[3652] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[5445] | 3 | * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[3652] | 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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[4068] | 22 | using System;
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| 23 | using System.Collections.Generic;
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[3652] | 24 | using System.Linq;
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[4068] | 25 | using HeuristicLab.Analysis;
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[4722] | 26 | using HeuristicLab.Common;
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[3652] | 27 | using HeuristicLab.Core;
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| 28 | using HeuristicLab.Data;
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[4068] | 29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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[3652] | 30 | using HeuristicLab.Operators;
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| 31 | using HeuristicLab.Optimization;
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| 32 | using HeuristicLab.Parameters;
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| 33 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[5863] | 34 | using HeuristicLab.PluginInfrastructure;
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[4068] | 35 | using HeuristicLab.Problems.DataAnalysis.Evaluators;
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[3652] | 36 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 37 |
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| 38 | namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers {
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| 39 | /// <summary>
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| 40 | /// "An operator for analyzing the quality of symbolic regression solutions symbolic expression tree encoding."
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| 41 | /// </summary>
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[3681] | 42 | [Item("SymbolicRegressionModelQualityAnalyzer", "An operator for analyzing the quality of symbolic regression solutions symbolic expression tree encoding.")]
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[3652] | 43 | [StorableClass]
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[5863] | 44 | [NonDiscoverableType]
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[3996] | 45 | public sealed class SymbolicRegressionModelQualityAnalyzer : SingleSuccessorOperator, ISymbolicRegressionAnalyzer {
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[3652] | 46 | private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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| 47 | private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
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| 48 | private const string ProblemDataParameterName = "ProblemData";
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| 49 | private const string ResultsParameterName = "Results";
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[3666] | 50 |
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[3710] | 51 | private const string TrainingMeanSquaredErrorQualityParameterName = "Mean squared error (training)";
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| 52 | private const string MinTrainingMeanSquaredErrorQualityParameterName = "Min mean squared error (training)";
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| 53 | private const string MaxTrainingMeanSquaredErrorQualityParameterName = "Max mean squared error (training)";
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| 54 | private const string AverageTrainingMeanSquaredErrorQualityParameterName = "Average mean squared error (training)";
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| 55 | private const string BestTrainingMeanSquaredErrorQualityParameterName = "Best mean squared error (training)";
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[3666] | 56 |
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[3710] | 57 | private const string TrainingAverageRelativeErrorQualityParameterName = "Average relative error (training)";
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| 58 | private const string MinTrainingAverageRelativeErrorQualityParameterName = "Min average relative error (training)";
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| 59 | private const string MaxTrainingAverageRelativeErrorQualityParameterName = "Max average relative error (training)";
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| 60 | private const string AverageTrainingAverageRelativeErrorQualityParameterName = "Average average relative error (training)";
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| 61 | private const string BestTrainingAverageRelativeErrorQualityParameterName = "Best average relative error (training)";
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[3666] | 62 |
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[3710] | 63 | private const string TrainingRSquaredQualityParameterName = "R² (training)";
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| 64 | private const string MinTrainingRSquaredQualityParameterName = "Min R² (training)";
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| 65 | private const string MaxTrainingRSquaredQualityParameterName = "Max R² (training)";
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| 66 | private const string AverageTrainingRSquaredQualityParameterName = "Average R² (training)";
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| 67 | private const string BestTrainingRSquaredQualityParameterName = "Best R² (training)";
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[3666] | 68 |
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[3710] | 69 | private const string TestMeanSquaredErrorQualityParameterName = "Mean squared error (test)";
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| 70 | private const string MinTestMeanSquaredErrorQualityParameterName = "Min mean squared error (test)";
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| 71 | private const string MaxTestMeanSquaredErrorQualityParameterName = "Max mean squared error (test)";
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| 72 | private const string AverageTestMeanSquaredErrorQualityParameterName = "Average mean squared error (test)";
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| 73 | private const string BestTestMeanSquaredErrorQualityParameterName = "Best mean squared error (test)";
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[3666] | 74 |
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[3710] | 75 | private const string TestAverageRelativeErrorQualityParameterName = "Average relative error (test)";
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| 76 | private const string MinTestAverageRelativeErrorQualityParameterName = "Min average relative error (test)";
