| 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2010 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 System.Linq;
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| 24 | using HeuristicLab.Common;
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| 25 | using HeuristicLab.Core;
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| 26 | using HeuristicLab.Data;
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| 27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 28 | using HeuristicLab.Operators;
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| 29 | using HeuristicLab.Optimization;
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| 30 | using HeuristicLab.Parameters;
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| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 32 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 33 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
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| 34 |
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| 35 | namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers {
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| 36 | public class SymbolicRegressionTournamentPruning : SingleSuccessorOperator, ISymbolicRegressionAnalyzer {
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| 37 | private const string RandomParameterName = "Random";
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| 38 | private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
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| 39 | private const string DataAnalysisProblemDataParameterName = "DataAnalysisProblemData";
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| 40 | private const string SamplesStartParameterName = "SamplesStart";
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| 41 | private const string SamplesEndParameterName = "SamplesEnd";
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| 42 | private const string EvaluatorParameterName = "Evaluator";
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| 43 | private const string MaximizationParameterName = "Maximization";
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| 44 | private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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| 45 | private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
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| 46 | private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
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| 47 | private const string MaxPruningRatioParameterName = "MaxPruningRatio";
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| 48 | private const string TournamentSizeParameterName = "TournamentSize";
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| 49 | private const string PopulationPercentileStartParameterName = "PopulationPercentileStart";
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| 50 | private const string PopulationPercentileEndParameterName = "PopulationPercentileEnd";
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| 51 | private const string QualityGainWeightParameterName = "QualityGainWeight";
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| 52 | private const string IterationsParameterName = "Iterations";
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| 53 | private const string FirstPruningGenerationParameterName = "FirstPruningGeneration";
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| 54 | private const string PruningFrequencyParameterName = "PruningFrequency";
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| 55 | private const string GenerationParameterName = "Generations";
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| 56 | private const string ResultsParameterName = "Results";
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| 57 |
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| 58 | #region parameter properties
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| 59 | public ILookupParameter<IRandom> RandomParameter {
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| 60 | get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
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| 61 | }
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| 62 | public ScopeTreeLookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
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| 63 | get { return (ScopeTreeLookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
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| 64 | }
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| 65 | public ILookupParameter<DataAnalysisProblemData> DataAnalysisProblemDataParameter {
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| 66 | get { return (ILookupParameter<DataAnalysisProblemData>)Parameters[DataAnalysisProblemDataParameterName]; }
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| 67 | }
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| 68 | public ILookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
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| 69 | get { return (ILookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
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| 70 | }
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| 71 | public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
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| 72 | get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
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| 73 | }
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| 74 | public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
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| 75 | get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
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| 76 | }
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| 77 | public IValueLookupParameter<IntValue> SamplesStartParameter {
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| 78 | get { return (IValueLookupParameter<IntValue>)Parameters[SamplesStartParameterName]; }
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| 79 | }
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| 80 | public IValueLookupParameter<IntValue> SamplesEndParameter {
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| 81 | get { return (IValueLookupParameter<IntValue>)Parameters[SamplesEndParameterName]; }
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| 82 | }
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| 83 | public ILookupParameter<ISymbolicRegressionEvaluator> EvaluatorParameter {
