1 | using System;
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2 | using System.Collections;
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3 | using System.Globalization;
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4 | using System.Linq;
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5 | using System.Runtime.CompilerServices;
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6 | using HeuristicLab.Problems.DataAnalysis;
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7 | using HeuristicLab.Random;
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8 | using Microsoft.VisualStudio.TestTools.UnitTesting;
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9 |
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10 | namespace HeuristicLab.Algorithms.DataAnalysis.GradientBoostedTrees {
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11 | [TestClass()]
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12 | public class Test {
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13 | [TestMethod]
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14 | [TestCategory("Algorithms.DataAnalysis")]
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15 | [TestProperty("Time", "short")]
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16 | public void DecisionTreeTest() {
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17 | {
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18 | var xy = new double[,]
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19 | {
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20 | {1, 20, 0},
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21 | {1, 20, 0},
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22 | {2, 10, 0},
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23 | {2, 10, 0},
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24 | };
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25 | var allVariables = new string[] { "y", "x1", "x2" };
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26 |
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27 | // x1 <= 15 -> 1
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28 | // x1 > 15 -> 2
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29 | BuildTree(xy, allVariables, 10);
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30 | }
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31 |
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32 |
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33 | {
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34 | var xy = new double[,]
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35 | {
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36 | {1, 20, 1},
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37 | {1, 20, -1},
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38 | {2, 10, -1},
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39 | {2, 10, 1},
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40 | };
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41 | var allVariables = new string[] { "y", "x1", "x2" };
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42 |
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43 | // ignore irrelevant variables
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44 | // x1 <= 15 -> 1
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45 | // x1 > 15 -> 2
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46 | BuildTree(xy, allVariables, 10);
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47 | }
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48 |
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49 | {
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50 | // split must be by x1 first
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51 | var xy = new double[,]
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52 | {
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53 | {1, 20, 1},
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54 | {2, 20, -1},
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55 | {3, 10, -1},
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56 | {4, 10, 1},
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57 | };
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58 |
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59 | var allVariables = new string[] { "y", "x1", "x2" };
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60 |
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61 | // x1 <= 15 AND x2 <= 0 -> 3
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62 | // x1 <= 15 AND x2 > 0 -> 4
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63 | // x1 > 15 AND x2 <= 0 -> 1
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64 | // x1 > 15 AND x2 > 0 -> 2
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65 | BuildTree(xy, allVariables, 10);
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66 | }
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67 |
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68 | {
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69 | // averaging ys
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70 | var xy = new double[,]
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71 | {
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72 | {0.5, 20, 1},
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73 | {1.5, 20, 1},
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74 | {1.5, 20, -1},
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75 | {2.5, 20, -1},
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76 | {2.5, 10, -1},
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77 | {3.5, 10, -1},
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78 | {3.5, 10, 1},
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79 | {4.5, 10, 1},
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80 | };
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81 |
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82 | var allVariables = new string[] { "y", "x1", "x2" };
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83 |
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84 | // x1 <= 15 AND x2 <= 0 -> 3
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85 | // x1 <= 15 AND x2 > 0 -> 4
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86 | // x1 > 15 AND x2 <= 0 -> 1
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87 | // x1 > 15 AND x2 > 0 -> 2
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88 | BuildTree(xy, allVariables, 10);
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89 | }
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90 |
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91 |
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92 | {
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93 | // diagonal split (no split possible)
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94 | var xy = new double[,]
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95 | {
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96 | {10, 1, 1},
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97 | {1, 1, 2},
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98 | {1, 2, 1},
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99 | {10, 2, 2},
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100 | };
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101 |
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102 | var allVariables = new string[] { "y", "x1", "x2" };
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103 |
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104 | // split cannot be found
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105 | BuildTree(xy, allVariables, 3);
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106 | }
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107 | {
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108 | // almost diagonal split
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109 | var xy = new double[,]
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110 | {
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111 | {10, 1, 1},
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112 | {1, 1, 2},
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113 | {1, 2, 1},
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114 | {10.1, 2, 2},
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115 | };
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116 |
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117 | var allVariables = new string[] { "y", "x1", "x2" };
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118 | // x1 <= 1.5 AND x2 <= 1.5 -> 10
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119 | // x1 <= 1.5 AND x2 > 1.5 -> 1
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120 | // x1 > 1.5 AND x2 <= 1.5 -> 1
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121 | // x1 > 1.5 AND x2 > 1.5 -> 10.1
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122 | BuildTree(xy, allVariables, 3);
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123 | }
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124 | {
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125 | // unbalanced split
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126 | var xy = new double[,]
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127 | {
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128 | {-1, 1, 1},
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129 | {-1, 1, 2},
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130 | {0.9, 2, 1},
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131 | {1.1, 2, 2},
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132 | };
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133 |
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134 | var allVariables = new string[] { "y", "x1", "x2" };
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135 | // x1 <= 1.5 -> -1.0
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136 | // x1 > 1.5 AND x2 <= 1.5 -> 0.9
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137 | // x1 > 1.5 AND x2 > 1.5 -> 1.1
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138 | BuildTree(xy, allVariables, 3);
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139 | }
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140 |
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141 | }
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142 |
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143 | private void BuildTree(double[,] xy, string[] allVariables, int maxDepth) {
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144 | int nRows = xy.GetLength(0);
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145 | var allowedInputs = allVariables.Skip(1);
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146 | var dataset = new Dataset(allVariables, xy);
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147 | var problemData = new RegressionProblemData(dataset, allowedInputs, allVariables.First());
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148 | problemData.TrainingPartition.Start = 0;
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149 | problemData.TrainingPartition.End = nRows;
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150 | problemData.TestPartition.Start = nRows;
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151 | problemData.TestPartition.End = nRows;
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152 | var rand = new MersenneTwister(31415);
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153 | var builder = new RegressionTreeBuilder(problemData, rand);
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154 | var model = (GradientBoostedTreesModel)builder.CreateRegressionTree(maxDepth, 1, 1); // maximal depth and use all rows and cols
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155 | var constM = model.Models.First() as ConstantRegressionModel;
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156 | var treeM = model.Models.Skip(1).First() as RegressionTreeModel;
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157 | WriteTree(treeM.tree, 0, "", constM.Constant);
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158 | Console.WriteLine();
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159 | }
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160 |
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161 | private void WriteTree(RegressionTreeModel.TreeNode[] tree, int idx, string partialRule, double offset) {
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162 | var n = tree[idx];
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163 | if (n.varName == RegressionTreeModel.TreeNode.NO_VARIABLE) {
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164 | Console.WriteLine("{0} -> {1:F}", partialRule, n.val + offset);
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165 | } else {
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166 | WriteTree(tree, n.leftIdx,
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167 | string.Format(CultureInfo.InvariantCulture, "{0}{1}{2} <= {3:F}",
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168 | partialRule,
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169 | string.IsNullOrEmpty(partialRule) ? "" : " and ",
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170 | n.varName,
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171 | n.val), offset);
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172 | WriteTree(tree, n.rightIdx,
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173 | string.Format(CultureInfo.InvariantCulture, "{0}{1}{2} > {3:F}",
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174 | partialRule,
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175 | string.IsNullOrEmpty(partialRule) ? "" : " and ",
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176 | n.varName,
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177 | n.val), offset);
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178 | }
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179 | }
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180 | }
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181 | }
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