[5607] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[16720] | 3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[5607] | 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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[8723] | 22 | using System.Linq;
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[5607] | 23 | using HeuristicLab.Common;
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| 24 | using HeuristicLab.Core;
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| 25 | using HeuristicLab.Data;
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[11332] | 26 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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[5914] | 27 | using HeuristicLab.Optimization;
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[16720] | 28 | using HEAL.Attic;
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[5607] | 29 |
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[5624] | 30 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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[5607] | 31 | /// <summary>
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| 32 | /// Represents a symbolic regression solution (model + data) and attributes of the solution like accuracy and complexity
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| 33 | /// </summary>
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[16720] | 34 | [StorableType("88E56AF9-AD72-47E4-A613-8875703BD927")]
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[5607] | 35 | [Item(Name = "SymbolicRegressionSolution", Description = "Represents a symbolic regression solution (model + data) and attributes of the solution like accuracy and complexity.")]
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[5717] | 36 | public sealed class SymbolicRegressionSolution : RegressionSolution, ISymbolicRegressionSolution {
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[5975] | 37 | private const string ModelLengthResultName = "Model Length";
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| 38 | private const string ModelDepthResultName = "Model Depth";
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[5736] | 39 |
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[8723] | 40 | private const string EstimationLimitsResultsResultName = "Estimation Limits Results";
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| 41 | private const string EstimationLimitsResultName = "Estimation Limits";
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| 42 | private const string TrainingUpperEstimationLimitHitsResultName = "Training Upper Estimation Limit Hits";
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| 43 | private const string TestLowerEstimationLimitHitsResultName = "Test Lower Estimation Limit Hits";
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| 44 | private const string TrainingLowerEstimationLimitHitsResultName = "Training Lower Estimation Limit Hits";
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| 45 | private const string TestUpperEstimationLimitHitsResultName = "Test Upper Estimation Limit Hits";
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| 46 | private const string TrainingNaNEvaluationsResultName = "Training NaN Evaluations";
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| 47 | private const string TestNaNEvaluationsResultName = "Test NaN Evaluations";
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| 48 |
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[5624] | 49 | public new ISymbolicRegressionModel Model {
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| 50 | get { return (ISymbolicRegressionModel)base.Model; }
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[5717] | 51 | set { base.Model = value; }
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[5607] | 52 | }
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[5624] | 53 | ISymbolicDataAnalysisModel ISymbolicDataAnalysisSolution.Model {
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| 54 | get { return (ISymbolicDataAnalysisModel)base.Model; }
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[5607] | 55 | }
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[5736] | 56 | public int ModelLength {
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| 57 | get { return ((IntValue)this[ModelLengthResultName].Value).Value; }
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| 58 | private set { ((IntValue)this[ModelLengthResultName].Value).Value = value; }
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| 59 | }
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[5607] | 60 |
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[5736] | 61 | public int ModelDepth {
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| 62 | get { return ((IntValue)this[ModelDepthResultName].Value).Value; }
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| 63 | private set { ((IntValue)this[ModelDepthResultName].Value).Value = value; }
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| 64 | }
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| 65 |
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[8723] | 66 | private ResultCollection EstimationLimitsResultCollection {
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| 67 | get { return (ResultCollection)this[EstimationLimitsResultsResultName].Value; }
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| 68 | }
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| 69 | public DoubleLimit EstimationLimits {
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| 70 | get { return (DoubleLimit)EstimationLimitsResultCollection[EstimationLimitsResultName].Value; }
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| 71 | }
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| 72 |
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| 73 | public int TrainingUpperEstimationLimitHits {
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| 74 | get { return ((IntValue)EstimationLimitsResultCollection[TrainingUpperEstimationLimitHitsResultName].Value).Value; }
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| 75 | private set { ((IntValue)EstimationLimitsResultCollection[TrainingUpperEstimationLimitHitsResultName].Value).Value = value; }
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| 76 | }
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| 77 | public int TestUpperEstimationLimitHits {
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| 78 | get { return ((IntValue)EstimationLimitsResultCollection[TestUpperEstimationLimitHitsResultName].Value).Value; }
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| 79 | private set { ((IntValue)EstimationLimitsResultCollection[TestUpperEstimationLimitHitsResultName].Value).Value = value; }
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| 80 | }
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| 81 | public int TrainingLowerEstimationLimitHits {
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| 82 | get { return ((IntValue)EstimationLimitsResultCollection[TrainingLowerEstimationLimitHitsResultName].Value).Value; }
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| 83 | private set { ((IntValue)EstimationLimitsResultCollection[TrainingLowerEstimationLimitHitsResultName].Value).Value = value; }
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| 84 | }
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| 85 | public int TestLowerEstimationLimitHits {
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| 86 | get { return ((IntValue)EstimationLimitsResultCollection[TestLowerEstimationLimitHitsResultName].Value).Value; }
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| 87 | private set { ((IntValue)EstimationLimitsResultCollection[TestLowerEstimationLimitHitsResultName].Value).Value = value; }
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| 88 | }
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| 89 | public int TrainingNaNEvaluations {
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| 90 | get { return ((IntValue)EstimationLimitsResultCollection[TrainingNaNEvaluationsResultName].Value).Value; }
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| 91 | private set { ((IntValue)EstimationLimitsResultCollection[TrainingNaNEvaluationsResultName].Value).Value = value; }
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| 92 | }
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| 93 | public int TestNaNEvaluations {
