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

Timestamp:

10/29/08 11:21:04 (16 years ago)

Author:

gkronber

Message:

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

File:

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

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

@@ -27 +27 @@
 using HeuristicLab.Data;
 using HeuristicLab.GP.StructureIdentification;
+using HeuristicLab.DataAnalysis;
 
 namespace HeuristicLab.GP.StructureIdentification.Classification {
-  public class AccuracyEvaluator : GPEvaluatorBase {
+  public class AccuracyEvaluator : GPClassificationEvaluatorBase {
     private const double EPSILON = 1.0E-6;
-    private double[] classesArr;
-    private double[] thresholds;
-    private DoubleData accuracy;
     public override string Description {
       get {
@@ -43 +41 @@
       : base() {
       AddVariableInfo(new VariableInfo("Accuracy", "The total accuracy of the model (ratio of correctly classified instances to total number of instances)", typeof(DoubleData), 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) {
-      accuracy = GetVariableValue<DoubleData>("Accuracy", scope, false, false);
+    public override void Evaluate(IScope scope, BakedTreeEvaluator evaluator, Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end) {
+      DoubleData accuracy = GetVariableValue<DoubleData>("Accuracy", scope, false, false);
       if(accuracy == null) {
         accuracy = new DoubleData();
@@ -53 +50 @@
       }
 
-      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;
-      }
-
-      return base.Apply(scope);
-    }
-
-    public override void Evaluate(int start, int end) {
       int nSamples = end - start;
       int nCorrect = 0;
       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);
         double estClass = double.NaN;
         // if estimation is lower than the smallest threshold value -> estimated class is the lower class
-        if(est < thresholds[0]) estClass = classesArr[0];
+        if(est < thresholds[0]) estClass = classes[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 if(est >= thresholds[thresholds.Length - 1]) estClass = classes[classes.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];
+              estClass = classes[k];
               break;
             }
           }
         }
-        SetOriginalValue(sample, estClass);
         if(Math.Abs(estClass - origClass) < EPSILON) nCorrect++;
       }