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
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.Collections.Generic;
23using System.Linq;
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 {
31  [Item("Mean absolute error Evaluator", "Calculates the mean absolute error of a symbolic regression solution.")]
32  [StorableClass]
33  public class SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
34    public override bool Maximization { get { return false; } }
35    [StorableConstructor]
36    protected SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator(bool deserializing) : base(deserializing) { }
37    protected SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator(SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator original, Cloner cloner)
38      : base(original, cloner) {
39    }
40    public override IDeepCloneable Clone(Cloner cloner) {
41      return new SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator(this, cloner);
42    }
43    public SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator() : base() { }
44
45    public override IOperation InstrumentedApply() {
46      var solution = SymbolicExpressionTreeParameter.ActualValue;
47      IEnumerable<int> rows = GenerateRowsToEvaluate();
48
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);
65      QualityParameter.ActualValue = new DoubleValue(quality);
66
67      return base.InstrumentedApply();
68    }
69
70    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
71      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
72      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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) {
77      OnlineCalculatorError errorState;
78
79      double mae;
80      if (applyLinearScaling) {
81        var maeCalculator = new OnlineMeanAbsoluteErrorCalculator();
82        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, maeCalculator, problemData.Dataset.Rows);
83        errorState = maeCalculator.ErrorState;
84        mae = maeCalculator.MeanAbsoluteError;
85      } else {
86        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
87        mae = OnlineMeanAbsoluteErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
88      }
89      if (errorState != OnlineCalculatorError.None) return double.NaN;
90      return mae;
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      ApplyLinearScalingParameter.ExecutionContext = context;
97
98      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
99
100      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
101      EstimationLimitsParameter.ExecutionContext = null;
102      ApplyLinearScalingParameter.ExecutionContext = null;
103
104      return mse;
105    }
106  }
107}
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