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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveProblem.cs @ 5809

Last change on this file since 5809 was 5809, checked in by mkommend, 13 years ago

#1418: Reintegrated branch into trunk.

File size: 5.0 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
21using System.Linq;
22using HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Parameters;
25using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
26
27namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
28  [Item("Symbolic Classification Problem (single objective)", "Represents a single objective symbolic classfication problem.")]
29  [StorableClass]
30  [Creatable("Problems")]
31  public class SymbolicClassificationSingleObjectiveProblem : SymbolicDataAnalysisSingleObjectiveProblem<IClassificationProblemData, ISymbolicClassificationSingleObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, IClassificationProblem {
32    private const double PunishmentFactor = 10;
33    private const int InitialMaximumTreeDepth = 8;
34    private const int InitialMaximumTreeLength = 25;
35    private const string EstimationLimitsParameterName = "EstimationLimits";
36    private const string EstimationLimitsParameterDescription = "The lower and upper limit for the estimated value that can be returned by the symbolic classification model.";
37
38    #region parameter properties
39    public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter {
40      get { return (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
41    }
42    #endregion
43    #region properties
44    public DoubleLimit EstimationLimits {
45      get { return EstimationLimitsParameter.Value; }
46    }
47    #endregion
48    [StorableConstructor]
49    protected SymbolicClassificationSingleObjectiveProblem(bool deserializing) : base(deserializing) { }
50    protected SymbolicClassificationSingleObjectiveProblem(SymbolicClassificationSingleObjectiveProblem original, Cloner cloner) : base(original, cloner) { }
51    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicClassificationSingleObjectiveProblem(this, cloner); }
52
53    public SymbolicClassificationSingleObjectiveProblem()
54      : base(new ClassificationProblemData(), new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
55      Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription, new DoubleLimit()));
56
57      MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
58      MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
59
60      InitializeOperators();
61      UpdateEstimationLimits();
62    }
63
64    private void InitializeOperators() {
65      Operators.Add(new SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer());
66      Operators.Add(new SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer());
67      Operators.Add(new SymbolicClassificationSingleObjectiveOverfittingAnalyzer());
68      ParameterizeOperators();
69    }
70
71    private void UpdateEstimationLimits() {
72      if (ProblemData.TrainingPartition.Start < ProblemData.TrainingPartition.End) {
73        var targetValues = ProblemData.Dataset.GetVariableValues(ProblemData.TargetVariable, ProblemData.TrainingPartition.Start, ProblemData.TrainingPartition.End);
74        var mean = targetValues.Average();
75        var range = targetValues.Max() - targetValues.Min();
76        EstimationLimits.Upper = mean + PunishmentFactor * range;
77        EstimationLimits.Lower = mean - PunishmentFactor * range;
78      }
79    }
80
81    protected override void OnProblemDataChanged() {
82      base.OnProblemDataChanged();
83      UpdateEstimationLimits();
84    }
85
86    protected override void ParameterizeOperators() {
87      base.ParameterizeOperators();
88      if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
89        var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
90        foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
91          op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
92        }
93      }
94    }
95
96    public override void ImportProblemDataFromFile(string fileName) {
97      ClassificationProblemData problemData = ClassificationProblemData.ImportFromFile(fileName);
98      ProblemData = problemData;
99    }
100  }
101}
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