Trees_RF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import Counter\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "trees = pd.read_csv('C:\\\\Temp\\\\MRes\\\\training_data\\\\AB_3cat.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def prepare_xy(file):\n",
    "#     trees = pd.read_csv(file)\n",
    "#     trees = trees.drop(columns=['geometry'])\n",
    "#     trees = trees.to_numpy()\n",
    "#     X, y = trees[:,1:], trees[:,0]\n",
    "#     X = X.astype('float32')\n",
    "#     y = y.astype('str')\n",
    "#     y = LabelEncoder().fit_transform(y)\n",
    "#     return(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENUS</th>\n",
       "      <th>All.returns.above.2</th>\n",
       "      <th>All.returns.above.mean</th>\n",
       "      <th>All.returns.above.mode</th>\n",
       "      <th>Canopy.relief.ratio</th>\n",
       "      <th>First.returns.above.2</th>\n",
       "      <th>First.returns.above.mean</th>\n",
       "      <th>First.returns.above.mode</th>\n",
       "      <th>H01TH</th>\n",
       "      <th>H05TH</th>\n",
       "      <th>H10TH</th>\n",
       "      <th>H15TH</th>\n",
       "      <th>H20TH</th>\n",
       "      <th>H25TH</th>\n",
       "      <th>H30TH</th>\n",
       "      <th>H35TH</th>\n",
       "      <th>H40TH</th>\n",
       "      <th>H45TH</th>\n",
       "      <th>H50TH</th>\n",
       "      <th>H55TH</th>\n",
       "      <th>H60TH</th>\n",
       "      <th>H65TH</th>\n",
       "      <th>H70TH</th>\n",
       "      <th>H75TH</th>\n",
       "      <th>H80TH</th>\n",
       "      <th>H90TH</th>\n",
       "      <th>H95TH</th>\n",
       "      <th>H99TH</th>\n",
       "      <th>HCV</th>\n",
       "      <th>HKUR</th>\n",
       "      <th>HMAX</th>\n",
       "      <th>HMEADIAN</th>\n",
       "      <th>HMEAN</th>\n",
       "      <th>HMODE</th>\n",
       "      <th>HSD</th>\n",
       "      <th>HSKE</th>\n",
       "      <th>HVAR</th>\n",
       "      <th>I01TH</th>\n",
       "      <th>I05TH</th>\n",
       "      <th>I10TH</th>\n",
       "      <th>I15TH</th>\n",
       "      <th>I20TH</th>\n",
       "      <th>I25TH</th>\n",
       "      <th>I30TH</th>\n",
       "      <th>I35TH</th>\n",
       "      <th>I40TH</th>\n",
       "      <th>I45TH</th>\n",
       "      <th>I50TH</th>\n",
       "      <th>I55TH</th>\n",
       "      <th>I60TH</th>\n",
       "      <th>ICV</th>\n",
       "      <th>IKUR</th>\n",
       "      <th>IMAX</th>\n",
       "      <th>IMEADIAN</th>\n",
       "      <th>IMEAN</th>\n",
       "      <th>IMODE</th>\n",
       "      <th>IP65</th>\n",
       "      <th>IP70</th>\n",
       "      <th>IP75</th>\n",
       "      <th>IP80</th>\n",
       "      <th>IP90</th>\n",
       "      <th>IP95</th>\n",
       "      <th>IP99</th>\n",
       "      <th>ISD</th>\n",
       "      <th>ISKE</th>\n",
       "      <th>IVAR</th>\n",
       "      <th>Percentage.all.returns.above.mean</th>\n",
       "      <th>Percentage.all.returns.above.mode</th>\n",
       "      <th>Percentage.first.returns.above.mean</th>\n",
       "      <th>Percentage.first.returns.above.mode</th>\n",
       "      <th>Return.1.count.above.2</th>\n",
       "      <th>Return.2.count.above.2</th>\n",
       "      <th>Return.3.count.2</th>\n",
       "      <th>Return.4.count.2</th>\n",
       "      <th>Return.5.count.2</th>\n",
       "      <th>Return.6.count.2</th>\n",
       "      <th>Return.7.count.above.2</th>\n",
       "      <th>Total.all.return.count</th>\n",
       "      <th>Total.all.return.count.above.2</th>\n",
       "      <th>Total.first.return.count</th>\n",
       "      <th>b_max</th>\n",
       "      <th>b_mean</th>\n",
       "      <th>b_median</th>\n",
       "      <th>b_min</th>\n",
       "      <th>crownArea</th>\n",
       "      <th>g_max</th>\n",
       "      <th>g_mean</th>\n",
       "      <th>g_median</th>\n",
       "      <th>g_min</th>\n",
       "      <th>geometry</th>\n",
       "      <th>r_max</th>\n",
       "      <th>r_mean</th>\n",
       "      <th>r_median</th>\n",
       "      <th>r_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Tilia</td>\n",
       "      <td>1920</td>\n",
       "      <td>827</td>\n",
       "      <td>942</td>\n",
       "      <td>0.329585</td>\n",
       "      <td>1416</td>\n",
       "      <td>723</td>\n",
       "      <td>811</td>\n",
       "      <td>5.04</td>\n",
       "      <td>5.18</td>\n",
       "      <td>5.33</td>\n",
       "      <td>5.45</td>\n",
       "      <td>5.62</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.88</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.13</td>\n",
       "      <td>6.24</td>\n",
       "      <td>6.33</td>\n",
       "      <td>6.43</td>\n",
       "      <td>6.55</td>\n",
       "      <td>6.70</td>\n",
       "      <td>6.91</td>\n",
       "      <td>7.14</td>\n",
       "      <td>7.63</td>\n",
       "      <td>7.91</td>\n",
       "      <td>8.10</td>\n",
       "      <td>8.71</td>\n",
       "      <td>14.31</td>\n",
       "      <td>2.47</td>\n",
       "      <td>9.49</td>\n",
       "      <td>6.33</td>\n",
       "      <td>6.48</td>\n",
       "      <td>6.34</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.86</td>\n",
       "      <td>488.38</td>\n",
       "      <td>689.8</td>\n",
       "      <td>920.6</td>\n",
       "      <td>1076.70</td>\n",
       "      <td>1279.0</td>\n",
       "      <td>1446.0</td>\n",
       "      <td>1622.4</td>\n",
       "      <td>1828.00</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2187.0</td>\n",
       "      <td>2396.0</td>\n",
       "      <td>2594.9</td>\n",
       "      <td>2845.8</td>\n",
       "      <td>57.01</td>\n",
       "      <td>2.96</td>\n",
       "      <td>8143</td>\n",
       "      <td>2396.0</td>\n",
       "      <td>2670.93</td>\n",
       "      <td>879</td>\n",
       "      <td>3064.40</td>\n",
       "      <td>3321.5</td>\n",
       "      <td>3622.75</td>\n",
       "      <td>4373.3</td>\n",
       "      <td>4933.4</td>\n",
       "      <td>5497.20</td>\n",
       "      <td>6831.53</td>\n",
       "      <td>1522.59</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2318274.94</td>\n",
       "      <td>43.072917</td>\n",
       "      <td>49.062500</td>\n",
       "      <td>51.059322</td>\n",
       "      <td>57.274011</td>\n",
       "      <td>1416</td>\n",
       "      <td>433</td>\n",
       "      <td>66</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1920</td>\n",
       "      <td>1416</td>\n",
       "      <td>142</td>\n",
       "      <td>64.965399</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "      <td>67.05</td>\n",
       "      <td>181</td>\n",
       "      <td>94.354064</td>\n",
       "      <td>89.0</td>\n",
       "      <td>36</td>\n",
       "      <td>POLYGON ((423712.8 564644.1, 423712.8 564643.8...</td>\n",
       "      <td>200</td>\n",
       "      <td>104.325578</td>\n",
       "      <td>93.0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Acer</td>\n",
       "      <td>1015</td>\n",
       "      <td>572</td>\n",
       "      <td>798</td>\n",
       "      <td>0.556663</td>\n",
       "      <td>708</td>\n",
       "      <td>527</td>\n",
       "      <td>244</td>\n",
       "      <td>5.06</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.65</td>\n",
       "      <td>6.16</td>\n",
       "      <td>6.70</td>\n",
       "      <td>7.15</td>\n",
       "      <td>7.57</td>\n",
       "      <td>7.95</td>\n",
       "      <td>8.42</td>\n",
       "      <td>9.15</td>\n",
       "      <td>9.59</td>\n",
       "      <td>10.04</td>\n",
       "      <td>10.25</td>\n",
       "      <td>10.43</td>\n",
       "      <td>10.69</td>\n",
       "      <td>10.90</td>\n",
       "      <td>11.36</td>\n",
       "      <td>11.48</td>\n",
       "      <td>11.63</td>\n",
       "      <td>11.96</td>\n",
       "      <td>23.84</td>\n",
       "      <td>1.77</td>\n",
       "      <td>12.20</td>\n",
       "      <td>9.59</td>\n",
       "      <td>9.01</td>\n",
