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.Collections.Generic;
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24 | using System.Linq;
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25 | using System.Threading;
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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 HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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32 | using HeuristicLab.Problems.DataAnalysis;
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33 | using LibSVM;
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34 |
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35 | namespace HeuristicLab.Algorithms.DataAnalysis {
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36 | /// <summary>
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37 | /// Support vector machine regression data analysis algorithm.
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38 | /// </summary>
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39 | [Item("Support Vector Regression (SVM)", "Support vector machine regression data analysis algorithm (wrapper for libSVM).")]
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40 | [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 110)]
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41 | [StorableClass]
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42 | public sealed class SupportVectorRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
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43 | private const string SvmTypeParameterName = "SvmType";
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44 | private const string KernelTypeParameterName = "KernelType";
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45 | private const string CostParameterName = "Cost";
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46 | private const string NuParameterName = "Nu";
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47 | private const string GammaParameterName = "Gamma";
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48 | private const string EpsilonParameterName = "Epsilon";
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49 | private const string DegreeParameterName = "Degree";
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50 | private const string CreateSolutionParameterName = "CreateSolution";
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51 |
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52 | #region parameter properties
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53 | public IConstrainedValueParameter<StringValue> SvmTypeParameter {
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54 | get { return (IConstrainedValueParameter<StringValue>)Parameters[SvmTypeParameterName]; }
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55 | }
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56 | public IConstrainedValueParameter<StringValue> KernelTypeParameter {
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57 | get { return (IConstrainedValueParameter<StringValue>)Parameters[KernelTypeParameterName]; }
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58 | }
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59 | public IValueParameter<DoubleValue> NuParameter {
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60 | get { return (IValueParameter<DoubleValue>)Parameters[NuParameterName]; }
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61 | }
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62 | public IValueParameter<DoubleValue> CostParameter {
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63 | get { return (IValueParameter<DoubleValue>)Parameters[CostParameterName]; }
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64 | }
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65 | public IValueParameter<DoubleValue> GammaParameter {
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66 | get { return (IValueParameter<DoubleValue>)Parameters[GammaParameterName]; }
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67 | }
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68 | public IValueParameter<DoubleValue> EpsilonParameter {
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69 | get { return (IValueParameter<DoubleValue>)Parameters[EpsilonParameterName]; }
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70 | }
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71 | public IValueParameter<IntValue> DegreeParameter {
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72 | get { return (IValueParameter<IntValue>)Parameters[DegreeParameterName]; }
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73 | }
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74 | public IFixedValueParameter<BoolValue> CreateSolutionParameter {
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75 | get { return (IFixedValueParameter<BoolValue>)Parameters[CreateSolutionParameterName]; }
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76 | }
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77 | #endregion
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78 | #region properties
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79 | public StringValue SvmType {
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80 | get { return SvmTypeParameter.Value; }
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81 | set { SvmTypeParameter.Value = value; }
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82 | }
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83 | public StringValue KernelType {
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84 | get { return KernelTypeParameter.Value; }
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85 | set { KernelTypeParameter.Value = value; }
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86 | }
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87 | public DoubleValue Nu {
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88 | get { return NuParameter.Value; }
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89 | }
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90 | public DoubleValue Cost {
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91 | get { return CostParameter.Value; }
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92 | }
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93 | public DoubleValue Gamma {
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94 | get { return GammaParameter.Value; }
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95 | }
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96 | public DoubleValue Epsilon {
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97 | get { return EpsilonParameter.Value; }
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98 | }
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99 | public IntValue Degree {
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100 | get { return DegreeParameter.Value; }
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101 | }
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102 | public bool CreateSolution {
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103 | get { return CreateSolutionParameter.Value.Value; }
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104 | set { CreateSolutionParameter.Value.Value = value; }
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105 | }
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106 | #endregion
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107 | [StorableConstructor]
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108 | private SupportVectorRegression(bool deserializing) : base(deserializing) { }
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109 | private SupportVectorRegression(SupportVectorRegression original, Cloner cloner)
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110 | : base(original, cloner) {
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111 | }
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112 | public SupportVectorRegression()
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113 | : base() {
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114 | Problem = new RegressionProblem();
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115 |
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116 | List<StringValue> svrTypes = (from type in new List<string> { "NU_SVR", "EPSILON_SVR" }
