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source: branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveProblem.cs @ 5685

Last change on this file since 5685 was 5685, checked in by gkronber, 13 years ago

#1418 Implemented validation best solution analyzers for symbolic classification and regression, added analyzers to symbolic data analysis problem classes and changed details of parameter wiring in problem classes.

File size: 5.6 KB
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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.Persistence.Default.CompositeSerializers.Storable;
27using HeuristicLab.Parameters;
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> {
34    private const double PunishmentFactor = 10;
35    private const int InitialMaximumTreeDepth = 8;
36    private const int InitialMaximumTreeLength = 25;
37    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
38    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
39    private const string LowerEstimationLimitParameterDescription = "The lower limit for the estimated value that can be returned by the symbolic regression model.";
40    private const string UpperEstimationLimitParameterDescription = "The upper limit for the estimated value that can be returned by the symbolic regression model.";
41
42    #region parameter properties
43    public IFixedValueParameter<DoubleValue> LowerEstimationLimitParameter {
44      get { return (IFixedValueParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
45    }
46    public IFixedValueParameter<DoubleValue> UpperEstimationLimitParameter {
47      get { return (IFixedValueParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
48    }
49    #endregion
50    #region properties
51    public DoubleValue LowerEstimationLimit {
52      get { return LowerEstimationLimitParameter.Value; }
53    }
54    public DoubleValue UpperEstimationLimit {
55      get { return UpperEstimationLimitParameter.Value; }
56    }
57    #endregion
58    [StorableConstructor]
59    protected SymbolicRegressionMultiObjectiveProblem(bool deserializing) : base(deserializing) { }
60    protected SymbolicRegressionMultiObjectiveProblem(SymbolicRegressionMultiObjectiveProblem original, Cloner cloner) : base(original, cloner) { }
61    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionMultiObjectiveProblem(this, cloner); }
62
63    public SymbolicRegressionMultiObjectiveProblem()
64      : base(new RegressionProblemData(), new SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
65      Parameters.Add(new FixedValueParameter<DoubleValue>(LowerEstimationLimitParameterName, LowerEstimationLimitParameterDescription, new DoubleValue()));
66      Parameters.Add(new FixedValueParameter<DoubleValue>(UpperEstimationLimitParameterName, UpperEstimationLimitParameterDescription, new DoubleValue()));
67
68      Maximization = new BoolArray(new bool[] { false, false });
69      MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
70      MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
71
72      InitializeOperators();
73    }
74
75    private void InitializeOperators() {
76      Operators.Add(new SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer());
77      Operators.Add(new SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer());
78      ParameterizeOperators();
79    }
80
81    private void UpdateEstimationLimits() {
82      if (ProblemData.TrainingPartitionStart.Value < ProblemData.TrainingPartitionEnd.Value) {
83        var targetValues = ProblemData.Dataset.GetVariableValues(ProblemData.TargetVariable, ProblemData.TrainingPartitionStart.Value, ProblemData.TrainingPartitionEnd.Value);
84        var mean = targetValues.Average();
85        var range = targetValues.Max() - targetValues.Min();
86        UpperEstimationLimit.Value = mean + PunishmentFactor * range;
87        LowerEstimationLimit.Value = mean - PunishmentFactor * range;
88      }
89    }
90
91    protected override void OnProblemDataChanged() {
92      base.OnProblemDataChanged();
93      UpdateEstimationLimits();
94    }
95
96    protected override void ParameterizeOperators() {
97      base.ParameterizeOperators();
98      var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
99      foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedEvaluator<IRegressionProblemData>>()) {
100        op.LowerEstimationLimitParameter.ActualName = LowerEstimationLimitParameterName;
101        op.UpperEstimationLimitParameter.ActualName = UpperEstimationLimitParameterName;
102      }
103    }
104
105    public override void ImportProblemDataFromFile(string fileName) {
106      RegressionProblemData problemData = RegressionProblemData.ImportFromFile(fileName);
107      ProblemData = problemData;
108    }
109  }
110}
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