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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer.cs @ 8531

Last change on this file since 8531 was 8531, checked in by mkommend, 10 years ago

#1919: Refactored calculation of thresholds for SymbolicDiscriminantFunctionClassficationModels and removed the automatic recalculation of thresholds during solution creation.

File size: 5.3 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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 HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Data;
25using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
26using HeuristicLab.Parameters;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
30  /// <summary>
31  /// An operator that analyzes the training best symbolic classification solution for single objective symbolic classification problems.
32  /// </summary>
33  [Item("SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic classification solution for single objective symbolic classification problems.")]
34  [StorableClass]
35  public sealed class SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingBestSolutionAnalyzer<ISymbolicClassificationSolution>,
36    ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
37    private const string ProblemDataParameterName = "ProblemData";
38    private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
39    private const string EstimationLimitsParameterName = "UpperEstimationLimit";
40    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
41    #region parameter properties
42    public ILookupParameter<IClassificationProblemData> ProblemDataParameter {
43      get { return (ILookupParameter<IClassificationProblemData>)Parameters[ProblemDataParameterName]; }
44    }
45    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
46      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
47    }
48    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
49      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
50    }
51    public IValueParameter<BoolValue> ApplyLinearScalingParameter {
52      get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
53    }
54    #endregion
55    #region properties
56    public BoolValue ApplyLinearScaling {
57      get { return ApplyLinearScalingParameter.Value; }
58    }
59    #endregion
60
61    [StorableConstructor]
62    private SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
63    private SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer(SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
64    public SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer()
65      : base() {
66      Parameters.Add(new LookupParameter<IClassificationProblemData>(ProblemDataParameterName, "The problem data for the symbolic classification solution."));
67      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
68      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic classification model."));
69      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic classification solution should be linearly scaled.", new BoolValue(false)));
70    }
71    public override IDeepCloneable Clone(Cloner cloner) {
72      return new SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer(this, cloner);
73    }
74
75    protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
76      var model = new SymbolicDiscriminantFunctionClassificationModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
77      if (ApplyLinearScaling.Value) SymbolicDiscriminantFunctionClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
78
79      SymbolicDiscriminantFunctionClassificationModel.SetAccuracyMaximizingThresholds(model, ProblemDataParameter.ActualValue);
80      return new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
81    }
82  }
83}
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