Changeset 18027 for branches/3026_IntegrationIntoSymSpace/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolutionBase.cs

Timestamp:

07/20/21 18:13:55 (3 years ago)

Author:

dpiringe

Message:

#3026

• merged trunk into branch

Location:

branches/3026_IntegrationIntoSymSpace

Files:

• . (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis/3.4 (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolutionBase.cs (modified) (6 diffs)

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

@@ -40 +40 @@
     private const string TrainingRSquaredResultName = "Pearson's R² (training)";
     private const string TestRSquaredResultName = "Pearson's R² (test)";
+    private const string TrainingNormalizedGiniCoefficientResultName = "Norm. Gini coeff. (training, discriminant values)";
+    private const string TestNormalizedGiniCoefficientResultName = "Norm. Gini coeff. (test, discriminant values)";
+
 
     public new IDiscriminantFunctionClassificationModel Model {
@@ -71 +74 @@
       private set { ((DoubleValue)this[TestRSquaredResultName].Value).Value = value; }
     }
+    public double TrainingNormalizedGiniCoefficientForDiscriminantValues {
+      get { return ((DoubleValue)this[TrainingNormalizedGiniCoefficientResultName].Value).Value; }
+      protected set { ((DoubleValue)this[TrainingNormalizedGiniCoefficientResultName].Value).Value = value; }
+    }
+    public double TestNormalizedGiniCoefficientForDiscriminantValues {
+      get { return ((DoubleValue)this[TestNormalizedGiniCoefficientResultName].Value).Value; }
+      protected set { ((DoubleValue)this[TestNormalizedGiniCoefficientResultName].Value).Value = value; }
+    }
     #endregion
 
@@ -85 +96 @@
       Add(new Result(TrainingRSquaredResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition", new DoubleValue()));
       Add(new Result(TestRSquaredResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition", new DoubleValue()));
+      Add(new Result(TrainingNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the discriminant values produced by the model on the training partition.", new DoubleValue()));
+      Add(new Result(TestNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the discriminant values produced by the model on the test partition.", new DoubleValue()));
       RegisterEventHandler();
     }
@@ -90 +103 @@
     [StorableHook(HookType.AfterDeserialization)]
     private void AfterDeserialization() {
+      #region backwards compatibility
+      if (!ContainsKey(TrainingNormalizedGiniCoefficientResultName)) {
+        Add(new Result(TrainingNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the discriminant values produced by the model on the training partition.", new DoubleValue()));
+        Add(new Result(TestNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the discriminant values produced by the model on the test partition.", new DoubleValue()));
+        double[] estimatedTrainingValues = EstimatedTrainingValues.ToArray(); // cache values
+        double[] originalTrainingValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToArray();
+        double[] estimatedTestValues = EstimatedTestValues.ToArray(); // cache values
+        double[] originalTestValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToArray();
+        double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out var errorState);
+        if (errorState != OnlineCalculatorError.None) trainingNormalizedGini = double.NaN;
+        double testNormalizedGini = NormalizedGiniCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+        if (errorState != OnlineCalculatorError.None) testNormalizedGini = double.NaN;
+
+        TrainingNormalizedGiniCoefficientForDiscriminantValues = trainingNormalizedGini;
+        TestNormalizedGiniCoefficientForDiscriminantValues = testNormalizedGini;
+      }
+      #endregion
       RegisterEventHandler();
     }
@@ -106 +136 @@
 
       double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
+      TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR * trainingR : double.NaN;
       double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-      TestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
+      TestRSquared = errorState == OnlineCalculatorError.None ? testR * testR : double.NaN;
 
       double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
@@ -115 +145 @@
       if (errorState != OnlineCalculatorError.None) testNormalizedGini = double.NaN;
 
-      TrainingNormalizedGiniCoefficient = trainingNormalizedGini;
-      TestNormalizedGiniCoefficient = testNormalizedGini;
+      TrainingNormalizedGiniCoefficientForDiscriminantValues = trainingNormalizedGini;
+      TestNormalizedGiniCoefficientForDiscriminantValues = testNormalizedGini;
     }