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117 | select new StringValue(type).AsReadOnly())
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118 | .ToList();
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119 | ItemSet<StringValue> svrTypeSet = new ItemSet<StringValue>(svrTypes);
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120 | List<StringValue> kernelTypes = (from type in new List<string> { "LINEAR", "POLY", "SIGMOID", "RBF" }
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121 | select new StringValue(type).AsReadOnly())
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122 | .ToList();
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123 | ItemSet<StringValue> kernelTypeSet = new ItemSet<StringValue>(kernelTypes);
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124 | Parameters.Add(new ConstrainedValueParameter<StringValue>(SvmTypeParameterName, "The type of SVM to use.", svrTypeSet, svrTypes[0]));
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125 | Parameters.Add(new ConstrainedValueParameter<StringValue>(KernelTypeParameterName, "The kernel type to use for the SVM.", kernelTypeSet, kernelTypes[3]));
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126 | Parameters.Add(new ValueParameter<DoubleValue>(NuParameterName, "The value of the nu parameter of the nu-SVR.", new DoubleValue(0.5)));
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127 | 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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128 | Parameters.Add(new ValueParameter<DoubleValue>(GammaParameterName, "The value of the gamma parameter in the kernel function.", new DoubleValue(1.0)));
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129 | Parameters.Add(new ValueParameter<DoubleValue>(EpsilonParameterName, "The value of the epsilon parameter for epsilon-SVR.", new DoubleValue(0.1)));
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130 | Parameters.Add(new ValueParameter<IntValue>(DegreeParameterName, "The degree parameter for the polynomial kernel function.", new IntValue(3)));
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131 | 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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132 | Parameters[CreateSolutionParameterName].Hidden = true;
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133 | }
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134 | [StorableHook(HookType.AfterDeserialization)]
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135 | private void AfterDeserialization() {
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136 | #region backwards compatibility (change with 3.4)
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137 |
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138 | if (!Parameters.ContainsKey(DegreeParameterName)) {
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139 | Parameters.Add(new ValueParameter<IntValue>(DegreeParameterName,
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140 | "The degree parameter for the polynomial kernel function.", new IntValue(3)));
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141 | }
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142 | if (!Parameters.ContainsKey(CreateSolutionParameterName)) {
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143 | 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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144 | Parameters[CreateSolutionParameterName].Hidden = true;
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145 | }
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146 | #endregion
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147 | }
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148 |
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149 | public override IDeepCloneable Clone(Cloner cloner) {
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150 | return new SupportVectorRegression(this, cloner);
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151 | }
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152 |
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153 | #region support vector regression
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154 | protected override void Run(CancellationToken cancellationToken) {
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155 | IRegressionProblemData problemData = Problem.ProblemData;
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156 | IEnumerable<string> selectedInputVariables = problemData.AllowedInputVariables;
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157 | int nSv;
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158 | ISupportVectorMachineModel model;
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159 | Run(problemData, selectedInputVariables, SvmType.Value, KernelType.Value, Cost.Value, Nu.Value, Gamma.Value, Epsilon.Value, Degree.Value, out model, out nSv);
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160 |
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161 | if (CreateSolution) {
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162 | var solution = new SupportVectorRegressionSolution((SupportVectorMachineModel)model, (IRegressionProblemData)problemData.Clone());
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163 | Results.Add(new Result("Support vector regression solution", "The support vector regression solution.", solution));
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164 | }
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165 |
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166 | Results.Add(new Result("Number of support vectors", "The number of support vectors of the SVR solution.", new IntValue(nSv)));
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167 |
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168 |
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169 | {
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170 | // calculate regression model metrics
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171 | var ds = problemData.Dataset;
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172 | var trainRows = problemData.TrainingIndices;
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173 | var testRows = problemData.TestIndices;
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174 | var yTrain = ds.GetDoubleValues(problemData.TargetVariable, trainRows);
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175 | var yTest = ds.GetDoubleValues(problemData.TargetVariable, testRows);
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176 | var yPredTrain = model.GetEstimatedValues(ds, trainRows).ToArray();
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177 | var yPredTest = model.GetEstimatedValues(ds, testRows).ToArray();
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178 |
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179 | OnlineCalculatorError error;
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180 | var trainMse = OnlineMeanSquaredErrorCalculator.Calculate(yPredTrain, yTrain, out error);
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181 | if (error != OnlineCalculatorError.None) trainMse = double.MaxValue;
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182 | var testMse = OnlineMeanSquaredErrorCalculator.Calculate(yPredTest, yTest, out error);
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183 | if (error != OnlineCalculatorError.None) testMse = double.MaxValue;
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184 |
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185 | Results.Add(new Result("Mean squared error (training)", "The mean of squared errors of the SVR solution on the training partition.", new DoubleValue(trainMse)));
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186 | Results.Add(new Result("Mean squared error (test)", "The mean of squared errors of the SVR solution on the test partition.", new DoubleValue(testMse)));
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187 |
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188 |
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189 | var trainMae = OnlineMeanAbsoluteErrorCalculator.Calculate(yPredTrain, yTrain, out error);
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190 | if (error != OnlineCalculatorError.None) trainMae = double.MaxValue;
