Changeset 12216 for branches/SensitivityEvaluator/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective

Timestamp:

03/17/15 15:56:40 (10 years ago)

Author:

ehopf

Message:

#2361: Removed redundant calculations.

File:

• branches/SensitivityEvaluator/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs (modified) (3 diffs)

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

@@ -30 +30 @@
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification.SingleObjective {
-  [Item("Weighted Performance Measures Evaluator", "Calculates the quality a symbolic classification solution based on three weighted measures(normalized mean squared error, false negative rate and false positve rate).")]
+  [Item("Weighted Performance Measures Evaluator", "Calculates the quality of a symbolic classification solution based on three weighted measures(normalized mean squared error, false negative rate(1-sensitivity) and false positve rate(1-specificity)).")]
   [StorableClass]
   public class SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
@@ -101 +101 @@
                 IEnumerable<int> rows, bool applyLinearScaling, ISymbolicClassificationModelCreator modelCreator, double normalizedMeanSquaredErrorWeightingFactor, double falseNegativeRateWeightingFactor, double falsePositiveRateWeightingFactor) {
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
-      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
+      IEnumerable<double> targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
       OnlineCalculatorError errorState;
       double nmse;
 
       //calculate performance measures
-      var model = modelCreator.CreateSymbolicClassificationModel(solution, interpreter, lowerEstimationLimit, upperEstimationLimit);
+      var model = (ISymbolicDiscriminantFunctionClassificationModel)modelCreator.CreateSymbolicClassificationModel(solution, interpreter, lowerEstimationLimit, upperEstimationLimit);
       string positiveClassName = problemData.PositiveClass;
-      model.RecalculateModelParameters(problemData, rows);
+      double[] classValues, thresholds;
+      model.ThresholdCalculator.Calculate(problemData, estimatedValues, targetClassValues, out classValues, out thresholds);
+      model.SetThresholdsAndClassValues(thresholds, classValues);
       var performanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, problemData.GetClassValue(positiveClassName));
-      var estimatedClassValues = model.GetEstimatedClassValues(problemData.Dataset, rows);
-      performanceCalculator.Calculate(estimatedClassValues, targetValues);
+      var estimatedClassValues = model.GetEstimatedClassValues(estimatedValues);
+      performanceCalculator.Calculate(targetClassValues, estimatedClassValues);
       if (performanceCalculator.ErrorState != OnlineCalculatorError.None) 
         return Double.NaN;
@@ -119 +121 @@
       if (applyLinearScaling) {
         var nmseCalculator = new OnlineNormalizedMeanSquaredErrorCalculator();
-        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, nmseCalculator, problemData.Dataset.Rows);
+        CalculateWithScaling(targetClassValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, nmseCalculator, problemData.Dataset.Rows);
         errorState = nmseCalculator.ErrorState;
         nmse = nmseCalculator.NormalizedMeanSquaredError;
       } else {
         IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
-        nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
+        nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetClassValues, boundedEstimatedValues, out errorState);
       }
       if (errorState != OnlineCalculatorError.None) return Double.NaN;