1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2019 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 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 HEAL.Attic;
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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 collects the training Pareto-best symbolic classificatino solutions for single objective symbolic classificatino problems.
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31 | /// </summary>
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32 | [Item("SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer", "An operator that collects the training Pareto-best symbolic classification solutions for single objective symbolic classification problems.")]
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33 | [StorableType("881573CA-7246-4203-B5C9-10793325A008")]
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34 | public sealed class SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingParetoBestSolutionAnalyzer<IClassificationProblemData, ISymbolicClassificationSolution>, ISymbolicClassificationModelCreatorOperator {
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35 | private const string ModelCreatorParameterName = "ModelCreator";
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36 | #region parameter properties
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37 | public IValueLookupParameter<ISymbolicClassificationModelCreator> ModelCreatorParameter {
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38 | get { return (IValueLookupParameter<ISymbolicClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
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39 | }
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40 | ILookupParameter<ISymbolicClassificationModelCreator> ISymbolicClassificationModelCreatorOperator.ModelCreatorParameter {
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41 | get { return ModelCreatorParameter; }
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42 | }
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43 | #endregion
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44 |
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45 | [StorableConstructor]
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46 | private SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer(StorableConstructorFlag _) : base(_) { }
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47 | private SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer(SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
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48 | public SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer()
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49 | : base() {
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50 | Parameters.Add(new ValueLookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, ""));
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51 | }
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52 | public override IDeepCloneable Clone(Cloner cloner) {
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53 | return new SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer(this, cloner);
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54 | }
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55 |
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56 | [StorableHook(HookType.AfterDeserialization)]
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57 | private void AfterDeserialization() {
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58 | // BackwardsCompatibility3.4
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59 | #region Backwards compatible code, remove with 3.5
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60 | if (!Parameters.ContainsKey(ModelCreatorParameterName))
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61 | Parameters.Add(new ValueLookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, ""));
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62 | #endregion
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63 | }
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64 |
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65 | protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree) {
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66 | var model = ModelCreatorParameter.ActualValue.CreateSymbolicClassificationModel(ProblemDataParameter.ActualValue.TargetVariable, (ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
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67 | if (ApplyLinearScalingParameter.ActualValue.Value) model.Scale(ProblemDataParameter.ActualValue);
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68 |
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69 | model.RecalculateModelParameters(ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TrainingIndices);
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70 | return model.CreateClassificationSolution((IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
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71 | }
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72 | }
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73 | }
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