source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolutionBase.cs @ 6961

Last change on this file since 6961 was 6961, checked in by mkommend, 11 years ago

#1670: Corrected calculation of DataAnalysisSolution results and modified online calculators to have more meaningful parameter names.

File size: 8.5 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;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Optimization;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30
31namespace HeuristicLab.Problems.DataAnalysis {
32  /// <summary>
33  /// Represents a classification solution that uses a discriminant function and classification thresholds.
34  /// </summary>
35  [StorableClass]
36  [Item("DiscriminantFunctionClassificationSolution", "Represents a classification solution that uses a discriminant function and classification thresholds.")]
37  public abstract class DiscriminantFunctionClassificationSolutionBase : ClassificationSolutionBase, IDiscriminantFunctionClassificationSolution {
38    private const string TrainingMeanSquaredErrorResultName = "Mean squared error (training)";
39    private const string TestMeanSquaredErrorResultName = "Mean squared error (test)";
40    private const string TrainingRSquaredResultName = "Pearson's R² (training)";
41    private const string TestRSquaredResultName = "Pearson's R² (test)";
42
43    public new IDiscriminantFunctionClassificationModel Model {
44      get { return (IDiscriminantFunctionClassificationModel)base.Model; }
45      protected set {
46        if (value != null && value != Model) {
47          if (Model != null) {
48            Model.ThresholdsChanged -= new EventHandler(Model_ThresholdsChanged);
49          }
50          value.ThresholdsChanged += new EventHandler(Model_ThresholdsChanged);
51          base.Model = value;
52        }
53      }
54    }
55
56    #region Results
57    public double TrainingMeanSquaredError {
58      get { return ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value; }
59      private set { ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value = value; }
60    }
61    public double TestMeanSquaredError {
62      get { return ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value; }
63      private set { ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value = value; }
64    }
65    public double TrainingRSquared {
66      get { return ((DoubleValue)this[TrainingRSquaredResultName].Value).Value; }
67      private set { ((DoubleValue)this[TrainingRSquaredResultName].Value).Value = value; }
68    }
69    public double TestRSquared {
70      get { return ((DoubleValue)this[TestRSquaredResultName].Value).Value; }
71      private set { ((DoubleValue)this[TestRSquaredResultName].Value).Value = value; }
72    }
73    #endregion
74
75    [StorableConstructor]
76    protected DiscriminantFunctionClassificationSolutionBase(bool deserializing) : base(deserializing) { }
77    protected DiscriminantFunctionClassificationSolutionBase(DiscriminantFunctionClassificationSolutionBase original, Cloner cloner)
78      : base(original, cloner) {
79      RegisterEventHandler();
80    }
81    protected DiscriminantFunctionClassificationSolutionBase(IDiscriminantFunctionClassificationModel model, IClassificationProblemData problemData)
82      : base(model, problemData) {
83      Add(new Result(TrainingMeanSquaredErrorResultName, "Mean of squared errors of the model on the training partition", new DoubleValue()));
84      Add(new Result(TestMeanSquaredErrorResultName, "Mean of squared errors of the model on the test partition", new DoubleValue()));
85      Add(new Result(TrainingRSquaredResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition", new DoubleValue()));
86      Add(new Result(TestRSquaredResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition", new DoubleValue()));
87
88      RegisterEventHandler();
89    }
90
91    [StorableHook(HookType.AfterDeserialization)]
92    private void AfterDeserialization() {
93      RegisterEventHandler();
94    }
95
96    protected override void OnModelChanged() {
97      DeregisterEventHandler();
98      SetAccuracyMaximizingThresholds();
99      RegisterEventHandler();
100      base.OnModelChanged();
101    }
102
103    protected void CalculateRegressionResults() {
104      double[] estimatedTrainingValues = EstimatedTrainingValues.ToArray(); // cache values
105      double[] originalTrainingValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes).ToArray();
106      double[] estimatedTestValues = EstimatedTestValues.ToArray(); // cache values
107      double[] originalTestValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndizes).ToArray();
108
109      OnlineCalculatorError errorState;
110      double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
111      TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
112      double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
113      TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
114
115      double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
116      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
117      double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
118      TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
119
120      double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
121      if (errorState != OnlineCalculatorError.None) trainingNormalizedGini = double.NaN;
122      double testNormalizedGini = NormalizedGiniCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
123      if (errorState != OnlineCalculatorError.None) testNormalizedGini = double.NaN;
124
125      TrainingNormalizedGiniCoefficient = trainingNormalizedGini;
126      TestNormalizedGiniCoefficient = testNormalizedGini;
127    }
128
129    private void RegisterEventHandler() {
130      Model.ThresholdsChanged += new EventHandler(Model_ThresholdsChanged);
131    }
132    private void DeregisterEventHandler() {
133      Model.ThresholdsChanged -= new EventHandler(Model_ThresholdsChanged);
134    }
135    private void Model_ThresholdsChanged(object sender, EventArgs e) {
136      OnModelThresholdsChanged(e);
137    }
138
139    public void SetAccuracyMaximizingThresholds() {
140      double[] classValues;
141      double[] thresholds;
142      var targetClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes);
143      AccuracyMaximizationThresholdCalculator.CalculateThresholds(ProblemData, EstimatedTrainingValues, targetClassValues, out classValues, out thresholds);
144
145      Model.SetThresholdsAndClassValues(thresholds, classValues);
146    }
147
148    public void SetClassDistibutionCutPointThresholds() {
149      double[] classValues;
150      double[] thresholds;
151      var targetClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes);
152      NormalDistributionCutPointsThresholdCalculator.CalculateThresholds(ProblemData, EstimatedTrainingValues, targetClassValues, out classValues, out thresholds);
153
154      Model.SetThresholdsAndClassValues(thresholds, classValues);
155    }
156
157    protected virtual void OnModelThresholdsChanged(EventArgs e) {
158      CalculateResults();
159    }
160
161    public abstract IEnumerable<double> EstimatedValues { get; }
162    public abstract IEnumerable<double> EstimatedTrainingValues { get; }
163    public abstract IEnumerable<double> EstimatedTestValues { get; }
164
165    public abstract IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows);
166  }
167}
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