| 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.Analysis;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 29 | using HeuristicLab.Operators;
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| 30 | using HeuristicLab.Optimization;
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| 31 | using HeuristicLab.Parameters;
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| 32 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 33 | using HeuristicLab.Problems.DataAnalysis.Evaluators;
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| 34 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 35 | using System;
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| 36 |
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| 37 | namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers {
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| 38 | [Item("OverfittingAnalyzer", "")]
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| 39 | [StorableClass]
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| 40 | public sealed class OverfittingAnalyzer : SingleSuccessorOperator, ISymbolicRegressionAnalyzer {
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| 41 | private const string RandomParameterName = "Random";
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| 42 | private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
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| 43 | private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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| 44 | private const string ProblemDataParameterName = "ProblemData";
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| 45 | private const string ValidationSamplesStartParameterName = "SamplesStart";
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| 46 | private const string ValidationSamplesEndParameterName = "SamplesEnd";
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| 47 | private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
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| 48 | private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
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| 49 | private const string EvaluatorParameterName = "Evaluator";
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| 50 | private const string MaximizationParameterName = "Maximization";
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| 51 | private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
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| 52 |
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| 53 | #region parameter properties
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| 54 | public ILookupParameter<IRandom> RandomParameter {
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| 55 | get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
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| 56 | }
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| 57 | public ScopeTreeLookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
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| 58 | get { return (ScopeTreeLookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
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| 59 | }
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| 60 | public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
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| 61 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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| 62 | }
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| 63 | public ScopeTreeLookupParameter<DoubleValue> ValidationQualityParameter {
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| 64 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["ValidationQuality"]; }
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| 65 | }
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| 66 | public IValueLookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
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| 67 | get { return (IValueLookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
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| 68 | }
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| 69 | public ILookupParameter<ISymbolicRegressionEvaluator> EvaluatorParameter {
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| 70 | get { return (ILookupParameter<ISymbolicRegressionEvaluator>)Parameters[EvaluatorParameterName]; }
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| 71 | }
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| 72 | public ILookupParameter<BoolValue> MaximizationParameter {
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| 73 | get { return (ILookupParameter<BoolValue>)Parameters[MaximizationParameterName]; }
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| 74 | }
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| 75 | public IValueLookupParameter<DataAnalysisProblemData> ProblemDataParameter {
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| 76 | get { return (IValueLookupParameter<DataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
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| 77 | }
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| 78 | public IValueLookupParameter<IntValue> ValidationSamplesStartParameter {
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| 79 | get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesStartParameterName]; }
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| 80 | }
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| 81 | public IValueLookupParameter<IntValue> ValidationSamplesEndParameter {
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| 82 | get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesEndParameterName]; }
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| 83 | }
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| 84 | public IValueParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
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| 85 | get { return (IValueParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
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| 86 | }
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| 87 |
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| 88 | public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
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| 89 | get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
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| 90 | }
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| 91 | public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
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| 92 | get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
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| 93 | }
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| 94 | public ILookupParameter<PercentValue> RelativeValidationQualityParameter {
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| 95 | get { return (ILookupParameter<PercentValue>)Parameters["RelativeValidationQuality"]; }
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| 96 | }
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| 97 | //public IValueLookupParameter<PercentValue> RelativeValidationQualityLowerLimitParameter {
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| 98 | // get { return (IValueLookupParameter<PercentValue>)Parameters["RelativeValidationQualityLowerLimit"]; }
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| 99 | //}
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| 100 | //public IValueLookupParameter<PercentValue> RelativeValidationQualityUpperLimitParameter {
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| 101 | // get { return (IValueLookupParameter<PercentValue>)Parameters["RelativeValidationQualityUpperLimit"]; }
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| 102 | //}
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| 103 | public ILookupParameter<DoubleValue> TrainingValidationQualityCorrelationParameter {
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| 104 | get { return (ILookupParameter<DoubleValue>)Parameters["TrainingValidationCorrelation"]; }
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| 105 | }
