| 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.Regression.Symbolic;
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| 34 | using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers;
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| 35 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 36 |
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| 37 | namespace HeuristicLab.Problems.DataAnalysis.Classification {
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| 38 | [Item("ValidationBestSymbolicClassificationSolutionAnalyzer", "An operator that analyzes the validation best symbolic classification solution.")]
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| 39 | [StorableClass]
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| 40 | public class ValidationBestSymbolicClassificationSolutionAnalyzer : SingleSuccessorOperator, ISymbolicClassificationAnalyzer {
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| 41 | private const string MaximizationParameterName = "Maximization";
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| 42 | private const string GenerationsParameterName = "Generations";
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| 43 | private const string RandomParameterName = "Random";
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| 44 | private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
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| 45 | private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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| 46 |
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| 47 | private const string ClassificationProblemDataParameterName = "ClassificationProblemData";
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| 48 | private const string EvaluatorParameterName = "Evaluator";
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| 49 | private const string ValidationSamplesStartParameterName = "SamplesStart";
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| 50 | private const string ValidationSamplesEndParameterName = "SamplesEnd";
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| 51 | private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
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| 52 | private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
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| 53 | private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
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| 54 | private const string CalculateSolutionComplexityParameterName = "CalculateSolutionComplexity";
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| 55 | private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
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| 56 |
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| 57 | private const string ResultsParameterName = "Results";
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| 58 | private const string BestValidationQualityParameterName = "Best validation quality";
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| 59 | private const string BestValidationSolutionParameterName = "Best validation solution";
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| 60 | private const string BestSolutionAccuracyTrainingParameterName = "Best solution accuracy (training)";
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| 61 | private const string BestSolutionAccuracyTestParameterName = "Best solution accuracy (test)";
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| 62 | private const string BestSolutionLengthParameterName = "Best solution length (validation)";
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| 63 | private const string BestSolutionHeightParameterName = "Best solution height (validiation)";
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| 64 | private const string VariableFrequenciesParameterName = "VariableFrequencies";
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| 65 |
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| 66 | #region parameter properties
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| 67 | public ILookupParameter<BoolValue> MaximizationParameter {
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| 68 | get { return (ILookupParameter<BoolValue>)Parameters[MaximizationParameterName]; }
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| 69 | }
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| 70 | public ILookupParameter<IntValue> GenerationsParameter {
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| 71 | get { return (ILookupParameter<IntValue>)Parameters[GenerationsParameterName]; }
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| 72 | }
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| 73 | public ILookupParameter<IRandom> RandomParameter {
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| 74 | get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
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| 75 | }
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| 76 | public ScopeTreeLookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
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| 77 | get { return (ScopeTreeLookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
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| 78 | }
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| 79 | public IValueLookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
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| 80 | get { return (IValueLookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
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| 81 | }
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| 82 | public ILookupParameter<ClassificationProblemData> ClassificationProblemDataParameter {
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| 83 | get { return (ILookupParameter<ClassificationProblemData>)Parameters[ClassificationProblemDataParameterName]; }
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| 84 | }
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| 85 | public ILookupParameter<ISymbolicClassificationEvaluator> EvaluatorParameter {
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| 86 | get { return (ILookupParameter<ISymbolicClassificationEvaluator>)Parameters[EvaluatorParameterName]; }
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| 87 | }
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| 88 | public IValueLookupParameter<IntValue> ValidationSamplesStartParameter {
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| 89 | get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesStartParameterName]; }
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| 90 | }
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| 91 | public IValueLookupParameter<IntValue> ValidationSamplesEndParameter {
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| 92 | get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesEndParameterName]; }
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| 93 | }
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| 94 | public IValueParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
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| 95 | get { return (IValueParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
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| 96 | }
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| 97 | public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
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| 98 | get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
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| 99 | }
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| 100 | public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