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191 | var testMae = OnlineMeanAbsoluteErrorCalculator.Calculate(yPredTest, yTest, out error);
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192 | if (error != OnlineCalculatorError.None) testMae = double.MaxValue;
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193 |
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194 | Results.Add(new Result("Mean absolute error (training)", "The mean of absolute errors of the SVR solution on the training partition.", new DoubleValue(trainMae)));
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195 | Results.Add(new Result("Mean absolute error (test)", "The mean of absolute errors of the SVR solution on the test partition.", new DoubleValue(testMae)));
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196 |
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197 |
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198 | var trainRelErr = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(yPredTrain, yTrain, out error);
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199 | if (error != OnlineCalculatorError.None) trainRelErr = double.MaxValue;
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200 | var testRelErr = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(yPredTest, yTest, out error);
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201 | if (error != OnlineCalculatorError.None) testRelErr = double.MaxValue;
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202 |
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203 | Results.Add(new Result("Average relative error (training)", "The mean of relative errors of the SVR solution on the training partition.", new DoubleValue(trainRelErr)));
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204 | Results.Add(new Result("Average relative error (test)", "The mean of relative errors of the SVR solution on the test partition.", new DoubleValue(testRelErr)));
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205 | }
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206 | }
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207 |
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208 | // BackwardsCompatibility3.4
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209 | #region Backwards compatible code, remove with 3.5
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210 | // for compatibility with old API
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211 | public static SupportVectorRegressionSolution CreateSupportVectorRegressionSolution(
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212 | IRegressionProblemData problemData, IEnumerable<string> allowedInputVariables,
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213 | string svmType, string kernelType, double cost, double nu, double gamma, double epsilon, int degree,
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214 | out double trainingR2, out double testR2, out int nSv) {
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215 | ISupportVectorMachineModel model;
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216 | Run(problemData, allowedInputVariables, svmType, kernelType, cost, nu, gamma, epsilon, degree, out model, out nSv);
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217 |
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218 | var solution = new SupportVectorRegressionSolution((SupportVectorMachineModel)model, (IRegressionProblemData)problemData.Clone());
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219 | trainingR2 = solution.TrainingRSquared;
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220 | testR2 = solution.TestRSquared;
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221 | return solution;
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222 | }
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223 | #endregion
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224 |
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225 | public static void Run(IRegressionProblemData problemData, IEnumerable<string> allowedInputVariables,
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226 | string svmType, string kernelType, double cost, double nu, double gamma, double epsilon, int degree,
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227 | out ISupportVectorMachineModel model, out int nSv) {
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228 | var dataset = problemData.Dataset;
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229 | string targetVariable = problemData.TargetVariable;
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230 | IEnumerable<int> rows = problemData.TrainingIndices;
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231 |
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232 | svm_parameter parameter = new svm_parameter {
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233 | svm_type = GetSvmType(svmType),
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234 | kernel_type = GetKernelType(kernelType),
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235 | C = cost,
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236 | nu = nu,
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237 | gamma = gamma,
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238 | p = epsilon,
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239 | cache_size = 500,
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240 | probability = 0,
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241 | eps = 0.001,
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242 | degree = degree,
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243 | shrinking = 1,
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244 | coef0 = 0
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245 | };
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246 |
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247 | svm_problem problem = SupportVectorMachineUtil.CreateSvmProblem(dataset, targetVariable, allowedInputVariables, rows);
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248 | RangeTransform rangeTransform = RangeTransform.Compute(problem);
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249 | svm_problem scaledProblem = rangeTransform.Scale(problem);
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250 | var svmModel = svm.svm_train(scaledProblem, parameter);
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251 | nSv = svmModel.SV.Length;
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252 |
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253 | model = new SupportVectorMachineModel(svmModel, rangeTransform, targetVariable, allowedInputVariables);
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254 | }
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255 |
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256 | private static int GetSvmType(string svmType) {
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257 | if (svmType == "NU_SVR") return svm_parameter.NU_SVR;
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258 | if (svmType == "EPSILON_SVR") return svm_parameter.EPSILON_SVR;
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259 | throw new ArgumentException("Unknown SVM type");
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260 | }
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261 |
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262 | private static int GetKernelType(string kernelType) {
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263 | if (kernelType == "LINEAR") return svm_parameter.LINEAR;
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264 | if (kernelType == "POLY") return svm_parameter.POLY;
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265 | if (kernelType == "SIGMOID") return svm_parameter.SIGMOID;
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266 | if (kernelType == "RBF") return svm_parameter.RBF;
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267 | throw new ArgumentException("Unknown kernel type");
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268 | }
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269 | #endregion
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270 | }
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271 | }
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