[13368] | 1 | #region License Information
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[10072] | 2 | /* HeuristicLab
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[12012] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[10072] | 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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[10968] | 19 | *
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| 20 | * Author: Sabine Winkler
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[10072] | 21 | */
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[10968] | 22 |
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[10072] | 23 | #endregion
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| 24 |
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| 25 | using System.Linq;
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| 26 | using HeuristicLab.Common;
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| 27 | using HeuristicLab.Core;
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[10073] | 28 | using HeuristicLab.Encodings.IntegerVectorEncoding;
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[10072] | 29 | using HeuristicLab.Parameters;
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| 30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[10073] | 31 | using HeuristicLab.Problems.DataAnalysis;
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| 32 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 33 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
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[10072] | 34 |
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[10073] | 35 | namespace HeuristicLab.Problems.GrammaticalEvolution {
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[13239] | 36 | [Item("Grammatical Evolution Symbolic Regression Problem (GE)",
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[10974] | 37 | "Represents grammatical evolution for single objective symbolic regression problems.")]
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[14711] | 38 | [StorableType("F09DC82F-2826-469A-8B7C-806C49B6B1A4")]
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[12504] | 39 | [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 180)]
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[10075] | 40 | public class GESymbolicRegressionSingleObjectiveProblem : GESymbolicDataAnalysisSingleObjectiveProblem<IRegressionProblemData, IGESymbolicRegressionSingleObjectiveEvaluator, IIntegerVectorCreator>,
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| 41 | IRegressionProblem {
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[10072] | 42 | private const double PunishmentFactor = 10;
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[10328] | 43 | private const int InitialMaximumTreeLength = 30;
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[10072] | 44 | private const string EstimationLimitsParameterName = "EstimationLimits";
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[10276] | 45 | private const string EstimationLimitsParameterDescription
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| 46 | = "The limits for the estimated value that can be returned by the symbolic regression model.";
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[10072] | 47 |
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| 48 | #region parameter properties
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| 49 | public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter {
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| 50 | get { return (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
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| 51 | }
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| 52 | #endregion
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| 53 | #region properties
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| 54 | public DoubleLimit EstimationLimits {
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| 55 | get { return EstimationLimitsParameter.Value; }
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| 56 | }
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| 57 | #endregion
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| 58 | [StorableConstructor]
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[10073] | 59 | protected GESymbolicRegressionSingleObjectiveProblem(bool deserializing) : base(deserializing) { }
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| 60 | protected GESymbolicRegressionSingleObjectiveProblem(GESymbolicRegressionSingleObjectiveProblem original, Cloner cloner)
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[10072] | 61 | : base(original, cloner) {
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| 62 | RegisterEventHandlers();
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| 63 | }
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[10073] | 64 | public override IDeepCloneable Clone(Cloner cloner) { return new GESymbolicRegressionSingleObjectiveProblem(this, cloner); }
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[10072] | 65 |
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[10073] | 66 | public GESymbolicRegressionSingleObjectiveProblem()
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[10263] | 67 | : base(new RegressionProblemData(), new GESymbolicRegressionSingleObjectiveEvaluator(), new UniformRandomIntegerVectorCreator()) {
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[10072] | 68 | Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
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| 69 |
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| 70 | EstimationLimitsParameter.Hidden = true;
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| 71 |
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| 72 |
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| 73 | ApplyLinearScalingParameter.Value.Value = true;
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[10974] | 74 | Maximization.Value = Evaluator.Maximization;
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[10072] | 75 | MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
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| 76 |
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| 77 | RegisterEventHandlers();
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| 78 | InitializeOperators();
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| 79 | UpdateEstimationLimits();
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| 80 | }
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| 81 |
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| 82 | [StorableHook(HookType.AfterDeserialization)]
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| 83 | private void AfterDeserialization() {
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| 84 | RegisterEventHandlers();
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| 85 | }
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| 86 |
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| 87 | private void RegisterEventHandlers() {
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[10974] | 88 | // when the ge evaluator itself changes
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| 89 | EvaluatorParameter.ValueChanged += (sender, args) => {
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| 90 | // register a new hander for the symbreg evaluator in the ge evaluator
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| 91 | // hacky because we the evaluator does not have an event for changes of the maximization property
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| 92 | EvaluatorParameter.Value.EvaluatorParameter.ValueChanged +=
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| 93 | (_, __) => Maximization.Value = Evaluator.Maximization;
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| 94 | };
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| 95 | EvaluatorParameter.Value.EvaluatorParameter.ValueChanged +=
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| 96 | (sender, args) => Maximization.Value = Evaluator.Maximization;
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[10072] | 97 | }
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| 98 |
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| 99 |
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| 100 | private void InitializeOperators() {
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| 101 | Operators.Add(new SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer());
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| 102 | Operators.Add(new SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer());
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| 103 | Operators.Add(new SymbolicRegressionSingleObjectiveOverfittingAnalyzer());
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| 104 | Operators.Add(new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer());
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| 105 | Operators.Add(new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer());
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| 106 |
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| 107 | ParameterizeOperators();
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| 108 | }
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| 109 |
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| 110 | private void UpdateEstimationLimits() {
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| 111 | if (ProblemData.TrainingIndices.Any()) {
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| 112 | var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
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| 113 | var mean = targetValues.Average();
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| 114 | var range = targetValues.Max() - targetValues.Min();
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| 115 | EstimationLimits.Upper = mean + PunishmentFactor * range;
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| 116 | EstimationLimits.Lower = mean - PunishmentFactor * range;
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| 117 | } else {
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| 118 | EstimationLimits.Upper = double.MaxValue;
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| 119 | EstimationLimits.Lower = double.MinValue;
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| 120 | }
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| 121 | }
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| 122 |
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| 123 | protected override void OnProblemDataChanged() {
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| 124 | base.OnProblemDataChanged();
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| 125 | UpdateEstimationLimits();
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| 126 | }
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| 127 |
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| 128 | protected override void ParameterizeOperators() {
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| 129 | base.ParameterizeOperators();
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| 130 | if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
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| 131 | var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
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| 132 | foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
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| 133 | op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
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| 134 | }
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| 135 | }
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| 136 | }
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| 137 | }
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| 138 | }
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