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source: stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveProblem.cs @ 12009

Last change on this file since 12009 was 12009, checked in by ascheibe, 9 years ago

#2212 updated copyright year

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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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.Parameters;
26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27
28namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
29  [Item("Symbolic Regression Problem (single objective)", "Represents a single objective symbolic regression problem.")]
30  [StorableClass]
31  [Creatable("Problems")]
32  public class SymbolicRegressionSingleObjectiveProblem : SymbolicDataAnalysisSingleObjectiveProblem<IRegressionProblemData, ISymbolicRegressionSingleObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, IRegressionProblem {
33    private const double PunishmentFactor = 10;
34    private const int InitialMaximumTreeDepth = 8;
35    private const int InitialMaximumTreeLength = 25;
36    private const string EstimationLimitsParameterName = "EstimationLimits";
37    private const string EstimationLimitsParameterDescription = "The limits for the estimated value that can be returned by the symbolic regression model.";
38
39    #region parameter properties
40    public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter {
41      get { return (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
42    }
43    #endregion
44    #region properties
45    public DoubleLimit EstimationLimits {
46      get { return EstimationLimitsParameter.Value; }
47    }
48    #endregion
49    [StorableConstructor]
50    protected SymbolicRegressionSingleObjectiveProblem(bool deserializing) : base(deserializing) { }
51    protected SymbolicRegressionSingleObjectiveProblem(SymbolicRegressionSingleObjectiveProblem original, Cloner cloner)
52      : base(original, cloner) {
53      RegisterEventHandlers();
54    }
55    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionSingleObjectiveProblem(this, cloner); }
56
57    public SymbolicRegressionSingleObjectiveProblem()
58      : base(new RegressionProblemData(), new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
59      Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
60
61      EstimationLimitsParameter.Hidden = true;
62
63
64      ApplyLinearScalingParameter.Value.Value = true;
65      Maximization.Value = true;
66      MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
67      MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
68
69      RegisterEventHandlers();
70      ConfigureGrammarSymbols();
71      InitializeOperators();
72      UpdateEstimationLimits();
73    }
74
75    [StorableHook(HookType.AfterDeserialization)]
76    private void AfterDeserialization() {
77      RegisterEventHandlers();
78      // compatibility
79      bool changed = false;
80      if (!Operators.OfType<SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer>().Any()) {
81        Operators.Add(new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer());
82        changed = true;
83      }
84      if (!Operators.OfType<SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer>().Any()) {
85        Operators.Add(new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer());
86        changed = true;
87      }
88      if (!Operators.OfType<SymbolicRegressionSolutionsAnalyzer>().Any()) {
89        Operators.Add(new SymbolicRegressionSolutionsAnalyzer());
90        changed = true;
91      }
92      if (changed) {
93        ParameterizeOperators();
94      }
95    }
96
97    private void RegisterEventHandlers() {
98      SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols();
99    }
100
101    private void ConfigureGrammarSymbols() {
102      var grammar = SymbolicExpressionTreeGrammar as TypeCoherentExpressionGrammar;
103      if (grammar != null) grammar.ConfigureAsDefaultRegressionGrammar();
104    }
105
106    private void InitializeOperators() {
107      Operators.Add(new SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer());
108      Operators.Add(new SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer());
109      Operators.Add(new SymbolicRegressionSingleObjectiveOverfittingAnalyzer());
110      Operators.Add(new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer());
111      Operators.Add(new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer());
112      Operators.Add(new SymbolicRegressionSolutionsAnalyzer());
113
114      ParameterizeOperators();
115    }
116
117    private void UpdateEstimationLimits() {
118      if (ProblemData.TrainingIndices.Any()) {
119        var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
120        var mean = targetValues.Average();
121        var range = targetValues.Max() - targetValues.Min();
122        EstimationLimits.Upper = mean + PunishmentFactor * range;
123        EstimationLimits.Lower = mean - PunishmentFactor * range;
124      } else {
125        EstimationLimits.Upper = double.MaxValue;
126        EstimationLimits.Lower = double.MinValue;
127      }
128    }
129
130    protected override void OnProblemDataChanged() {
131      base.OnProblemDataChanged();
132      UpdateEstimationLimits();
133    }
134
135    protected override void ParameterizeOperators() {
136      base.ParameterizeOperators();
137      if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
138        var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
139        foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
140          op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
141        }
142      }
143    }
144  }
145}
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