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| 106 | public IValueLookupParameter<DoubleValue> LowerCorrelationLimitParameter {
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| 107 | get { return (IValueLookupParameter<DoubleValue>)Parameters["LowerCorrelationLimit"]; }
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| 108 | }
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| 109 | public IValueLookupParameter<DoubleValue> UpperCorrelationLimitParameter {
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| 110 | get { return (IValueLookupParameter<DoubleValue>)Parameters["UpperCorrelationLimit"]; }
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| 111 | }
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| 112 | public ILookupParameter<BoolValue> OverfittingParameter {
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| 113 | get { return (ILookupParameter<BoolValue>)Parameters["Overfitting"]; }
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| 114 | }
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| 115 | public ILookupParameter<ResultCollection> ResultsParameter {
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| 116 | get { return (ILookupParameter<ResultCollection>)Parameters["Results"]; }
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| 117 | }
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| 118 | public ILookupParameter<DoubleValue> InitialTrainingQualityParameter {
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| 119 | get { return (ILookupParameter<DoubleValue>)Parameters["InitialTrainingQuality"]; }
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| 120 | }
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| 121 | public ILookupParameter<ItemList<DoubleMatrix>> TrainingAndValidationQualitiesParameter {
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| 122 | get { return (ILookupParameter<ItemList<DoubleMatrix>>)Parameters["TrainingAndValidationQualities"]; }
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| 123 | }
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| 124 | public IValueLookupParameter<DoubleValue> PercentileParameter {
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| 125 | get { return (IValueLookupParameter<DoubleValue>)Parameters["Percentile"]; }
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| 126 | }
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| 127 | #endregion
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| 128 | #region properties
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| 129 | public IRandom Random {
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| 130 | get { return RandomParameter.ActualValue; }
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| 131 | }
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| 132 | public ItemArray<SymbolicExpressionTree> SymbolicExpressionTree {
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| 133 | get { return SymbolicExpressionTreeParameter.ActualValue; }
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| 134 | }
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| 135 | public ISymbolicExpressionTreeInterpreter SymbolicExpressionTreeInterpreter {
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| 136 | get { return SymbolicExpressionTreeInterpreterParameter.ActualValue; }
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| 137 | }
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| 138 | public ISymbolicRegressionEvaluator Evaluator {
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| 139 | get { return EvaluatorParameter.ActualValue; }
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| 140 | }
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| 141 | public BoolValue Maximization {
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| 142 | get { return MaximizationParameter.ActualValue; }
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| 143 | }
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| 144 | public DataAnalysisProblemData ProblemData {
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| 145 | get { return ProblemDataParameter.ActualValue; }
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| 146 | }
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| 147 | public IntValue ValidiationSamplesStart {
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| 148 | get { return ValidationSamplesStartParameter.ActualValue; }
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| 149 | }
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| 150 | public IntValue ValidationSamplesEnd {
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| 151 | get { return ValidationSamplesEndParameter.ActualValue; }
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| 152 | }
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| 153 | public PercentValue RelativeNumberOfEvaluatedSamples {
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| 154 | get { return RelativeNumberOfEvaluatedSamplesParameter.Value; }
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| 155 | }
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| 156 |
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| 157 | public DoubleValue UpperEstimationLimit {
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| 158 | get { return UpperEstimationLimitParameter.ActualValue; }
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| 159 | }
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| 160 | public DoubleValue LowerEstimationLimit {
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| 161 | get { return LowerEstimationLimitParameter.ActualValue; }
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| 162 | }
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| 163 | #endregion
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| 164 |
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| 165 | public OverfittingAnalyzer()
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| 166 | : base() {
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| 167 | Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "The random generator to use."));
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| 168 | Parameters.Add(new LookupParameter<ISymbolicRegressionEvaluator>(EvaluatorParameterName, "The evaluator which should be used to evaluate the solution on the validation set."));
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| 169 | Parameters.Add(new ScopeTreeLookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to analyze."));
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| 170 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality"));
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| 171 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("ValidationQuality"));
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| 172 | Parameters.Add(new LookupParameter<BoolValue>(MaximizationParameterName, "The direction of optimization."));
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| 173 | Parameters.Add(new ValueLookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used for the analysis of symbolic expression trees."));
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| 174 | Parameters.Add(new ValueLookupParameter<DataAnalysisProblemData>(ProblemDataParameterName, "The problem data for which the symbolic expression tree is a solution."));
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| 175 | Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesStartParameterName, "The first index of the validation partition of the data set."));
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| 176 | Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesEndParameterName, "The last index of the validation partition of the data set."));
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| 177 | Parameters.Add(new ValueParameter<PercentValue>(RelativeNumberOfEvaluatedSamplesParameterName, "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation between the start and end index.", new PercentValue(1)));
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| 178 | Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper estimation limit that was set for the evaluation of the symbolic expression trees."));
