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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/SymbolicRegressionProblem.cs @ 4800

Last change on this file since 4800 was 4722, checked in by swagner, 14 years ago

Merged cloning refactoring branch back into trunk (#922)

File size: 8.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 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;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Optimization;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers;
31
32namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
33  [Item("Symbolic Regression Problem (single objective)", "Represents a single objective symbolic regression problem.")]
34  [Creatable("Problems")]
35  [StorableClass]
36  public sealed class SymbolicRegressionProblem : SymbolicRegressionProblemBase, ISingleObjectiveDataAnalysisProblem {
37
38    #region Parameter Properties
39    public ValueParameter<BoolValue> MaximizationParameter {
40      get { return (ValueParameter<BoolValue>)Parameters["Maximization"]; }
41    }
42    IParameter ISingleObjectiveProblem.MaximizationParameter {
43      get { return MaximizationParameter; }
44    }
45    public new ValueParameter<ISymbolicRegressionEvaluator> EvaluatorParameter {
46      get { return (ValueParameter<ISymbolicRegressionEvaluator>)Parameters["Evaluator"]; }
47    }
48    IParameter IProblem.EvaluatorParameter {
49      get { return EvaluatorParameter; }
50    }
51    public OptionalValueParameter<DoubleValue> BestKnownQualityParameter {
52      get { return (OptionalValueParameter<DoubleValue>)Parameters["BestKnownQuality"]; }
53    }
54    IParameter ISingleObjectiveProblem.BestKnownQualityParameter {
55      get { return BestKnownQualityParameter; }
56    }
57    #endregion
58
59    #region Properties
60    public new ISymbolicRegressionEvaluator Evaluator {
61      get { return EvaluatorParameter.Value; }
62      set { EvaluatorParameter.Value = value; }
63    }
64    ISingleObjectiveEvaluator ISingleObjectiveProblem.Evaluator {
65      get { return EvaluatorParameter.Value; }
66    }
67    IEvaluator IProblem.Evaluator {
68      get { return EvaluatorParameter.Value; }
69    }
70    public DoubleValue BestKnownQuality {
71      get { return BestKnownQualityParameter.Value; }
72    }
73    #endregion
74
75    [StorableConstructor]
76    private SymbolicRegressionProblem(bool deserializing) : base(deserializing) { }
77    private SymbolicRegressionProblem(SymbolicRegressionProblem original, Cloner cloner)
78      : base(original, cloner) {
79      RegisterParameterEvents();
80      RegisterParameterValueEvents();
81    }
82
83    public SymbolicRegressionProblem()
84      : base() {
85      var evaluator = new SymbolicRegressionPearsonsRSquaredEvaluator();
86      Parameters.Add(new ValueParameter<BoolValue>("Maximization", "Set to false as the error of the regression model should be minimized.", (BoolValue)new BoolValue(true)));
87      Parameters.Add(new ValueParameter<ISymbolicRegressionEvaluator>("Evaluator", "The operator which should be used to evaluate symbolic regression solutions.", evaluator));
88      Parameters.Add(new OptionalValueParameter<DoubleValue>("BestKnownQuality", "The minimal error value that reached by symbolic regression solutions for the problem."));
89
90      evaluator.QualityParameter.ActualName = "TrainingPearsonR2";
91
92      InitializeOperators();
93      ParameterizeEvaluator();
94
95      RegisterParameterEvents();
96      RegisterParameterValueEvents();
97    }
98
99    public override IDeepCloneable Clone(Cloner cloner) {
100      return new SymbolicRegressionProblem(this, cloner);
101    }
102
103    private void RegisterParameterValueEvents() {
104      EvaluatorParameter.ValueChanged += new EventHandler(EvaluatorParameter_ValueChanged);
105    }
106
107    private void RegisterParameterEvents() { }
108
109    #region event handling
110    protected override void OnDataAnalysisProblemChanged(EventArgs e) {
111      base.OnDataAnalysisProblemChanged(e);
112      BestKnownQualityParameter.Value = null;
113      // paritions could be changed
114      ParameterizeEvaluator();
115      ParameterizeAnalyzers();
116    }
117
