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source: trunk/sources/HeuristicLab.GP.StructureIdentification/Evaluators/EarlyStoppingMeanSquaredErrorEvaluator.cs @ 1109

Last change on this file since 1109 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.Operators;
29
30namespace HeuristicLab.GP.StructureIdentification {
31  public class EarlyStoppingMeanSquaredErrorEvaluator : MeanSquaredErrorEvaluator {
32    public override string Description {
33      get {
34        return @"Evaluates 'FunctionTree' for all samples of the dataset and calculates the mean-squared-error
35for the estimated values vs. the real values of 'TargetVariable'.
36This operator stops the computation as soon as an upper limit for the mean-squared-error is reached.";
37      }
38    }
39
40    public EarlyStoppingMeanSquaredErrorEvaluator()
41      : base() {
42      AddVariableInfo(new VariableInfo("QualityLimit", "The upper limit of the MSE which is used as early stopping criterion.", typeof(DoubleData), VariableKind.In));
43    }
44
45    // evaluates the function-tree for the given target-variable and the whole dataset and returns the MSE
46    public override void Evaluate(IScope scope, BakedTreeEvaluator evaluator, HeuristicLab.DataAnalysis.Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
47      double qualityLimit = GetVariableValue<DoubleData>("QualityLimit", scope, false).Data;
48      DoubleData mse = GetVariableValue<DoubleData>("MSE", scope, false, false);
49      if (mse == null) {
50        mse = new DoubleData();
51        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("MSE"), mse));
52      }
53
54      double errorsSquaredSum = 0;
55      int rows = end - start;
56      for (int sample = start; sample < end; sample++) {
57        double estimated = evaluator.Evaluate(sample);
58        double original = dataset.GetValue(sample, targetVariable);
59        if (updateTargetValues) {
60          dataset.SetValue(sample, targetVariable, estimated);
61        }
62        if (!double.IsNaN(original) && !double.IsInfinity(original)) {
63          double error = estimated - original;
64          errorsSquaredSum += error * error;
65        }
66        // check the limit and stop as soon as we hit the limit
67        if (errorsSquaredSum / rows >= qualityLimit) {
68          mse.Data = errorsSquaredSum / (sample - start + 1); // return estimated MSE (when the remaining errors are on average the same)
69          return;
70        }
71      }
72      errorsSquaredSum /= rows;
73      if (double.IsNaN(errorsSquaredSum) || double.IsInfinity(errorsSquaredSum)) {
74        errorsSquaredSum = double.MaxValue;
75      }
76
77      mse.Data = errorsSquaredSum;
78    }
79  }
80}
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