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| 101 | get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
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| 102 | }
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| 103 | public IValueLookupParameter<BoolValue> ApplyLinearScalingParameter {
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| 104 | get { return (IValueLookupParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
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| 105 | }
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| 106 | public ILookupParameter<DataTable> VariableFrequenciesParameter {
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| 107 | get { return (ILookupParameter<DataTable>)Parameters[VariableFrequenciesParameterName]; }
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| 108 | }
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| 109 | public IValueParameter<BoolValue> CalculateSolutionComplexityParameter {
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| 110 | get { return (IValueParameter<BoolValue>)Parameters[CalculateSolutionComplexityParameterName]; }
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| 111 | }
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| 112 |
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| 113 | public ILookupParameter<ResultCollection> ResultsParameter {
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| 114 | get { return (ILookupParameter<ResultCollection>)Parameters[ResultsParameterName]; }
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| 115 | }
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| 116 | public ILookupParameter<DoubleValue> BestValidationQualityParameter {
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| 117 | get { return (ILookupParameter<DoubleValue>)Parameters[BestValidationQualityParameterName]; }
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| 118 | }
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| 119 | public ILookupParameter<SymbolicClassificationSolution> BestValidationSolutionParameter {
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| 120 | get { return (ILookupParameter<SymbolicClassificationSolution>)Parameters[BestValidationSolutionParameterName]; }
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| 121 | }
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| 122 | public ILookupParameter<DoubleValue> BestSolutionAccuracyTrainingParameter {
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| 123 | get { return (ILookupParameter<DoubleValue>)Parameters[BestSolutionAccuracyTrainingParameterName]; }
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| 124 | }
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| 125 | public ILookupParameter<DoubleValue> BestSolutionAccuracyTestParameter {
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| 126 | get { return (ILookupParameter<DoubleValue>)Parameters[BestSolutionAccuracyTestParameterName]; }
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| 127 | }
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| 128 | public ILookupParameter<IntValue> BestSolutionLengthParameter {
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| 129 | get { return (ILookupParameter<IntValue>)Parameters[BestSolutionLengthParameterName]; }
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| 130 | }
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| 131 | public ILookupParameter<IntValue> BestSolutionHeightParameter {
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| 132 | get { return (ILookupParameter<IntValue>)Parameters[BestSolutionHeightParameterName]; }
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| 133 | }
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| 134 | #endregion
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| 135 | #region properties
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| 136 | public BoolValue Maximization {
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| 137 | get { return MaximizationParameter.ActualValue; }
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| 138 | }
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| 139 | public IntValue Generations {
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| 140 | get { return GenerationsParameter.ActualValue; }
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| 141 | }
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| 142 | public IRandom Random {
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| 143 | get { return RandomParameter.ActualValue; }
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| 144 | }
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| 145 | public ItemArray<SymbolicExpressionTree> SymbolicExpressionTree {
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| 146 | get { return SymbolicExpressionTreeParameter.ActualValue; }
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| 147 | }
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| 148 | public ISymbolicExpressionTreeInterpreter SymbolicExpressionTreeInterpreter {
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| 149 | get { return SymbolicExpressionTreeInterpreterParameter.ActualValue; }
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| 150 | }
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| 151 |
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| 152 | public ClassificationProblemData ClassificationProblemData {
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| 153 | get { return ClassificationProblemDataParameter.ActualValue; }
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| 154 | }
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| 155 | public ISymbolicClassificationEvaluator Evaluator {
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| 156 | get { return EvaluatorParameter.ActualValue; }
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| 157 | }
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| 158 | public IntValue ValidiationSamplesStart {
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| 159 | get { return ValidationSamplesStartParameter.ActualValue; }
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| 160 | }
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| 161 | public IntValue ValidationSamplesEnd {
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| 162 | get { return ValidationSamplesEndParameter.ActualValue; }
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| 163 | }
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| 164 | public PercentValue RelativeNumberOfEvaluatedSamples {
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| 165 | get { return RelativeNumberOfEvaluatedSamplesParameter.Value; }
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| 166 | }
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| 167 | public DoubleValue UpperEstimationLimit {
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| 168 | get { return UpperEstimationLimitParameter.ActualValue; }
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| 169 | }
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| 170 | public DoubleValue LowerEstimationLimit {
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| 171 | get { return LowerEstimationLimitParameter.ActualValue; }
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| 172 | }
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| 173 | public BoolValue ApplyLinearScaling {
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| 174 | get { return ApplyLinearScalingParameter.ActualValue; }
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| 175 | set { ApplyLinearScalingParameter.ActualValue = value; }
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| 176 | }
