 0.402035 0.619626 0.815758 0.749760 0.910653 0.497142 0.452315 \n",
"568 0.020497 0.124084 0.036043 0.000000 0.000000 0.257441 0.100682 \n",
"\n",
"[569 rows x 31 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_scaled"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"scores = cross_validation.cross_val_score(clf, X_scaled, Y, cv = 5, scoring = 'accuracy')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"scores = [ 0.92173913 0.94782609 0.98230088 0.96460177 0.97345133]\n",
"scores mean accuracy= 0.957983839938\n",
"scores standard deviation = 0.0214066240102\n"
]
}
],
"source": [
"print \"scores =\", scores\n",
"print \"scores mean accuracy=\", scores.mean()\n",
"print \"scores standard deviation =\", scores.std()\n",
"#mean accuracy is not appreciably changed from this rescaling of the data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Tune the model using automated parametric grid search via LogisticRegressionCV. Explain your intution behind what is being tuned."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[mean: 0.96309, std: 0.01132, params: {'penalty': 'l1', 'C': 10000},\n",
" mean: 0.64148, std: 0.21932, params: {'penalty': 'l2', 'C': 10000},\n",
" mean: 0.96309, std: 0.01132, params: {'penalty': 'l1', 'C': 1000},\n",
" mean: 0.64148, std: 0.21932, params: {'penalty': 'l2', 'C': 1000},\n",
" mean: 0.96661, std: 0.00244, params: {'penalty': 'l1', 'C': 100},\n",
" mean: 0.64323, std: 0.22152, params: {'penalty': 'l2', 'C': 100},\n",
" mean: 0.95958, std: 0.01083, params: {'penalty': 'l1', 'C': 10},\n",
" mean: 0.64323, std: 0.22152, params: {'penalty': 'l2', 'C': 10},\n",
" mean: 0.95431, std: 0.01083, params: {'penalty': 'l1', 'C': 1},\n",
" mean: 0.64323, std: 0.22152, params: {'penalty': 'l2', 'C': 1},\n",
" mean: 0.92267, std: 0.02439, params: {'penalty': 'l1', 'C': 0.1},\n",
" mean: 0.64148, std: 0.21932, params: {'penalty': 'l2', 'C': 0.1},\n",
" mean: 0.91564, std: 0.01555, params: {'penalty': 'l1', 'C': 0.01},\n",
" mean: 0.64148, std: 0.21932, params: {'penalty': 'l2', 'C': 0.01},\n",
" mean: 0.91916, std: 0.01371, params: {'penalty': 'l1', 'C': 0.001},\n",
" mean: 0.64323, std: 0.22152, params: {'penalty': 'l2', 'C': 0.001},\n",
" mean: 0.37961, std: 0.00440, params: {'penalty': 'l1', 'C': 0.0001},\n",
" mean: 0.64148, std: 0.21932, params: {'penalty': 'l2', 'C': 0.0001}]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import grid_search, linear_model\n",
"\n",
"model = linear_model.LogisticRegression()\n",
"\n",
"params = {'penalty': ['l1', 'l2'], \n",
" 'C': [10000, 1000, 100, 10, 1, 0.1, 0.01, 0.001, 0.0001]\n",
" }\n",
"\n",
"search = grid_search.GridSearchCV(model, params)\n",
"search.fit(X, Y)\n",
"search.grid_scores_\n",
"\n",
"#First for type of regularization, l1 or l2, we see that l2 is much worse with this data set. In l1 regularization, some of the \n",
"#feature weights will go to zero, inducing sparsity on the model. This is preferred if some of the features are not good predictors. \n",
"#Next, we have C, which is the inverse of the regularization coefficient (lambda) i.e. the larger C means the smaller the regularization\n",
"#coefficient, giving more freedom for feature weights to vary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Q: What should we do to prevent overfitting so our model generalizes well to the test data?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#There are a number of possible answers to this question, ranging from generic in the context of machine learning to specific for\n",
"#the chosen model.\n",
"\n",
"#Let's start with answers specific to the models chosen for this challenge. Two models have been used: a random forest classifier\n",
"#and a logistic regression classifier. For the random forest classifier, increasing the number of estimators (i.e. trees) will\n",
"#decrease overfitting through an averaging of classifications by each individual tree. min_samples_split helps reduce overfitting\n",
"#by not splitting nodes which have fewer than 4 (or the given threshold) training points. max_depth can play a role in reducing\n",
"#overfitting by limiting the overall size of the tree (by controlling the depth), limiting us to considering a smaller subset of\n",
"#variables per tree, and possibly through an effect mediated through ensuring terminal nodes have more training points.\n",
"#min_samples_leaf is similar to min_samples_split; after a split, if a created node has fewer training points than the threshold,\n",
"#the split is discarded.\n",
"\n",
"#For the logistic regression classifier, the primary method to reduce overfitting is to prevent the model from taking on extreme\n",
"#values for feature weights, which is prevented through the presence of a regularizer term.\n",
"\n",
"#In the general case, we can do averaging of models, for example in bagging, we can include regularization terms in our models,\n",
"#we can select a different model that is more appropriate for the data, we can try to get more data for training, and, finally,\n",
"#we can inlude cross-validation in our training process."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Q: What was the best C?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#The best value for C was 100."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Create Two Plots that describe the data and discuss your results"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#first, we'll look at a plot of the two principal components of the cell data\n",