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| 77 | private const string MaxTestAverageRelativeErrorQualityParameterName = "Max average relative error (test)";
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| 78 | private const string AverageTestAverageRelativeErrorQualityParameterName = "Average average relative error (test)";
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| 79 | private const string BestTestAverageRelativeErrorQualityParameterName = "Best average relative error (test)";
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[3666] | 80 |
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[3710] | 81 | private const string TestRSquaredQualityParameterName = "R² (test)";
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| 82 | private const string MinTestRSquaredQualityParameterName = "Min R² (test)";
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| 83 | private const string MaxTestRSquaredQualityParameterName = "Max R² (test)";
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| 84 | private const string AverageTestRSquaredQualityParameterName = "Average R² (test)";
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| 85 | private const string BestTestRSquaredQualityParameterName = "Best R² (test)";
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[3666] | 86 |
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[3710] | 87 | private const string RSquaredValuesParameterName = "R²";
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| 88 | private const string MeanSquaredErrorValuesParameterName = "Mean squared error";
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| 89 | private const string RelativeErrorValuesParameterName = "Average relative error";
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[3666] | 90 |
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[3652] | 91 | private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
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| 92 | private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
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| 93 |
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[7214] | 94 | public bool EnabledByDefault {
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| 95 | get { return true; }
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| 96 | }
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| 97 |
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[3652] | 98 | #region parameter properties
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[3681] | 99 | public ScopeTreeLookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
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| 100 | get { return (ScopeTreeLookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
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[3652] | 101 | }
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[3681] | 102 | public IValueLookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
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| 103 | get { return (IValueLookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
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[3652] | 104 | }
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[3681] | 105 | public IValueLookupParameter<DataAnalysisProblemData> ProblemDataParameter {
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| 106 | get { return (IValueLookupParameter<DataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
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[3652] | 107 | }
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| 108 | public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
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| 109 | get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
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| 110 | }
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| 111 | public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
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| 112 | get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
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| 113 | }
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[3681] | 114 | public ILookupParameter<ResultCollection> ResultsParameter {
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| 115 | get { return (ILookupParameter<ResultCollection>)Parameters[ResultsParameterName]; }
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| 116 | }
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[3652] | 117 | #endregion
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[3996] | 118 | #region properties
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| 119 | public DoubleValue UpperEstimationLimit {
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| 120 | get { return UpperEstimationLimitParameter.ActualValue; }
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| 121 | }
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| 122 | public DoubleValue LowerEstimationLimit {
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| 123 | get { return LowerEstimationLimitParameter.ActualValue; }
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| 124 | }
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| 125 | #endregion
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[3652] | 126 |
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[4722] | 127 | [StorableConstructor]
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| 128 | private SymbolicRegressionModelQualityAnalyzer(bool deserializing) : base(deserializing) { }
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| 129 | private SymbolicRegressionModelQualityAnalyzer(SymbolicRegressionModelQualityAnalyzer original, Cloner cloner) : base(original, cloner) { }
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[3681] | 130 | public SymbolicRegressionModelQualityAnalyzer()
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[3652] | 131 | : base() {
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[3659] | 132 | Parameters.Add(new ScopeTreeLookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to analyze."));
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[3681] | 133 | Parameters.Add(new ValueLookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used to calculate the output values of the symbolic expression tree."));
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| 134 | Parameters.Add(new ValueLookupParameter<DataAnalysisProblemData>(ProblemDataParameterName, "The problem data containing the input varaibles for the symbolic regression problem."));
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[3652] | 135 | Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit that should be used as cut off value for the output values of symbolic expression trees."));
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| 136 | Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit that should be used as cut off value for the output values of symbolic expression trees."));
