[16641] | 1 | #region License Information
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[5607] | 2 | /* HeuristicLab
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[16641] | 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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[16735] | 22 | using System;
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[8723] | 23 | using System.Linq;
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[16713] | 24 | using HEAL.Attic;
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[5607] | 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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[11332] | 28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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[5914] | 29 | using HeuristicLab.Optimization;
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[16556] | 30 | using HeuristicLab.Problems.DataAnalysis.Implementation;
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[5607] | 31 |
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[5624] | 32 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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[5607] | 33 | /// <summary>
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| 34 | /// Represents a symbolic regression solution (model + data) and attributes of the solution like accuracy and complexity
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| 35 | /// </summary>
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[16641] | 36 | [StorableType("88E56AF9-AD72-47E4-A613-8875703BD927")]
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[5607] | 37 | [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] | 38 | public sealed class SymbolicRegressionSolution : RegressionSolution, ISymbolicRegressionSolution {
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[5975] | 39 | private const string ModelLengthResultName = "Model Length";
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| 40 | private const string ModelDepthResultName = "Model Depth";
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[5736] | 41 |
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[8723] | 42 | private const string EstimationLimitsResultsResultName = "Estimation Limits Results";
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| 43 | private const string EstimationLimitsResultName = "Estimation Limits";
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| 44 | private const string TrainingUpperEstimationLimitHitsResultName = "Training Upper Estimation Limit Hits";
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| 45 | private const string TestLowerEstimationLimitHitsResultName = "Test Lower Estimation Limit Hits";
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| 46 | private const string TrainingLowerEstimationLimitHitsResultName = "Training Lower Estimation Limit Hits";
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| 47 | private const string TestUpperEstimationLimitHitsResultName = "Test Upper Estimation Limit Hits";
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| 48 | private const string TrainingNaNEvaluationsResultName = "Training NaN Evaluations";
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| 49 | private const string TestNaNEvaluationsResultName = "Test NaN Evaluations";
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| 50 |
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[16589] | 51 | private const string IntervalEvaluationResultName = "Interval Evaluation";
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[16556] | 52 | private const string EstimatedDerivationInterval = "Interval";
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| 53 |
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[5624] | 54 | public new ISymbolicRegressionModel Model {
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| 55 | get { return (ISymbolicRegressionModel)base.Model; }
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[5717] | 56 | set { base.Model = value; }
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[5607] | 57 | }
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[5624] | 58 | ISymbolicDataAnalysisModel ISymbolicDataAnalysisSolution.Model {
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| 59 | get { return (ISymbolicDataAnalysisModel)base.Model; }
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[5607] | 60 | }
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[5736] | 61 | public int ModelLength {
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| 62 | get { return ((IntValue)this[ModelLengthResultName].Value).Value; }
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| 63 | private set { ((IntValue)this[ModelLengthResultName].Value).Value = value; }
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| 64 | }
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[5607] | 65 |
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[5736] | 66 | public int ModelDepth {
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| 67 | get { return ((IntValue)this[ModelDepthResultName].Value).Value; }
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| 68 | private set { ((IntValue)this[ModelDepthResultName].Value).Value = value; }
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| 69 | }
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| 70 |
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[8723] | 71 | private ResultCollection EstimationLimitsResultCollection {
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| 72 | get { return (ResultCollection)this[EstimationLimitsResultsResultName].Value; }
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| 73 | }
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| 74 | public DoubleLimit EstimationLimits {
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| 75 | get { return (DoubleLimit)EstimationLimitsResultCollection[EstimationLimitsResultName].Value; }
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| 76 | }
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| 77 |
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| 78 | public int TrainingUpperEstimationLimitHits {
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| 79 | get { return ((IntValue)EstimationLimitsResultCollection[TrainingUpperEstimationLimitHitsResultName].Value).Value; }
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| 80 | private set { ((IntValue)EstimationLimitsResultCollection[TrainingUpperEstimationLimitHitsResultName].Value).Value = value; }
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| 81 | }
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| 82 | public int TestUpperEstimationLimitHits {
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| 83 | get { return ((IntValue)EstimationLimitsResultCollection[TestUpperEstimationLimitHitsResultName].Value).Value; }
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| 84 | private set { ((IntValue)EstimationLimitsResultCollection[TestUpperEstimationLimitHitsResultName].Value).Value = value; }
