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.Linq;
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23 | using HeuristicLab.Common;
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24 | using HeuristicLab.Core;
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25 | using HeuristicLab.Data;
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26 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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27 | using HeuristicLab.Optimization;
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28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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29 |
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30 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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31 | /// <summary>
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32 | /// Represents a symbolic regression solution (model + data) and attributes of the solution like accuracy and complexity
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33 | /// </summary>
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34 | [StorableClass]
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35 | [Item(Name = "SymbolicRegressionSolution", Description = "Represents a symbolic regression solution (model + data) and attributes of the solution like accuracy and complexity.")]
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36 | public sealed class SymbolicRegressionSolution : RegressionSolution, ISymbolicRegressionSolution {
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37 | private const string ModelLengthResultName = "Model Length";
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38 | private const string ModelDepthResultName = "Model Depth";
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39 |
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40 | private const string EstimationLimitsResultsResultName = "Estimation Limits Results";
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41 | private const string EstimationLimitsResultName = "Estimation Limits";
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42 | private const string TrainingUpperEstimationLimitHitsResultName = "Training Upper Estimation Limit Hits";
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43 | private const string TestLowerEstimationLimitHitsResultName = "Test Lower Estimation Limit Hits";
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44 | private const string TrainingLowerEstimationLimitHitsResultName = "Training Lower Estimation Limit Hits";
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45 | private const string TestUpperEstimationLimitHitsResultName = "Test Upper Estimation Limit Hits";
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46 | private const string TrainingNaNEvaluationsResultName = "Training NaN Evaluations";
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47 | private const string TestNaNEvaluationsResultName = "Test NaN Evaluations";
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48 |
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49 | public new ISymbolicRegressionModel Model {
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50 | get { return (ISymbolicRegressionModel)base.Model; }
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51 | set { base.Model = value; }
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52 | }
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53 | ISymbolicDataAnalysisModel ISymbolicDataAnalysisSolution.Model {
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54 | get { return (ISymbolicDataAnalysisModel)base.Model; }
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55 | }
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56 | public int ModelLength {
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57 | get { return ((IntValue)this[ModelLengthResultName].Value).Value; }
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58 | private set { ((IntValue)this[ModelLengthResultName].Value).Value = value; }
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59 | }
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60 |
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61 | public int ModelDepth {
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62 | get { return ((IntValue)this[ModelDepthResultName].Value).Value; }
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63 | private set { ((IntValue)this[ModelDepthResultName].Value).Value = value; }
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64 | }
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65 |
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66 | private ResultCollection EstimationLimitsResultCollection {
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67 | get { return (ResultCollection)this[EstimationLimitsResultsResultName].Value; }
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68 | }
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69 | public DoubleLimit EstimationLimits {
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70 | get { return (DoubleLimit)EstimationLimitsResultCollection[EstimationLimitsResultName].Value; }
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71 | }
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72 |
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73 | public int TrainingUpperEstimationLimitHits {
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74 | get { return ((IntValue)EstimationLimitsResultCollection[TrainingUpperEstimationLimitHitsResultName].Value).Value; }
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75 | private set { ((IntValue)EstimationLimitsResultCollection[TrainingUpperEstimationLimitHitsResultName].Value).Value = value; }
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76 | }
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77 | public int TestUpperEstimationLimitHits {
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78 | get { return ((IntValue)EstimationLimitsResultCollection[TestUpperEstimationLimitHitsResultName].Value).Value; }
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79 | private set { ((IntValue)EstimationLimitsResultCollection[TestUpperEstimationLimitHitsResultName].Value).Value = value; }
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80 | }
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81 | public int TrainingLowerEstimationLimitHits {
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82 | get { return ((IntValue)EstimationLimitsResultCollection[TrainingLowerEstimationLimitHitsResultName].Value).Value; }
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83 | private set { ((IntValue)EstimationLimitsResultCollection[TrainingLowerEstimationLimitHitsResultName].Value).Value = value; }
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84 | }
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85 | public int TestLowerEstimationLimitHits {
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86 | get { return ((IntValue)EstimationLimitsResultCollection[TestLowerEstimationLimitHitsResultName].Value).Value; }
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87 | private set { ((IntValue)EstimationLimitsResultCollection[TestLowerEstimationLimitHitsResultName].Value).Value = value; }
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88 | }
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89 | public int TrainingNaNEvaluations {
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90 | get { return ((IntValue)EstimationLimitsResultCollection[TrainingNaNEvaluationsResultName].Value).Value; }
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91 | private set { ((IntValue)EstimationLimitsResultCollection[TrainingNaNEvaluationsResultName].Value).Value = value; }
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92 | }
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93 | public int TestNaNEvaluations {
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94 | get { return ((IntValue)EstimationLimitsResultCollection[TestNaNEvaluationsResultName].Value).Value; }
