1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2015 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.IO;
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25 | using System.Linq;
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26 | using System.Text;
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27 | using HeuristicLab.Common;
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28 | using HeuristicLab.Core;
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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 LibSVM;
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32 |
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33 | namespace HeuristicLab.Algorithms.DataAnalysis {
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34 | /// <summary>
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35 | /// Represents a support vector machine model.
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36 | /// </summary>
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37 | [StorableType("0AB8F0B0-DB90-488D-9EB4-CB7D81566655")]
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38 | [Item("SupportVectorMachineModel", "Represents a support vector machine model.")]
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39 | public sealed class SupportVectorMachineModel : NamedItem, ISupportVectorMachineModel {
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40 |
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41 | private svm_model model;
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42 | /// <summary>
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43 | /// Gets or sets the SVM model.
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44 | /// </summary>
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45 | public svm_model Model {
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46 | get { return model; }
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47 | set {
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48 | if (value != model) {
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49 | if (value == null) throw new ArgumentNullException();
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50 | model = value;
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51 | OnChanged(EventArgs.Empty);
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52 | }
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53 | }
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54 | }
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55 |
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56 | /// <summary>
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57 | /// Gets or sets the range transformation for the model.
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58 | /// </summary>
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59 | private RangeTransform rangeTransform;
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60 | public RangeTransform RangeTransform {
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61 | get { return rangeTransform; }
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62 | set {
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63 | if (value != rangeTransform) {
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64 | if (value == null) throw new ArgumentNullException();
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65 | rangeTransform = value;
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66 | OnChanged(EventArgs.Empty);
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67 | }
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68 | }
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69 | }
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70 |
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71 | public Dataset SupportVectors {
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72 | get {
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73 | var data = new double[Model.sv_coef.Length, allowedInputVariables.Count()];
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74 | for (int i = 0; i < Model.sv_coef.Length; i++) {
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75 | var sv = Model.SV[i];
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76 | for (int j = 0; j < sv.Length; j++) {
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77 | data[i, j] = sv[j].value;
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78 | }
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79 | }
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80 | return new Dataset(allowedInputVariables, data);
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81 | }
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82 | }
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83 |
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84 | [Storable]
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85 | private string targetVariable;
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86 | [Storable]
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87 | private string[] allowedInputVariables;
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88 | [Storable]
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89 | private double[] classValues; // only for SVM classification models
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90 |
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91 | [StorableConstructor]
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92 | private SupportVectorMachineModel(bool deserializing) : base(deserializing) { }
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93 | private SupportVectorMachineModel(SupportVectorMachineModel original, Cloner cloner)
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94 | : base(original, cloner) {
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95 | // only using a shallow copy here! (gkronber)
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96 | this.model = original.model;
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97 | this.rangeTransform = original.rangeTransform;
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98 | this.targetVariable = original.targetVariable;
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99 | this.allowedInputVariables = (string[])original.allowedInputVariables.Clone();
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100 | if (original.classValues != null)
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101 | this.classValues = (double[])original.classValues.Clone();
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102 | }
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103 | public SupportVectorMachineModel(svm_model model, RangeTransform rangeTransform, string targetVariable, IEnumerable<string> allowedInputVariables, IEnumerable<double> classValues)
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104 | : this(model, rangeTransform, targetVariable, allowedInputVariables) {
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105 | this.classValues = classValues.ToArray();
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106 | }
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107 | public SupportVectorMachineModel(svm_model model, RangeTransform rangeTransform, string targetVariable, IEnumerable<string> allowedInputVariables)
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108 | : base() {
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109 | this.name = ItemName;
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110 | this.description = ItemDescription;
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111 | this.model = model;
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112 | this.rangeTransform = rangeTransform;
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113 | this.targetVariable = targetVariable;
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114 | this.allowedInputVariables = allowedInputVariables.ToArray();
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115 | }
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116 |
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117 | public override IDeepCloneable Clone(Cloner cloner) {
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118 | return new SupportVectorMachineModel(this, cloner);
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119 | }
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120 |
