source: branches/2971_named_intervals/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionMeanRelativeErrorEvaluator.cs @ 16628

Last change on this file since 16628 was 16628, checked in by gkronber, 4 months ago

#2971: made branch compile with current version of trunk

File size: 4.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 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 HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HEAL.Attic;
31
32namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
33  [Item("Mean relative error Evaluator", "Evaluator for symbolic regression models that calculates the mean relative error avg( |y' - y| / (|y| + 1))." +
34                                         "The +1 is necessary to handle data with the value of 0.0 correctly. " +
35                                         "Notice: Linear scaling is ignored for this evaluator.")]
36  [StorableType("07CA387A-27F2-4932-8FF7-921AF057EA20")]
37  public class SymbolicRegressionMeanRelativeErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
38    public override bool Maximization { get { return false; } }
39    [StorableConstructor]
40    protected SymbolicRegressionMeanRelativeErrorEvaluator(StorableConstructorFlag _) : base(_) { }
41    protected SymbolicRegressionMeanRelativeErrorEvaluator(SymbolicRegressionMeanRelativeErrorEvaluator original, Cloner cloner)
42      : base(original, cloner) {
43    }
44    public override IDeepCloneable Clone(Cloner cloner) {
45      return new SymbolicRegressionMeanRelativeErrorEvaluator(this, cloner);
46    }
47    public SymbolicRegressionMeanRelativeErrorEvaluator() : base() { }
48
49    public override IOperation InstrumentedApply() {
50      var solution = SymbolicExpressionTreeParameter.ActualValue;
51      IEnumerable<int> rows = GenerateRowsToEvaluate();
52
53      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
54      QualityParameter.ActualValue = new DoubleValue(quality);
55
56      return base.InstrumentedApply();
57    }
58
59    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows) {
60      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
61      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
62      IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
63
64      var relResiduals = boundedEstimatedValues.Zip(targetValues, (e, t) => Math.Abs(t - e) / (Math.Abs(t) + 1.0));
65
66      OnlineCalculatorError errorState;
67      OnlineCalculatorError varErrorState;
68      double mre;
69      double variance;
70      OnlineMeanAndVarianceCalculator.Calculate(relResiduals, out mre, out variance, out errorState, out varErrorState);
71      if (errorState != OnlineCalculatorError.None) return double.NaN;
72      return mre;
73    }
74
75    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
76      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
77      EstimationLimitsParameter.ExecutionContext = context;
78
79      double mre = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
80
81      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
82      EstimationLimitsParameter.ExecutionContext = null;
83
84      return mre;
85    }
86  }
87}
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