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95 | private set { ((IntValue)EstimationLimitsResultCollection[TestNaNEvaluationsResultName].Value).Value = value; }
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96 | }
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97 |
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98 | [StorableConstructor]
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99 | private SymbolicRegressionSolution(bool deserializing) : base(deserializing) { }
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100 | private SymbolicRegressionSolution(SymbolicRegressionSolution original, Cloner cloner)
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101 | : base(original, cloner) {
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102 | }
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103 | public SymbolicRegressionSolution(ISymbolicRegressionModel model, IRegressionProblemData problemData)
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104 | : base(model, problemData) {
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105 | foreach (var node in model.SymbolicExpressionTree.Root.IterateNodesPrefix().OfType<SymbolicExpressionTreeTopLevelNode>())
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106 | node.SetGrammar(null);
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107 |
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108 | Add(new Result(ModelLengthResultName, "Length of the symbolic regression model.", new IntValue()));
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109 | Add(new Result(ModelDepthResultName, "Depth of the symbolic regression model.", new IntValue()));
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110 |
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111 | ResultCollection estimationLimitResults = new ResultCollection();
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112 | estimationLimitResults.Add(new Result(EstimationLimitsResultName, "", new DoubleLimit()));
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113 | estimationLimitResults.Add(new Result(TrainingUpperEstimationLimitHitsResultName, "", new IntValue()));
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114 | estimationLimitResults.Add(new Result(TestUpperEstimationLimitHitsResultName, "", new IntValue()));
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115 | estimationLimitResults.Add(new Result(TrainingLowerEstimationLimitHitsResultName, "", new IntValue()));
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116 | estimationLimitResults.Add(new Result(TestLowerEstimationLimitHitsResultName, "", new IntValue()));
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117 | estimationLimitResults.Add(new Result(TrainingNaNEvaluationsResultName, "", new IntValue()));
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118 | estimationLimitResults.Add(new Result(TestNaNEvaluationsResultName, "", new IntValue()));
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119 | Add(new Result(EstimationLimitsResultsResultName, "Results concerning the estimation limits of symbolic regression solution", estimationLimitResults));
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120 | RecalculateResults();
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121 | }
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122 |
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123 | public override IDeepCloneable Clone(Cloner cloner) {
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124 | return new SymbolicRegressionSolution(this, cloner);
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125 | }
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126 |
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127 | [StorableHook(HookType.AfterDeserialization)]
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128 | private void AfterDeserialization() {
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129 | if (!ContainsKey(EstimationLimitsResultsResultName)) {
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130 | ResultCollection estimationLimitResults = new ResultCollection();
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131 | estimationLimitResults.Add(new Result(EstimationLimitsResultName, "", new DoubleLimit()));
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132 | estimationLimitResults.Add(new Result(TrainingUpperEstimationLimitHitsResultName, "", new IntValue()));
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133 | estimationLimitResults.Add(new Result(TestUpperEstimationLimitHitsResultName, "", new IntValue()));
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134 | estimationLimitResults.Add(new Result(TrainingLowerEstimationLimitHitsResultName, "", new IntValue()));
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135 | estimationLimitResults.Add(new Result(TestLowerEstimationLimitHitsResultName, "", new IntValue()));
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136 | estimationLimitResults.Add(new Result(TrainingNaNEvaluationsResultName, "", new IntValue()));
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137 | estimationLimitResults.Add(new Result(TestNaNEvaluationsResultName, "", new IntValue()));
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138 | Add(new Result(EstimationLimitsResultsResultName, "Results concerning the estimation limits of symbolic regression solution", estimationLimitResults));
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139 | CalculateResults();
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140 | }
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141 | }
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142 |
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143 | protected override void RecalculateResults() {
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144 | base.RecalculateResults();
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145 | CalculateResults();
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146 | }
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147 |
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148 | private void CalculateResults() {
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149 | ModelLength = Model.SymbolicExpressionTree.Length;
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150 | ModelDepth = Model.SymbolicExpressionTree.Depth;
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151 |
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152 | EstimationLimits.Lower = Model.LowerEstimationLimit;
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153 | EstimationLimits.Upper = Model.UpperEstimationLimit;
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154 |
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155 | TrainingUpperEstimationLimitHits = EstimatedTrainingValues.Count(x => x.IsAlmost(Model.UpperEstimationLimit));
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156 | TestUpperEstimationLimitHits = EstimatedTestValues.Count(x => x.IsAlmost(Model.UpperEstimationLimit));
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157 | TrainingLowerEstimationLimitHits = EstimatedTrainingValues.Count(x => x.IsAlmost(Model.LowerEstimationLimit));
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158 | TestLowerEstimationLimitHits = EstimatedTestValues.Count(x => x.IsAlmost(Model.LowerEstimationLimit));
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159 | TrainingNaNEvaluations = Model.Interpreter.GetSymbolicExpressionTreeValues(Model.SymbolicExpressionTree, ProblemData.Dataset, ProblemData.TrainingIndices).Count(double.IsNaN);
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160 | TestNaNEvaluations = Model.Interpreter.GetSymbolicExpressionTreeValues(Model.SymbolicExpressionTree, ProblemData.Dataset, ProblemData.TestIndices).Count(double.IsNaN);
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161 | }
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162 | }
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163 | }
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