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| 84 | get { return (ILookupParameter<ISymbolicRegressionEvaluator>)Parameters[EvaluatorParameterName]; }
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| 85 | }
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| 86 | public ILookupParameter<BoolValue> MaximizationParameter {
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| 87 | get { return (ILookupParameter<BoolValue>)Parameters[MaximizationParameterName]; }
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| 88 | }
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| 89 | public IValueLookupParameter<DoubleValue> MaxPruningRatioParameter {
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| 90 | get { return (IValueLookupParameter<DoubleValue>)Parameters[MaxPruningRatioParameterName]; }
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| 91 | }
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| 92 | public IValueLookupParameter<IntValue> TournamentSizeParameter {
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| 93 | get { return (IValueLookupParameter<IntValue>)Parameters[TournamentSizeParameterName]; }
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| 94 | }
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| 95 | public IValueLookupParameter<DoubleValue> PopulationPercentileStartParameter {
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| 96 | get { return (IValueLookupParameter<DoubleValue>)Parameters[PopulationPercentileStartParameterName]; }
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| 97 | }
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| 98 | public IValueLookupParameter<DoubleValue> PopulationPercentileEndParameter {
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| 99 | get { return (IValueLookupParameter<DoubleValue>)Parameters[PopulationPercentileEndParameterName]; }
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| 100 | }
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| 101 | public IValueLookupParameter<DoubleValue> QualityGainWeightParameter {
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| 102 | get { return (IValueLookupParameter<DoubleValue>)Parameters[QualityGainWeightParameterName]; }
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| 103 | }
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| 104 | public IValueLookupParameter<IntValue> IterationsParameter {
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| 105 | get { return (IValueLookupParameter<IntValue>)Parameters[IterationsParameterName]; }
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| 106 | }
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| 107 | public IValueLookupParameter<IntValue> FirstPruningGenerationParameter {
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| 108 | get { return (IValueLookupParameter<IntValue>)Parameters[FirstPruningGenerationParameterName]; }
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| 109 | }
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| 110 | public IValueLookupParameter<IntValue> PruningFrequencyParameter {
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| 111 | get { return (IValueLookupParameter<IntValue>)Parameters[PruningFrequencyParameterName]; }
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| 112 | }
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| 113 | public ILookupParameter<IntValue> GenerationParameter {
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| 114 | get { return (ILookupParameter<IntValue>)Parameters[GenerationParameterName]; }
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| 115 | }
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| 116 | public ILookupParameter<ResultCollection> ResultsParameter {
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| 117 | get { return (ILookupParameter<ResultCollection>)Parameters[ResultsParameterName]; }
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| 118 | }
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| 119 | #endregion
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| 120 | #region properties
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| 121 | public IRandom Random {
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| 122 | get { return RandomParameter.ActualValue; }
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| 123 | }
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| 124 | public ItemArray<SymbolicExpressionTree> SymbolicExpressionTree {
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| 125 | get { return SymbolicExpressionTreeParameter.ActualValue; }
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| 126 | }
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| 127 | public DataAnalysisProblemData DataAnalysisProblemData {
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| 128 | get { return DataAnalysisProblemDataParameter.ActualValue; }
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| 129 | }
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| 130 | public ISymbolicExpressionTreeInterpreter SymbolicExpressionTreeInterpreter {
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| 131 | get { return SymbolicExpressionTreeInterpreterParameter.ActualValue; }
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| 132 | }
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| 133 | public DoubleValue UpperEstimationLimit {
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| 134 | get { return UpperEstimationLimitParameter.ActualValue; }
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| 135 | }
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| 136 | public DoubleValue LowerEstimationLimit {
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| 137 | get { return LowerEstimationLimitParameter.ActualValue; }
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| 138 | }
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| 139 | public IntValue SamplesStart {
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| 140 | get { return SamplesStartParameter.ActualValue; }
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| 141 | }
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| 142 | public IntValue SamplesEnd {
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| 143 | get { return SamplesEndParameter.ActualValue; }
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| 144 | }
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| 145 | public ISymbolicRegressionEvaluator Evaluator {
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| 146 | get { return EvaluatorParameter.ActualValue; }
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| 147 | }
