1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Linq;
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24 | using System.Threading;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Data;
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28 | using HeuristicLab.Optimization;
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29 | using HeuristicLab.Parameters;
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30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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31 | using HeuristicLab.PluginInfrastructure;
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32 | using HeuristicLab.Problems.DataAnalysis;
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33 |
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34 | namespace HeuristicLab.Algorithms.DataAnalysis {
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35 | [Item("Kernel Ridge Regression", "Kernelized ridge regression e.g. for radial basis function (RBF) regression.")]
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36 | [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 100)]
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37 | [StorableClass]
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38 | public sealed class KernelRidgeRegression : BasicAlgorithm {
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39 | private const string SolutionResultName = "Kernel ridge regression solution";
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40 |
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41 | public override bool SupportsPause {
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42 | get { return false; }
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43 | }
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44 | public override Type ProblemType {
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45 | get { return typeof(IRegressionProblem); }
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46 | }
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47 | public new IRegressionProblem Problem {
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48 | get { return (IRegressionProblem) base.Problem; }
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49 | set { base.Problem = value; }
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50 | }
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51 |
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52 | #region parameter names
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53 | private const string KernelParameterName = "Kernel";
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54 | private const string ScaleInputVariablesParameterName = "ScaleInputVariables";
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55 | private const string LambdaParameterName = "LogLambda";
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56 | private const string BetaParameterName = "Beta";
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57 | #endregion
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58 |
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59 | #region parameter properties
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60 | public IConstrainedValueParameter<IKernel> KernelParameter {
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61 | get { return (IConstrainedValueParameter<IKernel>) Parameters[KernelParameterName]; }
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62 | }
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63 |
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64 | public IFixedValueParameter<BoolValue> ScaleInputVariablesParameter {
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65 | get { return (IFixedValueParameter<BoolValue>) Parameters[ScaleInputVariablesParameterName]; }
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66 | }
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67 |
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68 | public IFixedValueParameter<DoubleValue> LogLambdaParameter {
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69 | get { return (IFixedValueParameter<DoubleValue>) Parameters[LambdaParameterName]; }
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70 | }
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71 |
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72 | public IFixedValueParameter<DoubleValue> BetaParameter {
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73 | get { return (IFixedValueParameter<DoubleValue>) Parameters[BetaParameterName]; }
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74 | }
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75 | #endregion
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76 |
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77 | #region properties
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78 | public IKernel Kernel {
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79 | get { return KernelParameter.Value; }
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80 | }
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81 |
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82 | public bool ScaleInputVariables {
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83 | get { return ScaleInputVariablesParameter.Value.Value; }
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84 | set { ScaleInputVariablesParameter.Value.Value = value; }
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85 | }
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86 |
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87 | public double LogLambda {
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88 | get { return LogLambdaParameter.Value.Value; }
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89 | set { LogLambdaParameter.Value.Value = value; }
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90 | }
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91 |
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92 | public double Beta {
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93 | get { return BetaParameter.Value.Value; }
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94 | set { BetaParameter.Value.Value = value; }
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95 | }
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96 | #endregion
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97 |
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98 | [StorableConstructor]
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99 | private KernelRidgeRegression(bool deserializing) : base(deserializing) { }
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100 | private KernelRidgeRegression(KernelRidgeRegression original, Cloner cloner)
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101 | : base(original, cloner) { }
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102 | public KernelRidgeRegression() {
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103 | Problem = new RegressionProblem();
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104 | var values = new ItemSet<IKernel>(ApplicationManager.Manager.GetInstances<IKernel>());
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105 | Parameters.Add(new ConstrainedValueParameter<IKernel>(KernelParameterName, "The kernel", values, values.OfType<GaussianKernel>().FirstOrDefault()));
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106 | Parameters.Add(new FixedValueParameter<BoolValue>(ScaleInputVariablesParameterName, "Set to true if the input variables should be scaled to the interval [0..1]", new BoolValue(true)));
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107 | Parameters.Add(new FixedValueParameter<DoubleValue>(LambdaParameterName, "The log10-transformed weight for the regularization term lambda [-inf..+inf]. Small values produce more complex models, large values produce models with larger errors. Set to very small value (e.g. -1.0e15) for almost exact approximation", new DoubleValue(-2)));
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108 | Parameters.Add(new FixedValueParameter<DoubleValue>(BetaParameterName, "The inverse width of the kernel ]0..+inf]. The distance between points is divided by this value before being plugged into the kernel.", new DoubleValue(2)));
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109 | }
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110 |
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111 | public override IDeepCloneable Clone(Cloner cloner) {
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112 | return new KernelRidgeRegression(this, cloner);
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113 | }
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114 |
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115 | protected override void Run(CancellationToken cancellationToken) {
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116 | double rmsError, looCvRMSE;
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117 | var kernel = Kernel;
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118 | kernel.Beta = Beta;
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119 | var solution = CreateRadialBasisRegressionSolution(Problem.ProblemData, kernel, Math.Pow(10, LogLambda), ScaleInputVariables, out rmsError, out looCvRMSE);
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120 | Results.Add(new Result(SolutionResultName, "The kernel ridge regression solution.", solution));
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121 | Results.Add(new Result("RMSE (test)", "The root mean squared error of the solution on the test set.", new DoubleValue(rmsError)));
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122 | Results.Add(new Result("RMSE (LOO-CV)", "The leave-one-out-cross-validation root mean squared error", new DoubleValue(looCvRMSE)));
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123 | }
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124 |
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125 | public static IRegressionSolution CreateRadialBasisRegressionSolution(IRegressionProblemData problemData, ICovarianceFunction kernel, double lambda, bool scaleInputs, out double rmsError, out double looCvRMSE) {
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126 | var model = KernelRidgeRegressionModel.Create(problemData.Dataset, problemData.TargetVariable, problemData.AllowedInputVariables, problemData.TrainingIndices, scaleInputs, kernel, lambda);
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127 | rmsError = double.NaN;
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128 | if (problemData.TestIndices.Any()) {
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129 | rmsError = Math.Sqrt(model.GetEstimatedValues(problemData.Dataset, problemData.TestIndices)
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130 | .Zip(problemData.TargetVariableTestValues, (a, b) => (a - b) * (a - b))
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131 | .Average());
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132 | }
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133 | var solution = model.CreateRegressionSolution((IRegressionProblemData) problemData.Clone());
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134 | solution.Model.Name = "Kernel ridge regression model";
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135 | solution.Name = SolutionResultName;
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136 | looCvRMSE = model.LooCvRMSE;
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137 | return solution;
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138 | }
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139 | }
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140 | } |
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