1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | * and the BEACON Center for the Study of Evolution in Action.
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5 | *
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6 | * This file is part of HeuristicLab.
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7 | *
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8 | * HeuristicLab is free software: you can redistribute it and/or modify
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9 | * it under the terms of the GNU General Public License as published by
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10 | * the Free Software Foundation, either version 3 of the License, or
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11 | * (at your option) any later version.
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12 | *
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13 | * HeuristicLab is distributed in the hope that it will be useful,
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14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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16 | * GNU General Public License for more details.
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17 | *
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18 | * You should have received a copy of the GNU General Public License
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19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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20 | */
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21 | #endregion
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22 |
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23 | using System.Linq;
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24 | using System.Threading;
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25 | using HeuristicLab.Analysis;
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26 | using HeuristicLab.Common;
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27 | using HeuristicLab.Core;
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28 | using HeuristicLab.Data;
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29 | using HeuristicLab.Optimization;
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30 | using HeuristicLab.Parameters;
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31 | using HEAL.Attic;
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32 | using HeuristicLab.PluginInfrastructure;
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33 | using HeuristicLab.Problems.DataAnalysis;
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34 |
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35 | namespace HeuristicLab.Algorithms.DataAnalysis {
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36 | [Item("Gradient Boosted Trees (GBT)", "Gradient boosted trees algorithm. Specific implementation of gradient boosting for regression trees. Friedman, J. \"Greedy Function Approximation: A Gradient Boosting Machine\", IMS 1999 Reitz Lecture.")]
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37 | [StorableType("8CCB55BD-4935-4868-855F-D3E5D55127AA")]
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38 | [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 125)]
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39 | public class GradientBoostedTreesAlgorithm : FixedDataAnalysisAlgorithm<IRegressionProblem> {
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40 | #region ParameterNames
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41 | private const string IterationsParameterName = "Iterations";
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42 | private const string MaxSizeParameterName = "Maximum Tree Size";
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43 | private const string NuParameterName = "Nu";
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44 | private const string RParameterName = "R";
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45 | private const string MParameterName = "M";
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46 | private const string SeedParameterName = "Seed";
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47 | private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
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48 | private const string LossFunctionParameterName = "LossFunction";
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49 | private const string UpdateIntervalParameterName = "UpdateInterval";
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50 | private const string CreateSolutionParameterName = "CreateSolution";
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51 | #endregion
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52 |
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53 | #region ParameterProperties
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54 | public IFixedValueParameter<IntValue> IterationsParameter {
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55 | get { return (IFixedValueParameter<IntValue>)Parameters[IterationsParameterName]; }
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56 | }
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57 | public IFixedValueParameter<IntValue> MaxSizeParameter {
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58 | get { return (IFixedValueParameter<IntValue>)Parameters[MaxSizeParameterName]; }
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59 | }
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60 | public IFixedValueParameter<DoubleValue> NuParameter {
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61 | get { return (IFixedValueParameter<DoubleValue>)Parameters[NuParameterName]; }
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62 | }
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63 | public IFixedValueParameter<DoubleValue> RParameter {
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64 | get { return (IFixedValueParameter<DoubleValue>)Parameters[RParameterName]; }
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65 | }
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66 | public IFixedValueParameter<DoubleValue> MParameter {
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67 | get { return (IFixedValueParameter<DoubleValue>)Parameters[MParameterName]; }
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68 | }
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69 | public IFixedValueParameter<IntValue> SeedParameter {
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70 | get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
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71 | }
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72 | public FixedValueParameter<BoolValue> SetSeedRandomlyParameter {
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73 | get { return (FixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
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74 | }
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75 | public IConstrainedValueParameter<ILossFunction> LossFunctionParameter {
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76 | get { return (IConstrainedValueParameter<ILossFunction>)Parameters[LossFunctionParameterName]; }
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77 | }
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78 | public IFixedValueParameter<IntValue> UpdateIntervalParameter {
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79 | get { return (IFixedValueParameter<IntValue>)Parameters[UpdateIntervalParameterName]; }
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80 | }
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81 | public IFixedValueParameter<BoolValue> CreateSolutionParameter {
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82 | get { return (IFixedValueParameter<BoolValue>)Parameters[CreateSolutionParameterName]; }
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83 | }
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84 | #endregion
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85 |
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86 | #region Properties
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87 | public int Iterations {
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88 | get { return IterationsParameter.Value.Value; }
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89 | set { IterationsParameter.Value.Value = value; }
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90 | }
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91 | public int Seed {
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92 | get { return SeedParameter.Value.Value; }
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93 | set { SeedParameter.Value.Value = value; }
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94 | }
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95 | public bool SetSeedRandomly {
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96 | get { return SetSeedRandomlyParameter.Value.Value; }