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| 148 | public BoolValue Maximization {
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| 149 | get { return MaximizationParameter.ActualValue; }
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| 150 | }
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| 151 | public DoubleValue MaxPruningRatio {
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| 152 | get { return MaxPruningRatioParameter.ActualValue; }
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| 153 | }
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| 154 | public IntValue TournamentSize {
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| 155 | get { return TournamentSizeParameter.ActualValue; }
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| 156 | }
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| 157 | public DoubleValue PopulationPercentileStart {
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| 158 | get { return PopulationPercentileStartParameter.ActualValue; }
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| 159 | }
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| 160 | public DoubleValue PopulationPercentileEnd {
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| 161 | get { return PopulationPercentileEndParameter.ActualValue; }
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| 162 | }
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| 163 | public DoubleValue QualityGainWeight {
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| 164 | get { return QualityGainWeightParameter.ActualValue; }
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| 165 | }
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| 166 | public IntValue Iterations {
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| 167 | get { return IterationsParameter.ActualValue; }
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| 168 | }
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| 169 | public IntValue PruningFrequency {
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| 170 | get { return PruningFrequencyParameter.ActualValue; }
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| 171 | }
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| 172 | public IntValue FirstPruningGeneration {
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| 173 | get { return FirstPruningGenerationParameter.ActualValue; }
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| 174 | }
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| 175 | public IntValue Generation {
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| 176 | get { return GenerationParameter.ActualValue; }
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| 177 | }
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| 178 | #endregion
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| 179 | protected SymbolicRegressionTournamentPruning(bool deserializing) : base(deserializing) { }
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| 180 | public SymbolicRegressionTournamentPruning()
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| 181 | : base() {
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| 182 | Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "A random number generator."));
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| 183 | Parameters.Add(new ScopeTreeLookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to prune."));
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| 184 | Parameters.Add(new LookupParameter<DataAnalysisProblemData>(DataAnalysisProblemDataParameterName, "The data analysis problem data to use for branch impact evaluation."));
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| 185 | Parameters.Add(new LookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter to use for node impact evaluation"));
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| 186 | Parameters.Add(new ValueLookupParameter<IntValue>(SamplesStartParameterName, "The first row index of the dataset partition to use for branch impact evaluation."));
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| 187 | Parameters.Add(new ValueLookupParameter<IntValue>(SamplesEndParameterName, "The last row index of the dataset partition to use for branch impact evaluation."));
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| 188 | Parameters.Add(new LookupParameter<ISymbolicRegressionEvaluator>(EvaluatorParameterName, "The evaluator that should be used to determine which branches are not relevant."));
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| 189 | Parameters.Add(new LookupParameter<BoolValue>(MaximizationParameterName, "The direction of optimization."));
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| 190 | Parameters.Add(new ValueLookupParameter<DoubleValue>(MaxPruningRatioParameterName, "The maximal relative size of the pruned branch.", new DoubleValue(0.5)));
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| 191 | Parameters.Add(new ValueLookupParameter<IntValue>(TournamentSizeParameterName, "The number of branches to compare for pruning", new IntValue(10)));
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| 192 | Parameters.Add(new ValueLookupParameter<DoubleValue>(PopulationPercentileStartParameterName, "The start of the population percentile to consider for pruning.", new DoubleValue(0.25)));
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| 193 | Parameters.Add(new ValueLookupParameter<DoubleValue>(PopulationPercentileEndParameterName, "The end of the population percentile to consider for pruning.", new DoubleValue(0.75)));
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| 194 | Parameters.Add(new ValueLookupParameter<DoubleValue>(QualityGainWeightParameterName, "The weight of the quality gain relative to the size gain.", new DoubleValue(1.0)));
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| 195 | Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper estimation limit to use for evaluation."));
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| 196 | Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower estimation limit to use for evaluation."));
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| 197 | Parameters.Add(new ValueLookupParameter<IntValue>(IterationsParameterName, "The number of pruning iterations to apply for each tree.", new IntValue(1)));
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| 198 | Parameters.Add(new ValueLookupParameter<IntValue>(FirstPruningGenerationParameterName, "The first generation when pruning should be applied.", new IntValue(1)));
