Changeset 479 for trunk/sources/HeuristicLab.StructureIdentification/Evaluation/MCCEvaluator.cs

Timestamp:

08/10/08 11:08:07 (16 years ago)

Author:

gkronber

Message:

implemented #242 (All GP evaluators should support the 'UseEstimatedTargetValues' switch for autoregressive modelling).
Also used the chance to remove a lot of the code duplication and thus improve the readability of all GP evaluators.

File:

• trunk/sources/HeuristicLab.StructureIdentification/Evaluation/MCCEvaluator.cs (modified) (6 diffs)

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

@@ -32 +32 @@
 namespace HeuristicLab.StructureIdentification {
   public class MCCEvaluator : GPEvaluatorBase {
+    private double limit;
+    private double[] original = new double[1];
+    private double[] estimated = new double[1];
     public override string Description {
       get {
@@ -37 +40 @@
       }
     }
-
     public MCCEvaluator()
       : base() {
@@ -43 +45 @@
     }
 
-    private double[] original = new double[1];
-    private double[] estimated = new double[1];
-    public override double Evaluate(IScope scope, IFunctionTree functionTree, int targetVariable, Dataset dataset) {
-      int trainingStart = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
-      int trainingEnd = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
-      int nSamples = trainingEnd-trainingStart;
-      double limit = GetVariableValue<DoubleData>("ClassSeparation", scope, true).Data;
+    public override IOperation Apply(IScope scope) {
+      limit = GetVariableValue<DoubleData>("ClassSeparation", scope, true).Data;
+      return base.Apply(scope);
+    }
+
+    public override double Evaluate(int start, int end) {
+      int nSamples = end - start;
       if(estimated.Length != nSamples) {
         estimated = new double[nSamples];
@@ -57 +59 @@
       double positive = 0;
       double negative = 0;
-      double targetMean = dataset.GetMean(targetVariable, trainingStart, trainingEnd);
-      for(int sample = trainingStart; sample < trainingEnd; sample++) {
-        double est = evaluator.Evaluate(sample);
-        double orig = dataset.GetValue(sample, targetVariable);
-        if(double.IsNaN(est) || double.IsInfinity(est)) {
-          est = targetMean + maximumPunishment;
-        } else if(est > targetMean + maximumPunishment) {
-          est = targetMean + maximumPunishment;
-        } else if(est < targetMean - maximumPunishment) {
-          est = targetMean - maximumPunishment;
-        }
-        estimated[sample-trainingStart] = est;
-        original[sample-trainingStart] = orig;
+      for(int sample = start; sample < end; sample++) {
+        double est = GetEstimatedValue(sample);
+        double orig = GetOriginalValue(sample);
+        estimated[sample - start] = est;
+        original[sample - start] = orig;
         if(orig >= limit) positive++;
         else negative++;
@@ -79 +73 @@
       double tn = negative;
       double fp = 0;
-      for(int i = original.Length-1; i >= 0 ; i--) {
+      for(int i = original.Length - 1; i >= 0; i--) {
         if(original[i] >= limit) {
           tp++; fn--;
@@ -90 +84 @@
         }
       }
-      scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data = totalEvaluatedNodes + treeSize * (trainingEnd - trainingStart);
       return best_mcc;
     }