       "      <td>10.90</td>\n",
       "      <td>2.15</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>4.61</td>\n",
       "      <td>453.00</td>\n",
       "      <td>517.0</td>\n",
       "      <td>620.6</td>\n",
       "      <td>808.20</td>\n",
       "      <td>1050.8</td>\n",
       "      <td>1462.5</td>\n",
       "      <td>2222.2</td>\n",
       "      <td>3103.00</td>\n",
       "      <td>3745.0</td>\n",
       "      <td>4342.3</td>\n",
       "      <td>4888.0</td>\n",
       "      <td>5400.2</td>\n",
       "      <td>5907.4</td>\n",
       "      <td>62.31</td>\n",
       "      <td>1.67</td>\n",
       "      <td>10579</td>\n",
       "      <td>4888.0</td>\n",
       "      <td>4533.38</td>\n",
       "      <td>444</td>\n",
       "      <td>6305.30</td>\n",
       "      <td>6624.6</td>\n",
       "      <td>6900.50</td>\n",
       "      <td>7711.5</td>\n",
       "      <td>8054.4</td>\n",
       "      <td>8659.20</td>\n",
       "      <td>9642.54</td>\n",
       "      <td>2824.58</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>7978231.46</td>\n",
       "      <td>56.354680</td>\n",
       "      <td>78.620690</td>\n",
       "      <td>74.435028</td>\n",
       "      <td>34.463277</td>\n",
       "      <td>708</td>\n",
       "      <td>206</td>\n",
       "      <td>77</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1015</td>\n",
       "      <td>1015</td>\n",
       "      <td>708</td>\n",
       "      <td>127</td>\n",
       "      <td>60.392079</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45.45</td>\n",
       "      <td>153</td>\n",
       "      <td>83.174697</td>\n",
       "      <td>82.0</td>\n",
       "      <td>37</td>\n",
       "      <td>POLYGON ((423819.9 564730.2, 423819.9 564729.9...</td>\n",
       "      <td>153</td>\n",
       "      <td>76.669527</td>\n",
       "      <td>76.0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Acer</td>\n",
       "      <td>366</td>\n",
       "      <td>202</td>\n",
       "      <td>189</td>\n",
       "      <td>0.440053</td>\n",
       "      <td>337</td>\n",
       "      <td>199</td>\n",
       "      <td>187</td>\n",
       "      <td>5.05</td>\n",
       "      <td>5.18</td>\n",
       "      <td>5.26</td>\n",
       "      <td>5.35</td>\n",
       "      <td>5.49</td>\n",
       "      <td>5.62</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.79</td>\n",
       "      <td>5.97</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.28</td>\n",
       "      <td>6.33</td>\n",
       "      <td>6.40</td>\n",
       "      <td>6.48</td>\n",
       "      <td>6.56</td>\n",
       "      <td>6.76</td>\n",
       "      <td>6.91</td>\n",
       "      <td>7.02</td>\n",
       "      <td>7.31</td>\n",
       "      <td>9.77</td>\n",
       "      <td>1.98</td>\n",
       "      <td>7.53</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.19</td>\n",
       "      <td>0.60</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.36</td>\n",
       "      <td>615.30</td>\n",
       "      <td>1255.0</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>2468.00</td>\n",
       "      <td>3191.0</td>\n",
       "      <td>3687.0</td>\n",
       "      <td>4109.0</td>\n",
       "      <td>4408.25</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>4954.5</td>\n",
       "      <td>5196.0</td>\n",
       "      <td>5527.0</td>\n",
       "      <td>5852.0</td>\n",
       "      <td>45.95</td>\n",
       "      <td>2.23</td>\n",
       "      <td>10346</td>\n",
       "      <td>5196.0</td>\n",
       "      <td>5328.16</td>\n",
       "      <td>4331</td>\n",
       "      <td>6300.25</td>\n",
       "      <td>6722.0</td>\n",
       "      <td>7164.50</td>\n",
       "      <td>8163.5</td>\n",
       "      <td>8880.0</td>\n",
       "      <td>9305.50</td>\n",
       "      <td>10190.25</td>\n",
       "      <td>2448.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5994906.73</td>\n",
       "      <td>55.191257</td>\n",
       "      <td>51.639344</td>\n",
       "      <td>59.050445</td>\n",
       "      <td>55.489614</td>\n",
       "      <td>337</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>366</td>\n",
       "      <td>366</td>\n",
       "      <td>337</td>\n",
       "      <td>124</td>\n",
       "      <td>64.554241</td>\n",
       "      <td>62.0</td>\n",
       "      <td>18</td>\n",
       "      <td>15.21</td>\n",
       "      <td>146</td>\n",
       "      <td>81.047995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>32</td>\n",
       "      <td>POLYGON ((423469.5 564817.8, 423469.5 564817.5...</td>\n",
       "      <td>139</td>\n",
       "      <td>77.166995</td>\n",
       "      <td>74.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Acer</td>\n",
       "      <td>177</td>\n",
       "      <td>126</td>\n",
       "      <td>96</td>\n",
       "      <td>0.802231</td>\n",
       "      <td>142</td>\n",
       "      <td>124</td>\n",
       "      <td>79</td>\n",
       "      <td>6.05</td>\n",
       "      <td>7.56</td>\n",
       "      <td>10.16</td>\n",
       "      <td>11.30</td>\n",
       "      <td>11.95</td>\n",
       "      <td>12.09</td>\n",
       "      <td>12.29</td>\n",
       "      <td>12.52</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.71</td>\n",
       "      <td>12.84</td>\n",
       "      <td>12.89</td>\n",
       "      <td>12.94</td>\n",
       "      <td>12.98</td>\n",
       "      <td>13.05</td>\n",
       "      <td>13.12</td>\n",
       "      <td>13.21</td>\n",
       "      <td>13.31</td>\n",
       "      <td>13.38</td>\n",
       "      <td>13.71</td>\n",
       "      <td>13.65</td>\n",
       "      <td>7.95</td>\n",
       "      <td>13.74</td>\n",
       "      <td>12.84</td>\n",
       "      <td>12.20</td>\n",
       "      <td>12.89</td>\n",
       "      <td>1.67</td>\n",
       "      <td>-2.34</td>\n",
       "      <td>2.77</td>\n",
       "      <td>483.76</td>\n",
       "      <td>658.4</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>1622.60</td>\n",
       "      <td>2280.2</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3809.2</td>\n",
       "      <td>4389.60</td>\n",
       "      <td>4794.2</td>\n",
       "      <td>5206.2</td>\n",
       "      <td>5568.0</td>\n",
       "      <td>5878.2</td>\n",
       "      <td>6266.0</td>\n",
       "      <td>48.64</td>\n",
       "      <td>2.00</td>\n",
       "      <td>9915</td>\n",
       "      <td>5568.0</td>\n",
       "      <td>5062.11</td>\n",
       "      <td>1228</td>\n",
       "      <td>6551.00</td>\n",
       "      <td>6797.0</td>\n",
       "      <td>7054.00</td>\n",
       "      <td>7679.2</td>\n",
       "      <td>7880.8</td>\n",
       "      <td>8214.60</td>\n",
       "      <td>9003.28</td>\n",
       "      <td>2462.13</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>6062069.68</td>\n",
       "      <td>71.186441</td>\n",
       "      <td>54.237288</td>\n",
       "      <td>87.323944</td>\n",
       "      <td>55.633803</td>\n",
       "      <td>142</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>177</td>\n",
       "      <td>142</td>\n",
       "      <td>135</td>\n",
       "      <td>64.611111</td>\n",
       "      <td>63.0</td>\n",
       "      <td>24</td>\n",
       "      <td>4.68</td>\n",
       "      <td>156</td>\n",
       "      <td>84.782051</td>\n",
       "      <td>83.0</td>\n",
       "      <td>44</td>\n",
       "      <td>POLYGON ((423754.2 564619.8, 423754.2 564619.5...</td>\n",
       "      <td>148</td>\n",
       "      <td>76.688034</td>\n",
       "      <td>75.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Acer</td>\n",
       "      <td>1028</td>\n",
       "      <td>476</td>\n",
       "      <td>735</td>\n",
       "      <td>0.470105</td>\n",
       "      <td>833</td>\n",
       "      <td>440</td>\n",
       "      <td>615</td>\n",
       "      <td>5.09</td>\n",
       "      <td>5.23</td>\n",
       "      <td>5.45</td>\n",
       "      <td>5.63</td>\n",
       "      <td>5.86</td>\n",
