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source: branches/3026_IntegrationIntoSymSpace/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveProblem.cs @ 18027

Last change on this file since 18027 was 18027, checked in by dpiringe, 3 years ago

#3026

  • merged trunk into branch
File size: 6.6 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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;
25using HeuristicLab.Data;
26using HeuristicLab.Optimization;
27using HeuristicLab.Parameters;
28using HEAL.Attic;
29
30namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
31  [Item("Symbolic Regression Problem (multi-objective)", "Represents a multi objective symbolic regression problem.")]
32  [StorableType("4A8D3658-66B3-48B4-B983-D46409045DBE")]
33  [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 110)]
34  public class SymbolicRegressionMultiObjectiveProblem : SymbolicDataAnalysisMultiObjectiveProblem<IRegressionProblemData, ISymbolicRegressionMultiObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, IRegressionProblem {
35    private const double PunishmentFactor = 10;
36    private const int InitialMaximumTreeDepth = 8;
37    private const int InitialMaximumTreeLength = 25;
38    private const string EstimationLimitsParameterName = "EstimationLimits";
39    private const string EstimationLimitsParameterDescription = "The lower and upper limit for the estimated value that can be returned by the symbolic regression model.";
40
41    #region parameter properties
42    public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter {
43      get { return (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
44    }
45    #endregion
46
47    #region properties
48    public DoubleLimit EstimationLimits {
49      get { return EstimationLimitsParameter.Value; }
50    }
51
52    #endregion
53
54    [StorableConstructor]
55    protected SymbolicRegressionMultiObjectiveProblem(StorableConstructorFlag _) : base(_) { }
56    protected SymbolicRegressionMultiObjectiveProblem(SymbolicRegressionMultiObjectiveProblem original, Cloner cloner)
57      : base(original, cloner) {
58      RegisterEventHandlers();
59    }
60    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionMultiObjectiveProblem(this, cloner); }
61
62    public SymbolicRegressionMultiObjectiveProblem() : this(new RegressionProblemData(), new SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) { }
63    public SymbolicRegressionMultiObjectiveProblem(IRegressionProblemData problemData, ISymbolicRegressionMultiObjectiveEvaluator evaluator, ISymbolicDataAnalysisSolutionCreator solutionCreator)
64      : base(problemData, evaluator, solutionCreator) {
65      Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
66
67      EstimationLimitsParameter.Hidden = true;
68
69      ApplyLinearScalingParameter.Value.Value = true;
70      Maximization = new BoolArray(new bool[] { true, false });
71      MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
72      MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
73
74      RegisterEventHandlers();
75      ConfigureGrammarSymbols();
76      InitializeOperators();
77      UpdateEstimationLimits();
78    }
79
80    [StorableHook(HookType.AfterDeserialization)]
81    private void AfterDeserialization() {
82      RegisterEventHandlers();
83    }
84
85    private void RegisterEventHandlers() {
86      SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols();
87    }
88
89    private void ConfigureGrammarSymbols() {
90      var grammar = SymbolicExpressionTreeGrammar as TypeCoherentExpressionGrammar;
91      if (grammar != null) grammar.ConfigureAsDefaultRegressionGrammar();
92    }
93
94    private void InitializeOperators() {
95      Operators.Add(new SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer());
96      Operators.Add(new SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer());
97      Operators.Add(new SymbolicExpressionTreePhenotypicSimilarityCalculator());
98      Operators.Add(new SymbolicRegressionPhenotypicDiversityAnalyzer(Operators.OfType<SymbolicExpressionTreePhenotypicSimilarityCalculator>()));
99      ParameterizeOperators();
100    }
101
102    private void UpdateEstimationLimits() {
103      if (ProblemData.TrainingIndices.Any()) {
104        var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
105        var mean = targetValues.Average();
106        var range = targetValues.Max() - targetValues.Min();
107        EstimationLimits.Upper = mean + PunishmentFactor * range;
108        EstimationLimits.Lower = mean - PunishmentFactor * range;
109      } else {
110        EstimationLimits.Upper = double.MaxValue;
111        EstimationLimits.Lower = double.MinValue;
112      }
113    }
114
115    protected override void OnProblemDataChanged() {
116      base.OnProblemDataChanged();
117      UpdateEstimationLimits();
118    }
119
120    protected override void ParameterizeOperators() {
121      base.ParameterizeOperators();
122      if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
123        var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
124        foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
125          op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
126        }
127      }
128
129      foreach (var op in Operators.OfType<ISolutionSimilarityCalculator>()) {
130        op.SolutionVariableName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
131        op.QualityVariableName = Evaluator.QualitiesParameter.ActualName;
132
133        if (op is SymbolicExpressionTreePhenotypicSimilarityCalculator) {
134          var phenotypicSimilarityCalculator = (SymbolicExpressionTreePhenotypicSimilarityCalculator)op;
135          phenotypicSimilarityCalculator.ProblemData = ProblemData;
136          phenotypicSimilarityCalculator.Interpreter = SymbolicExpressionTreeInterpreter;
137        }
138      }
139    }
140  }
141}
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