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| 85 | }
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| 86 | public int TrainingLowerEstimationLimitHits {
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| 87 | get { return ((IntValue)EstimationLimitsResultCollection[TrainingLowerEstimationLimitHitsResultName].Value).Value; }
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| 88 | private set { ((IntValue)EstimationLimitsResultCollection[TrainingLowerEstimationLimitHitsResultName].Value).Value = value; }
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| 89 | }
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| 90 | public int TestLowerEstimationLimitHits {
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| 91 | get { return ((IntValue)EstimationLimitsResultCollection[TestLowerEstimationLimitHitsResultName].Value).Value; }
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| 92 | private set { ((IntValue)EstimationLimitsResultCollection[TestLowerEstimationLimitHitsResultName].Value).Value = value; }
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| 93 | }
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| 94 | public int TrainingNaNEvaluations {
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| 95 | get { return ((IntValue)EstimationLimitsResultCollection[TrainingNaNEvaluationsResultName].Value).Value; }
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| 96 | private set { ((IntValue)EstimationLimitsResultCollection[TrainingNaNEvaluationsResultName].Value).Value = value; }
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| 97 | }
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| 98 | public int TestNaNEvaluations {
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| 99 | get { return ((IntValue)EstimationLimitsResultCollection[TestNaNEvaluationsResultName].Value).Value; }
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| 100 | private set { ((IntValue)EstimationLimitsResultCollection[TestNaNEvaluationsResultName].Value).Value = value; }
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| 101 | }
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| 102 |
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[16556] | 103 |
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[16627] | 104 | public NamedIntervals IntervalEvaluationCollection {
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| 105 | get { return (NamedIntervals)this[IntervalEvaluationResultName].Value; }
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| 106 | private set { this[IntervalEvaluationResultName].Value = value; }
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| 107 | }
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[16556] | 108 |
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| 109 |
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[16627] | 110 |
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[5607] | 111 | [StorableConstructor]
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[16627] | 112 | private SymbolicRegressionSolution(StorableConstructorFlag _) : base(_) { }
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[5717] | 113 | private SymbolicRegressionSolution(SymbolicRegressionSolution original, Cloner cloner)
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[5607] | 114 | : base(original, cloner) {
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| 115 | }
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[5624] | 116 | public SymbolicRegressionSolution(ISymbolicRegressionModel model, IRegressionProblemData problemData)
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| 117 | : base(model, problemData) {
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[11332] | 118 | foreach (var node in model.SymbolicExpressionTree.Root.IterateNodesPrefix().OfType<SymbolicExpressionTreeTopLevelNode>())
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| 119 | node.SetGrammar(null);
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| 120 |
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[5736] | 121 | Add(new Result(ModelLengthResultName, "Length of the symbolic regression model.", new IntValue()));
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| 122 | Add(new Result(ModelDepthResultName, "Depth of the symbolic regression model.", new IntValue()));
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[8723] | 123 |
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| 124 | ResultCollection estimationLimitResults = new ResultCollection();
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| 125 | estimationLimitResults.Add(new Result(EstimationLimitsResultName, "", new DoubleLimit()));
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| 126 | estimationLimitResults.Add(new Result(TrainingUpperEstimationLimitHitsResultName, "", new IntValue()));
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| 127 | estimationLimitResults.Add(new Result(TestUpperEstimationLimitHitsResultName, "", new IntValue()));
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| 128 | estimationLimitResults.Add(new Result(TrainingLowerEstimationLimitHitsResultName, "", new IntValue()));
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| 129 | estimationLimitResults.Add(new Result(TestLowerEstimationLimitHitsResultName, "", new IntValue()));
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| 130 | estimationLimitResults.Add(new Result(TrainingNaNEvaluationsResultName, "", new IntValue()));
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| 131 | estimationLimitResults.Add(new Result(TestNaNEvaluationsResultName, "", new IntValue()));
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| 132 | Add(new Result(EstimationLimitsResultsResultName, "Results concerning the estimation limits of symbolic regression solution", estimationLimitResults));
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[16635] | 133 | Add(new Result(IntervalEvaluationResultName, "Results concerning the derivation of symbolic regression solution", GetIntervalEvaluations()));
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[6588] | 134 | RecalculateResults();
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[16556] | 135 |
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[16627] | 136 | ;
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[5607] | 137 | }
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| 138 |
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| 139 | public override IDeepCloneable Clone(Cloner cloner) {
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| 140 | return new SymbolicRegressionSolution(this, cloner);
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| 141 | }
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[5729] | 142 |
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[8723] | 143 | [StorableHook(HookType.AfterDeserialization)]
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| 144 | private void AfterDeserialization() {
