source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator.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: 5.9 KB
RevLine 
[5500]1#region License Information
2/* HeuristicLab
[12012]3 * Copyright (C) 2002-2015 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;
[12973]23using System.Linq;
[5500]24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
[7677]31  [Item("Mean absolute error Evaluator", "Calculates the mean absolute error of a symbolic regression solution.")]
[5500]32  [StorableClass]
[7677]33  public class SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
[7672]34    public override bool Maximization { get { return false; } }
[5500]35    [StorableConstructor]
[7677]36    protected SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator(bool deserializing) : base(deserializing) { }
37    protected SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator(SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator original, Cloner cloner)
[5500]38      : base(original, cloner) {
39    }
40    public override IDeepCloneable Clone(Cloner cloner) {
[7677]41      return new SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator(this, cloner);
[5500]42    }
[7677]43    public SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator() : base() { }
[5505]44
[10291]45    public override IOperation InstrumentedApply() {
[5851]46      var solution = SymbolicExpressionTreeParameter.ActualValue;
[5500]47      IEnumerable<int> rows = GenerateRowsToEvaluate();
[5851]48
[12973]49      var problemData = ProblemDataParameter.ActualValue;
50      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
51      var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows).ToArray();
52      var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
53      var estimationLimits = EstimationLimitsParameter.ActualValue;
54
55      if (SaveEstimatedValuesToScope) {
56        var boundedValues = estimatedValues.LimitToRange(estimationLimits.Lower, estimationLimits.Upper).ToArray();
57        var scope = ExecutionContext.Scope;
58        if (scope.Variables.ContainsKey("EstimatedValues"))
59          scope.Variables["EstimatedValues"].Value = new DoubleArray(boundedValues);
60        else
61          scope.Variables.Add(new Core.Variable("EstimatedValues", new DoubleArray(boundedValues)));
62      }
63
64      double quality = Calculate(targetValues, estimatedValues, estimationLimits.Lower, estimationLimits.Upper, problemData, ApplyLinearScalingParameter.ActualValue.Value);
[5851]65      QualityParameter.ActualValue = new DoubleValue(quality);
66
[10291]67      return base.InstrumentedApply();
[5500]68    }
69
[7672]70    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
[5500]71      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
[7677]72      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
[12973]73      return Calculate(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, problemData, applyLinearScaling);
74    }
75
76    private static double Calculate(IEnumerable<double> targetValues, IEnumerable<double> estimatedValues, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, bool applyLinearScaling) {
[5942]77      OnlineCalculatorError errorState;
[7672]78
[8634]79      double mae;
[7672]80      if (applyLinearScaling) {
[7677]81        var maeCalculator = new OnlineMeanAbsoluteErrorCalculator();
[8113]82        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, maeCalculator, problemData.Dataset.Rows);
[7677]83        errorState = maeCalculator.ErrorState;
[8634]84        mae = maeCalculator.MeanAbsoluteError;
[8113]85      } else {
[8634]86        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
87        mae = OnlineMeanAbsoluteErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
[8113]88      }
[8664]89      if (errorState != OnlineCalculatorError.None) return double.NaN;
90      return mae;
[5500]91    }
[5607]92
[5613]93    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
[5722]94      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
[5770]95      EstimationLimitsParameter.ExecutionContext = context;
[8664]96      ApplyLinearScalingParameter.ExecutionContext = context;
[5722]97
[8664]98      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
[5722]99
100      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
[5770]101      EstimationLimitsParameter.ExecutionContext = null;
[8664]102      ApplyLinearScalingParameter.ExecutionContext = null;
[5722]103
104      return mse;
[5607]105    }
[5500]106  }
107}
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