[11145] | 1 | #region License Information
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| 2 |
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| 3 | /* HeuristicLab
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| 4 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 5 | *
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| 6 | * This file is part of HeuristicLab.
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| 7 | *
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| 8 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 9 | * it under the terms of the GNU General Public License as published by
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| 10 | * the Free Software Foundation, either version 3 of the License, or
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| 11 | * (at your option) any later version.
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| 12 | *
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| 13 | * HeuristicLab is distributed in the hope that it will be useful,
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| 14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 16 | * GNU General Public License for more details.
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| 17 | *
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| 18 | * You should have received a copy of the GNU General Public License
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| 19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 20 | */
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| 21 |
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| 22 | #endregion
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| 23 |
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| 24 | using System.Linq;
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[10469] | 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Parameters;
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| 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 29 |
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| 30 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
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| 31 | [StorableClass]
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| 32 | [Item("SymbolicClassificationPruningOperator", "An operator which prunes symbolic classificaton trees.")]
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| 33 | public class SymbolicClassificationPruningOperator : SymbolicDataAnalysisExpressionPruningOperator {
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| 34 | private const string ImpactValuesCalculatorParameterName = "ImpactValuesCalculator";
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| 35 | private const string ModelCreatorParameterName = "ModelCreator";
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| 36 |
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| 37 | #region parameter properties
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| 38 | public ILookupParameter<ISymbolicClassificationModelCreator> ModelCreatorParameter {
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| 39 | get { return (ILookupParameter<ISymbolicClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
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| 40 | }
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| 41 | #endregion
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| 42 |
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| 43 | protected SymbolicClassificationPruningOperator(SymbolicClassificationPruningOperator original, Cloner cloner)
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| 44 | : base(original, cloner) {
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| 45 | }
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[11145] | 46 |
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[10469] | 47 | public override IDeepCloneable Clone(Cloner cloner) {
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| 48 | return new SymbolicClassificationPruningOperator(this, cloner);
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| 49 | }
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| 50 |
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| 51 | [StorableConstructor]
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| 52 | protected SymbolicClassificationPruningOperator(bool deserializing) : base(deserializing) { }
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| 53 |
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| 54 | public SymbolicClassificationPruningOperator() {
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| 55 | Parameters.Add(new ValueParameter<ISymbolicDataAnalysisSolutionImpactValuesCalculator>(ImpactValuesCalculatorParameterName, new SymbolicClassificationSolutionImpactValuesCalculator()));
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| 56 | Parameters.Add(new LookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName));
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| 57 | }
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| 58 |
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| 59 | protected override ISymbolicDataAnalysisModel CreateModel() {
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[11145] | 60 | var model = ModelCreatorParameter.ActualValue.CreateSymbolicClassificationModel(SymbolicExpressionTree, Interpreter, EstimationLimits.Lower, EstimationLimits.Upper);
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[10469] | 61 | var problemData = (IClassificationProblemData)ProblemData;
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[11145] | 62 | var rows = problemData.TrainingIndices;
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[10469] | 63 | model.RecalculateModelParameters(problemData, rows);
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| 64 | return model;
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| 65 | }
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| 66 |
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| 67 | protected override double Evaluate(IDataAnalysisModel model) {
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| 68 | var classificationModel = (IClassificationModel)model;
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| 69 | var classificationProblemData = (IClassificationProblemData)ProblemData;
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[11145] | 70 | var trainingIndices = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
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[10469] | 71 | var estimatedValues = classificationModel.GetEstimatedClassValues(ProblemData.Dataset, trainingIndices);
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| 72 | var targetValues = ProblemData.Dataset.GetDoubleValues(classificationProblemData.TargetVariable, trainingIndices);
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| 73 | OnlineCalculatorError errorState;
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[11145] | 74 | var quality = OnlineAccuracyCalculator.Calculate(targetValues, estimatedValues, out errorState);
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[10469] | 75 | if (errorState != OnlineCalculatorError.None) return double.NaN;
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| 76 | return quality;
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| 77 | }
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| 78 | }
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| 79 | }
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