Changeset 702 for trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/ConfusionMatrixEvaluator.cs

Timestamp:

10/29/08 11:21:04 (15 years ago)

Author:

gkronber

Message:

fixed #328 by restructuring evaluation operators to remove state in evaluation operators.

File:

• trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/ConfusionMatrixEvaluator.cs (modified) (3 diffs)

trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/ConfusionMatrixEvaluator.cs

@@ -29 +29 @@
 
 namespace HeuristicLab.GP.StructureIdentification.Classification {
-  public class ConfusionMatrixEvaluator : GPEvaluatorBase {
-    private const double EPSILON = 1.0E-6;
-    private double[] classesArr;
-    private double[] thresholds;
-    private IntMatrixData matrix;
+  public class ConfusionMatrixEvaluator : GPClassificationEvaluatorBase {
     public override string Description {
       get {
@@ -43 +39 @@
       : base() {
       AddVariableInfo(new VariableInfo("ConfusionMatrix", "The confusion matrix of the model", typeof(IntMatrixData), VariableKind.New));
-      AddVariableInfo(new VariableInfo("TargetClassValues", "The original class values of target variable (for instance negative=0 and positive=1).", typeof(ItemList<DoubleData>), VariableKind.In));
     }
 
-    public override IOperation Apply(IScope scope) {
-      ItemList<DoubleData> classes = GetVariableValue<ItemList<DoubleData>>("TargetClassValues", scope, true);
-      classesArr = new double[classes.Count];
-      for(int i = 0; i < classesArr.Length; i++) classesArr[i] = classes[i].Data;
-      Array.Sort(classesArr);
-      thresholds = new double[classes.Count - 1];
-      for(int i = 0; i < classesArr.Length - 1; i++) {
-        thresholds[i] = (classesArr[i] + classesArr[i + 1]) / 2.0;
+    public override void Evaluate(IScope scope, BakedTreeEvaluator evaluator, HeuristicLab.DataAnalysis.Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end) {
+      IntMatrixData matrix = GetVariableValue<IntMatrixData>("ConfusionMatrix", scope, false, false);
+      if(matrix == null) {
+        matrix = new IntMatrixData(new int[classes.Length, classes.Length]);
+        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("ConfusionMatrix"), matrix));
       }
 
-      matrix = GetVariableValue<IntMatrixData>("ConfusionMatrix", scope, false, false);
-      if(matrix == null) {
-        matrix = new IntMatrixData(new int[classesArr.Length, classesArr.Length]);
-        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("ConfusionMatrix"), matrix));
-      }
-      return base.Apply(scope);
-    }
-
-    public override void Evaluate(int start, int end) {
       int nSamples = end - start;
       for(int sample = start; sample < end; sample++) {
-        double est = GetEstimatedValue(sample);
-        double origClass = GetOriginalValue(sample);
+        double est = evaluator.Evaluate(sample);
+        double origClass = dataset.GetValue(targetVariable,sample);
         int estClassIndex = -1;
         // if estimation is lower than the smallest threshold value -> estimated class is the lower class
         if(est < thresholds[0]) estClassIndex = 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]) estClassIndex = classesArr.Length - 1;
+        else if(est >= thresholds[thresholds.Length - 1]) estClassIndex = classes.Length - 1;
         else {
           // otherwise the estimated class is the class which upper threshold is larger than the estimated value
@@ -83 +66 @@
           }
         }
-        SetOriginalValue(sample, classesArr[estClassIndex]);
 
-        int origClassIndex = -1;
-        for(int i = 0; i < classesArr.Length; i++) {
-          if(IsEqual(origClass, classesArr[i])) origClassIndex = i;
+        // find the first threshold index that is larger to the original value
+        int origClassIndex = classes.Length-1;
+        for(int i = 0; i < thresholds.Length; i++) {
+          if(origClass < thresholds[i]) {
+            origClassIndex = i;
+            break;
+          }
         }
         matrix.Data[origClassIndex, estClassIndex]++;
       }
     }
-
-    private bool IsEqual(double x, double y) {
-      return Math.Abs(x - y) < EPSILON;
-    }
   }
 }