1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2011 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 HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.Optimization;
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28 | using HeuristicLab.Parameters;
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29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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30 | using HeuristicLab.Problems.DataAnalysis;
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31 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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32 | using System.Collections.Generic;
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33 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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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 classification data analysis algorithm.
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38 | /// </summary>
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39 | [Item("Support Vector Classification", "Support vector machine classification data analysis algorithm.")]
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40 | [Creatable("Data Analysis")]
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41 | [StorableClass]
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42 | public sealed class SupportVectorClassification : FixedDataAnalysisAlgorithm<IClassificationProblem> {
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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 |
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49 | #region parameter properties
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50 | public IValueParameter<StringValue> SvmTypeParameter {
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51 | get { return (IValueParameter<StringValue>)Parameters[SvmTypeParameterName]; }
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52 | }
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53 | public IValueParameter<StringValue> KernelTypeParameter {
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54 | get { return (IValueParameter<StringValue>)Parameters[KernelTypeParameterName]; }
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55 | }
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56 | public IValueParameter<DoubleValue> NuParameter {
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57 | get { return (IValueParameter<DoubleValue>)Parameters[NuParameterName]; }
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58 | }
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59 | public IValueParameter<DoubleValue> CostParameter {
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60 | get { return (IValueParameter<DoubleValue>)Parameters[CostParameterName]; }
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61 | }
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62 | public IValueParameter<DoubleValue> GammaParameter {
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63 | get { return (IValueParameter<DoubleValue>)Parameters[GammaParameterName]; }
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64 | }
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65 | #endregion
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66 | #region properties
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67 | public StringValue SvmType {
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68 | get { return SvmTypeParameter.Value; }
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69 | }
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70 | public StringValue KernelType {
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71 | get { return KernelTypeParameter.Value; }
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72 | }
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73 | public DoubleValue Nu {
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74 | get { return NuParameter.Value; }
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75 | }
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76 | public DoubleValue Cost {
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77 | get { return CostParameter.Value; }
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78 | }
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79 | public DoubleValue Gamma {
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80 | get { return GammaParameter.Value; }
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81 | }
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82 | #endregion
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83 | [StorableConstructor]
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84 | private SupportVectorClassification(bool deserializing) : base(deserializing) { }
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85 | private SupportVectorClassification(SupportVectorClassification original, Cloner cloner)
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86 | : base(original, cloner) {
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87 | }
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88 | public SupportVectorClassification()
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89 | : base() {
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90 | Problem = new ClassificationProblem();
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91 |
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92 | List<StringValue> svrTypes = (from type in new List<string> { "NU_SVC", "EPSILON_SVC" }
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93 | select new StringValue(type).AsReadOnly())
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94 | .ToList();
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95 | ItemSet<StringValue> svrTypeSet = new ItemSet<StringValue>(svrTypes);
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96 | List<StringValue> kernelTypes = (from type in new List<string> { "LINEAR", "POLY", "SIGMOID", "RBF" }
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97 | select new StringValue(type).AsReadOnly())
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98 | .ToList();
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99 | ItemSet<StringValue> kernelTypeSet = new ItemSet<StringValue>(kernelTypes);
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100 | Parameters.Add(new ConstrainedValueParameter<StringValue>(SvmTypeParameterName, "The type of SVM to use.", svrTypeSet, svrTypes[0]));
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101 | Parameters.Add(new ConstrainedValueParameter<StringValue>(KernelTypeParameterName, "The kernel type to use for the SVM.", kernelTypeSet, kernelTypes[3]));
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102 | Parameters.Add(new ValueParameter<DoubleValue>(NuParameterName, "The value of the nu parameter nu-SVC.", new DoubleValue(0.5)));
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103 | Parameters.Add(new ValueParameter<DoubleValue>(CostParameterName, "The value of the C (cost) parameter of C-SVC.", new DoubleValue(1.0)));
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104 | Parameters.Add(new ValueParameter<DoubleValue>(GammaParameterName, "The value of the gamma parameter in the kernel function.", new DoubleValue(1.0)));
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105 | }
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106 | [StorableHook(HookType.AfterDeserialization)]
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107 | private void AfterDeserialization() { }
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108 |
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109 | public override IDeepCloneable Clone(Cloner cloner) {
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110 | return new SupportVectorClassification(this, cloner);
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111 | }
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112 |
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113 | #region support vector classification
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114 | protected override void Run() {
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115 | IClassificationProblemData problemData = Problem.ProblemData;
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116 | IEnumerable<string> selectedInputVariables = problemData.AllowedInputVariables;
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117 | var solution = CreateSupportVectorClassificationSolution(problemData, selectedInputVariables, SvmType.Value, KernelType.Value, Cost.Value, Nu.Value, Gamma.Value);
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118 |
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119 | Results.Add(new Result("Support vector classification solution", "The support vector classification solution.", solution));
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120 | }
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121 |
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122 | public static SupportVectorClassificationSolution CreateSupportVectorClassificationSolution(IClassificationProblemData problemData, IEnumerable<string> allowedInputVariables,
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123 | string svmType, string kernelType, double cost, double nu, double gamma) {
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124 | Dataset dataset = problemData.Dataset;
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125 | string targetVariable = problemData.TargetVariable;
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126 | int start = problemData.TrainingPartitionStart.Value;
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127 | int end = problemData.TrainingPartitionEnd.Value;
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128 | IEnumerable<int> rows = Enumerable.Range(start, end - start);
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129 |
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130 | //extract SVM parameters from scope and set them
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131 | SVM.Parameter parameter = new SVM.Parameter();
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132 | parameter.SvmType = (SVM.SvmType)Enum.Parse(typeof(SVM.SvmType), svmType, true);
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133 | parameter.KernelType = (SVM.KernelType)Enum.Parse(typeof(SVM.KernelType), kernelType, true);
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134 | parameter.C = cost;
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135 | parameter.Nu = nu;
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136 | parameter.Gamma = gamma;
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137 | parameter.CacheSize = 500;
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138 | parameter.Probability = false;
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139 |
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140 |
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141 | SVM.Problem problem = SupportVectorMachineUtil.CreateSvmProblem(dataset, targetVariable, allowedInputVariables, rows);
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142 | SVM.RangeTransform rangeTransform = SVM.RangeTransform.Compute(problem);
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143 | SVM.Problem scaledProblem = SVM.Scaling.Scale(rangeTransform, problem);
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144 | var model = new SupportVectorMachineModel(SVM.Training.Train(scaledProblem, parameter), rangeTransform, targetVariable, allowedInputVariables, problemData.ClassValues);
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145 |
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146 | return new SupportVectorClassificationSolution(model, problemData);
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147 | }
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148 | #endregion
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149 | }
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150 | }
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