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| 179 | Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower estimation limit that was set for the evaluation of the symbolic expression trees."));
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| 180 | Parameters.Add(new LookupParameter<PercentValue>("RelativeValidationQuality"));
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| 181 | //Parameters.Add(new ValueLookupParameter<PercentValue>("RelativeValidationQualityUpperLimit", new PercentValue(0.05)));
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| 182 | //Parameters.Add(new ValueLookupParameter<PercentValue>("RelativeValidationQualityLowerLimit", new PercentValue(-0.05)));
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| 183 | Parameters.Add(new LookupParameter<DoubleValue>("TrainingValidationCorrelation"));
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| 184 | Parameters.Add(new ValueLookupParameter<DoubleValue>("LowerCorrelationLimit", new DoubleValue(0.65)));
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| 185 | Parameters.Add(new ValueLookupParameter<DoubleValue>("UpperCorrelationLimit", new DoubleValue(0.75)));
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| 186 | Parameters.Add(new LookupParameter<BoolValue>("Overfitting"));
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| 187 | Parameters.Add(new LookupParameter<ResultCollection>("Results"));
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| 188 | Parameters.Add(new LookupParameter<DoubleValue>("InitialTrainingQuality"));
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| 189 | Parameters.Add(new LookupParameter<ItemList<DoubleMatrix>>("TrainingAndValidationQualities"));
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| 190 | Parameters.Add(new ValueLookupParameter<DoubleValue>("Percentile", new DoubleValue(1)));
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| 191 |
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| 192 | }
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| 193 |
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| 194 | [StorableConstructor]
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| 195 | private OverfittingAnalyzer(bool deserializing) : base(deserializing) { }
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| 196 |
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| 197 | [StorableHook(HookType.AfterDeserialization)]
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| 198 | private void AfterDeserialization() {
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| 199 | if (!Parameters.ContainsKey("InitialTrainingQuality")) {
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| 200 | Parameters.Add(new LookupParameter<DoubleValue>("InitialTrainingQuality"));
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| 201 | }
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| 202 | //if (!Parameters.ContainsKey("RelativeValidationQualityUpperLimit")) {
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| 203 | // Parameters.Add(new ValueLookupParameter<PercentValue>("RelativeValidationQualityUpperLimit", new PercentValue(0.05)));
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| 204 | //}
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| 205 | //if (!Parameters.ContainsKey("RelativeValidationQualityLowerLimit")) {
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| 206 | // Parameters.Add(new ValueLookupParameter<PercentValue>("RelativeValidationQualityLowerLimit", new PercentValue(-0.05)));
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| 207 | //}
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| 208 | if (!Parameters.ContainsKey("TrainingAndValidationQualities")) {
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| 209 | Parameters.Add(new LookupParameter<ItemList<DoubleMatrix>>("TrainingAndValidationQualities"));
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| 210 | }
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| 211 | if (!Parameters.ContainsKey("Percentile")) {
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| 212 | Parameters.Add(new ValueLookupParameter<DoubleValue>("Percentile", new DoubleValue(1)));
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| 213 | }
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| 214 | if (!Parameters.ContainsKey("ValidationQuality")) {
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| 215 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("ValidationQuality"));
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| 216 | }
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| 217 | if (!Parameters.ContainsKey("LowerCorrelationLimit")) {
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| 218 | Parameters.Add(new ValueLookupParameter<DoubleValue>("LowerCorrelationLimit", new DoubleValue(0.65)));
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| 219 | }
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| 220 | if (!Parameters.ContainsKey("UpperCorrelationLimit")) {
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| 221 | Parameters.Add(new ValueLookupParameter<DoubleValue>("UpperCorrelationLimit", new DoubleValue(0.75)));
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| 222 | }
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| 223 |
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| 224 | }
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| 225 |
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| 226 | public override IOperation Apply() {
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| 227 | var trees = SymbolicExpressionTree;
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| 228 | ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
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| 229 | ItemArray<DoubleValue> validationQualities = ValidationQualityParameter.ActualValue;
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| 230 |
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| 231 | double correlationLimit;
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| 232 | if (OverfittingParameter.ActualValue != null && OverfittingParameter.ActualValue.Value) {
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| 233 | // if is already overfitting have to reach the upper limit to switch back to non-overfitting state
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| 234 | correlationLimit = UpperCorrelationLimitParameter.ActualValue.Value;
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| 235 | } else {
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| 236 | // if currently in non-overfitting state have to reach to lower limit to switch to overfitting state
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| 237 | correlationLimit = LowerCorrelationLimitParameter.ActualValue.Value;
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| 238 | }
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| 239 | //string targetVariable = ProblemData.TargetVariable.Value;
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| 240 |
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| 241 | //// select a random subset of rows in the validation set
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| 242 | //int validationStart = ValidiationSamplesStart.Value;
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| 243 | //int validationEnd = ValidationSamplesEnd.Value;
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| 244 | //int seed = Random.Next();
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| 245 | //int count = (int)((validationEnd - validationStart) * RelativeNumberOfEvaluatedSamples.Value);
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| 246 | //if (count == 0) count = 1;
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| 247 | //IEnumerable<int> rows = RandomEnumerable.SampleRandomNumbers(seed, validationStart, validationEnd, count);
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| 248 |
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| 249 | //double upperEstimationLimit = UpperEstimationLimit != null ? UpperEstimationLimit.Value : double.PositiveInfinity;
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| 250 | //double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity;
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| 251 |