"from sklearn import decomposition\n",
"pca_clf = decomposition.PCA(n_components=2, copy=True, whiten=False)\n",
"\n",
"pca_data = pca_clf.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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jjzR/mmpumBnDhw/n9ttvb0x7/vnnqa2tZcsz4/JDwUJEpAUvvPAC11xzDXPnzuWjjz5q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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1edda940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"pca_data = pandas.DataFrame(pca_data)\n",
"plt.scatter(pca_data[Y == 'M'][0], pca_data[Y == 'M'][1], c = 'r', label = 'M')\n",
"plt.scatter(pca_data[Y == 'B'][0], pca_data[Y == 'B'][1], c = 'b', label = 'B')\n",
"plt.title(\"First and second principal components of cell dataset\")\n",
"plt.legend(loc=\"best\")\n",
"plt.show()\n",
"\n",
"#This plot shows that the first principal component, x-axis, does not meaningfully allow us to distinguish between the two\n",
"#classes, but the second principal component, the y-axis, neatly separates between the begnin and malignant classes. The\n",
"#collected data on cell characteristics is quite adequate to distinguish between benign and malignant cell types"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Next we look at feature importance in our random forest classifier\n",
"clf = clf.fit(X, Y)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature ranking:\n",
"1. feature 23 (0.139947)\n",
"2. feature 28 (0.138124)\n",
"3. feature 21 (0.119059)\n",
"4. feature 24 (0.110824)\n",
"5. feature 8 (0.109997)\n",
"6. feature 4 (0.057285)\n",
"7. feature 1 (0.046989)\n",
"8. feature 7 (0.042839)\n",
"9. feature 3 (0.040832)\n",
"10. feature 14 (0.036347)\n",
"11. feature 27 (0.032266)\n",
"12. feature 22 (0.017686)\n",
"13. feature 26 (0.012835)\n",
"14. feature 2 (0.012681)\n",
"15. feature 13 (0.010368)\n",
"16. feature 11 (0.010106)\n",
"17. feature 25 (0.009750)\n",
"18. feature 6 (0.008942)\n",
"19. feature 29 (0.008660)\n",
"20. feature 30 (0.004636)\n",
"21. feature 17 (0.004429)\n",
"22. feature 5 (0.004028)\n",
"23. feature 0 (0.003427)\n",
"24. feature 18 (0.003209)\n",
"25. feature 10 (0.002570)\n",
"26. feature 20 (0.002473)\n",
"27. feature 12 (0.002389)\n",
"28. feature 9 (0.002092)\n",
"29. feature 15 (0.002005)\n",
"30. feature 16 (0.001614)\n",
"31. feature 19 (0.001589)\n"
]
},
{
"data": {
"image/png": 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6OdqvO0JD0mMnf1Lmo/Tnk/p/S+tr0+6q9fl80geteE6ibHuuInVZvTQifl8V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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2062bf60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#http://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html\n",
"import numpy as np\n",
"importances = clf.feature_importances_\n",
"std = np.std([tree.feature_importances_ for tree in clf.estimators_],\n",
" axis=0)\n",
"\n",
"indices = np.argsort(importances)[::-1]\n",
"\n",
"# Print the feature ranking\n",
"print(\"Feature ranking:\")\n",
"\n",
"for f in range(X.shape[1]):\n",
" print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))\n",
"\n",
"# Plot the feature importances of the forest\n",
"plt.figure()\n",
"plt.title(\"Feature importances\")\n",
"plt.bar(range(X.shape[1]), importances[indices],\n",
" color=\"r\", yerr=std[indices], align=\"center\")\n",
"plt.xticks(range(X.shape[1]), indices)\n",
"plt.xlim([-1, X.shape[1]])\n",
"plt.show()\n",
"\n",
"#There is considerable noise in this plot, as demonstrated by the size of the standard deviation error bars. However, this\n",
"#variable importance plot for our random forest classifier could be used by pathologists or molecular biologists to focus further\n",
"#study on the features 23, 24, 21, 28, 8, 4, 7, 3, 1, 27, and 14 as to why they may have greater predictive value in\n",
"#differentiating between benign and malignant cell types"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Provide a one-sentence summary for a non-technical audience. Then provide a longer paragraph-length technical explanation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Summary: The collected data on these breast cells effectively allows for discrimination between benign and malignant types with\n",
"#high accuracy -- 95.5% accuracy was achieved by considering all 30 measurements per cell.\n",
"\n",
"#Benign and malignant breast cells can be distinguished with high accuracy -- ~96% -- using the 30 provided cell measurements.\n",
"#Using a random forest classifier with 500 trees, the Gini inpurity measure (for continuous variables), and 5-fold cross validation\n",
"#I achieved a 95.5% accuracy rate, with std of 0.022, in distinguishing between benign and malignant cell types. The following\n",
"#precision and recall values were achieved with this classifier: 1) Benign (support = 118): Type 1 error rate = 0.026; Type 2\n",
"#error rate = 0.051; and 2) Malignant (support = 70): Type 1 error rate = 0.082; Type 2 error rate = 0.043. Finally, as the\n",
"#classes are linearly seperable (from the paper), or close to linearly seperable, as revealed by plotting the data along the two\n",
"#principal components, a simple logistic regression model with l1 penalty and C = 10000 achieved a mean accuracy of 0.95606 with \n",
"#std of 0.00887."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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