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source: branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer.cs @ 5720

Last change on this file since 5720 was 5720, checked in by gkronber, 13 years ago

#1418 Added upper and lower estimation bounds for symbolic classification and regression.

File size: 4.9 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
21
22using System.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
28using HeuristicLab.Operators;
29using HeuristicLab.Optimization;
30using HeuristicLab.Parameters;
31using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
32
33namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
34  /// <summary>
35  /// An operator that analyzes the validation best symbolic classification solution for single objective symbolic classification problems.
36  /// </summary>
37  [Item("SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer", "An operator that analyzes the validation best symbolic classification solution for single objective symbolic classification problems.")]
38  [StorableClass]
39  public sealed class SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationBestSolutionAnalyzer<ISymbolicClassificationSolution, ISymbolicClassificationSingleObjectiveEvaluator, IClassificationProblemData>,
40  ISymbolicDataAnalysisBoundedOperator {
41    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
42    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
43
44    #region parameter properties
45    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
46      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
47    }
48
49    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
50      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
51    }
52    #endregion
53
54    #region properties
55    public DoubleValue UpperEstimationLimit {
56      get { return UpperEstimationLimitParameter.ActualValue; }
57    }
58    public DoubleValue LowerEstimationLimit {
59      get { return LowerEstimationLimitParameter.ActualValue; }
60    }
61    #endregion
62    [StorableConstructor]
63    private SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
64    private SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer(SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
65    public SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer()
66      : base() {
67      Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit for the estimated values produced by the symbolic classification model."));
68      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit for the estimated values produced by the symbolic classification model."));
69    }
70    public override IDeepCloneable Clone(Cloner cloner) {
71      return new SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer(this, cloner);
72    }
73
74    protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
75      double[] classValues;
76      double[] thresholds;
77      // calculate thresholds on the whole training set even for the validation best solution
78      var estimatedValues = SymbolicDataAnalysisTreeInterpreter.GetSymbolicExpressionTreeValues(bestTree, ProblemData.Dataset, ProblemData.TrainingIndizes)
79        .LimitToRange(LowerEstimationLimit.Value, UpperEstimationLimit.Value);
80      var targetClassValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes);
81      AccuracyMaximizationThresholdCalculator.CalculateThresholds(ProblemData, estimatedValues, targetClassValues, out classValues, out thresholds);
82      var model = new SymbolicDiscriminantFunctionClassificationModel(bestTree, SymbolicDataAnalysisTreeInterpreter, classValues, thresholds, LowerEstimationLimit.Value, UpperEstimationLimit.Value);
83      return new SymbolicDiscriminantFunctionClassificationSolution(model, ProblemData);
84    }
85  }
86}
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