| 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2011 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 System.Linq;
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| 24 | using HeuristicLab.Common;
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| 25 | using HeuristicLab.Data;
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| 26 | using HeuristicLab.Optimization;
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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 {
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| 30 | [StorableClass]
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| 31 | public abstract class ClassificationSolutionBase : DataAnalysisSolution, IClassificationSolution {
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| 32 | private const string TrainingAccuracyResultName = "Accuracy (training)";
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| 33 | private const string TestAccuracyResultName = "Accuracy (test)";
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| 34 |
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| 35 | public new IClassificationModel Model {
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| 36 | get { return (IClassificationModel)base.Model; }
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| 37 | protected set { base.Model = value; }
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| 38 | }
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| 39 |
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| 40 | public new IClassificationProblemData ProblemData {
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| 41 | get { return (IClassificationProblemData)base.ProblemData; }
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| 42 | set { base.ProblemData = value; }
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| 43 | }
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| 44 |
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| 45 | #region Results
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| 46 | public double TrainingAccuracy {
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| 47 | get { return ((DoubleValue)this[TrainingAccuracyResultName].Value).Value; }
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| 48 | private set { ((DoubleValue)this[TrainingAccuracyResultName].Value).Value = value; }
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| 49 | }
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| 50 | public double TestAccuracy {
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| 51 | get { return ((DoubleValue)this[TestAccuracyResultName].Value).Value; }
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| 52 | private set { ((DoubleValue)this[TestAccuracyResultName].Value).Value = value; }
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| 53 | }
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| 54 | #endregion
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| 55 |
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| 56 | [StorableConstructor]
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| 57 | protected ClassificationSolutionBase(bool deserializing) : base(deserializing) { }
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| 58 | protected ClassificationSolutionBase(ClassificationSolutionBase original, Cloner cloner)
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| 59 | : base(original, cloner) {
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| 60 | }
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| 61 | protected ClassificationSolutionBase(IClassificationModel model, IClassificationProblemData problemData)
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| 62 | : base(model, problemData) {
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| 63 | Add(new Result(TrainingAccuracyResultName, "Accuracy of the model on the training partition (percentage of correctly classified instances).", new PercentValue()));
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| 64 | Add(new Result(TestAccuracyResultName, "Accuracy of the model on the test partition (percentage of correctly classified instances).", new PercentValue()));
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| 65 | }
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| 66 |
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| 67 | protected void CalculateResults() {
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| 68 | double[] estimatedTrainingClassValues = EstimatedTrainingClassValues.ToArray(); // cache values
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| 69 | double[] originalTrainingClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes).ToArray();
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| 70 | double[] estimatedTestClassValues = EstimatedTestClassValues.ToArray(); // cache values
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| 71 | double[] originalTestClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndizes).ToArray();
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| 72 |
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| 73 | OnlineCalculatorError errorState;
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| 74 | double trainingAccuracy = OnlineAccuracyCalculator.Calculate(estimatedTrainingClassValues, originalTrainingClassValues, out errorState);
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| 75 | if (errorState != OnlineCalculatorError.None) trainingAccuracy = double.NaN;
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| 76 | double testAccuracy = OnlineAccuracyCalculator.Calculate(estimatedTestClassValues, originalTestClassValues, out errorState);
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| 77 | if (errorState != OnlineCalculatorError.None) testAccuracy = double.NaN;
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| 78 |
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| 79 | TrainingAccuracy = trainingAccuracy;
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| 80 | TestAccuracy = testAccuracy;
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| 81 | }
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| 82 |
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| 83 | public abstract IEnumerable<double> EstimatedClassValues { get; }
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| 84 | public abstract IEnumerable<double> EstimatedTrainingClassValues { get; }
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| 85 | public abstract IEnumerable<double> EstimatedTestClassValues { get; }
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| 86 |
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| 87 | public abstract IEnumerable<double> GetEstimatedClassValues(IEnumerable<int> rows);
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| 88 | }
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| 89 | }
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