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source: branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/WeightCalculators/AverageThresholdCalculator.cs @ 8534

Last change on this file since 8534 was 8534, checked in by sforsten, 11 years ago

#1776:

  • merged r8508:8533 from trunk into branch
  • AverageThresholdCalculator and MedianThresholdCalculator can now handle multi class classification
  • changed combo boxes in ClassificationEnsembleSolutionView to drop down list
File size: 4.9 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;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis {
30  [StorableClass]
31  [Item("AverageThresholdCalculator", "")]
32  public class AverageThresholdCalculator : DiscriminantClassificationWeightCalculator {
33
34    public AverageThresholdCalculator()
35      : base() {
36    }
37    [StorableConstructor]
38    protected AverageThresholdCalculator(bool deserializing) : base(deserializing) { }
39    protected AverageThresholdCalculator(AverageThresholdCalculator original, Cloner cloner)
40      : base(original, cloner) {
41    }
42    public override IDeepCloneable Clone(Cloner cloner) {
43      return new AverageThresholdCalculator(this, cloner);
44    }
45
46    protected double[] threshold;
47    protected double[] classValues;
48
49    protected override IEnumerable<double> DiscriminantCalculateWeights(IEnumerable<IDiscriminantFunctionClassificationSolution> discriminantSolutions) {
50      classValues = discriminantSolutions.First().Model.ClassValues.ToArray();
51      var modelThresholds = discriminantSolutions.Select(x => x.Model.Thresholds.ToArray());
52      threshold = new double[modelThresholds.First().GetLength(0)];
53      for (int i = 0; i < modelThresholds.First().GetLength(0); i++) {
54        threshold[i] = modelThresholds.Select(x => x[i]).Average();
55      }
56      return Enumerable.Repeat<double>(1, discriminantSolutions.Count());
57    }
58
59    protected override double GetDiscriminantConfidence(IEnumerable<IDiscriminantFunctionClassificationSolution> solutions, int index, double estimatedClassValue) {
60      Dataset dataset = solutions.First().ProblemData.Dataset;
61      IList<double> values = solutions.Select(s => s.Model.GetEstimatedValues(dataset, Enumerable.Repeat(index, 1)).First()).ToList();
62      if (values.Count <= 0)
63        return double.NaN;
64      double avg = values.Average();
65      return GetAverageConfidence(avg, estimatedClassValue);
66    }
67
68    public override IEnumerable<double> GetDiscriminantConfidence(IEnumerable<IDiscriminantFunctionClassificationSolution> solutions, IEnumerable<int> indices, IEnumerable<double> estimatedClassValue) {
69      Dataset dataset = solutions.First().ProblemData.Dataset;
70      double[][] values = solutions.Select(s => s.Model.GetEstimatedValues(dataset, indices).ToArray()).ToArray();
71      double[] confidences = new double[indices.Count()];
72      double[] estimatedClassValueArr = estimatedClassValue.ToArray();
73
74      for (int i = 0; i < indices.Count(); i++) {
75        double avg = values.Select(x => x[i]).Average();
76        confidences[i] = GetAverageConfidence(avg, estimatedClassValueArr[i]);
77      }
78
79      return confidences;
80    }
81
82    protected double GetAverageConfidence(double avg, double estimatedClassValue) {
83      for (int i = 0; i < classValues.Length; i++) {
84        if (estimatedClassValue.Equals(classValues[i])) {
85          //special case: avgerage is higher than value of highest class
86          if (i == classValues.Length - 1 && avg > estimatedClassValue) {
87            return 1;
88          }
89          //special case: average is lower than value of lowest class
90          if (i == 0 && avg < estimatedClassValue) {
91            return 1;
92          }
93          //special case: average is not between threshold of estimated class value
94          if ((i < classValues.Length - 1 && avg >= threshold[i + 1]) || avg <= threshold[i]) {
95            return 0;
96          }
97
98          double thresholdToClassDistance, thresholdToAverageValueDistance;
99          if (avg >= classValues[i]) {
100            thresholdToClassDistance = threshold[i + 1] - classValues[i];
101            thresholdToAverageValueDistance = threshold[i + 1] - avg;
102          } else {
103            thresholdToClassDistance = classValues[i] - threshold[i];
104            thresholdToAverageValueDistance = avg - threshold[i];
105          }
106          return (1 / thresholdToClassDistance) * thresholdToAverageValueDistance;
107        }
108      }
109      return double.NaN;
110    }
111  }
112}
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