[5624] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12012] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[5624] | 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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[5777] | 23 | using System.Collections.Generic;
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[5624] | 24 | using System.Linq;
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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.Problems.DataAnalysis;
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[8609] | 32 | using LibSVM;
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[5624] | 33 |
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| 34 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 35 | /// <summary>
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| 36 | /// Support vector machine regression data analysis algorithm.
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| 37 | /// </summary>
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[6240] | 38 | [Item("Support Vector Regression", "Support vector machine regression data analysis algorithm (wrapper for libSVM).")]
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[12504] | 39 | [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 110)]
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[5624] | 40 | [StorableClass]
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| 41 | public sealed class SupportVectorRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
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| 42 | private const string SvmTypeParameterName = "SvmType";
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| 43 | private const string KernelTypeParameterName = "KernelType";
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| 44 | private const string CostParameterName = "Cost";
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| 45 | private const string NuParameterName = "Nu";
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| 46 | private const string GammaParameterName = "Gamma";
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| 47 | private const string EpsilonParameterName = "Epsilon";
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[8613] | 48 | private const string DegreeParameterName = "Degree";
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[5624] | 49 |
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| 50 | #region parameter properties
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[8121] | 51 | public IConstrainedValueParameter<StringValue> SvmTypeParameter {
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| 52 | get { return (IConstrainedValueParameter<StringValue>)Parameters[SvmTypeParameterName]; }
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[5624] | 53 | }
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[8121] | 54 | public IConstrainedValueParameter<StringValue> KernelTypeParameter {
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| 55 | get { return (IConstrainedValueParameter<StringValue>)Parameters[KernelTypeParameterName]; }
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[5624] | 56 | }
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| 57 | public IValueParameter<DoubleValue> NuParameter {
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| 58 | get { return (IValueParameter<DoubleValue>)Parameters[NuParameterName]; }
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| 59 | }
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| 60 | public IValueParameter<DoubleValue> CostParameter {
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| 61 | get { return (IValueParameter<DoubleValue>)Parameters[CostParameterName]; }
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| 62 | }
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| 63 | public IValueParameter<DoubleValue> GammaParameter {
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| 64 | get { return (IValueParameter<DoubleValue>)Parameters[GammaParameterName]; }
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| 65 | }
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| 66 | public IValueParameter<DoubleValue> EpsilonParameter {
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| 67 | get { return (IValueParameter<DoubleValue>)Parameters[EpsilonParameterName]; }
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| 68 | }
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[8613] | 69 | public IValueParameter<IntValue> DegreeParameter {
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| 70 | get { return (IValueParameter<IntValue>)Parameters[DegreeParameterName]; }
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| 71 | }
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[5624] | 72 | #endregion
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| 73 | #region properties
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| 74 | public StringValue SvmType {
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| 75 | get { return SvmTypeParameter.Value; }
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[8121] | 76 | set { SvmTypeParameter.Value = value; }
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[5624] | 77 | }
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| 78 | public StringValue KernelType {
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| 79 | get { return KernelTypeParameter.Value; }
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[8121] | 80 | set { KernelTypeParameter.Value = value; }
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[5624] | 81 | }
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| 82 | public DoubleValue Nu {
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| 83 | get { return NuParameter.Value; }
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| 84 | }
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| 85 | public DoubleValue Cost {
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| 86 | get { return CostParameter.Value; }
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| 87 | }
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| 88 | public DoubleValue Gamma {
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| 89 | get { return GammaParameter.Value; }
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| 90 | }
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| 91 | public DoubleValue Epsilon {
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| 92 | get { return EpsilonParameter.Value; }
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| 93 | }
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[8613] | 94 | public IntValue Degree {
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| 95 | get { return DegreeParameter.Value; }
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| 96 | }
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[5624] | 97 | #endregion
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| 98 | [StorableConstructor]
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| 99 | private SupportVectorRegression(bool deserializing) : base(deserializing) { }
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| 100 | private SupportVectorRegression(SupportVectorRegression original, Cloner cloner)
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| 101 | : base(original, cloner) {
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| 102 | }
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| 103 | public SupportVectorRegression()
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| 104 | : base() {
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[5649] | 105 | Problem = new RegressionProblem();
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| 106 |
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[5626] | 107 | List<StringValue> svrTypes = (from type in new List<string> { "NU_SVR", "EPSILON_SVR" }
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| 108 | select new StringValue(type).AsReadOnly())
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| 109 | .ToList();
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| 110 | ItemSet<StringValue> svrTypeSet = new ItemSet<StringValue>(svrTypes);
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| 111 | List<StringValue> kernelTypes = (from type in new List<string> { "LINEAR", "POLY", "SIGMOID", "RBF" }
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| 112 | select new StringValue(type).AsReadOnly())
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| 113 | .ToList();
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[5649] | 114 | ItemSet<StringValue> kernelTypeSet = new ItemSet<StringValue>(kernelTypes);
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[5626] | 115 | Parameters.Add(new ConstrainedValueParameter<StringValue>(SvmTypeParameterName, "The type of SVM to use.", svrTypeSet, svrTypes[0]));
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| 116 | Parameters.Add(new ConstrainedValueParameter<StringValue>(KernelTypeParameterName, "The kernel type to use for the SVM.", kernelTypeSet, kernelTypes[3]));
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| 117 | Parameters.Add(new ValueParameter<DoubleValue>(NuParameterName, "The value of the nu parameter of the nu-SVR.", new DoubleValue(0.5)));
