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

Last change on this file since 18103 was 18103, checked in by dpiringe, 2 years 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: 5.3 KB
RevLine 
[5500]1#region License Information
2/* HeuristicLab
[17180]3 * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[5500]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.Collections.Generic;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
[16565]27using HEAL.Attic;
[5500]28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
[7677]30  [Item("Maximum absolute error Evaluator", "Calculates the maximum squared error of a symbolic regression solution.")]
[16565]31  [StorableType("256A6405-D1EE-4D8D-963A-42C56FEE8571")]
[7677]32  public class SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
[7672]33    public override bool Maximization { get { return false; } }
[5500]34    [StorableConstructor]
[16565]35    protected SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator(StorableConstructorFlag _) : base(_) { }
[7677]36    protected SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator(SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator original, Cloner cloner)
[5500]37      : base(original, cloner) {
38    }
39    public override IDeepCloneable Clone(Cloner cloner) {
[7677]40      return new SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator(this, cloner);
[5500]41    }
[7677]42    public SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator() : base() { }
[5505]43
[10291]44    public override IOperation InstrumentedApply() {
[18103]45      var tree = SymbolicExpressionTreeParameter.ActualValue;
[5500]46      IEnumerable<int> rows = GenerateRowsToEvaluate();
[5851]47
[18103]48      double quality = Calculate(
49        tree, ProblemDataParameter.ActualValue,
50        rows, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
51        ApplyLinearScalingParameter.ActualValue.Value,
52        EstimationLimitsParameter.ActualValue.Lower,
53        EstimationLimitsParameter.ActualValue.Upper);
[5851]54      QualityParameter.ActualValue = new DoubleValue(quality);
55
[10291]56      return base.InstrumentedApply();
[5500]57    }
58
[18103]59    public static double Calculate(
60      ISymbolicExpressionTree tree,
61      IRegressionProblemData problemData,
62      IEnumerable<int> rows,
63      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
64      bool applyLinearScaling,
65      double lowerEstimationLimit,
66      double upperEstimationLimit) {
67      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
[7677]68      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
[5942]69      OnlineCalculatorError errorState;
[7672]70
71      double mse;
72      if (applyLinearScaling) {
[7677]73        var maeCalculator = new OnlineMaxAbsoluteErrorCalculator();
[8113]74        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, maeCalculator, problemData.Dataset.Rows);
[7677]75        errorState = maeCalculator.ErrorState;
76        mse = maeCalculator.MaxAbsoluteError;
[8113]77      } else {
78        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
[7677]79        mse = OnlineMaxAbsoluteErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
[8113]80      }
[8664]81      if (errorState != OnlineCalculatorError.None) return double.NaN;
82      return mse;
[5500]83    }
[5607]84
[5613]85    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
[5722]86      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
[5770]87      EstimationLimitsParameter.ExecutionContext = context;
[8664]88      ApplyLinearScalingParameter.ExecutionContext = context;
[5722]89
[18103]90      double mse = Calculate(
91        tree, problemData, rows,
92        SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
93        ApplyLinearScalingParameter.ActualValue.Value,
94        EstimationLimitsParameter.ActualValue.Lower,
95        EstimationLimitsParameter.ActualValue.Upper);
[5722]96
97      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
[5770]98      EstimationLimitsParameter.ExecutionContext = null;
[8664]99      ApplyLinearScalingParameter.ExecutionContext = null;
[5722]100
101      return mse;
[5607]102    }
[18095]103
[18103]104    public override double Evaluate(
105      ISymbolicExpressionTree tree,
106      IRegressionProblemData problemData,
107      IEnumerable<int> rows,
[18095]108      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
109      bool applyLinearScaling = true,
110      double lowerEstimationLimit = double.MinValue,
111      double upperEstimationLimit = double.MaxValue) {
[18103]112      return Calculate(tree, problemData, rows, interpreter, applyLinearScaling, lowerEstimationLimit, upperEstimationLimit);
[18095]113    }
[5500]114  }
[8664]115}
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