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| 177 | public DataTable VariableFrequencies {
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| 178 | get { return VariableFrequenciesParameter.ActualValue; }
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| 179 | }
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| 180 | public BoolValue CalculateSolutionComplexity {
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| 181 | get { return CalculateSolutionComplexityParameter.Value; }
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| 182 | set { CalculateSolutionComplexityParameter.Value = value; }
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| 183 | }
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| 184 |
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| 185 | public ResultCollection Results {
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| 186 | get { return ResultsParameter.ActualValue; }
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| 187 | }
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| 188 | public DoubleValue BestValidationQuality {
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| 189 | get { return BestValidationQualityParameter.ActualValue; }
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| 190 | protected set { BestValidationQualityParameter.ActualValue = value; }
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| 191 | }
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| 192 | public SymbolicClassificationSolution BestValidationSolution {
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| 193 | get { return BestValidationSolutionParameter.ActualValue; }
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| 194 | protected set { BestValidationSolutionParameter.ActualValue = value; }
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| 195 | }
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| 196 | public DoubleValue BestSolutionAccuracyTraining {
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| 197 | get { return BestSolutionAccuracyTrainingParameter.ActualValue; }
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| 198 | protected set { BestSolutionAccuracyTrainingParameter.ActualValue = value; }
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| 199 | }
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| 200 | public DoubleValue BestSolutionAccuracyTest {
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| 201 | get { return BestSolutionAccuracyTestParameter.ActualValue; }
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| 202 | protected set { BestSolutionAccuracyTestParameter.ActualValue = value; }
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| 203 | }
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| 204 | public IntValue BestSolutionLength {
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| 205 | get { return BestSolutionLengthParameter.ActualValue; }
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| 206 | set { BestSolutionLengthParameter.ActualValue = value; }
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| 207 | }
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| 208 | public IntValue BestSolutionHeight {
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| 209 | get { return BestSolutionHeightParameter.ActualValue; }
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| 210 | set { BestSolutionHeightParameter.ActualValue = value; }
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| 211 | }
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| 212 | #endregion
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| 213 |
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| 214 | [StorableConstructor]
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| 215 | protected ValidationBestSymbolicClassificationSolutionAnalyzer(bool deserializing) : base(deserializing) { }
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| 216 | protected ValidationBestSymbolicClassificationSolutionAnalyzer(ValidationBestSymbolicClassificationSolutionAnalyzer original, Cloner cloner)
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| 217 | : base(original, cloner) {
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| 218 | }
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| 219 | public ValidationBestSymbolicClassificationSolutionAnalyzer()
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| 220 | : base() {
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| 221 | Parameters.Add(new LookupParameter<BoolValue>(MaximizationParameterName, "The direction of optimization."));
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| 222 | Parameters.Add(new LookupParameter<IntValue>(GenerationsParameterName, "The number of generations calculated so far."));
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| 223 | Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "The random generator to use."));
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| 224 | Parameters.Add(new ScopeTreeLookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to analyze."));
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| 225 | Parameters.Add(new ValueLookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used for the analysis of symbolic expression trees."));
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| 226 | Parameters.Add(new LookupParameter<ClassificationProblemData>(ClassificationProblemDataParameterName, "The problem data for which the symbolic expression tree is a solution."));
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| 227 | Parameters.Add(new LookupParameter<ISymbolicClassificationEvaluator>(EvaluatorParameterName, "The evaluator which should be used to evaluate the solution on the validation set."));
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| 228 | Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesStartParameterName, "The first index of the validation partition of the data set."));
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| 229 | Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesEndParameterName, "The last index of the validation partition of the data set."));
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| 230 | 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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| 231 | 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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| 232 | 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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| 233 | Parameters.Add(new LookupParameter<DataTable>(VariableFrequenciesParameterName, "The variable frequencies table to use for the calculation of variable impacts"));
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| 234 | Parameters.Add(new ValueParameter<BoolValue>(CalculateSolutionComplexityParameterName, "Determines if the length and height of the validation best solution should be calculated.", new BoolValue(true)));
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| 235 | Parameters.Add(new ValueLookupParameter<BoolValue>(ApplyLinearScalingParameterName, "The switch determines if the best solution should be linearly scaled on the whole training set.", new BoolValue(false)));
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| 236 |
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| 237 | Parameters.Add(new ValueLookupParameter<ResultCollection>(ResultsParameterName, "The results collection where the analysis values should be stored."));
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| 238 | Parameters.Add(new LookupParameter<DoubleValue>(BestValidationQualityParameterName, "The validation quality of the best solution in the current run."));
