[10469] | 1 | using System.Linq;
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| 2 | using HeuristicLab.Common;
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| 3 | using HeuristicLab.Core;
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| 4 | using HeuristicLab.Data;
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| 5 | using HeuristicLab.Parameters;
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| 6 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 7 |
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| 8 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
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| 9 | [StorableClass]
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| 10 | [Item("SymbolicClassificationPruningOperator", "An operator which prunes symbolic classificaton trees.")]
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| 11 | public class SymbolicClassificationPruningOperator : SymbolicDataAnalysisExpressionPruningOperator {
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| 12 | private const string ImpactValuesCalculatorParameterName = "ImpactValuesCalculator";
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| 13 | private const string ModelCreatorParameterName = "ModelCreator";
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| 14 | private const string ApplyLinearScalingParmameterName = "ApplyLinearScaling";
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| 15 |
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| 16 | #region parameter properties
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| 17 | public ILookupParameter<ISymbolicClassificationModelCreator> ModelCreatorParameter {
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| 18 | get { return (ILookupParameter<ISymbolicClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
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| 19 | }
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| 20 |
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| 21 | public ILookupParameter<BoolValue> ApplyLinearScalingParameter {
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| 22 | get { return (ILookupParameter<BoolValue>)Parameters[ApplyLinearScalingParmameterName]; }
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| 23 | }
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| 24 | #endregion
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| 25 | #region properties
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| 26 | private ISymbolicClassificationModelCreator ModelCreator { get { return ModelCreatorParameter.ActualValue; } }
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| 27 | private BoolValue ApplyLinearScaling { get { return ApplyLinearScalingParameter.ActualValue; } }
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| 28 | #endregion
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| 29 |
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| 30 | protected SymbolicClassificationPruningOperator(SymbolicClassificationPruningOperator original, Cloner cloner)
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| 31 | : base(original, cloner) {
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| 32 | }
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| 33 | public override IDeepCloneable Clone(Cloner cloner) {
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| 34 | return new SymbolicClassificationPruningOperator(this, cloner);
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| 35 | }
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| 36 |
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| 37 | [StorableConstructor]
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| 38 | protected SymbolicClassificationPruningOperator(bool deserializing) : base(deserializing) { }
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| 39 |
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| 40 | public SymbolicClassificationPruningOperator() {
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| 41 | Parameters.Add(new ValueParameter<ISymbolicDataAnalysisSolutionImpactValuesCalculator>(ImpactValuesCalculatorParameterName, new SymbolicClassificationSolutionImpactValuesCalculator()));
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| 42 | Parameters.Add(new LookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName));
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| 43 | }
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| 44 |
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| 45 | protected override ISymbolicDataAnalysisModel CreateModel() {
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| 46 | var model = ModelCreator.CreateSymbolicClassificationModel(SymbolicExpressionTree, Interpreter, EstimationLimits.Lower, EstimationLimits.Upper);
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| 47 | var rows = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
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| 48 | var problemData = (IClassificationProblemData)ProblemData;
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| 49 | model.RecalculateModelParameters(problemData, rows);
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| 50 | return model;
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| 51 | }
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| 52 |
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| 53 | protected override double Evaluate(IDataAnalysisModel model) {
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| 54 | var classificationModel = (IClassificationModel)model;
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| 55 | var classificationProblemData = (IClassificationProblemData)ProblemData;
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| 56 | var trainingIndices = ProblemData.TrainingIndices.ToList();
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| 57 | var estimatedValues = classificationModel.GetEstimatedClassValues(ProblemData.Dataset, trainingIndices);
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| 58 | var targetValues = ProblemData.Dataset.GetDoubleValues(classificationProblemData.TargetVariable, trainingIndices);
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| 59 | OnlineCalculatorError errorState;
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| 60 | var quality = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, estimatedValues, out errorState);
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| 61 | if (errorState != OnlineCalculatorError.None) return double.NaN;
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| 62 | return quality;
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| 63 | }
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| 64 | }
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| 65 | }
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