Changeset 476 for trunk/sources/HeuristicLab.StructureIdentification

Timestamp:

08/09/08 12:18:20 (16 years ago)

Author:

gkronber

Message:

fixed a few bugs in classification evaluators

Location:

trunk/sources/HeuristicLab.StructureIdentification/Evaluation

Files:

• AccuracyEvaluator.cs (modified) (1 diff)
• ClassificationMeanSquaredErrorEvaluator.cs (modified) (2 diffs)

trunk/sources/HeuristicLab.StructureIdentification/Evaluation/AccuracyEvaluator.cs

@@ -64 +64 @@
         double origClass = dataset.GetValue(sample, targetVariable);
         double estClass = double.NaN;
-        if(est < classesArr[0]) estClass = classesArr[0];
-        else if(est > classesArr[classesArr.Length - 1]) estClass = classesArr[classesArr.Length - 1];
+        // if estimation is lower than the smallest threshold value -> estimated class is the lower class
+        if(est < thresholds[0]) estClass = classesArr[0];
+        // if estimation is larger (or equal) than the largest threshold value -> estimated class is the upper class
+        else if(est >= thresholds[thresholds.Length - 1]) estClass = classesArr[classesArr.Length - 1];  
         else {
+          // otherwise the estimated class is the class which upper threshold is larger than the estimated value
           for(int k = 0; k < thresholds.Length; k++) {
             if(thresholds[k] > est) {
-              estClass = classesArr[k + 1];
+              estClass = classesArr[k];
               break;
             }

trunk/sources/HeuristicLab.StructureIdentification/Evaluation/ClassificationMeanSquaredErrorEvaluator.cs

@@ -66 +66 @@
         }
         double error = estimated - original;
-        if(estimated < classesArr[0] ||
-          estimated > classesArr[classesArr.Length - 1]) {
+        // between classes use squared error
+        // on the lower end and upper end only add linear error if the absolute error is larger than 1
+        // the error>1.0 constraint is needed for balance because in the interval ]-1, 1[ the squared error is smaller than the absolute error
+        if(error < -1.0 && original == classesArr[0] && estimated < classesArr[0] ||
+          error > 1.0 && original == classesArr[classesArr.Length - 1] && estimated > classesArr[classesArr.Length - 1]) {
           errorsSquaredSum += Math.Abs(error); // only add linear error below the smallest class or above the largest class
         } else {
@@ -74 +77 @@
       }
 
-      errorsSquaredSum /= (trainingEnd-trainingStart);
+      errorsSquaredSum /= (trainingEnd - trainingStart);
       if(double.IsNaN(errorsSquaredSum) || double.IsInfinity(errorsSquaredSum)) {
         errorsSquaredSum = double.MaxValue;
       }
-      scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data = totalEvaluatedNodes + treeSize * (trainingEnd-trainingStart);
+      scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data = totalEvaluatedNodes + treeSize * (trainingEnd - trainingStart);
       return errorsSquaredSum;
     }