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Ignore:
Timestamp:
07/20/21 18:13:55 (3 years ago)
Author:
dpiringe
Message:

#3026

  • merged trunk into branch
Location:
branches/3026_IntegrationIntoSymSpace
Files:
4 edited

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  • branches/3026_IntegrationIntoSymSpace

  • branches/3026_IntegrationIntoSymSpace/HeuristicLab.Problems.DataAnalysis

  • branches/3026_IntegrationIntoSymSpace/HeuristicLab.Problems.DataAnalysis/3.4

  • branches/3026_IntegrationIntoSymSpace/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolutionBase.cs

    r17180 r18027  
    4040    private const string TrainingRSquaredResultName = "Pearson's R² (training)";
    4141    private const string TestRSquaredResultName = "Pearson's R² (test)";
     42    private const string TrainingNormalizedGiniCoefficientResultName = "Norm. Gini coeff. (training, discriminant values)";
     43    private const string TestNormalizedGiniCoefficientResultName = "Norm. Gini coeff. (test, discriminant values)";
     44
    4245
    4346    public new IDiscriminantFunctionClassificationModel Model {
     
    7174      private set { ((DoubleValue)this[TestRSquaredResultName].Value).Value = value; }
    7275    }
     76    public double TrainingNormalizedGiniCoefficientForDiscriminantValues {
     77      get { return ((DoubleValue)this[TrainingNormalizedGiniCoefficientResultName].Value).Value; }
     78      protected set { ((DoubleValue)this[TrainingNormalizedGiniCoefficientResultName].Value).Value = value; }
     79    }
     80    public double TestNormalizedGiniCoefficientForDiscriminantValues {
     81      get { return ((DoubleValue)this[TestNormalizedGiniCoefficientResultName].Value).Value; }
     82      protected set { ((DoubleValue)this[TestNormalizedGiniCoefficientResultName].Value).Value = value; }
     83    }
    7384    #endregion
    7485
     
    8596      Add(new Result(TrainingRSquaredResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition", new DoubleValue()));
    8697      Add(new Result(TestRSquaredResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition", new DoubleValue()));
     98      Add(new Result(TrainingNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the discriminant values produced by the model on the training partition.", new DoubleValue()));
     99      Add(new Result(TestNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the discriminant values produced by the model on the test partition.", new DoubleValue()));
    87100      RegisterEventHandler();
    88101    }
     
    90103    [StorableHook(HookType.AfterDeserialization)]
    91104    private void AfterDeserialization() {
     105      #region backwards compatibility
     106      if (!ContainsKey(TrainingNormalizedGiniCoefficientResultName)) {
     107        Add(new Result(TrainingNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the discriminant values produced by the model on the training partition.", new DoubleValue()));
     108        Add(new Result(TestNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the discriminant values produced by the model on the test partition.", new DoubleValue()));
     109        double[] estimatedTrainingValues = EstimatedTrainingValues.ToArray(); // cache values
     110        double[] originalTrainingValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToArray();
     111        double[] estimatedTestValues = EstimatedTestValues.ToArray(); // cache values
     112        double[] originalTestValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToArray();
     113        double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out var errorState);
     114        if (errorState != OnlineCalculatorError.None) trainingNormalizedGini = double.NaN;
     115        double testNormalizedGini = NormalizedGiniCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
     116        if (errorState != OnlineCalculatorError.None) testNormalizedGini = double.NaN;
     117
     118        TrainingNormalizedGiniCoefficientForDiscriminantValues = trainingNormalizedGini;
     119        TestNormalizedGiniCoefficientForDiscriminantValues = testNormalizedGini;
     120      }
     121      #endregion
    92122      RegisterEventHandler();
    93123    }
     
    106136
    107137      double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    108       TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
     138      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR * trainingR : double.NaN;
    109139      double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    110       TestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
     140      TestRSquared = errorState == OnlineCalculatorError.None ? testR * testR : double.NaN;
    111141
    112142      double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
     
    115145      if (errorState != OnlineCalculatorError.None) testNormalizedGini = double.NaN;
    116146
    117       TrainingNormalizedGiniCoefficient = trainingNormalizedGini;
    118       TestNormalizedGiniCoefficient = testNormalizedGini;
     147      TrainingNormalizedGiniCoefficientForDiscriminantValues = trainingNormalizedGini;
     148      TestNormalizedGiniCoefficientForDiscriminantValues = testNormalizedGini;
    119149    }
    120150
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