| 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.Core;
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| 26 | using HeuristicLab.Data;
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| 27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 28 | using HeuristicLab.Operators;
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| 29 | using HeuristicLab.Optimization;
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| 30 | using HeuristicLab.Parameters;
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| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 32 | using HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators;
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| 33 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 34 |
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| 35 | namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Analyzers {
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| 36 | /// <summary>
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| 37 | /// An operator that analyzes the validation best scaled symbolic vector regression solution.
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| 38 | /// </summary>
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| 39 | [Item("ValidationBestScaledSymbolicVectorRegressionSolutionAnalyzer", "An operator that analyzes the validation best scaled symbolic vector regression solution.")]
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| 40 | [StorableClass]
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| 41 | public sealed class ValidationBestScaledSymbolicVectorRegressionSolutionAnalyzer : SingleSuccessorOperator, IAnalyzer {
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| 42 | private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
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| 43 | private const string ScaledSymbolicExpressionTreeParameterName = "ScaledSymbolicExpressionTree";
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| 44 | private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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| 45 | private const string ProblemDataParameterName = "ProblemData";
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| 46 | private const string TrainingSamplesStartParameterName = "TrainingSamplesStart";
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| 47 | private const string TrainingSamplesEndParameterName = "TrainingSamplesEnd";
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| 48 | private const string ValidationSamplesStartParameterName = "ValidationSamplesStart";
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| 49 | private const string ValidationSamplesEndParameterName = "ValidationSamplesEnd";
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| 50 | private const string TestSamplesStartParameterName = "TestSamplesStart";
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| 51 | private const string TestSamplesEndParameterName = "TestSamplesEnd";
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| 52 | private const string QualityParameterName = "Quality";
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| 53 | private const string ScaledQualityParameterName = "ScaledQuality";
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| 54 | private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
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| 55 | private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
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| 56 | private const string AlphaParameterName = "Alpha";
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| 57 | private const string BetaParameterName = "Beta";
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| 58 | private const string BestSolutionParameterName = "Best solution (validation)";
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| 59 | private const string BestSolutionQualityParameterName = "Best solution quality (validation)";
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| 60 | private const string CurrentBestValidationQualityParameterName = "Current best validation quality";
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| 61 | private const string BestSolutionQualityValuesParameterName = "Validation Quality";
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| 62 | private const string ResultsParameterName = "Results";
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| 63 | private const string BestKnownQualityParameterName = "BestKnownQuality";
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| 64 |
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| 65 | #region parameter properties
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| 66 | public ScopeTreeLookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
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| 67 | get { return (ScopeTreeLookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
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| 68 | }
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| 69 | public ScopeTreeLookupParameter<DoubleArray> AlphaParameter {
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| 70 | get { return (ScopeTreeLookupParameter<DoubleArray>)Parameters[AlphaParameterName]; }
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| 71 | }
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| 72 | public ScopeTreeLookupParameter<DoubleArray> BetaParameter {
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| 73 | get { return (ScopeTreeLookupParameter<DoubleArray>)Parameters[BetaParameterName]; }
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| 74 | }
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| 75 | public IValueLookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
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| 76 | get { return (IValueLookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
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| 77 | }
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| 78 | public IValueLookupParameter<MultiVariateDataAnalysisProblemData> ProblemDataParameter {
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| 79 | get { return (IValueLookupParameter<MultiVariateDataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
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| 80 | }
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| 81 | public IValueLookupParameter<IntValue> ValidationSamplesStartParameter {
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| 82 | get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesStartParameterName]; }
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| 83 | }
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| 84 | public IValueLookupParameter<IntValue> ValidationSamplesEndParameter {
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| 85 | get { return (IValueLookupParameter<IntValue>)Parameters[ValidationSamplesEndParameterName]; }
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| 86 | }
