source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionMeanRelativeErrorEvaluator.cs @ 12973

Last change on this file since 12973 was 12973, checked in by bburlacu, 4 years ago

#2480: Implemented the necessary changes in the evaluators, and removed obsolete code from the phenotypic diversity analyzer.

File size: 5.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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;
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  [StorableClass]
36  public class SymbolicRegressionMeanRelativeErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
37    public override bool Maximization { get { return false; } }
38    [StorableConstructor]
39    protected SymbolicRegressionMeanRelativeErrorEvaluator(bool deserializing) : base(deserializing) { }
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 solution = SymbolicExpressionTreeParameter.ActualValue;
50      IEnumerable<int> rows = GenerateRowsToEvaluate();
51
52      var problemData = ProblemDataParameter.ActualValue;
53      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
54      var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows).ToArray();
55      var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
56      var estimationLimits = EstimationLimitsParameter.ActualValue;
57
58      if (SaveEstimatedValuesToScope) {
59        var boundedValues = estimatedValues.LimitToRange(estimationLimits.Lower, estimationLimits.Upper).ToArray();
60        var scope = ExecutionContext.Scope;
61        if (scope.Variables.ContainsKey("EstimatedValues"))
62          scope.Variables["EstimatedValues"].Value = new DoubleArray(boundedValues);
63        else
64          scope.Variables.Add(new Core.Variable("EstimatedValues", new DoubleArray(boundedValues)));
65      }
66
67      double quality = Calculate(targetValues, estimatedValues, estimationLimits.Lower, estimationLimits.Upper);
68      QualityParameter.ActualValue = new DoubleValue(quality);
69
70      return base.InstrumentedApply();
71    }
72
73    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows) {
74      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
75      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
76      return Calculate(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit);
77    }
78
79    private static double Calculate(IEnumerable<double> targetValues, IEnumerable<double> estimatedValues, double lowerEstimationLimit, double upperEstimationLimit) {
80      IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
81
82      var relResiduals = boundedEstimatedValues.Zip(targetValues, (e, t) => Math.Abs(t - e) / (Math.Abs(t) + 1.0));
83
84      OnlineCalculatorError errorState;
85      OnlineCalculatorError varErrorState;
86      double mre;
87      double variance;
88      OnlineMeanAndVarianceCalculator.Calculate(relResiduals, out mre, out variance, out errorState, out varErrorState);
89      if (errorState != OnlineCalculatorError.None) return double.NaN;
90      return mre;
91    }
92
93    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
94      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
95      EstimationLimitsParameter.ExecutionContext = context;
96
97      double mre = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
98
99      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
100      EstimationLimitsParameter.ExecutionContext = null;
101
102      return mre;
103    }
104  }
105}
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