       "      <td>6.06</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.33</td>\n",
       "      <td>6.45</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6.73</td>\n",
       "      <td>6.93</td>\n",
       "      <td>7.22</td>\n",
       "      <td>7.44</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.81</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.24</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.79</td>\n",
       "      <td>15.01</td>\n",
       "      <td>1.85</td>\n",
       "      <td>8.96</td>\n",
       "      <td>6.73</td>\n",
       "      <td>6.86</td>\n",
       "      <td>6.16</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.06</td>\n",
       "      <td>539.00</td>\n",
       "      <td>785.4</td>\n",
       "      <td>1127.4</td>\n",
       "      <td>1510.35</td>\n",
       "      <td>2142.4</td>\n",
       "      <td>2799.0</td>\n",
       "      <td>3320.9</td>\n",
       "      <td>3683.85</td>\n",
       "      <td>4060.2</td>\n",
       "      <td>4513.3</td>\n",
       "      <td>4733.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>5271.6</td>\n",
       "      <td>48.81</td>\n",
       "      <td>2.12</td>\n",
       "      <td>10206</td>\n",
       "      <td>4733.0</td>\n",
       "      <td>4454.68</td>\n",
       "      <td>4733</td>\n",
       "      <td>5569.85</td>\n",
       "      <td>5862.6</td>\n",
       "      <td>6121.50</td>\n",
       "      <td>6782.1</td>\n",
       "      <td>7135.3</td>\n",
       "      <td>7631.45</td>\n",
       "      <td>8745.74</td>\n",
       "      <td>2174.35</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>4727797.24</td>\n",
       "      <td>46.303502</td>\n",
       "      <td>71.498054</td>\n",
       "      <td>52.821128</td>\n",
       "      <td>73.829532</td>\n",
       "      <td>833</td>\n",
       "      <td>184</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1028</td>\n",
       "      <td>1028</td>\n",
       "      <td>833</td>\n",
       "      <td>131</td>\n",
       "      <td>57.770553</td>\n",
       "      <td>55.0</td>\n",
       "      <td>18</td>\n",
       "      <td>69.21</td>\n",
       "      <td>137</td>\n",
       "      <td>63.370611</td>\n",
       "      <td>61.0</td>\n",
       "      <td>32</td>\n",
       "      <td>POLYGON ((423400.2 564846.9, 423400.2 564846.6...</td>\n",
       "      <td>135</td>\n",
       "      <td>57.520590</td>\n",
       "      <td>56.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GENUS  All.returns.above.2  All.returns.above.mean  All.returns.above.mode  \\\n",
       "0  Tilia                 1920                     827                     942   \n",
       "1   Acer                 1015                     572                     798   \n",
       "2   Acer                  366                     202                     189   \n",
       "3   Acer                  177                     126                      96   \n",
       "4   Acer                 1028                     476                     735   \n",
       "\n",
       "   Canopy.relief.ratio  First.returns.above.2  First.returns.above.mean  \\\n",
       "0             0.329585                   1416                       723   \n",
       "1             0.556663                    708                       527   \n",
       "2             0.440053                    337                       199   \n",
       "3             0.802231                    142                       124   \n",
       "4             0.470105                    833                       440   \n",
       "\n",
       "   First.returns.above.mode  H01TH  H05TH  H10TH  H15TH  H20TH  H25TH  H30TH  \\\n",
       "0                       811   5.04   5.18   5.33   5.45   5.62   5.75   5.88   \n",
       "1                       244   5.06   5.34   5.65   6.16   6.70   7.15   7.57   \n",
       "2                       187   5.05   5.18   5.26   5.35   5.49   5.62   5.69   \n",
       "3                        79   6.05   7.56  10.16  11.30  11.95  12.09  12.29   \n",
       "4                       615   5.09   5.23   5.45   5.63   5.86   6.06   6.20   \n",
       "\n",
       "   H35TH  H40TH  H45TH  H50TH  H55TH  H60TH  H65TH  H70TH  H75TH  H80TH  \\\n",
       "0   6.01   6.13   6.24   6.33   6.43   6.55   6.70   6.91   7.14   7.63   \n",
       "1   7.95   8.42   9.15   9.59  10.04  10.25  10.43  10.69  10.90  11.36   \n",
       "2   5.79   5.97   6.15   6.21   6.28   6.33   6.40   6.48   6.56   6.76   \n",
       "3  12.52  12.62  12.71  12.84  12.89  12.94  12.98  13.05  13.12  13.21   \n",
       "4   6.33   6.45   6.60   6.73   6.93   7.22   7.44   7.66   7.81   8.05   \n",
       "\n",
       "   H90TH  H95TH  H99TH    HCV  HKUR   HMAX  HMEADIAN  HMEAN  HMODE   HSD  \\\n",
       "0   7.91   8.10   8.71  14.31  2.47   9.49      6.33   6.48   6.34  0.93   \n",
       "1  11.48  11.63  11.96  23.84  1.77  12.20      9.59   9.01  10.90  2.15   \n",
       "2   6.91   7.02   7.31   9.77  1.98   7.53      6.21   6.11   6.19  0.60   \n",
       "3  13.31  13.38  13.71  13.65  7.95  13.74     12.84  12.20  12.89  1.67   \n",
       "4   8.24   8.46   8.79  15.01  1.85   8.96      6.73   6.86   6.16  1.03   \n",
       "\n",
       "   HSKE  HVAR   I01TH   I05TH   I10TH    I15TH   I20TH   I25TH   I30TH  \\\n",
       "0  0.51  0.86  488.38   689.8   920.6  1076.70  1279.0  1446.0  1622.4   \n",
       "1 -0.39  4.61  453.00   517.0   620.6   808.20  1050.8  1462.5  2222.2   \n",
       "2 -0.05  0.36  615.30  1255.0  1680.0  2468.00  3191.0  3687.0  4109.0   \n",
       "3 -2.34  2.77  483.76   658.4  1315.0  1622.60  2280.2  3000.0  3809.2   \n",
       "4  0.04  1.06  539.00   785.4  1127.4  1510.35  2142.4  2799.0  3320.9   \n",
       "\n",
       "     I35TH   I40TH   I45TH   I50TH   I55TH   I60TH    ICV  IKUR   IMAX  \\\n",
       "0  1828.00  2019.0  2187.0  2396.0  2594.9  2845.8  57.01  2.96   8143   \n",
       "1  3103.00  3745.0  4342.3  4888.0  5400.2  5907.4  62.31  1.67  10579   \n",
       "2  4408.25  4700.0  4954.5  5196.0  5527.0  5852.0  45.95  2.23  10346   \n",
       "3  4389.60  4794.2  5206.2  5568.0  5878.2  6266.0  48.64  2.00   9915   \n",
       "4  3683.85  4060.2  4513.3  4733.0  4975.0  5271.6  48.81  2.12  10206   \n",
       "\n",
       "   IMEADIAN    IMEAN  IMODE     IP65    IP70     IP75    IP80    IP90  \\\n",
       "0    2396.0  2670.93    879  3064.40  3321.5  3622.75  4373.3  4933.4   \n",
       "1    4888.0  4533.38    444  6305.30  6624.6  6900.50  7711.5  8054.4   \n",
       "2    5196.0  5328.16   4331  6300.25  6722.0  7164.50  8163.5  8880.0   \n",
       "3    5568.0  5062.11   1228  6551.00  6797.0  7054.00  7679.2  7880.8   \n",
       "4    4733.0  4454.68   4733  5569.85  5862.6  6121.50  6782.1  7135.3   \n",
       "\n",
       "      IP95      IP99      ISD  ISKE        IVAR  \\\n",
       "0  5497.20   6831.53  1522.59  0.73  2318274.94   \n",
       "1  8659.20   9642.54  2824.58 -0.06  7978231.46   \n",
       "2  9305.50  10190.25  2448.45  0.00  5994906.73   \n",
       "3  8214.60   9003.28  2462.13 -0.41  6062069.68   \n",
       "4  7631.45   8745.74  2174.35 -0.18  4727797.24   \n",
       "\n",
       "   Percentage.all.returns.above.mean  Percentage.all.returns.above.mode  \\\n",
       "0                          43.072917                          49.062500   \n",
       "1                          56.354680                          78.620690   \n",
       "2                          55.191257                          51.639344   \n",
       "3                          71.186441                          54.237288   \n",
       "4                          46.303502                          71.498054   \n",
       "\n",
       "   Percentage.first.returns.above.mean  Percentage.first.returns.above.mode  \\\n",
       "0                            51.059322                            57.274011   \n",
       "1                            74.435028                            34.463277   \n",
       "2                            59.050445                            55.489614   \n",
