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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveProblem.cs @ 15583

Last change on this file since 15583 was 15583, checked in by swagner, 6 years ago

#2640: Updated year of copyrights in license headers

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[5618]1#region License Information
2/* HeuristicLab
[15583]3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[5618]4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System.Linq;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
[12103]25using HeuristicLab.Optimization;
[5716]26using HeuristicLab.Parameters;
[5618]27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
[12504]30  [Item("Symbolic Regression Problem (single-objective)", "Represents a single objective symbolic regression problem.")]
[5618]31  [StorableClass]
[12504]32  [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 100)]
[5759]33  public class SymbolicRegressionSingleObjectiveProblem : SymbolicDataAnalysisSingleObjectiveProblem<IRegressionProblemData, ISymbolicRegressionSingleObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, IRegressionProblem {
[5618]34    private const double PunishmentFactor = 10;
[5685]35    private const int InitialMaximumTreeDepth = 8;
36    private const int InitialMaximumTreeLength = 25;
[5770]37    private const string EstimationLimitsParameterName = "EstimationLimits";
38    private const string EstimationLimitsParameterDescription = "The limits for the estimated value that can be returned by the symbolic regression model.";
[5716]39
[5685]40    #region parameter properties
[5770]41    public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter {
42      get { return (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
[5685]43    }
44    #endregion
45    #region properties
[5770]46    public DoubleLimit EstimationLimits {
47      get { return EstimationLimitsParameter.Value; }
[5685]48    }
49    #endregion
[5618]50    [StorableConstructor]
51    protected SymbolicRegressionSingleObjectiveProblem(bool deserializing) : base(deserializing) { }
[8175]52    protected SymbolicRegressionSingleObjectiveProblem(SymbolicRegressionSingleObjectiveProblem original, Cloner cloner)
53      : base(original, cloner) {
54      RegisterEventHandlers();
55    }
[5618]56    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionSingleObjectiveProblem(this, cloner); }
57
58    public SymbolicRegressionSingleObjectiveProblem()
59      : base(new RegressionProblemData(), new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
[5847]60      Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
[5685]61
[5854]62      EstimationLimitsParameter.Hidden = true;
63
[8664]64
65      ApplyLinearScalingParameter.Value.Value = true;
[5618]66      Maximization.Value = true;
[5685]67      MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
68      MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
69
[8175]70      RegisterEventHandlers();
[6803]71      ConfigureGrammarSymbols();
[5685]72      InitializeOperators();
[5716]73      UpdateEstimationLimits();
[5618]74    }
75
[8130]76    [StorableHook(HookType.AfterDeserialization)]
77    private void AfterDeserialization() {
[8175]78      RegisterEventHandlers();
[8130]79      // compatibility
80      bool changed = false;
81      if (!Operators.OfType<SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer>().Any()) {
82        Operators.Add(new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer());
83        changed = true;
84      }
85      if (!Operators.OfType<SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer>().Any()) {
86        Operators.Add(new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer());
87        changed = true;
88      }
[10596]89      if (!Operators.OfType<SymbolicRegressionSolutionsAnalyzer>().Any()) {
90        Operators.Add(new SymbolicRegressionSolutionsAnalyzer());
91        changed = true;
92      }
[8130]93      if (changed) {
94        ParameterizeOperators();
95      }
96    }
97
[8175]98    private void RegisterEventHandlers() {
99      SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols();
100    }
101
[6803]102    private void ConfigureGrammarSymbols() {
103      var grammar = SymbolicExpressionTreeGrammar as TypeCoherentExpressionGrammar;
104      if (grammar != null) grammar.ConfigureAsDefaultRegressionGrammar();
105    }
106
[5685]107    private void InitializeOperators() {
108      Operators.Add(new SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer());
109      Operators.Add(new SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer());
[5747]110      Operators.Add(new SymbolicRegressionSingleObjectiveOverfittingAnalyzer());
[7726]111      Operators.Add(new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer());
[7734]112      Operators.Add(new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer());
[10596]113      Operators.Add(new SymbolicRegressionSolutionsAnalyzer());
[12103]114      Operators.Add(new SymbolicExpressionTreePhenotypicSimilarityCalculator());
115      Operators.Add(new SymbolicRegressionPhenotypicDiversityAnalyzer(Operators.OfType<SymbolicExpressionTreePhenotypicSimilarityCalculator>()) { DiversityResultName = "Phenotypic Diversity" });
[5685]116      ParameterizeOperators();
117    }
[5716]118
[5685]119    private void UpdateEstimationLimits() {
[8139]120      if (ProblemData.TrainingIndices.Any()) {
121        var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
[5618]122        var mean = targetValues.Average();
123        var range = targetValues.Max() - targetValues.Min();
[5770]124        EstimationLimits.Upper = mean + PunishmentFactor * range;
125        EstimationLimits.Lower = mean - PunishmentFactor * range;
[6754]126      } else {
127        EstimationLimits.Upper = double.MaxValue;
128        EstimationLimits.Lower = double.MinValue;
[5618]129      }
130    }
[5623]131
[5685]132    protected override void OnProblemDataChanged() {
133      base.OnProblemDataChanged();
134      UpdateEstimationLimits();
135    }
136
137    protected override void ParameterizeOperators() {
138      base.ParameterizeOperators();
[5770]139      if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
140        var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
141        foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
142          op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
143        }
[5685]144      }
[12103]145
146      foreach (var op in Operators.OfType<ISolutionSimilarityCalculator>()) {
147        op.SolutionVariableName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
148        op.QualityVariableName = Evaluator.QualityParameter.ActualName;
149
150        if (op is SymbolicExpressionTreePhenotypicSimilarityCalculator) {
151          var phenotypicSimilarityCalculator = (SymbolicExpressionTreePhenotypicSimilarityCalculator)op;
152          phenotypicSimilarityCalculator.ProblemData = ProblemData;
153          phenotypicSimilarityCalculator.Interpreter = SymbolicExpressionTreeInterpreter;
154        }
155      }
[5685]156    }
[5618]157  }
158}
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