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121 | #region IRegressionModel Members
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122 | public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
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123 | return GetEstimatedValuesHelper(dataset, rows);
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124 | }
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125 | public SupportVectorRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
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126 | return new SupportVectorRegressionSolution(this, new RegressionProblemData(problemData));
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127 | }
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128 | IRegressionSolution IRegressionModel.CreateRegressionSolution(IRegressionProblemData problemData) {
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129 | return CreateRegressionSolution(problemData);
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130 | }
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131 | #endregion
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132 |
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133 | #region IClassificationModel Members
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134 | public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
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135 | if (classValues == null) throw new NotSupportedException();
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136 | // return the original class value instead of the predicted value of the model
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137 | // svm classification only works for integer classes
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138 | foreach (var estimated in GetEstimatedValuesHelper(dataset, rows)) {
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139 | // find closest class
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140 | double bestDist = double.MaxValue;
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141 | double bestClass = -1;
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142 | for (int i = 0; i < classValues.Length; i++) {
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143 | double d = Math.Abs(estimated - classValues[i]);
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144 | if (d < bestDist) {
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145 | bestDist = d;
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146 | bestClass = classValues[i];
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147 | if (d.IsAlmost(0.0)) break; // exact match no need to look further
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148 | }
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149 | }
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150 | yield return bestClass;
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151 | }
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152 | }
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153 |
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154 | public SupportVectorClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
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155 | return new SupportVectorClassificationSolution(this, new ClassificationProblemData(problemData));
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156 | }
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157 | IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) {
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158 | return CreateClassificationSolution(problemData);
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159 | }
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160 | #endregion
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161 | private IEnumerable<double> GetEstimatedValuesHelper(IDataset dataset, IEnumerable<int> rows) {
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162 | // calculate predictions for the currently requested rows
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163 | svm_problem problem = SupportVectorMachineUtil.CreateSvmProblem(dataset, targetVariable, allowedInputVariables, rows);
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164 | svm_problem scaledProblem = rangeTransform.Scale(problem);
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165 |
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166 | for (int i = 0; i < problem.l; i++) {
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167 | yield return svm.svm_predict(Model, scaledProblem.x[i]);
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168 | }
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169 | }
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170 |
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171 | #region events
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172 | public event EventHandler Changed;
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173 | private void OnChanged(EventArgs e) {
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174 | var handlers = Changed;
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175 | if (handlers != null)
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176 | handlers(this, e);
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177 | }
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178 | #endregion
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179 |
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180 | #region persistence
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181 | [Storable]
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182 | private string ModelAsString {
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183 | get {
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184 | using (MemoryStream stream = new MemoryStream()) {
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185 | svm.svm_save_model(new StreamWriter(stream), Model);
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186 | stream.Seek(0, System.IO.SeekOrigin.Begin);
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187 | StreamReader reader = new StreamReader(stream);
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188 | return reader.ReadToEnd();
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189 | }
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190 | }
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191 | set {
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192 | using (MemoryStream stream = new MemoryStream(Encoding.ASCII.GetBytes(value))) {
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193 | model = svm.svm_load_model(new StreamReader(stream));
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194 | }
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195 | }
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196 | }
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197 | [Storable]
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198 | private string RangeTransformAsString {
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199 | get {
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200 | using (MemoryStream stream = new MemoryStream()) {
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201 | RangeTransform.Write(stream, RangeTransform);
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202 | stream.Seek(0, System.IO.SeekOrigin.Begin);
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203 | StreamReader reader = new StreamReader(stream);
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204 | return reader.ReadToEnd();
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205 | }
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206 | }
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207 | set {
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208 | using (MemoryStream stream = new MemoryStream(Encoding.ASCII.GetBytes(value))) {
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209 | RangeTransform = RangeTransform.Read(stream);
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210 | }
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211 | }
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212 | }
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213 | #endregion
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214 | }
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215 | }
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