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source: trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/3.3/AccuracyEvaluator.cs @ 1851

Last change on this file since 1851 was 1796, checked in by gkronber, 15 years ago

Refactored GP evaluation to make it possible to use different evaluators to interpret function trees. #615 (Evaluation of HL3 function trees should be equivalent to evaluation in HL2)

File size: 3.3 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2008 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;
23using System.Collections.Generic;
24using System.Linq;
25using System.Text;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.GP.StructureIdentification;
29using HeuristicLab.DataAnalysis;
30
31namespace HeuristicLab.GP.StructureIdentification.Classification {
32  public class AccuracyEvaluator : GPClassificationEvaluatorBase {
33    private const double EPSILON = 1.0E-6;
34    public override string Description {
35      get {
36        return @"Calculates the total accuracy of the model (ratio of correctly classified instances to total number of instances) given a model and the list of possible target class values.";
37      }
38    }
39
40    public AccuracyEvaluator()
41      : base() {
42      AddVariableInfo(new VariableInfo("Accuracy", "The total accuracy of the model (ratio of correctly classified instances to total number of instances)", typeof(DoubleData), VariableKind.New));
43    }
44
45    public override void Evaluate(IScope scope, ITreeEvaluator evaluator, IFunctionTree tree, Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end) {
46      DoubleData accuracy = GetVariableValue<DoubleData>("Accuracy", scope, false, false);
47      if (accuracy == null) {
48        accuracy = new DoubleData();
49        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("Accuracy"), accuracy));
50      }
51
52      int nSamples = end - start;
53      int nCorrect = 0;
54      for (int sample = start; sample < end; sample++) {
55        double est = evaluator.Evaluate(tree, sample);
56        double origClass = dataset.GetValue(sample, targetVariable);
57        double estClass = double.NaN;
58        // if estimation is lower than the smallest threshold value -> estimated class is the lower class
59        if (est < thresholds[0]) estClass = classes[0];
60        // if estimation is larger (or equal) than the largest threshold value -> estimated class is the upper class
61        else if (est >= thresholds[thresholds.Length - 1]) estClass = classes[classes.Length - 1];
62        else {
63          // otherwise the estimated class is the class which upper threshold is larger than the estimated value
64          for (int k = 0; k < thresholds.Length; k++) {
65            if (thresholds[k] > est) {
66              estClass = classes[k];
67              break;
68            }
69          }
70        }
71        if (Math.Abs(estClass - origClass) < EPSILON) nCorrect++;
72      }
73      accuracy.Data = nCorrect / (double)nSamples;
74    }
75  }
76}
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