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| 199 | Parameters.Add(new ValueLookupParameter<IntValue>(PruningFrequencyParameterName, "The frequency of pruning operations (1: every generation, 2: every second generation...)", new IntValue(1)));
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| 200 | Parameters.Add(new LookupParameter<IntValue>(GenerationParameterName, "The current generation."));
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| 201 | Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The results collection."));
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| 202 | }
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| 203 |
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| 204 | protected SymbolicRegressionTournamentPruning(SymbolicRegressionTournamentPruning original, Cloner cloner)
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| 205 | : base(original, cloner) {
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| 206 | }
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| 207 |
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| 208 | public override IDeepCloneable Clone(Cloner cloner) {
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| 209 | return new SymbolicRegressionTournamentPruning(this, cloner);
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| 210 | }
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| 211 |
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| 212 | [StorableHook(HookType.AfterDeserialization)]
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| 213 | private void AfterDeserialization() {
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| 214 | #region compatibility remove before releasing 3.3.1
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| 215 | if (!Parameters.ContainsKey(EvaluatorParameterName)) {
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| 216 | Parameters.Add(new LookupParameter<ISymbolicRegressionEvaluator>(EvaluatorParameterName, "The evaluator which should be used to evaluate the solution on the validation set."));
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| 217 | }
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| 218 | if (!Parameters.ContainsKey(MaximizationParameterName)) {
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| 219 | Parameters.Add(new LookupParameter<BoolValue>(MaximizationParameterName, "The direction of optimization."));
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| 220 | }
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| 221 | #endregion
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| 222 | }
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| 223 |
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| 224 | public override IOperation Apply() {
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| 225 | bool pruningCondition =
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| 226 | (Generation.Value >= FirstPruningGeneration.Value) &&
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| 227 | ((Generation.Value - FirstPruningGeneration.Value) % PruningFrequency.Value == 0);
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| 228 | if (pruningCondition) {
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| 229 | int n = SymbolicExpressionTree.Length;
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| 230 | double percentileStart = PopulationPercentileStart.Value;
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| 231 | double percentileEnd = PopulationPercentileEnd.Value;
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| 232 | // for each tree in the given percentile
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| 233 | var trees = SymbolicExpressionTree
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| 234 | .Skip((int)(n * percentileStart))
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| 235 | .Take((int)(n * (percentileEnd - percentileStart)));
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| 236 | foreach (var tree in trees) {
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| 237 | Prune(Random, tree, Iterations.Value, TournamentSize.Value,
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| 238 | DataAnalysisProblemData, SamplesStart.Value, SamplesEnd.Value,
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| 239 | SymbolicExpressionTreeInterpreter, Evaluator, Maximization.Value,
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| 240 | LowerEstimationLimit.Value, UpperEstimationLimit.Value,
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| 241 | MaxPruningRatio.Value, QualityGainWeight.Value);
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| 242 | }
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| 243 | }
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| 244 | return base.Apply();
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| 245 | }
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| 246 |
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| 247 | public static void Prune(IRandom random, SymbolicExpressionTree tree, int iterations, int tournamentSize,
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| 248 | DataAnalysisProblemData problemData, int samplesStart, int samplesEnd,
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| 249 | ISymbolicExpressionTreeInterpreter interpreter, ISymbolicRegressionEvaluator evaluator, bool maximization,
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| 250 | double lowerEstimationLimit, double upperEstimationLimit,
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| 251 | double maxPruningRatio, double qualityGainWeight) {
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| 252 | IEnumerable<int> rows = Enumerable.Range(samplesStart, samplesEnd - samplesStart)
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| 253 | .Where(i => i < problemData.TestSamplesStart.Value || problemData.TestSamplesEnd.Value <= i);
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| 254 | int originalSize = tree.Size;
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| 255 | double originalQuality = evaluator.Evaluate(interpreter, tree,
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| 256 | lowerEstimationLimit, upperEstimationLimit, problemData.Dataset, problemData.TargetVariable.Value, rows);
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| 257 |
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| 258 | int minPrunedSize = (int)(originalSize * (1 - maxPruningRatio));
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| 259 |
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| 260 | // tree for branch evaluation
