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source: branches/CEDMA-Refactoring-Ticket419/HeuristicLab.GP.StructureIdentification.Classification/ClassificationMeanSquaredErrorEvaluator.cs @ 922

Last change on this file since 922 was 712, checked in by gkronber, 16 years ago

fixed a stupid mistake introduced with r702 #328 (GP evaluation doesn't work in a thread parallel engine).

File size: 3.4 KB
Line 
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;
29
30namespace HeuristicLab.GP.StructureIdentification.Classification {
31  public class ClassificationMeanSquaredErrorEvaluator : GPClassificationEvaluatorBase {
32    private const double EPSILON = 1.0E-7;
33    public override string Description {
34      get {
35        return @"Evaluates 'FunctionTree' for all samples of 'DataSet' and calculates the mean-squared-error
36for the estimated values vs. the real values of 'TargetVariable'.";
37      }
38    }
39
40    public ClassificationMeanSquaredErrorEvaluator()
41      : base() {
42      AddVariableInfo(new VariableInfo("MSE", "The mean squared error of the model", typeof(DoubleData), VariableKind.New));
43    }
44
45    public override void Evaluate(IScope scope, BakedTreeEvaluator evaluator, HeuristicLab.DataAnalysis.Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end) {
46      double errorsSquaredSum = 0;
47      for (int sample = start; sample < end; sample++) {
48        double estimated = evaluator.Evaluate(sample);
49        double original = dataset.GetValue(sample, targetVariable);
50        if (!double.IsNaN(original) && !double.IsInfinity(original)) {
51          double error = estimated - original;
52          // between classes use squared error
53          // on the lower end and upper end only add linear error if the absolute error is larger than 1
54          // the error>1.0 constraint is needed for balance because in the interval ]-1, 1[ the squared error is smaller than the absolute error
55          if ((IsEqual(original, classes[0]) && error < -1.0) ||
56            (IsEqual(original, classes[classes.Length - 1]) && error > 1.0)) {
57            errorsSquaredSum += Math.Abs(error); // only add linear error below the smallest class or above the largest class
58          } else {
59            errorsSquaredSum += error * error;
60          }
61        }
62      }
63
64      errorsSquaredSum /= (end - start);
65      if (double.IsNaN(errorsSquaredSum) || double.IsInfinity(errorsSquaredSum)) {
66        errorsSquaredSum = double.MaxValue;
67      }
68
69      DoubleData mse = GetVariableValue<DoubleData>("MSE", scope, false, false);
70      if (mse == null) {
71        mse = new DoubleData();
72        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("MSE"), mse));
73      }
74
75      mse.Data = errorsSquaredSum;
76    }
77
78    private bool IsEqual(double x, double y) {
79      return Math.Abs(x - y) < EPSILON;
80    }
81  }
82}
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