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97 | set { SetSeedRandomlyParameter.Value.Value = value; }
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98 | }
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99 | public int MaxSize {
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100 | get { return MaxSizeParameter.Value.Value; }
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101 | set { MaxSizeParameter.Value.Value = value; }
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102 | }
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103 | public double Nu {
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104 | get { return NuParameter.Value.Value; }
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105 | set { NuParameter.Value.Value = value; }
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106 | }
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107 | public double R {
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108 | get { return RParameter.Value.Value; }
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109 | set { RParameter.Value.Value = value; }
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110 | }
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111 | public double M {
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112 | get { return MParameter.Value.Value; }
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113 | set { MParameter.Value.Value = value; }
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114 | }
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115 | public bool CreateSolution {
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116 | get { return CreateSolutionParameter.Value.Value; }
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117 | set { CreateSolutionParameter.Value.Value = value; }
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118 | }
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119 | #endregion
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120 |
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121 | #region ResultsProperties
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122 | private double ResultsBestQuality {
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123 | get { return ((DoubleValue)Results["Best Quality"].Value).Value; }
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124 | set { ((DoubleValue)Results["Best Quality"].Value).Value = value; }
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125 | }
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126 | private DataTable ResultsQualities {
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127 | get { return ((DataTable)Results["Qualities"].Value); }
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128 | }
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129 | #endregion
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130 |
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131 | [StorableConstructor]
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132 | protected GradientBoostedTreesAlgorithm(StorableConstructorFlag _) : base(_) { }
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133 |
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134 | protected GradientBoostedTreesAlgorithm(GradientBoostedTreesAlgorithm original, Cloner cloner)
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135 | : base(original, cloner) {
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136 | }
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137 |
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138 | public override IDeepCloneable Clone(Cloner cloner) {
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139 | return new GradientBoostedTreesAlgorithm(this, cloner);
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140 | }
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141 |
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142 | public GradientBoostedTreesAlgorithm() {
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143 | Problem = new RegressionProblem(); // default problem
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144 |
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145 | Parameters.Add(new FixedValueParameter<IntValue>(IterationsParameterName, "Number of iterations (set as high as possible, adjust in combination with nu, when increasing iterations also decrease nu)", new IntValue(1000)));
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146 | Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
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147 | Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
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148 | Parameters.Add(new FixedValueParameter<IntValue>(MaxSizeParameterName, "Maximal size of the tree learned in each step (prefer smaller sizes if possible)", new IntValue(10)));
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149 | Parameters.Add(new FixedValueParameter<DoubleValue>(RParameterName, "Ratio of training rows selected randomly in each step (0 < R <= 1)", new DoubleValue(0.5)));
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150 | Parameters.Add(new FixedValueParameter<DoubleValue>(MParameterName, "Ratio of variables selected randomly in each step (0 < M <= 1)", new DoubleValue(0.5)));
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151 | Parameters.Add(new FixedValueParameter<DoubleValue>(NuParameterName, "Learning rate nu (step size for the gradient update, should be small 0 < nu < 0.1)", new DoubleValue(0.002)));
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152 | Parameters.Add(new FixedValueParameter<IntValue>(UpdateIntervalParameterName, "", new IntValue(100)));
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153 | Parameters[UpdateIntervalParameterName].Hidden = true;
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154 | Parameters.Add(new FixedValueParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run", new BoolValue(true)));
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155 | Parameters[CreateSolutionParameterName].Hidden = true;
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156 |
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157 | var lossFunctions = ApplicationManager.Manager.GetInstances<ILossFunction>();
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158 | Parameters.Add(new ConstrainedValueParameter<ILossFunction>(LossFunctionParameterName, "The loss function", new ItemSet<ILossFunction>(lossFunctions)));
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159 | LossFunctionParameter.Value = LossFunctionParameter.ValidValues.First(f => f.ToString().Contains("Squared")); // squared error loss is the default
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160 | }
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161 |
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162 | [StorableHook(HookType.AfterDeserialization)]
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163 | private void AfterDeserialization() {
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164 | // BackwardsCompatibility3.4
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165 | #region Backwards compatible code, remove with 3.5
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166 | // parameter type has been changed
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167 | var lossFunctionParam = Parameters[LossFunctionParameterName] as ConstrainedValueParameter<StringValue>;
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168 | if (lossFunctionParam != null) {
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169 | Parameters.Remove(LossFunctionParameterName);
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170 | var selectedValue = lossFunctionParam.Value; // to be restored below
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171 |
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172 | var lossFunctions = ApplicationManager.Manager.GetInstances<ILossFunction>();
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173 | Parameters.Add(new ConstrainedValueParameter<ILossFunction>(LossFunctionParameterName, "The loss function", new ItemSet<ILossFunction>(lossFunctions)));
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174 | // try to restore selected value
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175 | var selectedLossFunction =
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176 | LossFunctionParameter.ValidValues.FirstOrDefault(f => f.ToString() == selectedValue.Value);
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177 | if (selectedLossFunction != null) {
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178 | LossFunctionParameter.Value = selectedLossFunction;
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179 | } else {
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180 | LossFunctionParameter.Value = LossFunctionParameter.ValidValues.First(f => f.ToString().Contains("Squared")); // default: SE
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181 | }
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182 | }