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[3681] | 137 | Parameters.Add(new ValueLookupParameter<DataTable>(MeanSquaredErrorValuesParameterName, "The data table to collect mean squared error values."));
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| 138 | Parameters.Add(new ValueLookupParameter<DataTable>(RSquaredValuesParameterName, "The data table to collect R² correlation coefficient values."));
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| 139 | Parameters.Add(new ValueLookupParameter<DataTable>(RelativeErrorValuesParameterName, "The data table to collect relative error values."));
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| 140 | Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The result collection where the best symbolic regression solution should be stored."));
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[3996] | 141 | }
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[3652] | 142 |
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[4722] | 143 | public override IDeepCloneable Clone(Cloner cloner) {
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| 144 | return new SymbolicRegressionModelQualityAnalyzer(this, cloner);
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| 145 | }
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[3681] | 146 |
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[3996] | 147 | public override IOperation Apply() {
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[5437] | 148 | double upperEstimationLimit = UpperEstimationLimit != null ? UpperEstimationLimit.Value : double.PositiveInfinity;
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| 149 | double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity;
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[3996] | 150 | Analyze(SymbolicExpressionTreeParameter.ActualValue, SymbolicExpressionTreeInterpreterParameter.ActualValue,
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[5437] | 151 | upperEstimationLimit, lowerEstimationLimit, ProblemDataParameter.ActualValue,
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[3996] | 152 | ResultsParameter.ActualValue);
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| 153 | return base.Apply();
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| 154 | }
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[3681] | 155 |
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[3996] | 156 | public static void Analyze(IEnumerable<SymbolicExpressionTree> trees, ISymbolicExpressionTreeInterpreter interpreter,
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| 157 | double upperEstimationLimit, double lowerEstimationLimit,
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[4468] | 158 | DataAnalysisProblemData problemData, ResultCollection results) {
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[3996] | 159 | int targetVariableIndex = problemData.Dataset.GetVariableIndex(problemData.TargetVariable.Value);
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[4468] | 160 | IEnumerable<double> originalTrainingValues = problemData.Dataset.GetEnumeratedVariableValues(targetVariableIndex, problemData.TrainingIndizes);
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| 161 | IEnumerable<double> originalTestValues = problemData.Dataset.GetEnumeratedVariableValues(targetVariableIndex, problemData.TestIndizes);
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[3996] | 162 | List<double> trainingMse = new List<double>();
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| 163 | List<double> trainingR2 = new List<double>();
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| 164 | List<double> trainingRelErr = new List<double>();
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| 165 | List<double> testMse = new List<double>();
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| 166 | List<double> testR2 = new List<double>();
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| 167 | List<double> testRelErr = new List<double>();
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[3652] | 168 |
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[3996] | 169 | OnlineMeanSquaredErrorEvaluator mseEvaluator = new OnlineMeanSquaredErrorEvaluator();
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| 170 | OnlineMeanAbsolutePercentageErrorEvaluator relErrEvaluator = new OnlineMeanAbsolutePercentageErrorEvaluator();
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| 171 | OnlinePearsonsRSquaredEvaluator r2Evaluator = new OnlinePearsonsRSquaredEvaluator();
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[3666] | 172 |
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[3996] | 173 | foreach (var tree in trees) {
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| 174 | #region training
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[4468] | 175 | var estimatedTrainingValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, problemData.TrainingIndizes);
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[3996] | 176 | mseEvaluator.Reset();
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| 177 | r2Evaluator.Reset();
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| 178 | relErrEvaluator.Reset();
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| 179 | var estimatedEnumerator = estimatedTrainingValues.GetEnumerator();
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| 180 | var originalEnumerator = originalTrainingValues.GetEnumerator();
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| 181 | while (estimatedEnumerator.MoveNext() & originalEnumerator.MoveNext()) {
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| 182 | double estimated = estimatedEnumerator.Current;
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| 183 | if (double.IsNaN(estimated)) estimated = upperEstimationLimit;
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| 184 | else estimated = Math.Min(upperEstimationLimit, Math.Max(lowerEstimationLimit, estimated));
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| 185 | mseEvaluator.Add(originalEnumerator.Current, estimated);
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| 186 | r2Evaluator.Add(originalEnumerator.Current, estimated);
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| 187 | relErrEvaluator.Add(originalEnumerator.Current, estimated);
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| 188 | }
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| 189 | if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()) {
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| 190 | throw new InvalidOperationException("Number of elements in estimated and original enumeration doesn't match.");
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| 191 | }
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| 192 | trainingMse.Add(mseEvaluator.MeanSquaredError);
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| 193 | trainingR2.Add(r2Evaluator.RSquared);
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| 194 | trainingRelErr.Add(relErrEvaluator.MeanAbsolutePercentageError);
