source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveProblem.cs @ 5847

Last change on this file since 5847 was 5847, checked in by mkommend, 11 years ago

#1418: Adapted data analysis classes to new parameter ctors.

File size: 4.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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.Parameters;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
30  [Item("Symbolic Regression Problem (multi objective)", "Represents a multi objective symbolic regression problem.")]
31  [StorableClass]
32  [Creatable("Problems")]
33  public class SymbolicRegressionMultiObjectiveProblem : SymbolicDataAnalysisMultiObjectiveProblem<IRegressionProblemData, ISymbolicRegressionMultiObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, IRegressionProblem {
34    private const double PunishmentFactor = 10;
35    private const int InitialMaximumTreeDepth = 8;
36    private const int InitialMaximumTreeLength = 25;
37    private const string EstimationLimitsParameterName = "EstimationLimits";
38    private const string EstimationLimitsParameterDescription = "The lower and upper limit for the estimated value that can be returned by the symbolic regression model.";
39
40    #region parameter properties
41    public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter {
42      get { return (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
43    }
44    #endregion
45
46    #region properties
47    public DoubleLimit EstimationLimits {
48      get { return EstimationLimitsParameter.Value; }
49    }
50
51    #endregion
52
53    [StorableConstructor]
54    protected SymbolicRegressionMultiObjectiveProblem(bool deserializing) : base(deserializing) { }
55    protected SymbolicRegressionMultiObjectiveProblem(SymbolicRegressionMultiObjectiveProblem original, Cloner cloner) : base(original, cloner) { }
56    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionMultiObjectiveProblem(this, cloner); }
57
58    public SymbolicRegressionMultiObjectiveProblem()
59      : base(new RegressionProblemData(), new SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
60      Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
61
62      Maximization = new BoolArray(new bool[] { true, false });
63      MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
64      MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
65
66      InitializeOperators();
67      UpdateEstimationLimits();
68    }
69
70    private void InitializeOperators() {
71      Operators.Add(new SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer());
72      Operators.Add(new SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer());
73      ParameterizeOperators();
74    }
75
76    private void UpdateEstimationLimits() {
77      if (ProblemData.TrainingPartition.Start < ProblemData.TrainingPartition.End) {
78        var targetValues = ProblemData.Dataset.GetVariableValues(ProblemData.TargetVariable, ProblemData.TrainingPartition.Start, ProblemData.TrainingPartition.End);
79        var mean = targetValues.Average();
80        var range = targetValues.Max() - targetValues.Min();
81        EstimationLimits.Upper = mean + PunishmentFactor * range;
82        EstimationLimits.Lower = mean - PunishmentFactor * range;
83      }
84    }
85
86    protected override void OnProblemDataChanged() {
87      base.OnProblemDataChanged();
88      UpdateEstimationLimits();
89    }
90
91    protected override void ParameterizeOperators() {
92      base.ParameterizeOperators();
93      if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
94        var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
95        foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
96          op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
97        }
98      }
99    }
100
101    public override void ImportProblemDataFromFile(string fileName) {
102      RegressionProblemData problemData = RegressionProblemData.ImportFromFile(fileName);
103      ProblemData = problemData;
104    }
105  }
106}
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