source: branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionMeanRelativeErrorEvaluator.cs @ 18103

Last change on this file since 18103 was 18103, checked in by dpiringe, 6 months ago

#3136

  • refactor the evaluation logic of NMSESingleObjectiveConstraintsEvaluator
  • refactor the new method Evaluate for PearsonRSquaredAverageSimilarityEvaluator
  • change the parameter order of some evaluate/calculate methods
File size: 5.0 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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 HEAL.Attic;
30
31namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
32  [Item("Mean relative error Evaluator", "Evaluator for symbolic regression models that calculates the mean relative error avg( |y' - y| / (|y| + 1))." +
33                                         "The +1 is necessary to handle data with the value of 0.0 correctly. " +
34                                         "Notice: Linear scaling is ignored for this evaluator.")]
35  [StorableType("8A5AAF93-5338-4E11-B3B2-3D9274329E5F")]
36  public class SymbolicRegressionMeanRelativeErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
37    public override bool Maximization { get { return false; } }
38    [StorableConstructor]
39    protected SymbolicRegressionMeanRelativeErrorEvaluator(StorableConstructorFlag _) : base(_) { }
40    protected SymbolicRegressionMeanRelativeErrorEvaluator(SymbolicRegressionMeanRelativeErrorEvaluator original, Cloner cloner)
41      : base(original, cloner) {
42    }
43    public override IDeepCloneable Clone(Cloner cloner) {
44      return new SymbolicRegressionMeanRelativeErrorEvaluator(this, cloner);
45    }
46    public SymbolicRegressionMeanRelativeErrorEvaluator() : base() { }
47
48    public override IOperation InstrumentedApply() {
49      var tree = SymbolicExpressionTreeParameter.ActualValue;
50      IEnumerable<int> rows = GenerateRowsToEvaluate();
51
52      double quality = Calculate(
53        tree, ProblemDataParameter.ActualValue, rows,
54        SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
55        EstimationLimitsParameter.ActualValue.Lower,
56        EstimationLimitsParameter.ActualValue.Upper);
57      QualityParameter.ActualValue = new DoubleValue(quality);
58
59      return base.InstrumentedApply();
60    }
61
62    public static double Calculate(
63      ISymbolicExpressionTree tree,
64      IRegressionProblemData problemData,
65      IEnumerable<int> rows,
66      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
67      double lowerEstimationLimit, double upperEstimationLimit) {
68      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
69      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
70      IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
71
72      var relResiduals = boundedEstimatedValues.Zip(targetValues, (e, t) => Math.Abs(t - e) / (Math.Abs(t) + 1.0));
73
74      OnlineCalculatorError errorState;
75      OnlineCalculatorError varErrorState;
76      double mre;
77      double variance;
78      OnlineMeanAndVarianceCalculator.Calculate(relResiduals, out mre, out variance, out errorState, out varErrorState);
79      if (errorState != OnlineCalculatorError.None) return double.NaN;
80      return mre;
81    }
82
83    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
84      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
85      EstimationLimitsParameter.ExecutionContext = context;
86
87      double mre = Calculate(
88        tree, problemData, rows,
89        SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
90        EstimationLimitsParameter.ActualValue.Lower,
91        EstimationLimitsParameter.ActualValue.Upper);
92
93      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
94      EstimationLimitsParameter.ExecutionContext = null;
95
96      return mre;
97    }
98
99    public override double Evaluate(
100      ISymbolicExpressionTree tree,
101      IRegressionProblemData problemData,
102      IEnumerable<int> rows,
103      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
104      bool applyLinearScaling = true,
105      double lowerEstimationLimit = double.MinValue,
106      double upperEstimationLimit = double.MaxValue) {
107      return Calculate(tree, problemData, rows, interpreter, lowerEstimationLimit, upperEstimationLimit);
108    }
109  }
110}
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