Changeset 8534 for branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/WeightCalculators/AverageThresholdCalculator.cs

Timestamp:

08/28/12 16:13:02 (12 years ago)

Author:

sforsten

Message:

#1776:

• merged r8508:8533 from trunk into branch
• AverageThresholdCalculator and MedianThresholdCalculator can now handle multi class classification
• changed combo boxes in ClassificationEnsembleSolutionView to drop down list

Location:

branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis

Files:

• . (modified) (1 prop)
• 3.4/Implementation/Classification/WeightCalculators/AverageThresholdCalculator.cs (modified) (3 diffs)

branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/WeightCalculators/AverageThresholdCalculator.cs

@@ -58 +58 @@
 
     protected override double GetDiscriminantConfidence(IEnumerable<IDiscriminantFunctionClassificationSolution> solutions, int index, double estimatedClassValue) {
-      // only works with binary classification
-      if (!classValues.Count().Equals(2))
-        return double.NaN;
       Dataset dataset = solutions.First().ProblemData.Dataset;
       IList<double> values = solutions.Select(s => s.Model.GetEstimatedValues(dataset, Enumerable.Repeat(index, 1)).First()).ToList();
@@ -70 +67 @@
 
     public override IEnumerable<double> GetDiscriminantConfidence(IEnumerable<IDiscriminantFunctionClassificationSolution> solutions, IEnumerable<int> indices, IEnumerable<double> estimatedClassValue) {
-      if (!classValues.Count().Equals(2))
-        return Enumerable.Repeat(double.NaN, indices.Count());
-
       Dataset dataset = solutions.First().ProblemData.Dataset;
       double[][] values = solutions.Select(s => s.Model.GetEstimatedValues(dataset, indices).ToArray()).ToArray();
@@ -87 +81 @@
 
     protected double GetAverageConfidence(double avg, double estimatedClassValue) {
-      if (estimatedClassValue.Equals(classValues[0])) {
-        if (avg < estimatedClassValue)
-          return 1;
-        else if (avg >= threshold[1])
-          return 0;
-        else {
-          double distance = threshold[1] - classValues[0];
-          return (1 / distance) * (threshold[1] - avg);
+      for (int i = 0; i < classValues.Length; i++) {
+        if (estimatedClassValue.Equals(classValues[i])) {
+          //special case: avgerage is higher than value of highest class
+          if (i == classValues.Length - 1 && avg > estimatedClassValue) {
+            return 1;
+          }
+          //special case: average is lower than value of lowest class
+          if (i == 0 && avg < estimatedClassValue) {
+            return 1;
+          }
+          //special case: average is not between threshold of estimated class value
+          if ((i < classValues.Length - 1 && avg >= threshold[i + 1]) || avg <= threshold[i]) {
+            return 0;
+          }
+
+          double thresholdToClassDistance, thresholdToAverageValueDistance;
+          if (avg >= classValues[i]) {
+            thresholdToClassDistance = threshold[i + 1] - classValues[i];
+            thresholdToAverageValueDistance = threshold[i + 1] - avg;
+          } else {
+            thresholdToClassDistance = classValues[i] - threshold[i];
+            thresholdToAverageValueDistance = avg - threshold[i];
+          }
+          return (1 / thresholdToClassDistance) * thresholdToAverageValueDistance;
         }
-      } else if (estimatedClassValue.Equals(classValues[1])) {
-        if (avg > estimatedClassValue)
-          return 1;
-        else if (avg <= threshold[1])
-          return 0;
-        else {
-          double distance = classValues[1] - threshold[1];
-          return (1 / distance) * (avg - threshold[1]);
-        }
-      } else
-        return double.NaN;
+      }
+      return double.NaN;
     }
   }