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| 239 | Parameters.Add(new LookupParameter<SymbolicClassificationSolution>(BestValidationSolutionParameterName, "The best solution on the validation data found in the current run."));
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| 240 | Parameters.Add(new LookupParameter<DoubleValue>(BestSolutionAccuracyTrainingParameterName, "The training accuracy of the best solution."));
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| 241 | Parameters.Add(new LookupParameter<DoubleValue>(BestSolutionAccuracyTestParameterName, "The test accuracy of the best solution."));
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| 242 | Parameters.Add(new LookupParameter<IntValue>(BestSolutionLengthParameterName, "The length of the best symbolic classification solution."));
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| 243 | Parameters.Add(new LookupParameter<IntValue>(BestSolutionHeightParameterName, "The height of the best symbolic classification solution."));
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| 244 | }
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| 245 |
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| 246 | [StorableHook(HookType.AfterDeserialization)]
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| 247 | private void AfterDeserialization() {
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| 248 | if (!Parameters.ContainsKey(CalculateSolutionComplexityParameterName)) {
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| 249 | Parameters.Add(new ValueParameter<BoolValue>(CalculateSolutionComplexityParameterName, "Determines if the length and height of the validation best solution should be calculated.", new BoolValue(true)));
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| 250 | }
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| 251 | if (!Parameters.ContainsKey(BestSolutionLengthParameterName)) {
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| 252 | Parameters.Add(new LookupParameter<IntValue>(BestSolutionLengthParameterName, "The length of the best symbolic classification solution."));
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| 253 | }
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| 254 | if (!Parameters.ContainsKey(BestSolutionHeightParameterName)) {
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| 255 | Parameters.Add(new LookupParameter<IntValue>(BestSolutionHeightParameterName, "The height of the best symbolic classification solution."));
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| 256 | }
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| 257 | if (!Parameters.ContainsKey(ApplyLinearScalingParameterName)) {
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| 258 | Parameters.Add(new ValueLookupParameter<BoolValue>(ApplyLinearScalingParameterName, "The switch determines if the best solution should be linearly scaled on the whole training set.", new BoolValue(false)));
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| 259 | }
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| 260 | }
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| 261 |
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| 262 | public override IDeepCloneable Clone(Cloner cloner) {
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| 263 | return new ValidationBestSymbolicClassificationSolutionAnalyzer(this, cloner);
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| 264 | }
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| 265 |
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| 266 | public override IOperation Apply() {
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| 267 | var trees = SymbolicExpressionTree;
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| 268 | string targetVariable = ClassificationProblemData.TargetVariable.Value;
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| 269 |
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| 270 | // select a random subset of rows in the validation set
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| 271 | int validationStart = ValidiationSamplesStart.Value;
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| 272 | int validationEnd = ValidationSamplesEnd.Value;
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| 273 | int seed = Random.Next();
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| 274 | int count = (int)((validationEnd - validationStart) * RelativeNumberOfEvaluatedSamples.Value);
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| 275 | if (count == 0) count = 1;
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| 276 | IEnumerable<int> rows = RandomEnumerable.SampleRandomNumbers(seed, validationStart, validationEnd, count)
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| 277 | .Where(row => row < ClassificationProblemData.TestSamplesStart.Value || ClassificationProblemData.TestSamplesEnd.Value <= row);
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| 278 |
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| 279 | double upperEstimationLimit = UpperEstimationLimit != null ? UpperEstimationLimit.Value : double.PositiveInfinity;
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| 280 | double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity;
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| 281 |
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| 282 | double bestQuality = Maximization.Value ? double.NegativeInfinity : double.PositiveInfinity;
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| 283 | SymbolicExpressionTree bestTree = null;
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| 284 |
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| 285 | foreach (var tree in trees) {
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| 286 | double quality = Evaluator.Evaluate(SymbolicExpressionTreeInterpreter, tree,
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| 287 | lowerEstimationLimit, upperEstimationLimit, ClassificationProblemData.Dataset,
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| 288 | targetVariable, rows);
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| 289 |
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| 290 | if ((Maximization.Value && quality > bestQuality) ||
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| 291 | (!Maximization.Value && quality < bestQuality)) {
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| 292 | bestQuality = quality;
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| 293 | bestTree = tree;
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| 294 | }
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| 295 | }
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| 296 |
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| 297 | // if the best validation tree is better than the current best solution => update
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| 298 | bool newBest =
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| 299 | BestValidationQuality == null ||
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| 300 | (Maximization.Value && bestQuality > BestValidationQuality.Value) ||
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| 301 | (!Maximization.Value && bestQuality < BestValidationQuality.Value);
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| 302 | if (newBest) {
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| 303 | if (ApplyLinearScaling.Value) {
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| 304 | double alpha, beta;
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| 305 | SymbolicRegressionScaledMeanSquaredErrorEvaluator.Calculate(SymbolicExpressionTreeInterpreter, bestTree,
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| 306 | lowerEstimationLimit, upperEstimationLimit,