       "3                            87.323944                            55.633803   \n",
       "4                            52.821128                            73.829532   \n",
       "\n",
       "   Return.1.count.above.2  Return.2.count.above.2  Return.3.count.2  \\\n",
       "0                    1416                     433                66   \n",
       "1                     708                     206                77   \n",
       "2                     337                      29                 0   \n",
       "3                     142                      32                 2   \n",
       "4                     833                     184                11   \n",
       "\n",
       "   Return.4.count.2  Return.5.count.2  Return.6.count.2  \\\n",
       "0                 5                 0                 0   \n",
       "1                22                 1                 1   \n",
       "2                 0                 0                 0   \n",
       "3                 1                 0                 0   \n",
       "4                 0                 0                 0   \n",
       "\n",
       "   Return.7.count.above.2  Total.all.return.count  \\\n",
       "0                       0                    1920   \n",
       "1                       0                    1015   \n",
       "2                       0                     366   \n",
       "3                       0                     177   \n",
       "4                       0                    1028   \n",
       "\n",
       "   Total.all.return.count.above.2  Total.first.return.count  b_max     b_mean  \\\n",
       "0                            1920                      1416    142  64.965399   \n",
       "1                            1015                       708    127  60.392079   \n",
       "2                             366                       337    124  64.554241   \n",
       "3                             177                       142    135  64.611111   \n",
       "4                            1028                       833    131  57.770553   \n",
       "\n",
       "   b_median  b_min  crownArea  g_max     g_mean  g_median  g_min  \\\n",
       "0      64.0      1      67.05    181  94.354064      89.0     36   \n",
       "1      58.0      1      45.45    153  83.174697      82.0     37   \n",
       "2      62.0     18      15.21    146  81.047995      81.0     32   \n",
       "3      63.0     24       4.68    156  84.782051      83.0     44   \n",
       "4      55.0     18      69.21    137  63.370611      61.0     32   \n",
       "\n",
       "                                            geometry  r_max      r_mean  \\\n",
       "0  POLYGON ((423712.8 564644.1, 423712.8 564643.8...    200  104.325578   \n",
       "1  POLYGON ((423819.9 564730.2, 423819.9 564729.9...    153   76.669527   \n",
       "2  POLYGON ((423469.5 564817.8, 423469.5 564817.5...    139   77.166995   \n",
       "3  POLYGON ((423754.2 564619.8, 423754.2 564619.5...    148   76.688034   \n",
       "4  POLYGON ((423400.2 564846.9, 423400.2 564846.6...    135   57.520590   \n",
       "\n",
       "   r_median  r_min  \n",
       "0      93.0     33  \n",
       "1      76.0     28  \n",
       "2      74.0     29  \n",
       "3      75.0     34  \n",
       "4      56.0     22  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trees.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "trees = trees.drop(columns=['geometry'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trees = trees.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = trees.iloc[:,1:], trees.iloc[:,0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.astype('float32')\n",
    "y = y.astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y = LabelEncoder().fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive score: 0.333 (0.000)\n"
     ]
    }
   ],
   "source": [
    "# Evaluate naive\n",
    "\n",
    "# Instantiate a DummyClassifier with 'most_frequent' strategy\n",
    "naive = DummyClassifier(strategy='most_frequent')\n",
    "\n",
    "# Create RepeatedStratifiedKFold cross-validator with 10 folds, 3 repeats and a seed of 1.\n",
    "cv = RepeatedStratifiedKFold(n_splits = 10, n_repeats = 3, random_state=1)\n",
    "\n",
    "# Calculate accuracy using `cross_val_score()` with model instantiated, data to fit, target variable, 'accuracy' scoring, cross validator, n_jobs=-1, and error_score set to 'raise'\n",
    "n_scores = cross_val_score(naive, X, y, scoring='accuracy', cv=cv, n_jobs=1, error_score='raise')\n",
    "\n",
    "# Print mean and standard deviation of n_scores: \n",
    "print('Naive score: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline score: 0.630 (0.064)\n"
     ]
    }
   ],
   "source": [
    "# Evaluate baseline model\n",
    "\n",
    "# Instantiate a DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()\n",
    "\n",
    "# Calculate accuracy using `cross_val_score()` with model instantiated, data to fit, target variable, 'accuracy' scoring, cross validator 'cv', and error_score set to 'raise'\n",
    "m_scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, error_score='raise')\n",
    "\n",
    "# Print mean and standard deviation of m_scores: \n",
    "print('Baseline score: %.3f (%.3f)' % (mean(m_scores), std(m_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import several other classifiers for ensemble\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline score: 0.730 (0.056)\n",
      "Baseline score: 0.502 (0.004)\n",
      "Baseline score: 0.677 (0.064)\n"
     ]
    }
   ],
   "source": [
    "cv = RepeatedStratifiedKFold(n_splits= 10, n_repeats=5, random_state= 1)\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "clf2 = SVC(gamma = 0.1)\n",
    "clf3 = AdaBoostClassifier()\n",
    "\n",
    "clf_scores = cross_val_score(clf, X, y, scoring='accuracy', error_score='raise', cv = cv)\n",
    "clf_scores2 = cross_val_score(clf2, X, y, scoring='accuracy', error_score='raise', cv = cv)\n",
    "clf_scores3 = cross_val_score(clf3, X, y, scoring='accuracy', error_score='raise', cv = cv)\n",
    "\n",
    "print('Baseline score: %.3f (%.3f)' % (mean(clf_scores), std(clf_scores)))\n",
    "print('Baseline score: %.3f (%.3f)' % (mean(clf_scores2), std(clf_scores2)))\n",
    "print('Baseline score: %.3f (%.3f)' % (mean(clf_scores3), std(clf_scores3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = cross_val_predict(clf, X, y, cv=10)\n",
    "conf_mat_rf = confusion_matrix(y, y_pred)\n",
    "\n",
    "y_pred = cross_val_predict(clf2, X, y, cv=10)\n",
    "conf_mat_svm = confusion_matrix(y, y_pred)\n",
    "\n",
    "y_pred = cross_val_predict(clf3, X, y, cv=10)\n",
    "conf_mat_ada = confusion_matrix(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def plot_confusion_matrix(cm,\n",
    "                          target_names,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=None,\n",
    "                          normalize=True):\n",
    "    \"\"\"\n",
    "    given a sklearn confusion matrix (cm), make a nice plot\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    cm:           confusion matrix from sklearn.metrics.confusion_matrix\n",
    "\n",
    "    target_names: given classification classes such as [0, 1, 2]\n",
    "                  the class names, for example: ['high', 'medium', 'low']\n",
    "\n",
    "    title:        the text to display at the top of the matrix\n",
    "\n",
    "    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm\n",
    "                  see http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "                  plt.get_cmap('jet') or plt.cm.Blues\n",
    "\n",
    "    normalize:    If False, plot the raw numbers\n",
    "                  If True, plot the proportions\n",
    "\n",
    "    Usage\n",
    "    -----\n",
    "    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by\n",
    "                                                              # sklearn.metrics.confusion_matrix\n",
    "                          normalize    = True,                # show proportions\n",