118    protected override void OnSolutionParameterNameChanged(EventArgs e) {
119      base.OnSolutionParameterNameChanged(e);
120      ParameterizeEvaluator();
121      ParameterizeAnalyzers();
122    }
123
124    protected override void OnEvaluatorChanged(EventArgs e) {
125      base.OnEvaluatorChanged(e);
126      ParameterizeEvaluator();
127      ParameterizeAnalyzers();
128      RaiseEvaluatorChanged(e);
129    }
130    #endregion
131
132    #region event handlers
133    private void EvaluatorParameter_ValueChanged(object sender, EventArgs e) {
134      OnEvaluatorChanged(e);
135    }
136    #endregion
137
138    #region Helpers
139    [StorableHook(HookType.AfterDeserialization)]
140    private void AfterDeserializationHook() {
141      // BackwardsCompatibility3.3
142      #region Backwards compatible code (remove with 3.4)
143      if (Operators == null || Operators.Count() == 0) InitializeOperators();
144      #endregion
145      RegisterParameterEvents();
146      RegisterParameterValueEvents();
147    }
148
149    private void InitializeOperators() {
150      AddOperator(new FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer());
151      ParameterizeAnalyzers();
152    }
153
154    private void ParameterizeEvaluator() {
155      Evaluator.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
156      Evaluator.RegressionProblemDataParameter.ActualName = DataAnalysisProblemDataParameter.Name;
157      Evaluator.SamplesStartParameter.Value = TrainingSamplesStart;
158      Evaluator.SamplesEndParameter.Value = TrainingSamplesEnd;
159    }
160
161    private void ParameterizeAnalyzers() {
162      foreach (var analyzer in Analyzers) {
163        analyzer.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
164        var fixedBestValidationSolutionAnalyzer = analyzer as FixedValidationBestScaledSymbolicRegressionSolutionAnalyzer;
165        if (fixedBestValidationSolutionAnalyzer != null) {
166          fixedBestValidationSolutionAnalyzer.ProblemDataParameter.ActualName = DataAnalysisProblemDataParameter.Name;
167          fixedBestValidationSolutionAnalyzer.UpperEstimationLimitParameter.ActualName = UpperEstimationLimitParameter.Name;
168          fixedBestValidationSolutionAnalyzer.LowerEstimationLimitParameter.ActualName = LowerEstimationLimitParameter.Name;
169          fixedBestValidationSolutionAnalyzer.SymbolicExpressionTreeInterpreterParameter.ActualName = SymbolicExpressionTreeInterpreterParameter.Name;
170          fixedBestValidationSolutionAnalyzer.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
171          fixedBestValidationSolutionAnalyzer.ValidationSamplesStartParameter.Value = ValidationSamplesStart;
172          fixedBestValidationSolutionAnalyzer.ValidationSamplesEndParameter.Value = ValidationSamplesEnd;
173          fixedBestValidationSolutionAnalyzer.BestKnownQualityParameter.ActualName = BestKnownQualityParameter.Name;
174        }
175
176        var bestValidationSolutionAnalyzer = analyzer as ValidationBestScaledSymbolicRegressionSolutionAnalyzer;
177        if (bestValidationSolutionAnalyzer != null) {
178          bestValidationSolutionAnalyzer.ProblemDataParameter.ActualName = DataAnalysisProblemDataParameter.Name;
179          bestValidationSolutionAnalyzer.UpperEstimationLimitParameter.ActualName = UpperEstimationLimitParameter.Name;
180          bestValidationSolutionAnalyzer.LowerEstimationLimitParameter.ActualName = LowerEstimationLimitParameter.Name;
181          bestValidationSolutionAnalyzer.SymbolicExpressionTreeInterpreterParameter.ActualName = SymbolicExpressionTreeInterpreterParameter.Name;
182          bestValidationSolutionAnalyzer.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
183          bestValidationSolutionAnalyzer.ValidationSamplesStartParameter.Value = ValidationSamplesStart;
184          bestValidationSolutionAnalyzer.ValidationSamplesEndParameter.Value = ValidationSamplesEnd;
185          bestValidationSolutionAnalyzer.BestKnownQualityParameter.ActualName = BestKnownQualityParameter.Name;
186          bestValidationSolutionAnalyzer.QualityParameter.ActualName = Evaluator.QualityParameter.ActualName;
187        }
188      }
189    }
190    #endregion
191  }
192}
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