1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2013 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.Encodings.IntegerVectorEncoding;
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26 | using HeuristicLab.Parameters;
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27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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28 | using HeuristicLab.Problems.DataAnalysis;
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29 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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30 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
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31 |
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32 | namespace HeuristicLab.Problems.GrammaticalEvolution {
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33 | [Item("Grammatical Evolution Symbolic Regression Problem (single objective)", "Represents a single objective symbolic regression problem, implemented in Grammatical Evolution.")]
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34 | [StorableClass]
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35 | [Creatable("Problems")]
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36 | public class GESymbolicRegressionSingleObjectiveProblem : GESymbolicDataAnalysisSingleObjectiveProblem<IRegressionProblemData, IGESymbolicRegressionSingleObjectiveEvaluator, IIntegerVectorCreator>,
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37 | IRegressionProblem {
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38 | private const double PunishmentFactor = 10;
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39 | //private const int InitialMaximumTreeDepth = 8;
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40 | private const int InitialMaximumTreeLength = 25;
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41 | private const string EstimationLimitsParameterName = "EstimationLimits";
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42 | private const string EstimationLimitsParameterDescription = "The limits for the estimated value that can be returned by the symbolic regression model.";
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43 |
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44 | #region parameter properties
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45 | public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter {
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46 | get { return (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
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47 | }
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48 | #endregion
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49 | #region properties
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50 | public DoubleLimit EstimationLimits {
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51 | get { return EstimationLimitsParameter.Value; }
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52 | }
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53 | #endregion
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54 | [StorableConstructor]
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55 | protected GESymbolicRegressionSingleObjectiveProblem(bool deserializing) : base(deserializing) { }
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56 | protected GESymbolicRegressionSingleObjectiveProblem(GESymbolicRegressionSingleObjectiveProblem original, Cloner cloner)
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57 | : base(original, cloner) {
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58 | RegisterEventHandlers();
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59 | }
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60 | public override IDeepCloneable Clone(Cloner cloner) { return new GESymbolicRegressionSingleObjectiveProblem(this, cloner); }
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61 |
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62 | public GESymbolicRegressionSingleObjectiveProblem()
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63 | : base(new RegressionProblemData(), new GESymbolicRegressionSingleObjectiveEvaluator(), new UniformRandomIntegerVectorCreator()) {
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64 | Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
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65 |
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66 | EstimationLimitsParameter.Hidden = true;
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67 |
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68 |
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69 | ApplyLinearScalingParameter.Value.Value = true;
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70 | Maximization.Value = true;
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71 | //MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
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72 | MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
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73 |
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74 | RegisterEventHandlers();
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75 | ConfigureGrammarSymbols();
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76 | InitializeOperators();
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77 | UpdateEstimationLimits();
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78 | }
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79 |
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80 | [StorableHook(HookType.AfterDeserialization)]
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81 | private void AfterDeserialization() {
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82 | RegisterEventHandlers();
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83 | }
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84 |
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85 | private void RegisterEventHandlers() {
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86 | SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols();
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87 | }
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88 |
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89 | private void ConfigureGrammarSymbols() {
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90 | var grammar = SymbolicExpressionTreeGrammar as TypeCoherentExpressionGrammar;
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91 | if (grammar != null) grammar.ConfigureAsDefaultRegressionGrammar();
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92 | }
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93 |
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94 | private void InitializeOperators() {
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95 | Operators.Add(new SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer());
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96 | Operators.Add(new SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer());
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97 | Operators.Add(new SymbolicRegressionSingleObjectiveOverfittingAnalyzer());
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98 | Operators.Add(new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer());
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99 | Operators.Add(new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer());
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100 |
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101 | ParameterizeOperators();
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102 | }
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103 |
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104 | private void UpdateEstimationLimits() {
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105 | if (ProblemData.TrainingIndices.Any()) {
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106 | var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
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107 | var mean = targetValues.Average();
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108 | var range = targetValues.Max() - targetValues.Min();
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109 | EstimationLimits.Upper = mean + PunishmentFactor * range;
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110 | EstimationLimits.Lower = mean - PunishmentFactor * range;
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111 | } else {
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112 | EstimationLimits.Upper = double.MaxValue;
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113 | EstimationLimits.Lower = double.MinValue;
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114 | }
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115 | }
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116 |
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117 | protected override void OnProblemDataChanged() {
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118 | base.OnProblemDataChanged();
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119 | UpdateEstimationLimits();
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120 | }
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121 |
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122 | protected override void ParameterizeOperators() {
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123 | base.ParameterizeOperators();
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124 | if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
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125 | var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
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126 | foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
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127 | op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
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128 | }
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129 | }
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130 | }
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131 | }
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132 | }
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