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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/MultiObjectiveSymbolicRegressionProblem.cs @ 6424

Last change on this file since 6424 was 5275, checked in by gkronber, 14 years ago

Merged changes from trunk to data analysis exploration branch and added fractional distance metric evaluator. #1142

File size: 5.1 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 HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Optimization;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
31  [Item("Symbolic Regression Problem (multi objective)", "Represents a multi objective symbolic regression problem.")]
32  [Creatable("Problems")]
33  [StorableClass]
34  public class MultiObjectiveSymbolicRegressionProblem : SymbolicRegressionProblemBase, IMultiObjectiveProblem {
35
36    #region Parameter Properties
37    public ValueParameter<BoolArray> MaximizationParameter {
38      get { return (ValueParameter<BoolArray>)Parameters["Maximization"]; }
39    }
40    IParameter IMultiObjectiveProblem.MaximizationParameter {
41      get { return MaximizationParameter; }
42    }
43    public new ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator> EvaluatorParameter {
44      get { return (ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator>)Parameters["Evaluator"]; }
45    }
46    IParameter IProblem.EvaluatorParameter {
47      get { return EvaluatorParameter; }
48    }
49    #endregion
50
51    #region Properties
52    public new IMultiObjectiveSymbolicRegressionEvaluator Evaluator {
53      get { return EvaluatorParameter.Value; }
54      set { EvaluatorParameter.Value = value; }
55    }
56    IMultiObjectiveEvaluator IMultiObjectiveProblem.Evaluator {
57      get { return EvaluatorParameter.Value; }
58    }
59    IEvaluator IProblem.Evaluator {
60      get { return EvaluatorParameter.Value; }
61    }
62    #endregion
63
64    [StorableConstructor]
65    protected MultiObjectiveSymbolicRegressionProblem(bool deserializing) : base(deserializing) { }
66    protected MultiObjectiveSymbolicRegressionProblem(MultiObjectiveSymbolicRegressionProblem original, Cloner cloner)
67      : base(original, cloner) {
68      RegisterParameterEvents();
69      RegisterParameterValueEvents();
70    }
71    public MultiObjectiveSymbolicRegressionProblem()
72      : base() {
73      var evaluator = new MultiObjectiveSymbolicRegressionMeanSquaredErrorEvaluator();
74      Parameters.Add(new ValueParameter<BoolArray>("Maximization", "Set to false as the error of the regression model should be minimized.", new BoolArray(new bool[] { false, false })));
75      Parameters.Add(new ValueParameter<IMultiObjectiveSymbolicRegressionEvaluator>("Evaluator", "The operator which should be used to evaluate symbolic regression solutions.", evaluator));
76
77      evaluator.QualitiesParameter.ActualName = "TrainingRSquared/Size";
78
79      ParameterizeEvaluator();
80
81      RegisterParameterEvents();
82      RegisterParameterValueEvents();
83    }
84
85    public override IDeepCloneable Clone(Cloner cloner) {
86      return new MultiObjectiveSymbolicRegressionProblem(this, cloner);
87    }
88
89    private void RegisterParameterValueEvents() {
90      EvaluatorParameter.ValueChanged += new EventHandler(EvaluatorParameter_ValueChanged);
91    }
92
93    private void RegisterParameterEvents() {
94    }
95
96    #region event handling
97    protected override void OnDataAnalysisProblemChanged(EventArgs e) {
98      base.OnDataAnalysisProblemChanged(e);
99      // paritions could be changed
100      ParameterizeEvaluator();
101    }
102    protected override void OnSolutionParameterNameChanged(EventArgs e) {
103      ParameterizeEvaluator();
104    }
105
106    protected override void OnEvaluatorChanged(EventArgs e) {
107      base.OnEvaluatorChanged(e);
108      ParameterizeEvaluator();
109      RaiseEvaluatorChanged(e);
110    }
111    #endregion
112
113    #region event handlers
114    private void EvaluatorParameter_ValueChanged(object sender, EventArgs e) {
115      OnEvaluatorChanged(e);
116    }
117    #endregion
118
119    #region Helpers
120    [StorableHook(HookType.AfterDeserialization)]
121    private void AfterDeserializationHook() {
122      RegisterParameterEvents();
123      RegisterParameterValueEvents();
124    }
125
126    private void ParameterizeEvaluator() {
127      Evaluator.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
128      Evaluator.RegressionProblemDataParameter.ActualName = DataAnalysisProblemDataParameter.Name;
129      Evaluator.SamplesStartParameter.Value = TrainingSamplesStart;
130      Evaluator.SamplesEndParameter.Value = TrainingSamplesEnd;
131    }
132    #endregion
133  }
134}
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