Changeset 12218 for branches/SensitivityEvaluator/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification

Timestamp:

03/17/15 17:22:57 (10 years ago)

Author:

ehopf

Message:

#2361: Added missing estimated values bound calculation and removed duplicate code. Note that the current implementation ignores the linear scaling option.

Location:

branches/SensitivityEvaluator/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4

Files:

• SingleObjective/SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs (modified) (3 diffs)
• SymbolicDiscriminantFunctionClassificationModel.cs (modified) (2 diffs)

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

@@ -102 +102 @@
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
+      IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
       OnlineCalculatorError errorState;
       double nmse;
@@ -109 +110 @@
       string positiveClassName = problemData.PositiveClass;
       double[] classValues, thresholds;
-      model.ThresholdCalculator.Calculate(problemData, estimatedValues, targetClassValues, out classValues, out thresholds);
+      model.ThresholdCalculator.Calculate(problemData, boundedEstimatedValues, targetClassValues, out classValues, out thresholds);
       model.SetThresholdsAndClassValues(thresholds, classValues);
       var performanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, problemData.GetClassValue(positiveClassName));
-      var estimatedClassValues = model.GetEstimatedClassValues(estimatedValues);
+      var estimatedClassValues = model.GetEstimatedClassValues(boundedEstimatedValues);
       performanceCalculator.Calculate(targetClassValues, estimatedClassValues);
       if (performanceCalculator.ErrorState != OnlineCalculatorError.None) 
@@ -125 +126 @@
         nmse = nmseCalculator.NormalizedMeanSquaredError;
       } else {
-        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
         nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetClassValues, boundedEstimatedValues, out errorState);
       }

branches/SensitivityEvaluator/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicDiscriminantFunctionClassificationModel.cs

@@ -88 +88 @@
       var classValuesArr = classValues.ToArray();
       var thresholdsArr = thresholds.ToArray();
-      if (thresholdsArr.Length != classValuesArr.Length || thresholdsArr.Length < 1) 
+      if (thresholdsArr.Length != classValuesArr.Length || thresholdsArr.Length < 1)
         throw new ArgumentException();
-      if (!double.IsNegativeInfinity(thresholds.First())) 
+      if (!double.IsNegativeInfinity(thresholds.First()))
         throw new ArgumentException();
 
@@ -113 +113 @@
     public override IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
       if (!Thresholds.Any() && !ClassValues.Any()) throw new ArgumentException("No thresholds and class values were set for the current symbolic classification model.");
-      foreach (var x in GetEstimatedValues(dataset, rows)) {
-        int classIndex = 0;
-        // find first threshold value which is larger than x => class index = threshold index + 1
-        for (int i = 0; i < thresholds.Length; i++) {
-          if (x > thresholds[i]) classIndex++;
-          else break;
-        }
-        yield return classValues.ElementAt(classIndex - 1);
-      }
+      var estimatedValues = GetEstimatedValues(dataset, rows);
+      return GetEstimatedClassValues(estimatedValues);
     }
     public IEnumerable<double> GetEstimatedClassValues(IEnumerable<double> estimatedValues) {