| 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("SymbolicRegressionOverfittingAnalyzer", "Calculates and tracks correlation of training and validation fitness of symbolic regression models.")]
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| 39 | [StorableClass]
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| 40 | public sealed class SymbolicRegressionOverfittingAnalyzer : 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 MaximizationParameterName = "Maximization";
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| 44 | private const string QualityParameterName = "Quality";
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| 45 | private const string ValidationQualityParameterName = "ValidationQuality";
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| 46 | private const string TrainingValidationCorrelationParameterName = "TrainingValidationCorrelation";
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| 47 | private const string TrainingValidationCorrelationTableParameterName = "TrainingValidationCorrelationTable";
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| 48 | private const string LowerCorrelationThresholdParameterName = "LowerCorrelationThreshold";
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| 49 | private const string UpperCorrelationThresholdParameterName = "UpperCorrelationThreshold";
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| 50 | private const string OverfittingParameterName = "IsOverfitting";
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| 51 | private const string ResultsParameterName = "Results";
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| 52 | private const string EvaluatorParameterName = "Evaluator";
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| 53 | private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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| 54 | private const string ProblemDataParameterName = "ProblemData";
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| 55 | private const string ValidationSamplesStartParameterName = "ValidationSamplesStart";
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| 56 | private const string ValidationSamplesEndParameterName = "ValidationSamplesEnd";
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| 57 | private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
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| 58 | private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
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| 59 | private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
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| 60 |
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| 61 | #region parameter properties
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| 62 | public ILookupParameter<IRandom> RandomParameter {
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| 63 | get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
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| 64 | }
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| 65 | public ScopeTreeLookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
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| 66 | get { return (ScopeTreeLookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
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| 67 | }
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| 68 | public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
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| 69 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters[QualityParameterName]; }
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| 70 | }
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| 71 | public ScopeTreeLookupParameter<DoubleValue> ValidationQualityParameter {
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| 72 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters[ValidationQualityParameterName]; }
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| 73 | }
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| 74 | public ILookupParameter<BoolValue> MaximizationParameter {
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| 75 | get { return (ILookupParameter<BoolValue>)Parameters[MaximizationParameterName]; }
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| 76 | }
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| 77 | public IValueLookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
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| 78 | get { return (IValueLookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
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| 79 | }
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| 80 | public ILookupParameter<ISymbolicRegressionEvaluator> EvaluatorParameter {
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| 81 | get { return (ILookupParameter<ISymbolicRegressionEvaluator>)Parameters[EvaluatorParameterName]; }
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| 82 | }
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| 83 | public IValueLookupParameter<DataAnalysisProblemData> ProblemDataParameter {
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| 84 | get { return (IValueLookupParameter<DataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
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| 85 | }
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| 86 | public IValueLookupParameter<IntValue> ValidationSamplesStartParameter {
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| 87 | get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesStartParameterName]; }
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| 88 | }
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| 89 | public IValueLookupParameter<IntValue> ValidationSamplesEndParameter {
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| 90 | get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesEndParameterName]; }
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| 91 | }
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| 92 | public IValueParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
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| 93 | get { return (IValueParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
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| 94 | }
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| 95 | public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
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| 96 | get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
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| 97 | }
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| 98 | public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
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| 99 | get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
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| 100 | }
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| 101 | public ILookupParameter<DoubleValue> TrainingValidationQualityCorrelationParameter {
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| 102 | get { return (ILookupParameter<DoubleValue>)Parameters[TrainingValidationCorrelationParameterName]; }
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| 103 | }
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| 104 | public ILookupParameter<DataTable> TrainingValidationQualityCorrelationTableParameter {
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| 105 | get { return (ILookupParameter<DataTable>)Parameters[TrainingValidationCorrelationTableParameterName]; }