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| 94 | get { return ((IntValue)EstimationLimitsResultCollection[TestNaNEvaluationsResultName].Value).Value; }
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| 95 | private set { ((IntValue)EstimationLimitsResultCollection[TestNaNEvaluationsResultName].Value).Value = value; }
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| 96 | }
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| 97 |
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[5607] | 98 | [StorableConstructor]
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[16720] | 99 | private SymbolicRegressionSolution(StorableConstructorFlag _) : base(_) { }
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[5717] | 100 | private SymbolicRegressionSolution(SymbolicRegressionSolution original, Cloner cloner)
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[5607] | 101 | : base(original, cloner) {
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| 102 | }
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[5624] | 103 | public SymbolicRegressionSolution(ISymbolicRegressionModel model, IRegressionProblemData problemData)
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| 104 | : base(model, problemData) {
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[11332] | 105 | foreach (var node in model.SymbolicExpressionTree.Root.IterateNodesPrefix().OfType<SymbolicExpressionTreeTopLevelNode>())
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| 106 | node.SetGrammar(null);
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| 107 |
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[5736] | 108 | Add(new Result(ModelLengthResultName, "Length of the symbolic regression model.", new IntValue()));
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| 109 | Add(new Result(ModelDepthResultName, "Depth of the symbolic regression model.", new IntValue()));
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[8723] | 110 |
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| 111 | ResultCollection estimationLimitResults = new ResultCollection();
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| 112 | estimationLimitResults.Add(new Result(EstimationLimitsResultName, "", new DoubleLimit()));
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| 113 | estimationLimitResults.Add(new Result(TrainingUpperEstimationLimitHitsResultName, "", new IntValue()));
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| 114 | estimationLimitResults.Add(new Result(TestUpperEstimationLimitHitsResultName, "", new IntValue()));
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| 115 | estimationLimitResults.Add(new Result(TrainingLowerEstimationLimitHitsResultName, "", new IntValue()));
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| 116 | estimationLimitResults.Add(new Result(TestLowerEstimationLimitHitsResultName, "", new IntValue()));
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| 117 | estimationLimitResults.Add(new Result(TrainingNaNEvaluationsResultName, "", new IntValue()));
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| 118 | estimationLimitResults.Add(new Result(TestNaNEvaluationsResultName, "", new IntValue()));
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| 119 | Add(new Result(EstimationLimitsResultsResultName, "Results concerning the estimation limits of symbolic regression solution", estimationLimitResults));
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[6588] | 120 | RecalculateResults();
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[5607] | 121 | }
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| 122 |
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| 123 | public override IDeepCloneable Clone(Cloner cloner) {
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| 124 | return new SymbolicRegressionSolution(this, cloner);
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| 125 | }
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[5729] | 126 |
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[8723] | 127 | [StorableHook(HookType.AfterDeserialization)]
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| 128 | private void AfterDeserialization() {
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| 129 | if (!ContainsKey(EstimationLimitsResultsResultName)) {
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| 130 | ResultCollection estimationLimitResults = new ResultCollection();
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| 131 | estimationLimitResults.Add(new Result(EstimationLimitsResultName, "", new DoubleLimit()));
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| 132 | estimationLimitResults.Add(new Result(TrainingUpperEstimationLimitHitsResultName, "", new IntValue()));
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| 133 | estimationLimitResults.Add(new Result(TestUpperEstimationLimitHitsResultName, "", new IntValue()));
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| 134 | estimationLimitResults.Add(new Result(TrainingLowerEstimationLimitHitsResultName, "", new IntValue()));
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| 135 | estimationLimitResults.Add(new Result(TestLowerEstimationLimitHitsResultName, "", new IntValue()));
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| 136 | estimationLimitResults.Add(new Result(TrainingNaNEvaluationsResultName, "", new IntValue()));
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| 137 | estimationLimitResults.Add(new Result(TestNaNEvaluationsResultName, "", new IntValue()));
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| 138 | Add(new Result(EstimationLimitsResultsResultName, "Results concerning the estimation limits of symbolic regression solution", estimationLimitResults));
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| 139 | CalculateResults();
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| 140 | }
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| 141 | }
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| 142 |
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[6411] | 143 | protected override void RecalculateResults() {
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[6602] | 144 | base.RecalculateResults();
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[8723] | 145 | CalculateResults();
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| 146 | }
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| 147 |
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| 148 | private void CalculateResults() {
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[5736] | 149 | ModelLength = Model.SymbolicExpressionTree.Length;
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| 150 | ModelDepth = Model.SymbolicExpressionTree.Depth;
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[8723] | 151 |
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| 152 | EstimationLimits.Lower = Model.LowerEstimationLimit;
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| 153 | EstimationLimits.Upper = Model.UpperEstimationLimit;
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| 154 |
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| 155 | TrainingUpperEstimationLimitHits = EstimatedTrainingValues.Count(x => x.IsAlmost(Model.UpperEstimationLimit));
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| 156 | TestUpperEstimationLimitHits = EstimatedTestValues.Count(x => x.IsAlmost(Model.UpperEstimationLimit));
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| 157 | TrainingLowerEstimationLimitHits = EstimatedTrainingValues.Count(x => x.IsAlmost(Model.LowerEstimationLimit));
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| 158 | TestLowerEstimationLimitHits = EstimatedTestValues.Count(x => x.IsAlmost(Model.LowerEstimationLimit));
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| 159 | TrainingNaNEvaluations = Model.Interpreter.GetSymbolicExpressionTreeValues(Model.SymbolicExpressionTree, ProblemData.Dataset, ProblemData.TrainingIndices).Count(double.IsNaN);
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| 160 | TestNaNEvaluations = Model.Interpreter.GetSymbolicExpressionTreeValues(Model.SymbolicExpressionTree, ProblemData.Dataset, ProblemData.TestIndices).Count(double.IsNaN);
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[5736] | 161 | }
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[5607] | 162 | }
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| 163 | }
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