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source: branches/DataAnalysis SolutionEnsembles/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolution.cs @ 5816

Last change on this file since 5816 was 5816, checked in by gkronber, 13 years ago

#1450 Added preliminary implementation for solution ensemble support.

File size: 4.9 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;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Data;
27using HeuristicLab.Optimization;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Problems.DataAnalysis {
31  /// <summary>
32  /// Represents a classification data analysis solution
33  /// </summary>
34  [StorableClass]
35  public class ClassificationSolution : DataAnalysisSolution, IClassificationSolution {
36    private const string TrainingAccuracyResultName = "Accuracy (training)";
37    private const string TestAccuracyResultName = "Accuracy (test)";
38
39    public new IClassificationModel Model {
40      get { return (IClassificationModel)base.Model; }
41      protected set { base.Model = value; }
42    }
43
44    public new IClassificationProblemData ProblemData {
45      get { return (IClassificationProblemData)base.ProblemData; }
46      protected set { base.ProblemData = value; }
47    }
48
49    public double TrainingAccuracy {
50      get { return ((DoubleValue)this[TrainingAccuracyResultName].Value).Value; }
51      private set { ((DoubleValue)this[TrainingAccuracyResultName].Value).Value = value; }
52    }
53
54    public double TestAccuracy {
55      get { return ((DoubleValue)this[TestAccuracyResultName].Value).Value; }
56      private set { ((DoubleValue)this[TestAccuracyResultName].Value).Value = value; }
57    }
58
59    [StorableConstructor]
60    protected ClassificationSolution(bool deserializing) : base(deserializing) { }
61    protected ClassificationSolution(ClassificationSolution original, Cloner cloner)
62      : base(original, cloner) {
63    }
64    public ClassificationSolution(IClassificationModel model, IClassificationProblemData problemData)
65      : base(model, problemData) {
66      Add(new Result(TrainingAccuracyResultName, "Accuracy of the model on the training partition (percentage of correctly classified instances).", new PercentValue()));
67      Add(new Result(TestAccuracyResultName, "Accuracy of the model on the test partition (percentage of correctly classified instances).", new PercentValue()));
68      RecalculateResults();
69    }
70
71    public override IDeepCloneable Clone(Cloner cloner) {
72      return new ClassificationSolution(this, cloner);
73    }
74
75    protected override void OnProblemDataChanged(EventArgs e) {
76      base.OnProblemDataChanged(e);
77      RecalculateResults();
78    }
79
80    protected override void OnModelChanged(EventArgs e) {
81      base.OnModelChanged(e);
82      RecalculateResults();
83    }
84
85    protected void RecalculateResults() {
86      double[] estimatedTrainingClassValues = EstimatedTrainingClassValues.ToArray(); // cache values
87      IEnumerable<double> originalTrainingClassValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes);
88      double[] estimatedTestClassValues = EstimatedTestClassValues.ToArray(); // cache values
89      IEnumerable<double> originalTestClassValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TestIndizes);
90
91      double trainingAccuracy = OnlineAccuracyEvaluator.Calculate(estimatedTrainingClassValues, originalTrainingClassValues);
92      double testAccuracy = OnlineAccuracyEvaluator.Calculate(estimatedTestClassValues, originalTestClassValues);
93
94      TrainingAccuracy = trainingAccuracy;
95      TestAccuracy = testAccuracy;
96    }
97
98    public virtual IEnumerable<double> EstimatedClassValues {
99      get {
100        return GetEstimatedClassValues(Enumerable.Range(0, ProblemData.Dataset.Rows));
101      }
102    }
103
104    public virtual IEnumerable<double> EstimatedTrainingClassValues {
105      get {
106        return GetEstimatedClassValues(ProblemData.TrainingIndizes);
107      }
108    }
109
110    public virtual IEnumerable<double> EstimatedTestClassValues {
111      get {
112        return GetEstimatedClassValues(ProblemData.TestIndizes);
113      }
114    }
115
116    public virtual IEnumerable<double> GetEstimatedClassValues(IEnumerable<int> rows) {
117      return Model.GetEstimatedClassValues(ProblemData.Dataset, rows);
118    }
119  }
120}
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