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183 | #endregion
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184 | }
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185 |
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186 | protected override void Run(CancellationToken cancellationToken) {
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187 | // Set up the algorithm
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188 | if (SetSeedRandomly) Seed = Random.RandomSeedGenerator.GetSeed();
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189 |
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190 | // Set up the results display
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191 | var iterations = new IntValue(0);
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192 | Results.Add(new Result("Iterations", iterations));
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193 |
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194 | var table = new DataTable("Qualities");
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195 | table.Rows.Add(new DataRow("Loss (train)"));
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196 | table.Rows.Add(new DataRow("Loss (test)"));
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197 | table.Rows["Loss (train)"].VisualProperties.StartIndexZero = true;
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198 | table.Rows["Loss (test)"].VisualProperties.StartIndexZero = true;
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199 |
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200 | Results.Add(new Result("Qualities", table));
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201 | var curLoss = new DoubleValue();
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202 | Results.Add(new Result("Loss (train)", curLoss));
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203 |
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204 | // init
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205 | var problemData = (IRegressionProblemData)Problem.ProblemData.Clone();
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206 | var lossFunction = LossFunctionParameter.Value;
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207 | var state = GradientBoostedTreesAlgorithmStatic.CreateGbmState(problemData, lossFunction, (uint)Seed, MaxSize, R, M, Nu);
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208 |
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209 | var updateInterval = UpdateIntervalParameter.Value.Value;
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210 | // Loop until iteration limit reached or canceled.
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211 | for (int i = 0; i < Iterations; i++) {
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212 | cancellationToken.ThrowIfCancellationRequested();
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213 |
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214 | GradientBoostedTreesAlgorithmStatic.MakeStep(state);
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215 |
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216 | // iteration results
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217 | if (i % updateInterval == 0) {
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218 | curLoss.Value = state.GetTrainLoss();
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219 | table.Rows["Loss (train)"].Values.Add(curLoss.Value);
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220 | table.Rows["Loss (test)"].Values.Add(state.GetTestLoss());
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221 | iterations.Value = i;
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222 | }
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223 | }
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224 |
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225 | // final results
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226 | iterations.Value = Iterations;
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227 | curLoss.Value = state.GetTrainLoss();
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228 | table.Rows["Loss (train)"].Values.Add(curLoss.Value);
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229 | table.Rows["Loss (test)"].Values.Add(state.GetTestLoss());
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230 |
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231 | // produce variable relevance
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232 | var orderedImpacts = state.GetVariableRelevance().Select(t => new { name = t.Key, impact = t.Value }).ToList();
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233 |
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234 | var impacts = new DoubleMatrix();
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235 | var matrix = impacts as IStringConvertibleMatrix;
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236 | matrix.Rows = orderedImpacts.Count;
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237 | matrix.RowNames = orderedImpacts.Select(x => x.name);
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238 | matrix.Columns = 1;
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239 | matrix.ColumnNames = new string[] { "Relative variable relevance" };
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240 |
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241 | int rowIdx = 0;
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242 | foreach (var p in orderedImpacts) {
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243 | matrix.SetValue(string.Format("{0:N2}", p.impact), rowIdx++, 0);
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244 | }
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245 |
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246 | Results.Add(new Result("Variable relevance", impacts));
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247 | Results.Add(new Result("Loss (test)", new DoubleValue(state.GetTestLoss())));
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248 |
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249 | // produce solution
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250 | if (CreateSolution) {
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251 | var model = state.GetModel();
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252 |
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253 | // for logistic regression we produce a classification solution
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254 | if (lossFunction is LogisticRegressionLoss) {
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255 | var classificationModel = new DiscriminantFunctionClassificationModel(model,
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256 | new AccuracyMaximizationThresholdCalculator());
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257 | var classificationProblemData = new ClassificationProblemData(problemData.Dataset,
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258 | problemData.AllowedInputVariables, problemData.TargetVariable, problemData.Transformations);
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259 | classificationProblemData.TrainingPartition.Start = Problem.ProblemData.TrainingPartition.Start;
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260 | classificationProblemData.TrainingPartition.End = Problem.ProblemData.TrainingPartition.End;
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261 | classificationProblemData.TestPartition.Start = Problem.ProblemData.TestPartition.Start;
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262 | classificationProblemData.TestPartition.End = Problem.ProblemData.TestPartition.End;
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263 |
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264 | classificationModel.SetThresholdsAndClassValues(new double[] { double.NegativeInfinity, 0.0 }, new[] { 0.0, 1.0 });
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265 |
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266 |
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267 | var classificationSolution = new DiscriminantFunctionClassificationSolution(classificationModel, classificationProblemData);
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268 | Results.Add(new Result("Solution", classificationSolution));
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269 | } else {
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270 | // otherwise we produce a regression solution
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271 | Results.Add(new Result("Solution", new GradientBoostedTreesSolution(model, problemData)));
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272 | }
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273 | }
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274 | }
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275 | }
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276 | }
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