    "                          target_names = y_labels_vals,       # list of names of the classes\n",
    "                          title        = best_estimator_name) # title of graph\n",
    "\n",
    "    Citiation\n",
    "    ---------\n",
    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "\n",
    "    \"\"\"\n",
    "    import matplotlib.pyplot as plt\n",
    "    import numpy as np\n",
    "    import itertools\n",
    "\n",
    "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
    "    misclass = 1 - accuracy\n",
    "\n",
    "    if cmap is None:\n",
    "        cmap = plt.get_cmap('Blues')\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "\n",
    "    if target_names is not None:\n",
    "        tick_marks = np.arange(len(target_names))\n",
    "        plt.xticks(tick_marks, target_names, rotation=45)\n",
    "        plt.yticks(tick_marks, target_names)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "\n",
    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        if normalize:\n",
    "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        else:\n",
    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
    "    \n",
    "    plt.savefig('C:\\\\Temp\\\\MRes\\\\Figures\\\\cm_svm_ab_3cat.eps')\n",
    "    \n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(conf_mat_rf, normalize = False, target_names = ['Acer', 'Prunus', 'Tilia'])\n",
    "plt.savefig('C:\\\\Temp\\\\MRes\\\\Figures\\\\cm_rf_ab_3cat.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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\" 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   ],
   "source": [
    "plot_confusion_matrix(conf_mat_svm, normalize = False, target_names = ['Acer', 'Prunus', 'Tilia'])\n",
    "#plt.savefig('C:\\\\Temp\\\\MRes\\\\Figures\\\\cm_svm_ab_3cat.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"957.8181818181819\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "plot_confusion_matrix(conf_mat_ada, normalize = False, target_names = ['Acer', 'Prunus', 'Tilia'])\n",
    "#plt.savefig('C:\\\\Temp\\\\MRes\\\\Figures\\\\cm_ada_ab_3cat.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "clf=RandomForestClassifier(n_estimators =10, random_state = 42, class_weight=\"balanced\")\n",
    "output = cross_validate(clf, X, y, cv=cv, scoring = 'accuracy', return_estimator =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features sorted by their score for estimator 0:\n",
      "                          importance\n",
      "ISD                         0.079368\n",
      "IVAR                        0.057862\n",
      "IP95                        0.052890\n",
      "I55TH                       0.029212\n",
      "ISKE                        0.028621\n",
      "...                              ...\n",
      "First.returns.above.mean    0.000733\n",
      "Return.6.count.2            0.000723\n",
      "I20TH                       0.000542\n",
      "Return.5.count.2            0.000125\n",
      "Return.7.count.above.2      0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 1:\n",
      "                        importance\n",
      "ISD                       0.094510\n",
      "IP95                      0.059378\n",
      "IVAR                      0.052717\n",
      "ISKE                      0.051841\n",
      "I55TH                     0.033876\n",
      "...                            ...\n",
      "Return.1.count.above.2    0.000989\n",
      "IP75                      0.000723\n",
      "Return.6.count.2          0.000000\n",
      "HMEAN                     0.000000\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 2:\n",
      "                          importance\n",
      "ISD                         0.083696\n",
      "IVAR                        0.065894\n",
      "IP95                        0.056297\n",
      "ISKE                        0.053089\n",
      "I45TH                       0.032209\n",
      "...                              ...\n",
      "Return.5.count.2            0.000568\n",
      "First.returns.above.mean    0.000380\n",
      "Return.6.count.2            0.000281\n",
      "Return.7.count.above.2      0.000000\n",
      "Return.2.count.above.2      0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 3:\n",
      "                        importance\n",
      "ISD                       0.089697\n",
      "IVAR                      0.067304\n",
      "IP95                      0.042991\n",
      "ISKE                      0.033188\n",
      "I55TH                     0.029885\n",
      "...                            ...\n",
      "H05TH                     0.001969\n",
      "Return.6.count.2          0.001608\n",
      "Return.5.count.2          0.001530\n",
      "H25TH                     0.001254\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 4:\n",
      "                        importance\n",
      "ISD                       0.102029\n",
      "IP95                      0.077168\n",
      "ISKE                      0.061094\n",
      "IVAR                      0.042170\n",
      "I55TH                     0.038478\n",
      "...                            ...\n",
      "H55TH                     0.000758\n",
      "Return.5.count.2          0.000262\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "H90TH                     0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 5:\n",
      "                        importance\n",
      "ISD                       0.100491\n",
      "IP95                      0.068656\n",
      "ISKE                      0.058715\n",
      "IVAR                      0.040923\n",
      "I55TH                     0.023714\n",
      "...                            ...\n",
      "Return.4.count.2          0.001454\n",
      "Return.3.count.2          0.000476\n",
      "HMEAN                     0.000406\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 6:\n",
      "                        importance\n",
      "ISD                       0.091941\n",
      "IP95                      0.067621\n",
      "IVAR                      0.058376\n",
      "ISKE                      0.052580\n",
      "I55TH                     0.028082\n",
      "...                            ...\n",
      "Return.1.count.above.2    0.001510\n",
      "IP70                      0.001492\n",
      "H55TH                     0.001212\n",
      "Return.6.count.2          0.001133\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 7:\n",
      "                                importance\n",
      "ISD                               0.103252\n",
      "ISKE                              0.059323\n",
      "IVAR                              0.051440\n",
      "IP95                              0.048329\n",
      "I55TH                             0.028266\n",
      "...                                    ...\n",
      "H01TH                             0.001163\n",
      "Total.all.return.count.above.2    0.001014\n",
      "Return.1.count.above.2            0.000428\n",
      "Return.7.count.above.2            0.000000\n",
      "Return.6.count.2                  0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 8:\n",
      "                        importance\n",
      "ISD                       0.077726\n",
      "IVAR                      0.066028\n",
      "ISKE                      0.046581\n",
      "IP95                      0.041899\n",
      "I55TH                     0.026482\n",
      "...                            ...\n",
      "I20TH                     0.002399\n",
      "I15TH                     0.001864\n",
      "HVAR                      0.001693\n",
      "Return.6.count.2          0.001063\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 9:\n",
      "                        importance\n",
      "ISD                       0.102240\n",
      "IP95                      0.059389\n",
      "IVAR                      0.053972\n",
      "ISKE                      0.034098\n",
      "I55TH                     0.022274\n",
      "...                            ...\n",
      "Return.6.count.2          0.001757\n",
      "H50TH                     0.001562\n",