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| 87 | public IValueLookupParameter<DoubleArray> UpperEstimationLimitParameter {
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| 88 | get { return (IValueLookupParameter<DoubleArray>)Parameters[UpperEstimationLimitParameterName]; }
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| 89 | }
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| 90 | public IValueLookupParameter<DoubleArray> LowerEstimationLimitParameter {
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| 91 | get { return (IValueLookupParameter<DoubleArray>)Parameters[LowerEstimationLimitParameterName]; }
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| 92 | }
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| 93 | public ILookupParameter<SymbolicExpressionTree> BestSolutionParameter {
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| 94 | get { return (ILookupParameter<SymbolicExpressionTree>)Parameters[BestSolutionParameterName]; }
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| 95 | }
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| 96 | public ILookupParameter<DoubleValue> BestSolutionQualityParameter {
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| 97 | get { return (ILookupParameter<DoubleValue>)Parameters[BestSolutionQualityParameterName]; }
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| 98 | }
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| 99 | public ILookupParameter<ResultCollection> ResultsParameter {
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| 100 | get { return (ILookupParameter<ResultCollection>)Parameters[ResultsParameterName]; }
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| 101 | }
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| 102 | public ILookupParameter<DoubleValue> BestKnownQualityParameter {
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| 103 | get { return (ILookupParameter<DoubleValue>)Parameters[BestKnownQualityParameterName]; }
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| 104 | }
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| 105 | #endregion
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| 106 | #region properties
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| 107 | public MultiVariateDataAnalysisProblemData ProblemData {
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| 108 | get { return ProblemDataParameter.ActualValue; }
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| 109 | }
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| 110 | public ItemArray<DoubleArray> Alpha {
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| 111 | get { return AlphaParameter.ActualValue; }
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| 112 | }
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| 113 | public ItemArray<DoubleArray> Beta {
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| 114 | get { return BetaParameter.ActualValue; }
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| 115 | }
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| 116 | public DoubleArray LowerEstimationLimit {
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| 117 | get { return LowerEstimationLimitParameter.ActualValue; }
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| 118 | }
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| 119 | public DoubleArray UpperEstimationLimit {
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| 120 | get { return UpperEstimationLimitParameter.ActualValue; }
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| 121 | }
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| 122 | #endregion
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| 123 |
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| 124 | public ValidationBestScaledSymbolicVectorRegressionSolutionAnalyzer()
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| 125 | : base() {
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| 126 | Parameters.Add(new ScopeTreeLookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to analyze."));
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| 127 | Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>(AlphaParameterName, "The alpha parameter for linear scaling."));
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| 128 | Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>(BetaParameterName, "The beta parameter for linear scaling."));
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| 129 | Parameters.Add(new ValueLookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used for the analysis of symbolic expression trees."));
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| 130 | Parameters.Add(new ValueLookupParameter<MultiVariateDataAnalysisProblemData>(ProblemDataParameterName, "The problem data for which the symbolic expression tree is a solution."));
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| 131 | Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesStartParameterName, "The first index of the validation partition of the data set."));
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| 132 | Parameters.Add(new ValueLookupParameter<IntValue>(ValidationSamplesEndParameterName, "The last index of the validation partition of the data set."));
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| 133 | Parameters.Add(new ValueLookupParameter<DoubleArray>(UpperEstimationLimitParameterName, "The upper estimation limit that was set for the evaluation of the symbolic expression trees."));
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| 134 | Parameters.Add(new ValueLookupParameter<DoubleArray>(LowerEstimationLimitParameterName, "The lower estimation limit that was set for the evaluation of the symbolic expression trees."));
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| 135 | Parameters.Add(new LookupParameter<SymbolicExpressionTree>(BestSolutionParameterName, "The best symbolic regression solution."));
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| 136 | Parameters.Add(new LookupParameter<DoubleValue>(BestSolutionQualityParameterName, "The quality of the best symbolic regression solution."));
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| 137 | Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The result collection where the best symbolic regression solution should be stored."));
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| 138 | Parameters.Add(new LookupParameter<DoubleValue>(BestKnownQualityParameterName, "The best known (validation) quality achieved on the data set."));
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| 139 |
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| 140 | }
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| 141 |
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| 142 | public override IOperation Apply() {
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| 143 | var trees = SymbolicExpressionTreeParameter.ActualValue;