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| 252 | //double bestQuality = Maximization.Value ? double.NegativeInfinity : double.PositiveInfinity;
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| 253 | //SymbolicExpressionTree bestTree = null;
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| 254 |
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| 255 | //List<double> validationQualities = new List<double>();
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| 256 | //foreach (var tree in trees) {
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| 257 | // double quality = Evaluator.Evaluate(SymbolicExpressionTreeInterpreter, tree,
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| 258 | // lowerEstimationLimit, upperEstimationLimit,
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| 259 | // ProblemData.Dataset, targetVariable,
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| 260 | // rows);
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| 261 | // validationQualities.Add(quality);
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| 262 | // //if ((Maximization.Value && quality > bestQuality) ||
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| 263 | // // (!Maximization.Value && quality < bestQuality)) {
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| 264 | // // bestQuality = quality;
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| 265 | // // bestTree = tree;
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| 266 | // //}
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| 267 | //}
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| 268 |
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| 269 | //if (RelativeValidationQualityParameter.ActualValue == null) {
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| 270 | // first call initialize the relative quality using the difference between average training and validation quality
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| 271 | double avgTrainingQuality = qualities.Select(x => x.Value).Average();
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| 272 | double avgValidationQuality = validationQualities.Select(x => x.Value).Average();
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| 273 |
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| 274 | if (Maximization.Value)
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| 275 | RelativeValidationQualityParameter.ActualValue = new PercentValue(avgValidationQuality / avgTrainingQuality - 1);
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| 276 | else {
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| 277 | RelativeValidationQualityParameter.ActualValue = new PercentValue(avgTrainingQuality / avgValidationQuality - 1);
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| 278 | }
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| 279 | //}
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| 280 |
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| 281 | // best first (only for maximization
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| 282 | var orderedDistinctPairs = (from index in Enumerable.Range(0, qualities.Length)
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| 283 | where qualities[index].Value > 0.0
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| 284 | select new { Training = qualities[index].Value, Validation = validationQualities[index].Value })
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| 285 | .OrderBy(x => -x.Training)
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| 286 | .ToList();
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| 287 |
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| 288 | int n = (int)Math.Round(PercentileParameter.ActualValue.Value * orderedDistinctPairs.Count);
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| 289 |
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| 290 | double[] validationArr = new double[n];
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| 291 | double[] trainingArr = new double[n];
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| 292 | double[,] qualitiesArr = new double[n, 2];
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| 293 | for (int i = 0; i < n; i++) {
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| 294 | validationArr[i] = orderedDistinctPairs[i].Validation;
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| 295 | trainingArr[i] = orderedDistinctPairs[i].Training;
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| 296 |
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| 297 | qualitiesArr[i, 0] = trainingArr[i];
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| 298 | qualitiesArr[i, 1] = validationArr[i];
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| 299 | }
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| 300 | double r = alglib.correlation.spearmanrankcorrelation(trainingArr, validationArr, n);
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| 301 | TrainingValidationQualityCorrelationParameter.ActualValue = new DoubleValue(r);
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| 302 | if (InitialTrainingQualityParameter.ActualValue == null)
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| 303 | InitialTrainingQualityParameter.ActualValue = new DoubleValue(avgValidationQuality);
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| 304 | bool overfitting =
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| 305 | avgTrainingQuality > InitialTrainingQualityParameter.ActualValue.Value && // better on training than in initial generation
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| 306 | // RelativeValidationQualityParameter.ActualValue.Value < 0.0 && // validation quality is worse than training quality
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| 307 | r < correlationLimit;
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| 308 |
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| 309 |
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| 310 | OverfittingParameter.ActualValue = new BoolValue(overfitting);
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| 311 | ItemList<DoubleMatrix> list = TrainingAndValidationQualitiesParameter.ActualValue;
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| 312 | if (list == null) {
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| 313 | TrainingAndValidationQualitiesParameter.ActualValue = new ItemList<DoubleMatrix>();
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| 314 | }
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| 315 | TrainingAndValidationQualitiesParameter.ActualValue.Add(new DoubleMatrix(qualitiesArr));
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| 316 | return base.Apply();
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| 317 | }
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| 318 |
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| 319 | [StorableHook(HookType.AfterDeserialization)]
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| 320 | private void Initialize() { }
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| 321 |
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| 322 | private static void AddValue(DataTable table, double data, string name, string description) {
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| 323 | DataRow row;
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| 324 | table.Rows.TryGetValue(name, out row);
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| 325 | if (row == null) {
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| 326 | row = new DataRow(name, description);
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| 327 | row.Values.Add(data);
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| 328 | table.Rows.Add(row);
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| 329 | } else {
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| 330 | row.Values.Add(data);
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| 331 | }
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| 332 | }
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| 333 | }
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| 334 | }
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