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| 118 | Parameters.Add(new ValueParameter<DoubleValue>(CostParameterName, "The value of the C (cost) parameter of epsilon-SVR and nu-SVR.", new DoubleValue(1.0)));
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[5624] | 119 | Parameters.Add(new ValueParameter<DoubleValue>(GammaParameterName, "The value of the gamma parameter in the kernel function.", new DoubleValue(1.0)));
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[5626] | 120 | Parameters.Add(new ValueParameter<DoubleValue>(EpsilonParameterName, "The value of the epsilon parameter for epsilon-SVR.", new DoubleValue(0.1)));
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[8613] | 121 | Parameters.Add(new ValueParameter<IntValue>(DegreeParameterName, "The degree parameter for the polynomial kernel function.", new IntValue(3)));
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[5624] | 122 | }
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| 123 | [StorableHook(HookType.AfterDeserialization)]
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[8613] | 124 | private void AfterDeserialization() {
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| 125 | #region backwards compatibility (change with 3.4)
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| 126 | if (!Parameters.ContainsKey(DegreeParameterName))
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| 127 | Parameters.Add(new ValueParameter<IntValue>(DegreeParameterName, "The degree parameter for the polynomial kernel function.", new IntValue(3)));
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| 128 | #endregion
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| 129 | }
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[5624] | 130 |
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| 131 | public override IDeepCloneable Clone(Cloner cloner) {
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| 132 | return new SupportVectorRegression(this, cloner);
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| 133 | }
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| 134 |
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| 135 | #region support vector regression
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| 136 | protected override void Run() {
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| 137 | IRegressionProblemData problemData = Problem.ProblemData;
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[5649] | 138 | IEnumerable<string> selectedInputVariables = problemData.AllowedInputVariables;
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[7306] | 139 | double trainR2, testR2;
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| 140 | int nSv;
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| 141 | var solution = CreateSupportVectorRegressionSolution(problemData, selectedInputVariables, SvmType.Value,
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[8613] | 142 | KernelType.Value, Cost.Value, Nu.Value, Gamma.Value, Epsilon.Value, Degree.Value,
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[7306] | 143 | out trainR2, out testR2, out nSv);
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[5624] | 144 |
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| 145 | Results.Add(new Result("Support vector regression solution", "The support vector regression solution.", solution));
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[7306] | 146 | Results.Add(new Result("Training R²", "The Pearson's R² of the SVR solution on the training partition.", new DoubleValue(trainR2)));
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| 147 | Results.Add(new Result("Test R²", "The Pearson's R² of the SVR solution on the test partition.", new DoubleValue(testR2)));
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| 148 | Results.Add(new Result("Number of support vectors", "The number of support vectors of the SVR solution.", new IntValue(nSv)));
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[5624] | 149 | }
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| 150 |
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| 151 | public static SupportVectorRegressionSolution CreateSupportVectorRegressionSolution(IRegressionProblemData problemData, IEnumerable<string> allowedInputVariables,
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[8613] | 152 | string svmType, string kernelType, double cost, double nu, double gamma, double epsilon, int degree,
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[7306] | 153 | out double trainingR2, out double testR2, out int nSv) {
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[12509] | 154 | var dataset = problemData.Dataset;
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[5624] | 155 | string targetVariable = problemData.TargetVariable;
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[8139] | 156 | IEnumerable<int> rows = problemData.TrainingIndices;
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[5624] | 157 |
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| 158 | //extract SVM parameters from scope and set them
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[8609] | 159 | svm_parameter parameter = new svm_parameter();
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| 160 | parameter.svm_type = GetSvmType(svmType);
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| 161 | parameter.kernel_type = GetKernelType(kernelType);
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[5624] | 162 | parameter.C = cost;
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[8609] | 163 | parameter.nu = nu;
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| 164 | parameter.gamma = gamma;
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| 165 | parameter.p = epsilon;
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| 166 | parameter.cache_size = 500;
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| 167 | parameter.probability = 0;
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| 168 | parameter.eps = 0.001;
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[8613] | 169 | parameter.degree = degree;
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[8609] | 170 | parameter.shrinking = 1;
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| 171 | parameter.coef0 = 0;
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[5624] | 172 |
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[7097] | 173 |
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[8609] | 174 |
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| 175 | svm_problem problem = SupportVectorMachineUtil.CreateSvmProblem(dataset, targetVariable, allowedInputVariables, rows);
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| 176 | RangeTransform rangeTransform = RangeTransform.Compute(problem);
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| 177 | svm_problem scaledProblem = rangeTransform.Scale(problem);
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| 178 | var svmModel = svm.svm_train(scaledProblem, parameter);
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| 179 | nSv = svmModel.SV.Length;
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[7306] | 180 | var model = new SupportVectorMachineModel(svmModel, rangeTransform, targetVariable, allowedInputVariables);
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| 181 | var solution = new SupportVectorRegressionSolution(model, (IRegressionProblemData)problemData.Clone());
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| 182 | trainingR2 = solution.TrainingRSquared;
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| 183 | testR2 = solution.TestRSquared;
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| 184 | return solution;
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[5624] | 185 | }
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[8609] | 186 |
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| 187 | private static int GetSvmType(string svmType) {
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| 188 | if (svmType == "NU_SVR") return svm_parameter.NU_SVR;
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| 189 | if (svmType == "EPSILON_SVR") return svm_parameter.EPSILON_SVR;
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| 190 | throw new ArgumentException("Unknown SVM type");
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| 191 | }
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| 192 |
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| 193 | private static int GetKernelType(string kernelType) {
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| 194 | if (kernelType == "LINEAR") return svm_parameter.LINEAR;
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| 195 | if (kernelType == "POLY") return svm_parameter.POLY;
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| 196 | if (kernelType == "SIGMOID") return svm_parameter.SIGMOID;
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| 197 | if (kernelType == "RBF") return svm_parameter.RBF;
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| 198 | throw new ArgumentException("Unknown kernel type");
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| 199 | }
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[5624] | 200 | #endregion
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| 201 | }
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| 202 | }
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