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source: branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer.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: 6.0 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
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 training best symbolic classification solution for multi objective symbolic classification problems.
36  /// </summary>
37  [Item("SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic classification solution for multi objective symbolic classification problems.")]
38  [StorableClass]
39  public sealed class SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisMultiObjectiveTrainingBestSolutionAnalyzer<ISymbolicClassificationSolution>,
40    ISymbolicDataAnalysisInterpreterOperator {
41    private const string ProblemDataParameterName = "ProblemData";
42    private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
43    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
44    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
45    #region parameter properties
46    public ILookupParameter<IClassificationProblemData> ProblemDataParameter {
47      get { return (ILookupParameter<IClassificationProblemData>)Parameters[ProblemDataParameterName]; }
48    }
49    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
50      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
51    }
52    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
53      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
54    }
55
56    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
57      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
58    }
59    #endregion
60    #region properties
61    public IClassificationProblemData ProblemData {
62      get { return ProblemDataParameter.ActualValue; }
63    }
64    public ISymbolicDataAnalysisExpressionTreeInterpreter SymbolicDataAnalysisTreeInterpreter {
65      get { return SymbolicDataAnalysisTreeInterpreterParameter.ActualValue; }
66    }
67    public DoubleValue UpperEstimationLimit {
68      get { return UpperEstimationLimitParameter.ActualValue; }
69    }
70    public DoubleValue LowerEstimationLimit {
71      get { return LowerEstimationLimitParameter.ActualValue; }
72    }
73    #endregion
74
75    [StorableConstructor]
76    private SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
77    private SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer(SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
78    public SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer()
79      : base() {
80      Parameters.Add(new LookupParameter<IClassificationProblemData>(ProblemDataParameterName, "The problem data for the symbolic classification solution."));
81      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
82      Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit for the estimated values produced by the symbolic classification model."));
83      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit for the estimated values produced by the symbolic classification model."));
84    }
85    public override IDeepCloneable Clone(Cloner cloner) {
86      return new SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer(this, cloner);
87    }
88
89
90    protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
91      double[] classValues;
92      double[] thresholds;
93      var estimatedValues = SymbolicDataAnalysisTreeInterpreter.GetSymbolicExpressionTreeValues(bestTree, ProblemData.Dataset, ProblemData.TrainingIndizes)
94        .LimitToRange(LowerEstimationLimit.Value, UpperEstimationLimit.Value);
95      var targetValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes);
96      AccuracyMaximizationThresholdCalculator.CalculateThresholds(ProblemData, estimatedValues, targetValues, out classValues, out thresholds);
97      var model = new SymbolicDiscriminantFunctionClassificationModel(bestTree, SymbolicDataAnalysisTreeInterpreter, classValues, thresholds, LowerEstimationLimit.Value, UpperEstimationLimit.Value);
98      return new SymbolicDiscriminantFunctionClassificationSolution(model, ProblemData);
99    }
100  }
101}
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