      "H70TH                     0.001478\n",
      "H20TH                     0.001381\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 10:\n",
      "                                     importance\n",
      "ISD                                    0.092461\n",
      "IVAR                                   0.060467\n",
      "IP95                                   0.042680\n",
      "ISKE                                   0.040973\n",
      "I55TH                                  0.029798\n",
      "...                                         ...\n",
      "Percentage.first.returns.above.mode    0.001924\n",
      "Return.7.count.above.2                 0.000000\n",
      "Return.6.count.2                       0.000000\n",
      "Return.5.count.2                       0.000000\n",
      "HMEAN                                  0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 11:\n",
      "                        importance\n",
      "ISD                       0.110469\n",
      "IP95                      0.057271\n",
      "IVAR                      0.050235\n",
      "ISKE                      0.033775\n",
      "I55TH                     0.025591\n",
      "...                            ...\n",
      "I10TH                     0.001566\n",
      "b_median                  0.001454\n",
      "H05TH                     0.000414\n",
      "Return.6.count.2          0.000089\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 12:\n",
      "                        importance\n",
      "ISD                       0.088626\n",
      "ISKE                      0.057362\n",
      "IP95                      0.051217\n",
      "IVAR                      0.047887\n",
      "I55TH                     0.032826\n",
      "...                            ...\n",
      "Return.1.count.above.2    0.000614\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "H55TH                     0.000000\n",
      "H50TH                     0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 13:\n",
      "                        importance\n",
      "ISD                       0.083982\n",
      "ISKE                      0.083047\n",
      "IP95                      0.059223\n",
      "IVAR                      0.044344\n",
      "IP80                      0.027473\n",
      "...                            ...\n",
      "Return.4.count.2          0.001664\n",
      "H70TH                     0.001577\n",
      "HMEAN                     0.001488\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 14:\n",
      "                        importance\n",
      "ISD                       0.080991\n",
      "IP95                      0.058885\n",
      "IVAR                      0.044656\n",
      "ISKE                      0.043924\n",
      "IP90                      0.026156\n",
      "...                            ...\n",
      "H90TH                     0.001948\n",
      "Return.6.count.2          0.001797\n",
      "Return.5.count.2          0.000360\n",
      "r_min                     0.000199\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 15:\n",
      "                        importance\n",
      "ISD                       0.088960\n",
      "IP95                      0.070066\n",
      "IVAR                      0.053460\n",
      "ISKE                      0.042101\n",
      "I55TH                     0.030331\n",
      "...                            ...\n",
      "H50TH                     0.001988\n",
      "g_mean                    0.000791\n",
      "Return.6.count.2          0.000586\n",
      "H70TH                     0.000411\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 16:\n",
      "                        importance\n",
      "ISD                       0.110982\n",
      "ISKE                      0.067027\n",
      "IP95                      0.048076\n",
      "I55TH                     0.036014\n",
      "IVAR                      0.033007\n",
      "...                            ...\n",
      "g_median                  0.001258\n",
      "Return.6.count.2          0.000000\n",
      "I20TH                     0.000000\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.5.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 17:\n",
      "                        importance\n",
      "ISD                       0.079463\n",
      "ISKE                      0.070242\n",
      "IP95                      0.053374\n",
      "ICV                       0.037148\n",
      "IVAR                      0.034242\n",
      "...                            ...\n",
      "H20TH                     0.002020\n",
      "Return.6.count.2          0.000629\n",
      "All.returns.above.mode    0.000385\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.5.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 18:\n",
      "                        importance\n",
      "ISD                       0.089950\n",
      "IP95                      0.052425\n",
      "ISKE                      0.038693\n",
      "IVAR                      0.037533\n",
      "I45TH                     0.030092\n",
      "...                            ...\n",
      "H55TH                     0.001081\n",
      "H15TH                     0.000360\n",
      "Return.5.count.2          0.000043\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 19:\n",
      "                                   importance\n",
      "ISD                                  0.086618\n",
      "IP95                                 0.066104\n",
      "IVAR                                 0.052333\n",
      "ISKE                                 0.052200\n",
      "I45TH                                0.028530\n",
      "...                                       ...\n",
      "Return.3.count.2                     0.000848\n",
      "I20TH                                0.000683\n",
      "Percentage.all.returns.above.mean    0.000305\n",
      "Return.7.count.above.2               0.000000\n",
      "Return.6.count.2                     0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 20:\n",
      "                        importance\n",
      "ISD                       0.093252\n",
      "ISKE                      0.073093\n",
      "IVAR                      0.060820\n",
      "I55TH                     0.038090\n",
      "IP95                      0.031579\n",
      "...                            ...\n",
      "g_mean                    0.002072\n",
      "IP70                      0.001816\n",
      "HMEADIAN                  0.000917\n",
      "Return.6.count.2          0.000000\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 21:\n",
      "                        importance\n",
      "ISD                       0.096058\n",
      "IVAR                      0.054596\n",
      "IP95                      0.054169\n",
      "ISKE                      0.037091\n",
      "IKUR                      0.035149\n",
      "...                            ...\n",
      "H40TH                     0.001906\n",
      "H05TH                     0.001453\n",
      "H10TH                     0.000879\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 22:\n",
      "                                importance\n",
      "ISD                               0.072004\n",
      "IVAR                              0.061814\n",
      "IP95                              0.047769\n",
      "ISKE                              0.044666\n",
      "I55TH                             0.036595\n",
      "...                                    ...\n",
      "Total.all.return.count.above.2    0.001553\n",
      "First.returns.above.mode          0.001143\n",
      "H20TH                             0.001110\n",
      "Return.6.count.2                  0.000701\n",
      "Return.7.count.above.2            0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 23:\n",
      "                        importance\n",
      "ISD                       0.090631\n",
      "IP95                      0.063091\n",
      "ISKE                      0.057749\n",
      "IVAR                      0.044548\n",
      "I55TH                     0.028992\n",