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| 106 | }
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| 107 | public IValueLookupParameter<DoubleValue> LowerCorrelationThresholdParameter {
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| 108 | get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerCorrelationThresholdParameterName]; }
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| 109 | }
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| 110 | public IValueLookupParameter<DoubleValue> UpperCorrelationThresholdParameter {
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| 111 | get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperCorrelationThresholdParameterName]; }
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| 112 | }
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| 113 | public ILookupParameter<BoolValue> OverfittingParameter {
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| 114 | get { return (ILookupParameter<BoolValue>)Parameters[OverfittingParameterName]; }
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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 BoolValue Maximization {
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| 125 | get { return MaximizationParameter.ActualValue; }
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| 126 | }
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| 127 | public ISymbolicExpressionTreeInterpreter SymbolicExpressionTreeInterpreter {
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| 128 | get { return SymbolicExpressionTreeInterpreterParameter.ActualValue; }
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| 129 | }
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| 130 | public ISymbolicRegressionEvaluator Evaluator {
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| 131 | get { return EvaluatorParameter.ActualValue; }
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| 132 | }
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| 133 | public DataAnalysisProblemData ProblemData {
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| 134 | get { return ProblemDataParameter.ActualValue; }
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| 135 | }
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| 136 | public IntValue ValidiationSamplesStart {
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| 137 | get { return ValidationSamplesStartParameter.ActualValue; }
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| 138 | }
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| 139 | public IntValue ValidationSamplesEnd {
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| 140 | get { return ValidationSamplesEndParameter.ActualValue; }
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| 141 | }
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| 142 | public PercentValue RelativeNumberOfEvaluatedSamples {
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| 143 | get { return RelativeNumberOfEvaluatedSamplesParameter.Value; }
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| 144 | }
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| 145 |
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| 146 | public DoubleValue UpperEstimationLimit {
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| 147 | get { return UpperEstimationLimitParameter.ActualValue; }
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| 148 | }
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| 149 | public DoubleValue LowerEstimationLimit {
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| 150 | get { return LowerEstimationLimitParameter.ActualValue; }
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| 151 | }
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| 152 | #endregion
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| 153 |
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| 154 | [StorableConstructor]
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| 155 | private SymbolicRegressionOverfittingAnalyzer(bool deserializing) : base(deserializing) { }
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| 156 | private SymbolicRegressionOverfittingAnalyzer(SymbolicRegressionOverfittingAnalyzer original, Cloner cloner) : base(original, cloner) { }
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| 157 | public SymbolicRegressionOverfittingAnalyzer()
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| 158 | : base() {
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| 159 | Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "The random generator to use."));
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| 160 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>(QualityParameterName, "Training fitness"));
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| 161 | Parameters.Add(new LookupParameter<BoolValue>(MaximizationParameterName, "The direction of optimization."));
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| 162 |
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| 163 | Parameters.Add(new ScopeTreeLookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to analyze."));
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| 164 | Parameters.Add(new LookupParameter<ISymbolicRegressionEvaluator>(EvaluatorParameterName, "The evaluator which should be used to evaluate the solution on the validation set."));
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| 165 | Parameters.Add(new ValueLookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used for the analysis of symbolic expression trees."));
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| 166 | Parameters.Add(new ValueLookupParameter<DataAnalysisProblemData>(ProblemDataParameterName, "The problem data for which the symbolic expression tree is a solution."));
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| 167 | Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesStartParameterName, "The first index of the validation partition of the data set."));
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| 168 | Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesEndParameterName, "The last index of the validation partition of the data set."));
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| 169 | 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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| 170 | 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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| 171 | 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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| 172 |
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| 173 | Parameters.Add(new LookupParameter<DoubleValue>(TrainingValidationCorrelationParameterName, "Correlation of training and validation fitnesses"));
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| 174 | Parameters.Add(new LookupParameter<DataTable>(TrainingValidationCorrelationTableParameterName, "Data table of training and validation fitness correlation values over the whole run."));
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| 175 | Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerCorrelationThresholdParameterName, "Lower threshold for correlation value that marks the boundary from non-overfitting to overfitting.", new DoubleValue(0.65)));
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| 176 | Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperCorrelationThresholdParameterName, "Upper threshold for correlation value that marks the boundary from overfitting to non-overfitting.", new DoubleValue(0.75)));
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| 177 | Parameters.Add(new LookupParameter<BoolValue>(OverfittingParameterName, "Boolean indicator for overfitting."));
