[14386] | 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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[14872] | 24 | using System.Threading;
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[14386] | 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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[15169] | 31 | using HeuristicLab.PluginInfrastructure;
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[14386] | 32 | using HeuristicLab.Problems.DataAnalysis;
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| 33 |
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[14936] | 34 | namespace HeuristicLab.Algorithms.DataAnalysis {
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[14887] | 35 | [Item("Kernel Ridge Regression", "Kernelized ridge regression e.g. for radial basis function (RBF) regression.")]
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[14386] | 36 | [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 100)]
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| 37 | [StorableClass]
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[15451] | 38 | public sealed class KernelRidgeRegression : BasicAlgorithm, IDataAnalysisAlgorithm<IRegressionProblem> {
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[14887] | 39 | private const string SolutionResultName = "Kernel ridge regression solution";
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[14386] | 40 |
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[14872] | 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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[14386] | 51 |
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[14872] | 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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[14887] | 55 | private const string LambdaParameterName = "LogLambda";
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| 56 | private const string BetaParameterName = "Beta";
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[14386] | 57 | #endregion
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| 58 |
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[14872] | 59 | #region parameter properties
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[15169] | 60 | public IConstrainedValueParameter<IKernel> KernelParameter {
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| 61 | get { return (IConstrainedValueParameter<IKernel>)Parameters[KernelParameterName]; }
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[14386] | 62 | }
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[14872] | 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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[14887] | 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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[14386] | 75 | #endregion
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| 76 |
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[14872] | 77 | #region properties
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[14887] | 78 | public IKernel Kernel {
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[14386] | 79 | get { return KernelParameter.Value; }
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| 80 | }
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| 81 |
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[14872] | 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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[14887] | 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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[14386] | 96 | #endregion
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| 97 |
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| 98 | [StorableConstructor]
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[14887] | 99 | private KernelRidgeRegression(bool deserializing) : base(deserializing) { }
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| 100 | private KernelRidgeRegression(KernelRidgeRegression original, Cloner cloner)
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[14386] | 101 | : base(original, cloner) {
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| 102 | }
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[14887] | 103 | public KernelRidgeRegression() {
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[14386] | 104 | Problem = new RegressionProblem();
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[15169] | 105 | var values = new ItemSet<IKernel>(ApplicationManager.Manager.GetInstances<IKernel>());
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| 106 | Parameters.Add(new ConstrainedValueParameter<IKernel>(KernelParameterName, "The kernel", values, values.OfType<GaussianKernel>().FirstOrDefault()));
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[14872] | 107 | 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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[14887] | 108 | 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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[15248] | 109 | 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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[14386] | 110 | }
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| 111 |
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| 112 | public override IDeepCloneable Clone(Cloner cloner) {
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[14887] | 113 | return new KernelRidgeRegression(this, cloner);
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[14386] | 114 | }
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| 115 |
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[14872] | 116 | protected override void Run(CancellationToken cancellationToken) {
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[14888] | 117 | double rmsError, looCvRMSE;
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[14887] | 118 | var kernel = Kernel;
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| 119 | kernel.Beta = Beta;
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[14888] | 120 | var solution = CreateRadialBasisRegressionSolution(Problem.ProblemData, kernel, Math.Pow(10, LogLambda), ScaleInputVariables, out rmsError, out looCvRMSE);
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[14887] | 121 | Results.Add(new Result(SolutionResultName, "The kernel ridge regression solution.", solution));
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[14872] | 122 | 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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[14888] | 123 | Results.Add(new Result("RMSE (LOO-CV)", "The leave-one-out-cross-validation root mean squared error", new DoubleValue(looCvRMSE)));
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[14386] | 124 | }
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| 125 |
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[14888] | 126 | public static IRegressionSolution CreateRadialBasisRegressionSolution(IRegressionProblemData problemData, ICovarianceFunction kernel, double lambda, bool scaleInputs, out double rmsError, out double looCvRMSE) {
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[15164] | 127 | var model = KernelRidgeRegressionModel.Create(problemData.Dataset, problemData.TargetVariable, problemData.AllowedInputVariables, problemData.TrainingIndices, scaleInputs, kernel, lambda);
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[14887] | 128 | rmsError = double.NaN;
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| 129 | if (problemData.TestIndices.Any()) {
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| 130 | rmsError = Math.Sqrt(model.GetEstimatedValues(problemData.Dataset, problemData.TestIndices)
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| 131 | .Zip(problemData.TargetVariableTestValues, (a, b) => (a - b) * (a - b))
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| 132 | .Average());
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| 133 | }
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[14872] | 134 | var solution = model.CreateRegressionSolution((IRegressionProblemData)problemData.Clone());
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[14887] | 135 | solution.Model.Name = "Kernel ridge regression model";
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| 136 | solution.Name = SolutionResultName;
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[14888] | 137 | looCvRMSE = model.LooCvRMSE;
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[14386] | 138 | return solution;
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| 139 | }
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| 140 | }
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| 141 | }
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