      "...                            ...\n",
      "Return.5.count.2          0.001847\n",
      "H90TH                     0.001779\n",
      "HMEADIAN                  0.001654\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 24:\n",
      "                        importance\n",
      "ISD                       0.098263\n",
      "ISKE                      0.069010\n",
      "IP95                      0.050987\n",
      "IVAR                      0.041471\n",
      "IP99                      0.024603\n",
      "...                            ...\n",
      "H50TH                     0.002233\n",
      "H01TH                     0.001926\n",
      "Return.5.count.2          0.000608\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 25:\n",
      "                                   importance\n",
      "ISD                                  0.081622\n",
      "ISKE                                 0.059384\n",
      "IP95                                 0.049825\n",
      "IVAR                                 0.039477\n",
      "I55TH                                0.027733\n",
      "...                                       ...\n",
      "H30TH                                0.000441\n",
      "Return.6.count.2                     0.000381\n",
      "Percentage.all.returns.above.mode    0.000304\n",
      "Return.7.count.above.2               0.000000\n",
      "Return.5.count.2                     0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 26:\n",
      "                          importance\n",
      "ISD                         0.082478\n",
      "IVAR                        0.061741\n",
      "ISKE                        0.054089\n",
      "IP95                        0.046688\n",
      "I55TH                       0.034292\n",
      "...                              ...\n",
      "First.returns.above.mean    0.001600\n",
      "I50TH                       0.001017\n",
      "Return.6.count.2            0.000307\n",
      "Return.7.count.above.2      0.000000\n",
      "H45TH                       0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 27:\n",
      "                        importance\n",
      "ISD                       0.104076\n",
      "IP95                      0.066454\n",
      "ISKE                      0.059906\n",
      "IVAR                      0.050499\n",
      "I55TH                     0.046246\n",
      "...                            ...\n",
      "H70TH                     0.001575\n",
      "H40TH                     0.001281\n",
      "H50TH                     0.001126\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 28:\n",
      "                        importance\n",
      "ISD                       0.102070\n",
      "IVAR                      0.054041\n",
      "IP95                      0.054035\n",
      "ISKE                      0.045025\n",
      "ICV                       0.024252\n",
      "...                            ...\n",
      "H25TH                     0.002106\n",
      "H70TH                     0.002019\n",
      "Return.6.count.2          0.000738\n",
      "HMEADIAN                  0.000634\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 29:\n",
      "                        importance\n",
      "ISD                       0.102554\n",
      "ISKE                      0.064499\n",
      "IP95                      0.057128\n",
      "I55TH                     0.033686\n",
      "IVAR                      0.031914\n",
      "...                            ...\n",
      "I50TH                     0.002048\n",
      "Return.5.count.2          0.001409\n",
      "H50TH                     0.001329\n",
      "Return.6.count.2          0.000358\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 30:\n",
      "                        importance\n",
      "ISD                       0.108566\n",
      "IVAR                      0.042683\n",
      "ISKE                      0.039004\n",
      "I60TH                     0.029566\n",
      "I55TH                     0.028770\n",
      "...                            ...\n",
      "HCV                       0.001790\n",
      "Return.2.count.above.2    0.001784\n",
      "Return.5.count.2          0.001643\n",
      "Return.6.count.2          0.001197\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 31:\n",
      "                        importance\n",
      "ISD                       0.086127\n",
      "ISKE                      0.061569\n",
      "IP95                      0.057258\n",
      "IVAR                      0.040663\n",
      "I55TH                     0.031433\n",
      "...                            ...\n",
      "Return.5.count.2          0.002359\n",
      "H75TH                     0.001733\n",
      "Return.6.count.2          0.001450\n",
      "H80TH                     0.000513\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 32:\n",
      "                        importance\n",
      "ISD                       0.106707\n",
      "IP95                      0.068341\n",
      "IVAR                      0.053411\n",
      "ISKE                      0.046749\n",
      "I55TH                     0.025673\n",
      "...                            ...\n",
      "I20TH                     0.001468\n",
      "Return.5.count.2          0.000826\n",
      "H55TH                     0.000071\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 33:\n",
      "                        importance\n",
      "ISD                       0.103771\n",
      "IP95                      0.053821\n",
      "IVAR                      0.049299\n",
      "ISKE                      0.047875\n",
      "I45TH                     0.033541\n",
      "...                            ...\n",
      "HMODE                     0.001462\n",
      "HMEADIAN                  0.000826\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "Return.5.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 34:\n",
      "                        importance\n",
      "ISD                       0.091707\n",
      "IP95                      0.058624\n",
      "IVAR                      0.052105\n",
      "ISKE                      0.042699\n",
      "I45TH                     0.031377\n",
      "...                            ...\n",
      "Return.5.count.2          0.001474\n",
      "All.returns.above.mode    0.001372\n",
      "Return.6.count.2          0.001228\n",
      "IMAX                      0.001193\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 35:\n",
      "                        importance\n",
      "ISD                       0.104735\n",
      "IP95                      0.066954\n",
      "IVAR                      0.053027\n",
      "ISKE                      0.045504\n",
      "I45TH                     0.034244\n",
      "...                            ...\n",
      "H25TH                     0.001143\n",
      "H90TH                     0.000974\n",
      "Return.6.count.2          0.000967\n",
      "Return.4.count.2          0.000692\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 36:\n",
      "                          importance\n",
      "ISD                         0.090347\n",
      "IVAR                        0.084895\n",
      "ISKE                        0.057305\n",
      "IP95                        0.050291\n",
      "I55TH                       0.022553\n",
      "...                              ...\n",
      "First.returns.above.mode    0.002229\n",
      "Return.4.count.2            0.002043\n",
      "Return.5.count.2            0.001235\n",
      "Return.6.count.2            0.000000\n",
      "Return.7.count.above.2      0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 37:\n",
      "                        importance\n",
      "ISD                       0.097743\n",
      "IP95                      0.059363\n",
      "ISKE                      0.052572\n",
      "IVAR                      0.039718\n",
      "I55TH                     0.034916\n",
      "...                            ...\n",
      "H05TH                     0.001850\n",
      "H55TH                     0.001097\n",
