[5557] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[16453] | 3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[5557] | 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 HeuristicLab.Common;
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| 23 | using HeuristicLab.Core;
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| 24 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 25 | using HeuristicLab.Parameters;
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| 26 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 27 |
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| 28 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
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| 29 | /// <summary>
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| 30 | /// An operator that analyzes the training best symbolic classification solution for single objective symbolic classification problems.
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| 31 | /// </summary>
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| 32 | [Item("SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic classification solution for single objective symbolic classification problems.")]
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| 33 | [StorableClass]
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[5649] | 34 | public sealed class SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingBestSolutionAnalyzer<ISymbolicClassificationSolution>,
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[8594] | 35 | ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator, ISymbolicClassificationModelCreatorOperator {
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[5649] | 36 | private const string ProblemDataParameterName = "ProblemData";
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[8594] | 37 | private const string ModelCreatorParameterName = "ModelCreator";
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[5649] | 38 | private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
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[5770] | 39 | private const string EstimationLimitsParameterName = "UpperEstimationLimit";
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[5649] | 40 | #region parameter properties
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| 41 | public ILookupParameter<IClassificationProblemData> ProblemDataParameter {
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| 42 | get { return (ILookupParameter<IClassificationProblemData>)Parameters[ProblemDataParameterName]; }
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| 43 | }
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[8594] | 44 | public IValueLookupParameter<ISymbolicClassificationModelCreator> ModelCreatorParameter {
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| 45 | get { return (IValueLookupParameter<ISymbolicClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
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| 46 | }
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| 47 | ILookupParameter<ISymbolicClassificationModelCreator> ISymbolicClassificationModelCreatorOperator.ModelCreatorParameter {
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| 48 | get { return ModelCreatorParameter; }
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| 49 | }
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[5649] | 50 | public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
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| 51 | get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
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| 52 | }
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[5770] | 53 | public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
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| 54 | get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
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[5720] | 55 | }
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[5649] | 56 | #endregion
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[5720] | 57 |
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[8664] | 58 |
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[5557] | 59 | [StorableConstructor]
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| 60 | private SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
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| 61 | private SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer(SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
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| 62 | public SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer()
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| 63 | : base() {
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[5685] | 64 | Parameters.Add(new LookupParameter<IClassificationProblemData>(ProblemDataParameterName, "The problem data for the symbolic classification solution."));
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[8594] | 65 | Parameters.Add(new ValueLookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, ""));
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[5685] | 66 | Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
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[5770] | 67 | Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic classification model."));
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[5557] | 68 | }
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[8594] | 69 |
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[5557] | 70 | public override IDeepCloneable Clone(Cloner cloner) {
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| 71 | return new SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer(this, cloner);
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| 72 | }
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[8594] | 73 | [StorableHook(HookType.AfterDeserialization)]
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| 74 | private void AfterDeserialization() {
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[8883] | 75 | // BackwardsCompatibility3.4
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| 76 | #region Backwards compatible code, remove with 3.5
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[8594] | 77 | if (!Parameters.ContainsKey(ModelCreatorParameterName))
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| 78 | Parameters.Add(new ValueLookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, ""));
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[8883] | 79 | #endregion
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[8594] | 80 | }
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[5557] | 81 |
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| 82 | protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
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[13941] | 83 | var model = ModelCreatorParameter.ActualValue.CreateSymbolicClassificationModel(ProblemDataParameter.ActualValue.TargetVariable, (ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
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[8972] | 84 | if (ApplyLinearScalingParameter.ActualValue.Value) model.Scale(ProblemDataParameter.ActualValue);
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[8531] | 85 |
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[8594] | 86 | model.RecalculateModelParameters(ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TrainingIndices);
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| 87 | return model.CreateClassificationSolution((IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
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[5557] | 88 | }
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| 89 | }
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| 90 | }
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