[16509] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 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.Parameters;
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| 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 29 |
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| 30 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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| 31 | [Item("New Constant Optimization Evaluator", "Calculates Pearson R² of a symbolic regression solution and optimizes the constant used.")]
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| 32 | [StorableClass]
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| 33 | public class ConstantsOptimizationEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
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| 34 | private const string ConstantOptimizationIterationsParameterName = "ConstantOptimizationIterations";
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| 35 | private const string ConstantOptimizationRowsPercentageParameterName = "ConstantOptimizationRowsPercentage";
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| 36 |
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| 37 | public IFixedValueParameter<IntValue> ConstantOptimizationIterationsParameter {
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| 38 | get { return (IFixedValueParameter<IntValue>)Parameters[ConstantOptimizationIterationsParameterName]; }
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| 39 | }
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| 40 | public IFixedValueParameter<PercentValue> ConstantOptimizationRowsPercentageParameter {
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| 41 | get { return (IFixedValueParameter<PercentValue>)Parameters[ConstantOptimizationRowsPercentageParameterName]; }
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| 42 | }
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| 43 |
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| 44 | public IntValue ConstantOptimizationIterations {
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| 45 | get { return ConstantOptimizationIterationsParameter.Value; }
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| 46 | }
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| 47 | public PercentValue ConstantOptimizationRowsPercentage {
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| 48 | get { return ConstantOptimizationRowsPercentageParameter.Value; }
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| 49 | }
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| 50 |
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| 51 | public override bool Maximization {
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| 52 | get { return true; }
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| 53 | }
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| 54 |
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| 55 | [StorableConstructor]
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| 56 | protected ConstantsOptimizationEvaluator(bool deserializing) : base(deserializing) { }
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| 57 | protected ConstantsOptimizationEvaluator(ConstantsOptimizationEvaluator original, Cloner cloner)
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| 58 | : base(original, cloner) {
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| 59 | }
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| 60 | public ConstantsOptimizationEvaluator()
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| 61 | : base() {
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| 62 | Parameters.Add(new FixedValueParameter<IntValue>(ConstantOptimizationIterationsParameterName, "Determines how many iterations should be calculated while optimizing the constant of a symbolic expression tree (0 indicates other or default stopping criterion).", new IntValue(10), true));
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| 63 | Parameters.Add(new FixedValueParameter<PercentValue>(ConstantOptimizationRowsPercentageParameterName, "Determines the percentage of the rows which should be used for constant optimization", new PercentValue(1), true));
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| 64 | }
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| 65 |
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| 66 | public override IDeepCloneable Clone(Cloner cloner) {
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| 67 | return new ConstantsOptimizationEvaluator(this, cloner);
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| 68 | }
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| 69 |
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| 70 | public override IOperation InstrumentedApply() {
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| 71 | var solution = SymbolicExpressionTreeParameter.ActualValue;
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| 72 | var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
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| 73 | var problemData = ProblemDataParameter.ActualValue;
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| 74 | var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
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| 75 | var estimationLimits = EstimationLimitsParameter.ActualValue;
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| 76 |
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| 77 | double quality;
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| 78 | var rowsPercentage = ConstantOptimizationRowsPercentage.Value;
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| 79 | var constantOptimizationRows = GenerateRowsToEvaluate(rowsPercentage);
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| 80 | quality = ConstantsOptimization.LMConstantsOptimizer.OptimizeConstants(solution, problemData.Dataset, problemData.TargetVariable, constantOptimizationRows, applyLinearScaling, ConstantOptimizationIterations.Value);
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| 81 | if (double.IsNaN(quality) || ConstantOptimizationRowsPercentage.Value != RelativeNumberOfEvaluatedSamplesParameter.ActualValue.Value) {
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| 82 | var evaluationRows = GenerateRowsToEvaluate();
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| 83 | quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, evaluationRows, applyLinearScaling);
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| 84 | }
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| 85 | QualityParameter.ActualValue = new DoubleValue(quality);
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| 86 |
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| 87 | return base.InstrumentedApply();
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| 88 | }
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| 89 |
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| 90 | public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
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| 91 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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| 92 | EstimationLimitsParameter.ExecutionContext = context;
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| 93 | ApplyLinearScalingParameter.ExecutionContext = context;
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| 94 |
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| 95 | // Pearson R² evaluator is used on purpose instead of the const-opt evaluator,
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| 96 | // because Evaluate() is used to get the quality of evolved models on
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| 97 | // different partitions of the dataset (e.g., best validation model)
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| 98 | double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
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| 99 |
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| 100 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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| 101 | EstimationLimitsParameter.ExecutionContext = null;
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| 102 | ApplyLinearScalingParameter.ExecutionContext = null;
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| 103 |
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| 104 | return r2;
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| 105 | }
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| 106 | }
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| 107 | }
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