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source: branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis/3.4/ClassificationEnsembleModel.cs @ 5681

Last change on this file since 5681 was 5662, checked in by gkronber, 14 years ago

#1418 added ensemble models for regression and classification.

File size: 3.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Operators;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Optimization;
31using System;
32
33namespace HeuristicLab.Problems.DataAnalysis {
34  /// <summary>
35  /// Represents classification solutions that contain an ensemble of multiple classification models
36  /// </summary>
37  [StorableClass]
38  [Item("ClassificationEnsembleModel", "A classification model that contains an ensemble of multiple classification models")]
39  public class ClassificationEnsembleModel : NamedItem, IClassificationEnsembleModel {
40
41    [Storable]
42    private List<IClassificationModel> models;
43    public IEnumerable<IClassificationModel> Models {
44      get { return new List<IClassificationModel>(models); }
45    }
46    [StorableConstructor]
47    protected ClassificationEnsembleModel(bool deserializing) : base(deserializing) { }
48    protected ClassificationEnsembleModel(ClassificationEnsembleModel original, Cloner cloner)
49      : base(original, cloner) {
50      this.models = original.Models.Select(m => cloner.Clone(m)).ToList();
51    }
52    public ClassificationEnsembleModel(IEnumerable<IClassificationModel> models)
53      : base() {
54      this.name = ItemName;
55      this.description = ItemDescription;
56      this.models = new List<IClassificationModel>(models);
57    }
58
59    public override IDeepCloneable Clone(Cloner cloner) {
60      return new ClassificationEnsembleModel(this, cloner);
61    }
62
63    #region IClassificationEnsembleModel Members
64
65    public IEnumerable<IEnumerable<double>> GetEstimatedClassValueVectors(Dataset dataset, IEnumerable<int> rows) {
66      var estimatedValuesEnumerators = (from model in models
67                                        select model.GetEstimatedClassValues(dataset, rows).GetEnumerator())
68                                       .ToList();
69
70      while (estimatedValuesEnumerators.All(en => en.MoveNext())) {
71        yield return from enumerator in estimatedValuesEnumerators
72                     select enumerator.Current;
73      }
74    }
75
76    #endregion
77
78    #region IClassificationModel Members
79
80    public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
81      foreach (var estimatedValuesVector in GetEstimatedClassValueVectors(dataset, rows)) {
82        // return the class which is most often occuring
83        yield return
84          estimatedValuesVector
85          .GroupBy(x => x)
86          .OrderBy(g => -g.Count())
87          .Select(g => g.Key)
88          .First();
89      }
90    }
91
92    #endregion
93  }
94}
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