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| 144 | IEnumerable<SymbolicExpressionTree> scaledTrees;
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| 145 | if (Alpha.Length == trees.Length) {
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| 146 | scaledTrees = from i in Enumerable.Range(0, trees.Length)
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| 147 | select SymbolicVectorRegressionSolutionLinearScaler.Scale(trees[i], Beta[i].ToArray(), Alpha[i].ToArray());
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| 148 | } else {
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| 149 | scaledTrees = trees;
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| 150 | }
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| 151 | IEnumerable<string> selectedTargetVariables = from item in ProblemData.TargetVariables.CheckedItems
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| 152 | select item.Value.Value;
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| 153 | var interpreter = SymbolicExpressionTreeInterpreterParameter.ActualValue;
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| 154 | int validationStart = ValidationSamplesStartParameter.ActualValue.Value;
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| 155 | int validationEnd = ValidationSamplesEndParameter.ActualValue.Value;
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| 156 | IEnumerable<int> rows = Enumerable.Range(validationStart, validationEnd - validationStart);
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| 157 | SymbolicExpressionTree bestTree = null;
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| 158 | double bestValidationError = double.PositiveInfinity;
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| 159 | foreach (var tree in scaledTrees) {
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| 160 | // calculate error on validation set
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| 161 | double validationMse = SymbolicVectorRegressionNormalizedMseEvaluator.Calculate(tree, interpreter, ProblemData, selectedTargetVariables, rows, LowerEstimationLimit, UpperEstimationLimit);
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| 162 | if (bestValidationError > validationMse) {
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| 163 | bestValidationError = validationMse;
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| 164 | bestTree = tree;
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| 165 | }
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| 166 | }
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| 167 | if (BestSolutionQualityParameter.ActualValue == null || BestSolutionQualityParameter.ActualValue.Value > bestValidationError) {
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| 168 | var bestSolution = bestTree;
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| 169 |
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| 170 | //bestSolution.Name = BestSolutionParameterName;
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| 171 | //solution.Description = "Best solution on validation partition found over the whole run.";
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| 172 |
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| 173 | BestSolutionParameter.ActualValue = bestSolution;
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| 174 | BestSolutionQualityParameter.ActualValue = new DoubleValue(bestValidationError);
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| 175 | }
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| 176 |
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| 177 | // update results
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| 178 | var results = ResultsParameter.ActualValue;
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| 179 | if (!results.ContainsKey(BestSolutionQualityValuesParameterName)) {
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| 180 | results.Add(new Result(BestSolutionParameterName, BestSolutionParameter.ActualValue));
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| 181 | results.Add(new Result(BestSolutionQualityValuesParameterName, new DataTable(BestSolutionQualityValuesParameterName, BestSolutionQualityValuesParameterName)));
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| 182 | results.Add(new Result(BestSolutionQualityParameterName, new DoubleValue()));
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| 183 | results.Add(new Result(CurrentBestValidationQualityParameterName, new DoubleValue()));
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| 184 | }
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| 185 | results[BestSolutionParameterName].Value = BestSolutionParameter.ActualValue;
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| 186 | results[BestSolutionQualityParameterName].Value = new DoubleValue(BestSolutionQualityParameter.ActualValue.Value);
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| 187 | results[CurrentBestValidationQualityParameterName].Value = new DoubleValue(bestValidationError);
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| 188 |
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| 189 | DataTable validationValues = (DataTable)results[BestSolutionQualityValuesParameterName].Value;
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| 190 | AddValue(validationValues, BestSolutionQualityParameter.ActualValue.Value, BestSolutionQualityParameterName, BestSolutionQualityParameterName);
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| 191 | AddValue(validationValues, bestValidationError, CurrentBestValidationQualityParameterName, CurrentBestValidationQualityParameterName);
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| 192 |
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| 193 | return base.Apply();
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| 194 | }
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| 195 |
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| 196 | private static void AddValue(DataTable table, double data, string name, string description) {
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| 197 | DataRow row;
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| 198 | table.Rows.TryGetValue(name, out row);
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| 199 | if (row == null) {
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| 200 | row = new DataRow(name, description);
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| 201 | row.Values.Add(data);
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| 202 | table.Rows.Add(row);
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| 203 | } else {
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| 204 | row.Values.Add(data);
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| 205 | }
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| 206 | }
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| 207 | }
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| 208 | }
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