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;
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24 | using System.Collections.Generic;
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25 | using System.Diagnostics.Contracts;
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26 | using System.Linq;
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27 | using HeuristicLab.Problems.DataAnalysis;
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28 | using HeuristicLab.Random;
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29 |
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30 | namespace HeuristicLab.Algorithms.DataAnalysis {
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31 | public static class GradientBoostedTreesAlgorithmStatic {
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32 | #region static API
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33 |
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34 | public interface IGbmState {
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35 | IRegressionModel GetModel();
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36 | double GetTrainLoss();
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37 | double GetTestLoss();
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38 | IEnumerable<KeyValuePair<string, double>> GetVariableRelevance();
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39 | }
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40 |
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41 | // created through factory method
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42 | // GbmState details are private API users can only use methods from IGbmState
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43 | private class GbmState : IGbmState {
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44 | internal IRegressionProblemData problemData { get; private set; }
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45 | internal ILossFunction lossFunction { get; private set; }
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46 | internal int maxSize { get; private set; }
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47 | internal double nu { get; private set; }
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48 | internal double r { get; private set; }
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49 | internal double m { get; private set; }
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50 | internal int[] trainingRows { get; private set; }
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51 | internal int[] testRows { get; private set; }
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52 | internal RegressionTreeBuilder treeBuilder { get; private set; }
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53 |
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54 | private readonly uint randSeed;
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55 | private MersenneTwister random { get; set; }
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56 |
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57 | // array members (allocate only once)
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58 | internal double[] pred;
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59 | internal double[] predTest;
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60 | internal double[] y;
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61 | internal int[] activeIdx;
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62 | internal double[] pseudoRes;
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63 |
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64 | private readonly IList<IRegressionModel> models;
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65 | private readonly IList<double> weights;
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66 |
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67 | public GbmState(IRegressionProblemData problemData, ILossFunction lossFunction, uint randSeed, int maxSize, double r, double m, double nu) {
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68 | // default settings for MaxSize, Nu and R
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69 | this.maxSize = maxSize;
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70 | this.nu = nu;
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71 | this.r = r;
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72 | this.m = m;
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73 |
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74 | this.randSeed = randSeed;
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75 | random = new MersenneTwister(randSeed);
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76 | this.problemData = problemData;
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77 | this.trainingRows = problemData.TrainingIndices.ToArray();
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78 | this.testRows = problemData.TestIndices.ToArray();
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79 | this.lossFunction = lossFunction;
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80 |
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81 | int nRows = trainingRows.Length;
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82 |
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83 | y = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, trainingRows).ToArray();
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84 |
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85 | treeBuilder = new RegressionTreeBuilder(problemData, random);
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86 |
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87 | activeIdx = Enumerable.Range(0, nRows).ToArray();
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88 |
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89 | var zeros = Enumerable.Repeat(0.0, nRows).ToArray();
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90 | double f0 = lossFunction.LineSearch(y, zeros, activeIdx, 0, nRows - 1); // initial constant value (mean for squared errors)
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91 | pred = Enumerable.Repeat(f0, nRows).ToArray();
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92 | predTest = Enumerable.Repeat(f0, testRows.Length).ToArray();
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93 | pseudoRes = new double[nRows];
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94 |
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95 | models = new List<IRegressionModel>();
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96 | weights = new List<double>();
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97 | // add constant model
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98 | models.Add(new ConstantModel(f0, problemData.TargetVariable));
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99 | weights.Add(1.0);
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100 | }
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101 |
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102 | public IRegressionModel GetModel() {
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103 | #pragma warning disable 618
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104 | var model = new GradientBoostedTreesModel(models, weights);
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105 | #pragma warning restore 618
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106 | // we don't know the number of iterations here but the number of weights is equal
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107 | // to the number of iterations + 1 (for the constant model)
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108 | // wrap the actual model in a surrogate that enables persistence and lazy recalculation of the model if necessary
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109 | return new GradientBoostedTreesModelSurrogate(problemData, randSeed, lossFunction, weights.Count - 1, maxSize, r, m, nu, model);
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110 | }
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111 | public IEnumerable<KeyValuePair<string, double>> GetVariableRelevance() {
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112 | return treeBuilder.GetVariableRelevance();
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113 | }
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114 |
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115 | public double GetTrainLoss() {
