source: branches/CEDMA-Refactoring-Ticket419/HeuristicLab.GP.StructureIdentification/Evaluators/MeanAbsolutePercentageOfRangeErrorEvaluator.cs @ 1246

Last change on this file since 1246 was 1246, checked in by gkronber, 12 years ago

Implemented first version of evaluation operator for the mean percentage error relative to the range of the original values of the target variable #505.

File size: 3.0 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;
29using HeuristicLab.DataAnalysis;
30
31namespace HeuristicLab.GP.StructureIdentification {
32  public class MeanAbsolutePercentageOfRangeErrorEvaluator : GPEvaluatorBase {
33    public override string Description {
34      get {
35        return @"Evaluates 'FunctionTree' for all samples of 'Dataset' and calculates
36the mean of the absolute percentage error (scale invariant) relative to the range of the target varibale of estimated values vs. real values of 'TargetVariable'.";
37      }
38    }
39
40    public MeanAbsolutePercentageOfRangeErrorEvaluator()
41      : base() {
42      AddVariableInfo(new VariableInfo("MAPRE", "The mean absolute percentage range error of the model", typeof(DoubleData), VariableKind.New));
43    }
44
45    public override void Evaluate(IScope scope, BakedTreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
46      double errorsSum = 0.0;
47      int n = 0;
48      double range = dataset.GetRange(targetVariable, start, end);
49      for (int sample = start; sample < end; sample++) {
50        double estimated = evaluator.Evaluate(sample);
51        double original = dataset.GetValue(sample, targetVariable);
52
53        if (updateTargetValues) {
54          dataset.SetValue(sample, targetVariable, estimated);
55        }
56
57        if (!double.IsNaN(original) && !double.IsInfinity(original) && original != 0.0) {
58          double percent_error = Math.Abs((estimated - original) / range);
59          errorsSum += percent_error;
60          n++;
61        }
62      }
63      double quality = errorsSum / n;
64      if (double.IsNaN(quality) || double.IsInfinity(quality))
65        quality = double.MaxValue;
66
67      // create a variable for the MAPRE
68      DoubleData mapre = GetVariableValue<DoubleData>("MAPRE", scope, false, false);
69      if (mapre == null) {
70        mapre = new DoubleData();
71        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("MAPRE"), mapre));
72      }
73
74      mapre.Data = quality;
75    }
76  }
77}
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