source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator.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: 4.7 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 HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
30  [Item("Maximum absolute error Evaluator", "Calculates the maximum squared error of a symbolic regression solution.")]
31  [StorableClass]
32  public class SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
33    public override bool Maximization { get { return false; } }
34    [StorableConstructor]
35    protected SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator(bool deserializing) : base(deserializing) { }
36    protected SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator(SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator original, Cloner cloner)
37      : base(original, cloner) {
38    }
39    public override IDeepCloneable Clone(Cloner cloner) {
40      return new SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator(this, cloner);
41    }
42    public SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator() : base() { }
43
44    public override IOperation InstrumentedApply() {
45      var solution = SymbolicExpressionTreeParameter.ActualValue;
46      IEnumerable<int> rows = GenerateRowsToEvaluate();
47
48      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
49      QualityParameter.ActualValue = new DoubleValue(quality);
50
51      return base.InstrumentedApply();
52    }
53
54    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
55      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
56      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
57      OnlineCalculatorError errorState;
58
59      double mse;
60      if (applyLinearScaling) {
61        var maeCalculator = new OnlineMaxAbsoluteErrorCalculator();
62        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, maeCalculator, problemData.Dataset.Rows);
63        errorState = maeCalculator.ErrorState;
64        mse = maeCalculator.MaxAbsoluteError;
65      } else {
66        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
67        mse = OnlineMaxAbsoluteErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
68      }
69      if (errorState != OnlineCalculatorError.None) return double.NaN;
70      return mse;
71    }
72
73    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
74      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
75      EstimationLimitsParameter.ExecutionContext = context;
76      ApplyLinearScalingParameter.ExecutionContext = context;
77
78      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
79
80      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
81      EstimationLimitsParameter.ExecutionContext = null;
82      ApplyLinearScalingParameter.ExecutionContext = null;
83
84      return mse;
85    }
86  }
87}
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