Changeset 12487

Timestamp:

06/22/15 16:55:29 (10 years ago)

Author:

ehopf

Message:

#2361: Fixed a bug that causes an exception when a ModelCreator is used that doesn´t implement the ISymbolicDiscriminantFunctionClassificationModelCreator interface. Note that the runtime can be further improved for those ModelCreators because of redundant calculations.

File:

• branches/SensitivityEvaluator/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs (modified) (1 diff)

branches/SensitivityEvaluator/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs

@@ -119 +119 @@
 
       //calculate performance measures
-      var model = (ISymbolicDiscriminantFunctionClassificationModel)modelCreator.CreateSymbolicClassificationModel(solution, interpreter, lowerEstimationLimit, upperEstimationLimit);
       string positiveClassName = problemData.PositiveClass;
       double[] classValues, thresholds;
-      model.ThresholdCalculator.Calculate(problemData, boundedEstimatedValues, targetClassValues, out classValues, out thresholds);
-      model.SetThresholdsAndClassValues(thresholds, classValues);
+      IEnumerable<double> estimatedClassValues = null;
+      ISymbolicDiscriminantFunctionClassificationModel m;
+
+      var model = modelCreator.CreateSymbolicClassificationModel(solution, interpreter, lowerEstimationLimit, upperEstimationLimit);
+      if ((m = model as ISymbolicDiscriminantFunctionClassificationModel) != null) {
+        m.ThresholdCalculator.Calculate(problemData, boundedEstimatedValues, targetClassValues, out classValues, out thresholds);
+        m.SetThresholdsAndClassValues(thresholds, classValues);
+        estimatedClassValues = m.GetEstimatedClassValues(boundedEstimatedValues);
+      } else {
+        model.RecalculateModelParameters(problemData, rows);
+        estimatedClassValues = model.GetEstimatedClassValues(problemData.Dataset, rows);
+      }
       var performanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, problemData.GetClassValue(positiveClassName));
-      var estimatedClassValues = model.GetEstimatedClassValues(boundedEstimatedValues);
       performanceCalculator.Calculate(targetClassValues, estimatedClassValues);
       if (performanceCalculator.ErrorState != OnlineCalculatorError.None)