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| 261 | SymbolicExpressionTree templateTree = (SymbolicExpressionTree)tree.Clone();
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| 262 | while (templateTree.Root.SubTrees[0].SubTrees.Count > 0) templateTree.Root.SubTrees[0].RemoveSubTree(0);
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| 263 |
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| 264 | SymbolicExpressionTree prunedTree = tree;
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| 265 | for (int iteration = 0; iteration < iterations; iteration++) {
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| 266 | SymbolicExpressionTree iterationBestTree = prunedTree;
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| 267 | double bestGain = double.PositiveInfinity;
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| 268 | int maxPrunedBranchSize = (int)(prunedTree.Size * maxPruningRatio);
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| 269 |
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| 270 | for (int i = 0; i < tournamentSize; i++) {
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| 271 | var clonedTree = (SymbolicExpressionTree)prunedTree.Clone();
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| 272 | int clonedTreeSize = clonedTree.Size;
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| 273 | var prunePoints = (from node in clonedTree.IterateNodesPostfix()
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| 274 | from subTree in node.SubTrees
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| 275 | let subTreeSize = subTree.GetSize()
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| 276 | where subTreeSize <= maxPrunedBranchSize
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| 277 | where clonedTreeSize - subTreeSize >= minPrunedSize
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| 278 | select new { Parent = node, Branch = subTree, SubTreeIndex = node.SubTrees.IndexOf(subTree) })
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| 279 | .ToList();
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| 280 | if (prunePoints.Count > 0) {
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| 281 | var selectedPrunePoint = prunePoints.SelectRandom(random);
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| 282 | templateTree.Root.SubTrees[0].AddSubTree(selectedPrunePoint.Branch);
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| 283 | IEnumerable<double> branchValues = interpreter.GetSymbolicExpressionTreeValues(templateTree, problemData.Dataset, rows);
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| 284 | double branchMean = branchValues.Average();
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| 285 | templateTree.Root.SubTrees[0].RemoveSubTree(0);
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| 286 |
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| 287 | selectedPrunePoint.Parent.RemoveSubTree(selectedPrunePoint.SubTreeIndex);
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| 288 | var constNode = CreateConstant(branchMean);
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| 289 | selectedPrunePoint.Parent.InsertSubTree(selectedPrunePoint.SubTreeIndex, constNode);
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| 290 |
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| 291 | double prunedQuality = evaluator.Evaluate(interpreter, clonedTree,
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| 292 | lowerEstimationLimit, upperEstimationLimit, problemData.Dataset, problemData.TargetVariable.Value, Enumerable.Range(samplesStart, samplesEnd - samplesStart));
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| 293 | double prunedSize = clonedTree.Size;
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| 294 | // deteriation in quality:
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| 295 | // exp: MSE : newMse < origMse (improvement) => prefer the larger improvement
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| 296 | // MSE : newMse > origMse (deteriation) => prefer the smaller deteriation
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| 297 | // MSE : minimize: newMse / origMse
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| 298 | // R² : newR² > origR² (improvment) => prefer the larger improvment
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| 299 | // R² : newR² < origR² (deteriation) => prefer smaller deteriation
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| 300 | // R² : minimize: origR² / newR²
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| 301 | double qualityDeteriation = maximization ? originalQuality / prunedQuality : prunedQuality / originalQuality;
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| 302 | // size of the pruned tree is always smaller than the size of the original tree
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| 303 | // same change in quality => prefer pruning operation that removes a larger tree
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| 304 | double gain = (qualityDeteriation * qualityGainWeight) /
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| 305 | (originalSize / prunedSize);
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| 306 | if (gain < bestGain) {
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| 307 | bestGain = gain;
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| 308 | iterationBestTree = clonedTree;
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| 309 | }
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| 310 | }
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| 311 | }
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| 312 | prunedTree = iterationBestTree;
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| 313 | }
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| 314 | tree.Root = prunedTree.Root;
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| 315 | }
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| 316 |
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| 317 | private static SymbolicExpressionTreeNode CreateConstant(double constantValue) {
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| 318 | var node = (ConstantTreeNode)(new Constant()).CreateTreeNode();
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| 319 | node.Value = constantValue;
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| 320 | return node;
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| 321 | }
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| 322 | }
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| 323 | }
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