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| 178 | Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The results collection."));
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| 179 | }
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| 180 |
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| 181 | [StorableHook(HookType.AfterDeserialization)]
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| 182 | private void AfterDeserialization() {
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| 183 | }
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| 184 |
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| 185 | public override IDeepCloneable Clone(Cloner cloner) {
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| 186 | return new SymbolicRegressionOverfittingAnalyzer(this, cloner);
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| 187 | }
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| 188 |
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| 189 | public override IOperation Apply() {
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| 190 | ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
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| 191 | double[] trainingArr = qualities.Select(x => x.Value).ToArray();
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| 192 | double[] validationArr = new double[trainingArr.Length];
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| 193 |
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| 194 | #region calculate validation fitness
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| 195 | string targetVariable = ProblemData.TargetVariable.Value;
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| 196 |
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| 197 | // select a random subset of rows in the validation set
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| 198 | int validationStart = ValidiationSamplesStart.Value;
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| 199 | int validationEnd = ValidationSamplesEnd.Value;
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| 200 | int seed = Random.Next();
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| 201 | int count = (int)((validationEnd - validationStart) * RelativeNumberOfEvaluatedSamples.Value);
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| 202 | if (count == 0) count = 1;
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| 203 | IEnumerable<int> rows = RandomEnumerable.SampleRandomNumbers(seed, validationStart, validationEnd, count)
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| 204 | .Where(row => row < ProblemData.TestSamplesStart.Value || ProblemData.TestSamplesEnd.Value <= row);
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| 205 |
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| 206 | double upperEstimationLimit = UpperEstimationLimit != null ? UpperEstimationLimit.Value : double.PositiveInfinity;
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| 207 | double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity;
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| 208 |
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| 209 | var trees = SymbolicExpressionTreeParameter.ActualValue;
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| 210 |
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| 211 | for (int i = 0; i < validationArr.Length; i++) {
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| 212 | var tree = trees[i];
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| 213 | double quality = Evaluator.Evaluate(SymbolicExpressionTreeInterpreter, tree,
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| 214 | lowerEstimationLimit, upperEstimationLimit,
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| 215 | ProblemData.Dataset, targetVariable,
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| 216 | rows);
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| 217 | validationArr[i] = quality;
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| 218 | }
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| 219 |
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| 220 | #endregion
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| 221 |
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| 222 |
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| 223 | double r = alglib.spearmancorr2(trainingArr, validationArr);
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| 224 |
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| 225 | TrainingValidationQualityCorrelationParameter.ActualValue = new DoubleValue(r);
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| 226 |
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| 227 | if (TrainingValidationQualityCorrelationTableParameter.ActualValue == null) {
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| 228 | var dataTable = new DataTable("Training and validation fitness correlation table", "Data table of training and validation fitness correlation values over the whole run.");
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| 229 | dataTable.Rows.Add(new DataRow("Training and validation fitness correlation", "Training and validation fitness correlation values"));
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| 230 | TrainingValidationQualityCorrelationTableParameter.ActualValue = dataTable;
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| 231 | ResultsParameter.ActualValue.Add(new Result(TrainingValidationCorrelationTableParameterName, dataTable));
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| 232 | }
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| 233 |
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| 234 | TrainingValidationQualityCorrelationTableParameter.ActualValue.Rows["Training and validation fitness correlation"].Values.Add(r);
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| 235 |
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| 236 | double correlationThreshold;
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| 237 | if (OverfittingParameter.ActualValue != null && OverfittingParameter.ActualValue.Value) {
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| 238 | // if is already overfitting => have to reach the upper threshold to switch back to non-overfitting state
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| 239 | correlationThreshold = UpperCorrelationThresholdParameter.ActualValue.Value;
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| 240 | } else {
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| 241 | // if currently in non-overfitting state => have to reach to lower threshold to switch to overfitting state
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| 242 | correlationThreshold = LowerCorrelationThresholdParameter.ActualValue.Value;
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| 243 | }
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| 244 | bool overfitting = r < correlationThreshold;
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| 245 |
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| 246 | OverfittingParameter.ActualValue = new BoolValue(overfitting);
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| 247 |
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| 248 | return base.Apply();
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| 249 | }
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| 250 | }
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| 251 | }
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