[5505] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12009] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[5505] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System.Collections.Generic;
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| 23 | using HeuristicLab.Common;
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| 24 | using HeuristicLab.Core;
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| 25 | using HeuristicLab.Data;
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| 26 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 28 |
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[5618] | 29 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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[5505] | 30 | [Item("Pearson R² & Tree size Evaluator", "Calculates the Pearson R² and the tree size of a symbolic regression solution.")]
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| 31 | [StorableClass]
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| 32 | public class SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
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| 33 | [StorableConstructor]
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| 34 | protected SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(bool deserializing) : base(deserializing) { }
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| 35 | protected SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator original, Cloner cloner)
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| 36 | : base(original, cloner) {
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| 37 | }
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| 38 | public override IDeepCloneable Clone(Cloner cloner) {
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| 39 | return new SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(this, cloner);
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| 40 | }
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| 41 |
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| 42 | public SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator() : base() { }
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| 43 |
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[5514] | 44 | public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } }
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| 45 |
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[10507] | 46 | public override IOperation InstrumentedApply() {
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[5505] | 47 | IEnumerable<int> rows = GenerateRowsToEvaluate();
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[5851] | 48 | var solution = SymbolicExpressionTreeParameter.ActualValue;
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[8664] | 49 | double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
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[5505] | 50 | QualitiesParameter.ActualValue = new DoubleArray(qualities);
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[10507] | 51 | return base.InstrumentedApply();
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[5505] | 52 | }
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| 53 |
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[8664] | 54 | public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
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[5505] | 55 | IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
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[8664] | 56 | IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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[5942] | 57 | OnlineCalculatorError errorState;
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[8664] | 58 |
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| 59 | double r2;
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| 60 | if (applyLinearScaling) {
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| 61 | var r2Calculator = new OnlinePearsonsRSquaredCalculator();
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| 62 | CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, r2Calculator, problemData.Dataset.Rows);
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| 63 | errorState = r2Calculator.ErrorState;
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| 64 | r2 = r2Calculator.RSquared;
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| 65 | } else {
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| 66 | IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
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| 67 | r2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
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| 68 | }
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| 69 |
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| 70 | if (errorState != OnlineCalculatorError.None) r2 = double.NaN;
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| 71 | return new double[2] { r2, solution.Length };
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[5505] | 72 | }
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[5613] | 73 |
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| 74 | public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
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[5722] | 75 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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[5770] | 76 | EstimationLimitsParameter.ExecutionContext = context;
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[8664] | 77 | ApplyLinearScalingParameter.ExecutionContext = context;
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[5722] | 78 |
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[8664] | 79 | double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
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[5722] | 80 |
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| 81 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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[5770] | 82 | EstimationLimitsParameter.ExecutionContext = null;
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[8664] | 83 | ApplyLinearScalingParameter.ExecutionContext = null;
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[5722] | 84 |
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| 85 | return quality;
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[5613] | 86 | }
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[5505] | 87 | }
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| 88 | }
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