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| 307 | ClassificationProblemData.Dataset, targetVariable,
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| 308 | ClassificationProblemData.TrainingIndizes, out beta, out alpha);
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| 309 |
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| 310 | // scale tree for solution
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| 311 | bestTree = SymbolicRegressionSolutionLinearScaler.Scale(bestTree, alpha, beta);
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| 312 | }
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| 313 | var model = new SymbolicRegressionModel((ISymbolicExpressionTreeInterpreter)SymbolicExpressionTreeInterpreter.Clone(),
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| 314 | bestTree);
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| 315 |
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| 316 | if (BestValidationSolution == null) {
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| 317 | BestValidationSolution = new SymbolicClassificationSolution(ClassificationProblemData, model, LowerEstimationLimit.Value, UpperEstimationLimit.Value);
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| 318 | BestValidationSolution.Name = BestValidationSolutionParameterName;
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| 319 | BestValidationSolution.Description = "Best solution on validation partition found over the whole run.";
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| 320 | BestValidationQuality = new DoubleValue(bestQuality);
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| 321 | } else {
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| 322 | BestValidationSolution.Model = model;
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| 323 | BestValidationQuality.Value = bestQuality;
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| 324 | }
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| 325 |
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| 326 | UpdateBestSolutionResults();
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| 327 | }
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| 328 | return base.Apply();
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| 329 | }
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| 330 |
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| 331 | private void UpdateBestSolutionResults() {
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| 332 | if (CalculateSolutionComplexity.Value) {
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| 333 | BestSolutionLength = new IntValue(BestValidationSolution.Model.SymbolicExpressionTree.Size);
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| 334 | BestSolutionHeight = new IntValue(BestValidationSolution.Model.SymbolicExpressionTree.Height);
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| 335 | if (!Results.ContainsKey(BestSolutionLengthParameterName)) {
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| 336 | Results.Add(new Result(BestSolutionLengthParameterName, "Length of the best solution on the validation set", new IntValue()));
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| 337 | Results.Add(new Result(BestSolutionHeightParameterName, "Height of the best solution on the validation set", new IntValue()));
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| 338 | }
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| 339 | Results[BestSolutionLengthParameterName].Value = BestSolutionLength;
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| 340 | Results[BestSolutionHeightParameterName].Value = BestSolutionHeight;
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| 341 | }
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| 342 |
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| 343 | BestSymbolicRegressionSolutionAnalyzer.UpdateBestSolutionResults(BestValidationSolution, ClassificationProblemData, Results, Generations, VariableFrequencies);
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| 344 |
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| 345 | IEnumerable<double> trainingValues = ClassificationProblemData.Dataset.GetEnumeratedVariableValues(
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| 346 | ClassificationProblemData.TargetVariable.Value, ClassificationProblemData.TrainingIndizes);
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| 347 | IEnumerable<double> testValues = ClassificationProblemData.Dataset.GetEnumeratedVariableValues(
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| 348 | ClassificationProblemData.TargetVariable.Value, ClassificationProblemData.TestIndizes);
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| 349 |
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| 350 | OnlineAccuracyEvaluator accuracyEvaluator = new OnlineAccuracyEvaluator();
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| 351 | var originalEnumerator = trainingValues.GetEnumerator();
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| 352 | var estimatedEnumerator = BestValidationSolution.EstimatedTrainingClassValues.GetEnumerator();
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| 353 | while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
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| 354 | accuracyEvaluator.Add(originalEnumerator.Current, estimatedEnumerator.Current);
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| 355 | }
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| 356 | double trainingAccuracy = accuracyEvaluator.Accuracy;
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| 357 |
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| 358 | accuracyEvaluator.Reset();
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| 359 | originalEnumerator = testValues.GetEnumerator();
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| 360 | estimatedEnumerator = BestValidationSolution.EstimatedTestClassValues.GetEnumerator();
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| 361 | while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
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| 362 | accuracyEvaluator.Add(originalEnumerator.Current, estimatedEnumerator.Current);
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| 363 | }
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| 364 | double testAccuracy = accuracyEvaluator.Accuracy;
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| 365 |
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| 366 | if (!Results.ContainsKey(BestSolutionAccuracyTrainingParameterName)) {
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| 367 | BestSolutionAccuracyTraining = new DoubleValue(trainingAccuracy);
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| 368 | BestSolutionAccuracyTest = new DoubleValue(testAccuracy);
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| 369 |
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| 370 | Results.Add(new Result(BestSolutionAccuracyTrainingParameterName, BestSolutionAccuracyTraining));
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| 371 | Results.Add(new Result(BestSolutionAccuracyTestParameterName, BestSolutionAccuracyTest));
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| 372 | } else {
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| 373 | BestSolutionAccuracyTraining.Value = trainingAccuracy;
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| 374 | BestSolutionAccuracyTest.Value = testAccuracy;
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| 375 | }
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| 376 | }
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| 377 | }
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| 378 | }
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