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| 195 | #endregion
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| 196 | #region test
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[4468] | 197 | var estimatedTestValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, problemData.TestIndizes);
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[3666] | 198 |
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[3996] | 199 | mseEvaluator.Reset();
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| 200 | r2Evaluator.Reset();
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| 201 | relErrEvaluator.Reset();
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| 202 | estimatedEnumerator = estimatedTestValues.GetEnumerator();
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| 203 | originalEnumerator = originalTestValues.GetEnumerator();
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| 204 | while (estimatedEnumerator.MoveNext() & originalEnumerator.MoveNext()) {
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| 205 | double estimated = estimatedEnumerator.Current;
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| 206 | if (double.IsNaN(estimated)) estimated = upperEstimationLimit;
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| 207 | else estimated = Math.Min(upperEstimationLimit, Math.Max(lowerEstimationLimit, estimated));
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| 208 | mseEvaluator.Add(originalEnumerator.Current, estimated);
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| 209 | r2Evaluator.Add(originalEnumerator.Current, estimated);
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| 210 | relErrEvaluator.Add(originalEnumerator.Current, estimated);
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| 211 | }
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| 212 | if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()) {
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| 213 | throw new InvalidOperationException("Number of elements in estimated and original enumeration doesn't match.");
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| 214 | }
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| 215 | testMse.Add(mseEvaluator.MeanSquaredError);
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| 216 | testR2.Add(r2Evaluator.RSquared);
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| 217 | testRelErr.Add(relErrEvaluator.MeanAbsolutePercentageError);
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| 218 | #endregion
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| 219 | }
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[3710] | 220 |
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[3996] | 221 | AddResultTableValues(results, MeanSquaredErrorValuesParameterName, "mean squared error (training)", trainingMse.Min(), trainingMse.Average(), trainingMse.Max());
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| 222 | AddResultTableValues(results, MeanSquaredErrorValuesParameterName, "mean squared error (test)", testMse.Min(), testMse.Average(), testMse.Max());
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| 223 | AddResultTableValues(results, RelativeErrorValuesParameterName, "mean relative error (training)", trainingRelErr.Min(), trainingRelErr.Average(), trainingRelErr.Max());
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| 224 | AddResultTableValues(results, RelativeErrorValuesParameterName, "mean relative error (test)", testRelErr.Min(), testRelErr.Average(), testRelErr.Max());
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| 225 | AddResultTableValues(results, RSquaredValuesParameterName, "Pearson's R² (training)", trainingR2.Min(), trainingR2.Average(), trainingR2.Max());
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| 226 | AddResultTableValues(results, RSquaredValuesParameterName, "Pearson's R² (test)", testR2.Min(), testR2.Average(), testR2.Max());
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[3652] | 227 | }
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[3681] | 228 |
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[3996] | 229 | private static void AddResultTableValues(ResultCollection results, string tableName, string valueName, double minValue, double avgValue, double maxValue) {
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| 230 | if (!results.ContainsKey(tableName)) {
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| 231 | results.Add(new Result(tableName, new DataTable(tableName)));
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| 232 | }
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| 233 | DataTable table = (DataTable)results[tableName].Value;
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| 234 | AddValue(table, minValue, "Min. " + valueName, string.Empty);
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| 235 | AddValue(table, avgValue, "Avg. " + valueName, string.Empty);
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| 236 | AddValue(table, maxValue, "Max. " + valueName, string.Empty);
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[3681] | 237 | }
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| 238 |
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[3996] | 239 | private static void AddValue(DataTable table, double data, string name, string description) {
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| 240 | DataRow row;
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| 241 | table.Rows.TryGetValue(name, out row);
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| 242 | if (row == null) {
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| 243 | row = new DataRow(name, description);
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| 244 | row.Values.Add(data);
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| 245 | table.Rows.Add(row);
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| 246 | } else {
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| 247 | row.Values.Add(data);
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| 248 | }
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[3681] | 249 | }
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| 250 |
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[3996] | 251 |
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| 252 | private static void SetResultValue(ResultCollection results, string name, double value) {
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| 253 | if (results.ContainsKey(name))
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| 254 | results[name].Value = new DoubleValue(value);
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| 255 | else
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| 256 | results.Add(new Result(name, new DoubleValue(value)));
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[3681] | 257 | }
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[3652] | 258 | }
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| 259 | }
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