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source: stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs @ 12099

Last change on this file since 12099 was 12009, checked in by ascheibe, 10 years ago

#2212 updated copyright year

File size: 4.8 KB
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[5505]1#region License Information
2/* HeuristicLab
[12009]3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[5505]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
[5618]29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
[5505]30  [Item("Pearson R² & Tree size Evaluator", "Calculates the Pearson R² and the tree size of a symbolic regression solution.")]
31  [StorableClass]
32  public class SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
33    [StorableConstructor]
34    protected SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(bool deserializing) : base(deserializing) { }
35    protected SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator original, Cloner cloner)
36      : base(original, cloner) {
37    }
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(this, cloner);
40    }
41
42    public SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator() : base() { }
43
[5514]44    public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } }
45
[10507]46    public override IOperation InstrumentedApply() {
[5505]47      IEnumerable<int> rows = GenerateRowsToEvaluate();
[5851]48      var solution = SymbolicExpressionTreeParameter.ActualValue;
[8664]49      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
[5505]50      QualitiesParameter.ActualValue = new DoubleArray(qualities);
[10507]51      return base.InstrumentedApply();
[5505]52    }
53
[8664]54    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
[5505]55      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
[8664]56      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
[5942]57      OnlineCalculatorError errorState;
[8664]58
59      double r2;
60      if (applyLinearScaling) {
61        var r2Calculator = new OnlinePearsonsRSquaredCalculator();
62        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, r2Calculator, problemData.Dataset.Rows);
63        errorState = r2Calculator.ErrorState;
64        r2 = r2Calculator.RSquared;
65      } else {
66        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
67        r2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
68      }
69
70      if (errorState != OnlineCalculatorError.None) r2 = double.NaN;
71      return new double[2] { r2, solution.Length };
[5505]72    }
[5613]73
74    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
[5722]75      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
[5770]76      EstimationLimitsParameter.ExecutionContext = context;
[8664]77      ApplyLinearScalingParameter.ExecutionContext = context;
[5722]78
[8664]79      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
[5722]80
81      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
[5770]82      EstimationLimitsParameter.ExecutionContext = null;
[8664]83      ApplyLinearScalingParameter.ExecutionContext = null;
[5722]84
85      return quality;
[5613]86    }
[5505]87  }
88}
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