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| 145 | if (!ContainsKey(EstimationLimitsResultsResultName)) {
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| 146 | ResultCollection estimationLimitResults = new ResultCollection();
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| 147 | estimationLimitResults.Add(new Result(EstimationLimitsResultName, "", new DoubleLimit()));
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| 148 | estimationLimitResults.Add(new Result(TrainingUpperEstimationLimitHitsResultName, "", new IntValue()));
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| 149 | estimationLimitResults.Add(new Result(TestUpperEstimationLimitHitsResultName, "", new IntValue()));
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| 150 | estimationLimitResults.Add(new Result(TrainingLowerEstimationLimitHitsResultName, "", new IntValue()));
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| 151 | estimationLimitResults.Add(new Result(TestLowerEstimationLimitHitsResultName, "", new IntValue()));
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| 152 | estimationLimitResults.Add(new Result(TrainingNaNEvaluationsResultName, "", new IntValue()));
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| 153 | estimationLimitResults.Add(new Result(TestNaNEvaluationsResultName, "", new IntValue()));
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| 154 | Add(new Result(EstimationLimitsResultsResultName, "Results concerning the estimation limits of symbolic regression solution", estimationLimitResults));
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| 155 | CalculateResults();
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| 156 | }
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| 157 | }
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| 158 |
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[6411] | 159 | protected override void RecalculateResults() {
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[6602] | 160 | base.RecalculateResults();
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[8723] | 161 | CalculateResults();
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| 162 | }
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| 163 |
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[16627] | 164 | private NamedIntervals GetIntervalEvaluations() {
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| 165 | var intervalEvaluation = new NamedIntervals();
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[16556] | 166 | var interpreter = new IntervalInterpreter();
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[16589] | 167 | var variableRanges = (ProblemData as RegressionProblemData)?.VariableRangesParameter.Value.VariableIntervals;
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[16556] | 168 |
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[16589] | 169 | if (variableRanges != null) {
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| 170 | intervalEvaluation.Add($"Target {ProblemData.TargetVariable}", new Interval(variableRanges[ProblemData.TargetVariable].LowerBound, variableRanges[ProblemData.TargetVariable].UpperBound));
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[16713] | 171 | intervalEvaluation.Add("Modell Interval", interpreter.GetSymbolicExpressionTreeInterval(Model.SymbolicExpressionTree, variableRanges));
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[16735] | 172 |
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[16589] | 173 | foreach (var derivate in variableRanges) {
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| 174 | if (derivate.Key != ProblemData.TargetVariable) {
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| 175 | var derived = DerivativeCalculator.Derive(Model.SymbolicExpressionTree, derivate.Key);
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[16713] | 176 | var derivedResultInterval = interpreter.GetSymbolicExpressionTreeInterval(derived, variableRanges);
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[16735] | 177 |
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| 178 | intervalEvaluation.Add(" \u2202f/\u2202" + derivate.Key,
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[16589] | 179 | new Interval(derivedResultInterval.LowerBound, derivedResultInterval.UpperBound));
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| 180 | }
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[16556] | 181 | }
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| 182 | }
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[16627] | 183 | return intervalEvaluation;
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[16556] | 184 | }
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[16635] | 185 |
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[8723] | 186 | private void CalculateResults() {
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[5736] | 187 | ModelLength = Model.SymbolicExpressionTree.Length;
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| 188 | ModelDepth = Model.SymbolicExpressionTree.Depth;
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[8723] | 189 |
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| 190 | EstimationLimits.Lower = Model.LowerEstimationLimit;
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| 191 | EstimationLimits.Upper = Model.UpperEstimationLimit;
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| 192 |
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| 193 | TrainingUpperEstimationLimitHits = EstimatedTrainingValues.Count(x => x.IsAlmost(Model.UpperEstimationLimit));
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| 194 | TestUpperEstimationLimitHits = EstimatedTestValues.Count(x => x.IsAlmost(Model.UpperEstimationLimit));
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| 195 | TrainingLowerEstimationLimitHits = EstimatedTrainingValues.Count(x => x.IsAlmost(Model.LowerEstimationLimit));
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| 196 | TestLowerEstimationLimitHits = EstimatedTestValues.Count(x => x.IsAlmost(Model.LowerEstimationLimit));
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| 197 | TrainingNaNEvaluations = Model.Interpreter.GetSymbolicExpressionTreeValues(Model.SymbolicExpressionTree, ProblemData.Dataset, ProblemData.TrainingIndices).Count(double.IsNaN);
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| 198 | TestNaNEvaluations = Model.Interpreter.GetSymbolicExpressionTreeValues(Model.SymbolicExpressionTree, ProblemData.Dataset, ProblemData.TestIndices).Count(double.IsNaN);
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[16627] | 199 |
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| 200 | IntervalEvaluationCollection = GetIntervalEvaluations();
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[5736] | 201 | }
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[5607] | 202 | }
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| 203 | }
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