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source: stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveProblem.cs @ 12009

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

#2212 updated copyright year

File size: 6.6 KB
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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
21using System.Linq;
22using HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Data;
25using HeuristicLab.Parameters;
26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27
28namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
29  [Item("Symbolic Classification Problem (multi objective)", "Represents a multi objective symbolic classfication problem.")]
30  [StorableClass]
31  [Creatable("Problems")]
32  public class SymbolicClassificationMultiObjectiveProblem : SymbolicDataAnalysisMultiObjectiveProblem<IClassificationProblemData, ISymbolicClassificationMultiObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, IClassificationProblem {
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 lower and upper limit for the estimated value that can be returned by the symbolic classification model.";
38    private const string ModelCreatorParameterName = "ModelCreator";
39
40
41    #region parameter properties
42    public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter {
43      get { return (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
44    }
45    public IValueParameter<ISymbolicClassificationModelCreator> ModelCreatorParameter {
46      get { return (IValueParameter<ISymbolicClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
47    }
48    #endregion
49    #region properties
50    public DoubleLimit EstimationLimits {
51      get { return EstimationLimitsParameter.Value; }
52    }
53    public ISymbolicClassificationModelCreator ModelCreator {
54      get { return ModelCreatorParameter.Value; }
55    }
56    #endregion
57    [StorableConstructor]
58    protected SymbolicClassificationMultiObjectiveProblem(bool deserializing) : base(deserializing) { }
59    protected SymbolicClassificationMultiObjectiveProblem(SymbolicClassificationMultiObjectiveProblem original, Cloner cloner)
60      : base(original, cloner) {
61      RegisterEventHandlers();
62    }
63    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicClassificationMultiObjectiveProblem(this, cloner); }
64
65    public SymbolicClassificationMultiObjectiveProblem()
66      : base(new ClassificationProblemData(), new SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
67      Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
68      Parameters.Add(new ValueParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, "", new AccuracyMaximizingThresholdsModelCreator()));
69
70      ApplyLinearScalingParameter.Value.Value = false;
71      EstimationLimitsParameter.Hidden = true;
72
73      Maximization = new BoolArray(new bool[] { false, false });
74      MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
75      MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
76
77
78      RegisterEventHandlers();
79      ConfigureGrammarSymbols();
80      InitializeOperators();
81      UpdateEstimationLimits();
82    }
83
84    [StorableHook(HookType.AfterDeserialization)]
85    private void AfterDeserialization() {
86      // BackwardsCompatibility3.4
87      #region Backwards compatible code, remove with 3.5
88      if (!Parameters.ContainsKey(ModelCreatorParameterName))
89        Parameters.Add(new ValueParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, "", new AccuracyMaximizingThresholdsModelCreator()));
90      #endregion
91      RegisterEventHandlers();
92    }
93
94    private void RegisterEventHandlers() {
95      SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols();
96      ModelCreatorParameter.NameChanged += (o, e) => ParameterizeOperators();
97    }
98
99    private void ConfigureGrammarSymbols() {
100      var grammar = SymbolicExpressionTreeGrammar as TypeCoherentExpressionGrammar;
101      if (grammar != null) grammar.ConfigureAsDefaultClassificationGrammar();
102    }
103
104    private void InitializeOperators() {
105      Operators.Add(new SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer());
106      Operators.Add(new SymbolicClassificationMultiObjectiveValidationBestSolutionAnalyzer());
107      ParameterizeOperators();
108    }
109
110    private void UpdateEstimationLimits() {
111      if (ProblemData.TrainingIndices.Any()) {
112        var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
113        var mean = targetValues.Average();
114        var range = targetValues.Max() - targetValues.Min();
115        EstimationLimits.Upper = mean + PunishmentFactor * range;
116        EstimationLimits.Lower = mean - PunishmentFactor * range;
117      } else {
118        EstimationLimits.Upper = double.MaxValue;
119        EstimationLimits.Lower = double.MinValue;
120      }
121    }
122
123    protected override void OnProblemDataChanged() {
124      base.OnProblemDataChanged();
125      UpdateEstimationLimits();
126    }
127
128    protected override void ParameterizeOperators() {
129      base.ParameterizeOperators();
130      if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
131        var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
132        foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>())
133          op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
134        foreach (var op in operators.OfType<ISymbolicClassificationModelCreatorOperator>())
135          op.ModelCreatorParameter.ActualName = ModelCreatorParameter.Name;
136      }
137    }
138  }
139}
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