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source: branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/WeightCalculators/MajorityVoteWeightCalculator.cs @ 8814

Last change on this file since 8814 was 8814, checked in by sforsten, 11 years ago

#1776:

  • improved performance of confidence calculation
  • fixed bug in median confidence calculation
  • fixed bug in average confidence calculation
  • confidence calculation is now easier for training and test
  • removed obsolete view ClassificationEnsembleSolutionConfidenceAccuracyDependence
File size: 3.9 KB
Line 
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 System.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27
28namespace HeuristicLab.Problems.DataAnalysis {
29  /// <summary>
30  /// Represents a weight calculator that gives every classification solution the same weight.
31  /// </summary>
32  [StorableClass]
33  [Item("MajorityVoteWeightCalculator", "Represents a weight calculator that gives every classification solution the same weight.")]
34  public class MajorityVoteWeightCalculator : ClassificationWeightCalculator {
35
36    public MajorityVoteWeightCalculator()
37      : base() {
38    }
39
40    [StorableConstructor]
41    protected MajorityVoteWeightCalculator(bool deserializing) : base(deserializing) { }
42    protected MajorityVoteWeightCalculator(MajorityVoteWeightCalculator original, Cloner cloner)
43      : base(original, cloner) {
44    }
45
46    public override IDeepCloneable Clone(Cloner cloner) {
47      return new MajorityVoteWeightCalculator(this, cloner);
48    }
49
50    protected override IEnumerable<double> CalculateWeights(IEnumerable<IClassificationSolution> classificationSolutions) {
51      return Enumerable.Repeat<double>(1, classificationSolutions.Count());
52    }
53
54    public override double GetConfidence(IEnumerable<IClassificationSolution> solutions, int index, double estimatedClassValue, CheckPoint handler) {
55      if (solutions.Count() < 1)
56        return double.NaN;
57      var votingSolutions = solutions.Where(s => handler(s.ProblemData, index));
58      if (votingSolutions.Count() < 1)
59        return double.NaN;
60      Dataset dataset = votingSolutions.First().ProblemData.Dataset;
61      int correctEstimated = votingSolutions.Select(s => s.Model.GetEstimatedClassValues(dataset, Enumerable.Repeat(index, 1)).First())
62                                      .Where(x => x.Equals(estimatedClassValue))
63                                      .Count();
64      return ((double)correctEstimated / (double)votingSolutions.Count() - 0.5) * 2;
65    }
66
67    public override IEnumerable<double> GetConfidence(IEnumerable<IClassificationSolution> solutions, IEnumerable<int> indices, IEnumerable<double> estimatedClassValue, CheckPoint handler) {
68      if (solutions.Count() < 1)
69        return Enumerable.Repeat(double.NaN, indices.Count());
70
71      List<int> indicesList = indices.ToList();
72      Dataset dataset = solutions.First().ProblemData.Dataset;
73      var solValues = solutions.ToDictionary(x => x, x => x.Model.GetEstimatedClassValues(dataset, indicesList).ToArray());
74      double[] estimatedClassValueArr = estimatedClassValue.ToArray();
75      double correctEstimated;
76      double[] confidences = new double[indices.Count()];
77
78      for (int i = 0; i < indicesList.Count; i++) {
79        var votingSolutions = solValues.Where(x => handler(x.Key.ProblemData, indicesList[i]));
80        correctEstimated = votingSolutions.Where(x => DoubleExtensions.IsAlmost(x.Value[i], estimatedClassValueArr[i])).Count();
81        confidences[i] = (correctEstimated / (double)votingSolutions.Count() - 0.5) * 2;
82      }
83
84      return confidences;
85    }
86  }
87}
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