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source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs @ 6588

Last change on this file since 6588 was 6588, checked in by mkommend, 13 years ago

#1600:

  • Corrected result descriptions in DataAnalysisSolution.
  • Added NMSE results in IRegressionSolution.
  • Split RegressionSolution into a concrete implementation class and an abstract base class RegressionSolutionBase that could also be used for RegressionEnsembleSolutions or CachingRegressionSolutions.
  • Moved calculation of results in specific regression solution implementations (e.g. SymbolicRegressionSolution).
File size: 8.7 KB
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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.Data;
26using HeuristicLab.Optimization;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis {
30  /// <summary>
31  /// Represents a regression data analysis solution
32  /// </summary>
33  [StorableClass]
34  public abstract class RegressionSolutionBase : DataAnalysisSolution, IRegressionSolution {
35    private const string TrainingMeanSquaredErrorResultName = "Mean squared error (training)";
36    private const string TestMeanSquaredErrorResultName = "Mean squared error (test)";
37    private const string TrainingSquaredCorrelationResultName = "Pearson's R² (training)";
38    private const string TestSquaredCorrelationResultName = "Pearson's R² (test)";
39    private const string TrainingRelativeErrorResultName = "Average relative error (training)";
40    private const string TestRelativeErrorResultName = "Average relative error (test)";
41    private const string TrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
42    private const string TestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
43
44    public new IRegressionModel Model {
45      get { return (IRegressionModel)base.Model; }
46      protected set { base.Model = value; }
47    }
48
49    public new IRegressionProblemData ProblemData {
50      get { return (IRegressionProblemData)base.ProblemData; }
51      protected set { base.ProblemData = value; }
52    }
53
54    public abstract IEnumerable<double> EstimatedValues { get; }
55    public abstract IEnumerable<double> EstimatedTrainingValues { get; }
56    public abstract IEnumerable<double> EstimatedTestValues { get; }
57    public abstract IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows);
58
59    #region Results
60    public double TrainingMeanSquaredError {
61      get { return ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value; }
62      private set { ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value = value; }
63    }
64    public double TestMeanSquaredError {
65      get { return ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value; }
66      private set { ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value = value; }
67    }
68    public double TrainingRSquared {
69      get { return ((DoubleValue)this[TrainingSquaredCorrelationResultName].Value).Value; }
70      private set { ((DoubleValue)this[TrainingSquaredCorrelationResultName].Value).Value = value; }
71    }
72    public double TestRSquared {
73      get { return ((DoubleValue)this[TestSquaredCorrelationResultName].Value).Value; }
74      private set { ((DoubleValue)this[TestSquaredCorrelationResultName].Value).Value = value; }
75    }
76    public double TrainingRelativeError {
77      get { return ((DoubleValue)this[TrainingRelativeErrorResultName].Value).Value; }
78      private set { ((DoubleValue)this[TrainingRelativeErrorResultName].Value).Value = value; }
79    }
80    public double TestRelativeError {
81      get { return ((DoubleValue)this[TestRelativeErrorResultName].Value).Value; }
82      private set { ((DoubleValue)this[TestRelativeErrorResultName].Value).Value = value; }
83    }
84    public double TrainingNormalizedMeanSquaredError {
85      get { return ((DoubleValue)this[TrainingNormalizedMeanSquaredErrorResultName].Value).Value; }
86      private set { ((DoubleValue)this[TrainingNormalizedMeanSquaredErrorResultName].Value).Value = value; }
87    }
88    public double TestNormalizedMeanSquaredError {
89      get { return ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value; }
90      private set { ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value = value; }
91    }
92    #endregion
93
94    [StorableConstructor]
95    protected RegressionSolutionBase(bool deserializing) : base(deserializing) { }
96    protected RegressionSolutionBase(RegressionSolutionBase original, Cloner cloner)
97      : base(original, cloner) {
98    }
99    protected RegressionSolutionBase(IRegressionModel model, IRegressionProblemData problemData)
100      : base(model, problemData) {
101      Add(new Result(TrainingMeanSquaredErrorResultName, "Mean of squared errors of the model on the training partition", new DoubleValue()));
102      Add(new Result(TestMeanSquaredErrorResultName, "Mean of squared errors of the model on the test partition", new DoubleValue()));
103      Add(new Result(TrainingSquaredCorrelationResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition", new DoubleValue()));
104      Add(new Result(TestSquaredCorrelationResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition", new DoubleValue()));
105      Add(new Result(TrainingRelativeErrorResultName, "Average of the relative errors of the model output and the actual values on the training partition", new PercentValue()));
106      Add(new Result(TestRelativeErrorResultName, "Average of the relative errors of the model output and the actual values on the test partition", new PercentValue()));
107      Add(new Result(TrainingNormalizedMeanSquaredErrorResultName, "Normalized mean of squared errors of the model on the training partition", new DoubleValue()));
108      Add(new Result(TestNormalizedMeanSquaredErrorResultName, "Normalized mean of squared errors of the model on the test partition", new DoubleValue()));
109    }
110
111    protected void CalculateResults() {
112      double[] estimatedTrainingValues = EstimatedTrainingValues.ToArray(); // cache values
113      double[] originalTrainingValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes).ToArray();
114      double[] estimatedTestValues = EstimatedTestValues.ToArray(); // cache values
115      double[] originalTestValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TestIndizes).ToArray();
116
117      OnlineCalculatorError errorState;
118      double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
119      TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
120      double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
121      TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
122
123      double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
124      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
125      double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
126      TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
127
128      double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
129      TrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
130      double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
131      TestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
132
133      double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
134      TrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
135      double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
136      TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
137    }
138  }
139}
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