      "I40TH                     0.000623\n",
      "Return.6.count.2          0.000578\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 38:\n",
      "                        importance\n",
      "ISD                       0.097624\n",
      "ISKE                      0.062109\n",
      "IVAR                      0.058975\n",
      "IP95                      0.045954\n",
      "I55TH                     0.030860\n",
      "...                            ...\n",
      "Return.6.count.2          0.001153\n",
      "b_median                  0.001115\n",
      "HMEADIAN                  0.000838\n",
      "Return.7.count.above.2    0.000281\n",
      "Return.5.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 39:\n",
      "                                importance\n",
      "ISD                               0.097992\n",
      "IVAR                              0.067585\n",
      "ISKE                              0.064330\n",
      "IP95                              0.040556\n",
      "I55TH                             0.022461\n",
      "...                                    ...\n",
      "Total.all.return.count.above.2    0.002078\n",
      "H01TH                             0.001055\n",
      "Return.2.count.above.2            0.001021\n",
      "Return.6.count.2                  0.000972\n",
      "Return.7.count.above.2            0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 40:\n",
      "                        importance\n",
      "ISD                       0.093202\n",
      "ISKE                      0.047800\n",
      "IP95                      0.046825\n",
      "IVAR                      0.041736\n",
      "I55TH                     0.032665\n",
      "...                            ...\n",
      "HMEADIAN                  0.001689\n",
      "H30TH                     0.001468\n",
      "Return.6.count.2          0.000842\n",
      "Return.5.count.2          0.000767\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 41:\n",
      "                          importance\n",
      "ISD                         0.101847\n",
      "IP95                        0.061672\n",
      "ISKE                        0.052802\n",
      "IVAR                        0.051566\n",
      "IP80                        0.025352\n",
      "...                              ...\n",
      "Total.first.return.count    0.002376\n",
      "IP65                        0.002076\n",
      "H01TH                       0.001603\n",
      "Return.6.count.2            0.001195\n",
      "Return.7.count.above.2      0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 42:\n",
      "                        importance\n",
      "ISD                       0.087732\n",
      "IVAR                      0.060007\n",
      "IP95                      0.055751\n",
      "ISKE                      0.052537\n",
      "I60TH                     0.025168\n",
      "...                            ...\n",
      "HMEAN                     0.001667\n",
      "Return.6.count.2          0.001262\n",
      "HMEADIAN                  0.000926\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.5.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 43:\n",
      "                        importance\n",
      "ISD                       0.082972\n",
      "IP95                      0.060976\n",
      "ISKE                      0.048401\n",
      "IVAR                      0.037229\n",
      "b_mean                    0.031089\n",
      "...                            ...\n",
      "All.returns.above.mode    0.001275\n",
      "HMEADIAN                  0.000895\n",
      "All.returns.above.2       0.000772\n",
      "I05TH                     0.000116\n",
      "Return.7.count.above.2    0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 44:\n",
      "                        importance\n",
      "ISD                       0.089964\n",
      "IP95                      0.055583\n",
      "IVAR                      0.054322\n",
      "ISKE                      0.039546\n",
      "I55TH                     0.033452\n",
      "...                            ...\n",
      "H05TH                     0.001688\n",
      "Return.5.count.2          0.001068\n",
      "H20TH                     0.000272\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 45:\n",
      "                          importance\n",
      "ISD                         0.090044\n",
      "IP95                        0.055870\n",
      "ISKE                        0.037741\n",
      "IVAR                        0.031974\n",
      "I45TH                       0.030643\n",
      "...                              ...\n",
      "H50TH                       0.001807\n",
      "First.returns.above.mean    0.001386\n",
      "Return.5.count.2            0.001303\n",
      "Return.6.count.2            0.000000\n",
      "Return.7.count.above.2      0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 46:\n",
      "                        importance\n",
      "ISD                       0.093985\n",
      "IP95                      0.058915\n",
      "ISKE                      0.045592\n",
      "IVAR                      0.043478\n",
      "IP65                      0.031195\n",
      "...                            ...\n",
      "H20TH                     0.001493\n",
      "HCV                       0.000998\n",
      "Return.6.count.2          0.000770\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.5.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 47:\n",
      "                        importance\n",
      "ISD                       0.111986\n",
      "ISKE                      0.062603\n",
      "IP95                      0.050916\n",
      "IVAR                      0.038366\n",
      "I55TH                     0.029402\n",
      "...                            ...\n",
      "I01TH                     0.001377\n",
      "Return.4.count.2          0.000982\n",
      "Return.6.count.2          0.000385\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.5.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 48:\n",
      "                        importance\n",
      "ISD                       0.111196\n",
      "IVAR                      0.062674\n",
      "ISKE                      0.058527\n",
      "IP95                      0.048832\n",
      "I55TH                     0.038320\n",
      "...                            ...\n",
      "I15TH                     0.001537\n",
      "H40TH                     0.001313\n",
      "Return.5.count.2          0.001205\n",
      "Return.7.count.above.2    0.000000\n",
      "Return.6.count.2          0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n",
      "Features sorted by their score for estimator 49:\n",
      "                          importance\n",
      "ISD                         0.081727\n",
      "IP95                        0.059255\n",
      "ISKE                        0.047129\n",
      "IVAR                        0.044857\n",
      "I45TH                       0.029919\n",
      "...                              ...\n",
      "IMEAN                       0.002202\n",
      "Total.first.return.count    0.001915\n",
      "H65TH                       0.001851\n",
      "Return.6.count.2            0.000000\n",
      "Return.7.count.above.2      0.000000\n",
      "\n",
      "[92 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "for idx,estimator in enumerate(output['estimator']):\n",
    "    print(\"Features sorted by their score for estimator {}:\".format(idx))\n",
    "    feature_importances = pd.DataFrame(estimator.feature_importances_,\n",
    "                                       index = X.columns,\n",
    "                                        columns=['importance']).sort_values('importance', ascending=False)\n",
    "    print(feature_importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = X.columns.values.tolist()\n",
    "importances = pd.Series(clf.feature_importances_,index=feature_names).sort_values(ascending=False)\n",
    "importances - importances.tolist()\n",
    "print(importances)"
   ]
  }
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