[8548] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2012 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 System.Collections.Generic;
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| 23 | using HeuristicLab.Common;
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| 24 | using HeuristicLab.Core;
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| 25 | using HeuristicLab.Data;
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| 26 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 28 |
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| 29 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
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| 30 | [Item("Penalty Score Evaluator", "Calculates the penalty score of a symbolic classification solution.")]
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| 31 | [StorableClass]
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| 32 | public class SymbolicClassificationSingleObjectivePenaltyScoreEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
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| 33 | public override bool Maximization { get { return false; } }
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| 34 |
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| 35 | [StorableConstructor]
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| 36 | protected SymbolicClassificationSingleObjectivePenaltyScoreEvaluator(bool deserializing) : base(deserializing) { }
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| 37 | protected SymbolicClassificationSingleObjectivePenaltyScoreEvaluator(SymbolicClassificationSingleObjectivePenaltyScoreEvaluator original, Cloner cloner) : base(original, cloner) { }
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| 38 | public SymbolicClassificationSingleObjectivePenaltyScoreEvaluator() : base() { }
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| 39 |
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| 40 | public override IDeepCloneable Clone(Cloner cloner) {
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| 41 | return new SymbolicClassificationSingleObjectivePenaltyScoreEvaluator(this, cloner);
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| 42 | }
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| 43 |
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| 44 | public override IOperation Apply() {
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| 45 | double quality = Evaluate(ExecutionContext, SymbolicExpressionTreeParameter.ActualValue, ProblemDataParameter.ActualValue, GenerateRowsToEvaluate());
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| 46 | QualityParameter.ActualValue = new DoubleValue(quality);
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| 47 | return base.Apply();
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| 48 | }
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| 49 |
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| 50 | public static double Calculate(IClassificationModel model, IClassificationProblemData problemData, IEnumerable<int> rows) {
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| 51 | var estimations = model.GetEstimatedClassValues(problemData.Dataset, rows).GetEnumerator();
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| 52 | if (!estimations.MoveNext()) return double.NaN;
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| 53 |
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| 54 | double penalty = 0.0;
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| 55 | foreach (var r in rows) {
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| 56 | var actualClass = problemData.Dataset.GetDoubleValue(problemData.TargetVariable, r);
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| 57 | penalty += problemData.GetClassificationPenalty(actualClass, estimations.Current);
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| 58 | estimations.MoveNext();
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| 59 | }
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| 60 | return penalty;
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| 61 | }
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| 62 |
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| 63 | public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IClassificationProblemData problemData, IEnumerable<int> rows) {
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| 64 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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| 65 | EstimationLimitsParameter.ExecutionContext = context;
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| 66 |
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| 67 | var model = new SymbolicDiscriminantFunctionClassificationModel(tree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
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| 68 | SymbolicDiscriminantFunctionClassificationModel.SetAccuracyMaximizingThresholds(model, problemData);
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| 69 | double penalty = Calculate(model, problemData, rows);
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| 70 |
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| 71 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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| 72 | EstimationLimitsParameter.ExecutionContext = null;
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| 73 |
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| 74 | return penalty;
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| 75 | }
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| 76 | }
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| 77 | }
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