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116 | int nRows = y.Length;
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117 | return lossFunction.GetLoss(y, pred) / nRows;
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118 | }
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119 | public double GetTestLoss() {
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120 | var yTest = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, testRows);
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121 | var nRows = testRows.Length;
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122 | return lossFunction.GetLoss(yTest, predTest) / nRows;
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123 | }
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124 |
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125 | internal void AddModel(IRegressionModel m, double weight) {
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126 | models.Add(m);
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127 | weights.Add(weight);
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128 | }
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129 | }
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130 |
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131 | // simple interface
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132 | public static GradientBoostedTreesSolution TrainGbm(IRegressionProblemData problemData, ILossFunction lossFunction, int maxSize, double nu, double r, double m, int maxIterations, uint randSeed = 31415) {
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133 | Contract.Assert(r > 0);
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134 | Contract.Assert(r <= 1.0);
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135 | Contract.Assert(nu > 0);
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136 | Contract.Assert(nu <= 1.0);
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137 |
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138 | var state = (GbmState)CreateGbmState(problemData, lossFunction, randSeed, maxSize, r, m, nu);
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139 |
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140 | for (int iter = 0; iter < maxIterations; iter++) {
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141 | MakeStep(state);
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142 | }
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143 |
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144 | var model = state.GetModel();
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145 | return new GradientBoostedTreesSolution(model, (IRegressionProblemData)problemData.Clone());
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146 | }
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147 |
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148 | // for custom stepping & termination
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149 | public static IGbmState CreateGbmState(IRegressionProblemData problemData, ILossFunction lossFunction, uint randSeed, int maxSize = 3, double r = 0.66, double m = 0.5, double nu = 0.01) {
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150 | // check input variables. Only double variables are allowed.
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151 | var invalidInputs =
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152 | problemData.AllowedInputVariables.Where(name => !problemData.Dataset.VariableHasType<double>(name));
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153 | if (invalidInputs.Any())
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154 | throw new NotSupportedException("Gradient tree boosting only supports real-valued variables. Unsupported inputs: " + string.Join(", ", invalidInputs));
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155 |
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156 | return new GbmState(problemData, lossFunction, randSeed, maxSize, r, m, nu);
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157 | }
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158 |
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159 | // use default settings for maxSize, nu, r from state
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160 | public static void MakeStep(IGbmState state) {
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161 | var gbmState = state as GbmState;
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162 | if (gbmState == null) throw new ArgumentException("state");
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163 |
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164 | MakeStep(gbmState, gbmState.maxSize, gbmState.nu, gbmState.r, gbmState.m);
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165 | }
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166 |
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167 | // allow dynamic adaptation of maxSize, nu and r (even though this is not used)
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168 | public static void MakeStep(IGbmState state, int maxSize, double nu, double r, double m) {
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169 | var gbmState = state as GbmState;
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170 | if (gbmState == null) throw new ArgumentException("state");
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171 |
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172 | var problemData = gbmState.problemData;
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173 | var lossFunction = gbmState.lossFunction;
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174 | var yPred = gbmState.pred;
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175 | var yPredTest = gbmState.predTest;
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176 | var treeBuilder = gbmState.treeBuilder;
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177 | var y = gbmState.y;
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178 | var activeIdx = gbmState.activeIdx;
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179 | var pseudoRes = gbmState.pseudoRes;
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180 | var trainingRows = gbmState.trainingRows;
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181 | var testRows = gbmState.testRows;
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182 |
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183 | // copy output of gradient function to pre-allocated rim array (pseudo-residual per row and model)
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184 | int rimIdx = 0;
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185 | foreach (var g in lossFunction.GetLossGradient(y, yPred)) {
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186 | pseudoRes[rimIdx++] = g;
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187 | }
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188 |
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189 | var tree = treeBuilder.CreateRegressionTreeForGradientBoosting(pseudoRes, yPred, maxSize, activeIdx, lossFunction, r, m);
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190 |
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191 | int i = 0;
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192 | foreach (var pred in tree.GetEstimatedValues(problemData.Dataset, trainingRows)) {
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193 | yPred[i] = yPred[i] + nu * pred;
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194 | i++;
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195 | }
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196 | // update predictions for validation set
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197 | i = 0;
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198 | foreach (var pred in tree.GetEstimatedValues(problemData.Dataset, testRows)) {
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199 | yPredTest[i] = yPredTest[i] + nu * pred;
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200 | i++;
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201 | }
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202 |
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203 | gbmState.AddModel(tree, nu);
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204 | }
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205 | #endregion
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206 | }
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207 | }
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