source: branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator.cs @ 17193

Last change on this file since 17193 was 17193, checked in by mkommend, 4 months ago

#2974: Merged trunk changes into branch.

File size: 4.7 KB
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[5500]1#region License Information
2/* HeuristicLab
[17193]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;
[16677]27using HEAL.Attic;
[5500]28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
[7677]30  [Item("Mean absolute error Evaluator", "Calculates the mean absolute error of a symbolic regression solution.")]
[16677]31  [StorableType("8ABB1AC3-0ACE-43AB-8E6B-B0FC925429DC")]
[7677]32  public class SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
[7672]33    public override bool Maximization { get { return false; } }
[5500]34    [StorableConstructor]
[16677]35    protected SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator(StorableConstructorFlag _) : base(_) { }
[7677]36    protected SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator(SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator original, Cloner cloner)
[5500]37      : base(original, cloner) {
38    }
39    public override IDeepCloneable Clone(Cloner cloner) {
[7677]40      return new SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator(this, cloner);
[5500]41    }
[7677]42    public SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator() : base() { }
[5505]43
[10291]44    public override IOperation InstrumentedApply() {
[5851]45      var solution = SymbolicExpressionTreeParameter.ActualValue;
[5500]46      IEnumerable<int> rows = GenerateRowsToEvaluate();
[5851]47
[12977]48      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
[5851]49      QualityParameter.ActualValue = new DoubleValue(quality);
50
[10291]51      return base.InstrumentedApply();
[5500]52    }
53
[7672]54    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
[5500]55      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
[7677]56      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
[5942]57      OnlineCalculatorError errorState;
[7672]58
[8634]59      double mae;
[7672]60      if (applyLinearScaling) {
[7677]61        var maeCalculator = new OnlineMeanAbsoluteErrorCalculator();
[8113]62        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, maeCalculator, problemData.Dataset.Rows);
[7677]63        errorState = maeCalculator.ErrorState;
[8634]64        mae = maeCalculator.MeanAbsoluteError;
[8113]65      } else {
[8634]66        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
67        mae = OnlineMeanAbsoluteErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
[8113]68      }
[8664]69      if (errorState != OnlineCalculatorError.None) return double.NaN;
70      return mae;
[5500]71    }
[5607]72
[5613]73    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
[5722]74      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
[5770]75      EstimationLimitsParameter.ExecutionContext = context;
[8664]76      ApplyLinearScalingParameter.ExecutionContext = context;
[5722]77
[8664]78      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
[5722]79
80      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
[5770]81      EstimationLimitsParameter.ExecutionContext = null;
[8664]82      ApplyLinearScalingParameter.ExecutionContext = null;
[5722]83
84      return mse;
[5607]85    }
[5500]86  }
87}
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