1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2018 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.Persistence.Default.CompositeSerializers.Storable;
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27 |
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28 | namespace HeuristicLab.Problems.DataAnalysis {
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29 | /// <summary>
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30 | /// Represents classification solutions that contain an ensemble of multiple classification models
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31 | /// </summary>
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32 | [StorableClass]
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33 | [Item("ClassificationEnsembleModel", "A classification model that contains an ensemble of multiple classification models")]
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34 | public class ClassificationEnsembleModel : ClassificationModel, IClassificationEnsembleModel {
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35 | public override IEnumerable<string> VariablesUsedForPrediction {
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36 | get { return models.SelectMany(x => x.VariablesUsedForPrediction).Distinct().OrderBy(x => x); }
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37 | }
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38 |
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39 | [Storable]
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40 | private List<IClassificationModel> models;
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41 | public IEnumerable<IClassificationModel> Models {
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42 | get { return new List<IClassificationModel>(models); }
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43 | }
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44 |
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45 | [StorableConstructor]
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46 | protected ClassificationEnsembleModel(bool deserializing) : base(deserializing) { }
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47 | protected ClassificationEnsembleModel(ClassificationEnsembleModel original, Cloner cloner)
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48 | : base(original, cloner) {
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49 | this.models = original.Models.Select(m => cloner.Clone(m)).ToList();
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50 | }
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51 |
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52 | public ClassificationEnsembleModel() : this(Enumerable.Empty<IClassificationModel>()) { }
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53 | public ClassificationEnsembleModel(IEnumerable<IClassificationModel> models)
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54 | : base(string.Empty) {
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55 | this.name = ItemName;
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56 | this.description = ItemDescription;
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57 | this.models = new List<IClassificationModel>(models);
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58 |
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59 | if (this.models.Any()) this.TargetVariable = this.models.First().TargetVariable;
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60 | }
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61 |
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62 | public override IDeepCloneable Clone(Cloner cloner) {
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63 | return new ClassificationEnsembleModel(this, cloner);
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64 | }
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65 |
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66 | public void Add(IClassificationModel model) {
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67 | if (string.IsNullOrEmpty(TargetVariable)) TargetVariable = model.TargetVariable;
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68 | models.Add(model);
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69 | }
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70 | public void Remove(IClassificationModel model) {
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71 | models.Remove(model);
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72 | if (!models.Any()) TargetVariable = string.Empty;
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73 | }
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74 |
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75 | public IEnumerable<IEnumerable<double>> GetEstimatedClassValueVectors(IDataset dataset, IEnumerable<int> rows) {
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76 | var estimatedValuesEnumerators = (from model in models
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77 | select model.GetEstimatedClassValues(dataset, rows).GetEnumerator())
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78 | .ToList();
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79 |
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80 | while (estimatedValuesEnumerators.All(en => en.MoveNext())) {
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81 | yield return from enumerator in estimatedValuesEnumerators
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82 | select enumerator.Current;
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83 | }
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84 | }
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85 |
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86 |
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87 | public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
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88 | foreach (var estimatedValuesVector in GetEstimatedClassValueVectors(dataset, rows)) {
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89 | // return the class which is most often occuring
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90 | yield return
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91 | estimatedValuesVector
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92 | .GroupBy(x => x)
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93 | .OrderBy(g => -g.Count())
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94 | .Select(g => g.Key)
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95 | .First();
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96 | }
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97 | }
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98 |
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99 | public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
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100 | return new ClassificationEnsembleSolution(models, new ClassificationEnsembleProblemData(problemData));
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101 | }
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102 |
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103 |
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104 | }
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105 | }
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