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source: branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer.cs @ 5722

Last change on this file since 5722 was 5722, checked in by gkronber, 13 years ago

#1418 fixed evaluator call from validation analyzers, fixed bugs in interactive simplifier view and added apply linear scaling flag to analyzers.

File size: 5.5 KB
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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    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
44
45    #region parameter properties
46    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
47      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
48    }
49
50    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
51      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
52    }
53
54    public IValueParameter<BoolValue> ApplyLinearScalingParameter {
55      get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
56    }
57    #endregion
58
59    #region properties
60    public DoubleValue UpperEstimationLimit {
61      get { return UpperEstimationLimitParameter.ActualValue; }
62    }
63    public DoubleValue LowerEstimationLimit {
64      get { return LowerEstimationLimitParameter.ActualValue; }
65    }
66    public BoolValue ApplyLinearScaling {
67      get { return ApplyLinearScalingParameter.Value; }
68    }
69    #endregion
70    [StorableConstructor]
71    private SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
72    private SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer(SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
73    public SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer()
74      : base() {
75      Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit for the estimated values produced by the symbolic classification model."));
76      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit for the estimated values produced by the symbolic classification model."));
77      Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic classification solution should be linearly scaled.", new BoolValue(false)));
78    }
79    public override IDeepCloneable Clone(Cloner cloner) {
80      return new SymbolicClassificationSingleObjectiveValidationBestSolutionAnalyzer(this, cloner);
81    }
82
83    protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
84      double[] classValues;
85      double[] thresholds;
86      // calculate thresholds on the whole training set even for the validation best solution
87      var estimatedValues = SymbolicDataAnalysisTreeInterpreter.GetSymbolicExpressionTreeValues(bestTree, ProblemData.Dataset, ProblemData.TrainingIndizes)
88        .LimitToRange(LowerEstimationLimit.Value, UpperEstimationLimit.Value);
89      var targetClassValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes);
90      AccuracyMaximizationThresholdCalculator.CalculateThresholds(ProblemData, estimatedValues, targetClassValues, out classValues, out thresholds);
91      var model = new SymbolicDiscriminantFunctionClassificationModel(bestTree, SymbolicDataAnalysisTreeInterpreter, classValues, thresholds, LowerEstimationLimit.Value, UpperEstimationLimit.Value);
92      return new SymbolicDiscriminantFunctionClassificationSolution(model, ProblemData);
93    }
94  }
95}
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