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.Core;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.Operators;
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28 | using HeuristicLab.Parameters;
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29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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30 | using HeuristicLab.Optimization;
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31 | using System;
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32 |
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33 | namespace HeuristicLab.Problems.DataAnalysis {
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34 | /// <summary>
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35 | /// Abstract base class for classification data analysis solutions
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36 | /// </summary>
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37 | [StorableClass]
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38 | public abstract class ClassificationSolution : DataAnalysisSolution, IClassificationSolution {
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39 | private const string TrainingAccuracyResultName = "Accuracy (training)";
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40 | private const string TestAccuracyResultName = "Accuracy (test)";
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41 | [StorableConstructor]
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42 | protected ClassificationSolution(bool deserializing) : base(deserializing) { }
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43 | protected ClassificationSolution(ClassificationSolution original, Cloner cloner)
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44 | : base(original, cloner) {
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45 | }
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46 | public ClassificationSolution(IClassificationModel model, IClassificationProblemData problemData)
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47 | : base(model, problemData) {
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48 | double[] estimatedTrainingClassValues = EstimatedTrainingClassValues.ToArray(); // cache values
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49 | IEnumerable<double> originalTrainingClassValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes);
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50 | double[] estimatedTestClassValues = EstimatedTestClassValues.ToArray(); // cache values
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51 | IEnumerable<double> originalTestClassValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TestIndizes);
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52 |
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53 | double trainingAccuracy = OnlineAccuracyEvaluator.Calculate(estimatedTrainingClassValues, originalTrainingClassValues);
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54 | double testAccuracy = OnlineAccuracyEvaluator.Calculate(estimatedTestClassValues, originalTestClassValues);
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55 |
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56 | Add(new Result(TrainingAccuracyResultName, "Accuracy of the model on the training partition (percentage of correctly classified instances).", new PercentValue(trainingAccuracy)));
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57 | Add(new Result(TestAccuracyResultName, "Accuracy of the model on the test partition (percentage of correctly classified instances).", new PercentValue(testAccuracy)));
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58 | }
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59 |
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60 | #region IClassificationSolution Members
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61 |
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62 | public new IClassificationModel Model {
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63 | get { return (IClassificationModel)base.Model; }
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64 | }
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65 |
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66 | public new IClassificationProblemData ProblemData {
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67 | get { return (IClassificationProblemData)base.ProblemData; }
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68 | }
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69 |
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70 | public virtual IEnumerable<double> EstimatedClassValues {
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71 | get {
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72 | return GetEstimatedClassValues(Enumerable.Range(0, ProblemData.Dataset.Rows));
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73 | }
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74 | }
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75 |
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76 | public virtual IEnumerable<double> EstimatedTrainingClassValues {
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77 | get {
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78 | return GetEstimatedClassValues(ProblemData.TrainingIndizes);
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79 | }
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80 | }
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81 |
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82 | public virtual IEnumerable<double> EstimatedTestClassValues {
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83 | get {
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84 | return GetEstimatedClassValues(ProblemData.TestIndizes);
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85 | }
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86 | }
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87 |
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88 | public virtual IEnumerable<double> GetEstimatedClassValues(IEnumerable<int> rows) {
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89 | return Model.GetEstimatedClassValues(ProblemData.Dataset, rows);
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90 | }
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91 | #endregion
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92 | }
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93 | }
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