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source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolution.cs @ 5991

Last change on this file since 5991 was 5942, checked in by mkommend, 14 years ago

#1453: Renamed IOnlineEvaluator to IOnlineCalculator

File size: 5.0 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  /// Abstract base class for classification data analysis solutions
33  /// </summary>
34  [StorableClass]
35  public abstract 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    protected override void OnProblemDataChanged(EventArgs e) {
72      base.OnProblemDataChanged(e);
73      RecalculateResults();
74    }
75
76    protected override void OnModelChanged(EventArgs e) {
77      base.OnModelChanged(e);
78      RecalculateResults();
79    }
80
81    protected void RecalculateResults() {
82      double[] estimatedTrainingClassValues = EstimatedTrainingClassValues.ToArray(); // cache values
83      IEnumerable<double> originalTrainingClassValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes);
84      double[] estimatedTestClassValues = EstimatedTestClassValues.ToArray(); // cache values
85      IEnumerable<double> originalTestClassValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TestIndizes);
86
87      OnlineCalculatorError errorState;
88      double trainingAccuracy = OnlineAccuracyCalculator.Calculate(estimatedTrainingClassValues, originalTrainingClassValues, out errorState);
89      if (errorState != OnlineCalculatorError.None) trainingAccuracy = double.NaN;
90      double testAccuracy = OnlineAccuracyCalculator.Calculate(estimatedTestClassValues, originalTestClassValues, out errorState);
91      if (errorState != OnlineCalculatorError.None) testAccuracy = double.NaN;
92
93      TrainingAccuracy = trainingAccuracy;
94      TestAccuracy = testAccuracy;
95    }
96
97    public virtual IEnumerable<double> EstimatedClassValues {
98      get {
99        return GetEstimatedClassValues(Enumerable.Range(0, ProblemData.Dataset.Rows));
100      }
101    }
102
103    public virtual IEnumerable<double> EstimatedTrainingClassValues {
104      get {
105        return GetEstimatedClassValues(ProblemData.TrainingIndizes);
106      }
107    }
108
109    public virtual IEnumerable<double> EstimatedTestClassValues {
110      get {
111        return GetEstimatedClassValues(ProblemData.TestIndizes);
112      }
113    }
114
115    public virtual IEnumerable<double> GetEstimatedClassValues(IEnumerable<int> rows) {
116      return Model.GetEstimatedClassValues(ProblemData.Dataset, rows);
117    }
118  }
119}
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