source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicClassificationSolutionImpactValuesCalculator.cs @ 10273

Last change on this file since 10273 was 10273, checked in by mkommend, 6 years ago

#2132: Adapted the SymbolicClassificationSolutionImpactValuesCalculator to manipulate the cloned tree instead of using the cloner itself for the node exchange.

File size: 3.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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 HeuristicLab.Common;
24using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
25
26namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
27  public class SymbolicClassificationSolutionImpactValuesCalculator : SymbolicDataAnalysisSolutionImpactValuesCalculator {
28    public override double CalculateReplacementValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows) {
29      var classificationModel = (ISymbolicClassificationModel)model;
30      var classificationProblemData = (IClassificationProblemData)problemData;
31
32      return CalculateReplacementValue(node, classificationModel.SymbolicExpressionTree, classificationModel.Interpreter, classificationProblemData.Dataset, rows);
33    }
34
35    public override double CalculateImpactValue(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node, IDataAnalysisProblemData problemData, IEnumerable<int> rows, double originalQuality = double.NaN) {
36      var classificationModel = (ISymbolicClassificationModel)model;
37      var classificationProblemData = (IClassificationProblemData)problemData;
38
39      var dataset = classificationProblemData.Dataset;
40      var targetClassValues = dataset.GetDoubleValues(classificationProblemData.TargetVariable, rows);
41
42      OnlineCalculatorError errorState;
43      if (double.IsNaN(originalQuality)) {
44        var originalClassValues = classificationModel.GetEstimatedClassValues(dataset, rows);
45        originalQuality = OnlineAccuracyCalculator.Calculate(targetClassValues, originalClassValues, out errorState);
46        if (errorState != OnlineCalculatorError.None) originalQuality = 0.0;
47      }
48
49      var replacementValue = CalculateReplacementValue(classificationModel, node, classificationProblemData, rows);
50      var constantNode = new ConstantTreeNode(new Constant()) { Value = replacementValue };
51
52      var cloner = new Cloner();
53      var tempModel = cloner.Clone(classificationModel);
54      var tempModelNode = (ISymbolicExpressionTreeNode)cloner.GetClone(node);
55
56      var tempModelParentNode = tempModelNode.Parent;
57      int i = tempModelParentNode.IndexOfSubtree(tempModelNode);
58      tempModelParentNode.RemoveSubtree(i);
59      tempModelParentNode.InsertSubtree(i, constantNode);
60
61      var estimatedClassValues = tempModel.GetEstimatedClassValues(dataset, rows);
62      double newQuality = OnlineAccuracyCalculator.Calculate(targetClassValues, estimatedClassValues, out errorState);
63      if (errorState != OnlineCalculatorError.None) newQuality = 0.0;
64
65      return originalQuality - newQuality;
66    }
67
68  }
69}
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