source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionMeanRelativeErrorEvaluator.cs @ 12977

Last change on this file since 12977 was 12977, checked in by bburlacu, 4 years ago

#2480: Reverted changes to the evaluators as they impact performance and memory usage.

File size: 4.5 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      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
53      QualityParameter.ActualValue = new DoubleValue(quality);
54
55      return base.InstrumentedApply();
56    }
57
58    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows) {
59      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
60      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
61      IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
62
63      var relResiduals = boundedEstimatedValues.Zip(targetValues, (e, t) => Math.Abs(t - e) / (Math.Abs(t) + 1.0));
64
65      OnlineCalculatorError errorState;
66      OnlineCalculatorError varErrorState;
67      double mre;
68      double variance;
69      OnlineMeanAndVarianceCalculator.Calculate(relResiduals, out mre, out variance, out errorState, out varErrorState);
70      if (errorState != OnlineCalculatorError.None) return double.NaN;
71      return mre;
72    }
73
74    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
75      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
76      EstimationLimitsParameter.ExecutionContext = context;
77
78      double mre = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
79
80      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
81      EstimationLimitsParameter.ExecutionContext = null;
82
83      return mre;
84    }
85  }
86}
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