Changeset 702 for trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/ClassificationMeanSquaredErrorEvaluator.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/ClassificationMeanSquaredErrorEvaluator.cs (modified) (4 diffs)

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

@@ -29 +29 @@
 
 namespace HeuristicLab.GP.StructureIdentification.Classification {
-  public class ClassificationMeanSquaredErrorEvaluator : MeanSquaredErrorEvaluator {
-    private const double EPSILON = 1.0E-6;
-    private double[] classesArr;
+  public class ClassificationMeanSquaredErrorEvaluator : GPClassificationEvaluatorBase {
+    private const double EPSILON = 1.0E-7;
     public override string Description {
       get {
@@ -41 +40 @@
     public ClassificationMeanSquaredErrorEvaluator()
       : base() {
-      AddVariableInfo(new VariableInfo("TargetClassValues", "The original class values of target variable (for instance negative=0 and positive=1).", typeof(ItemList<DoubleData>), VariableKind.In));
+      AddVariableInfo(new VariableInfo("MSE", "The mean squared error of the model", typeof(DoubleData), VariableKind.New));
     }
 
-    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);
-      return base.Apply(scope);
-    }
-
-    public override void Evaluate(int start, int end) {
+    public override void  Evaluate(IScope scope, BakedTreeEvaluator evaluator, HeuristicLab.DataAnalysis.Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end)
+{
       double errorsSquaredSum = 0;
       for(int sample = start; sample < end; sample++) {
-        double estimated = GetEstimatedValue(sample);
-        double original = GetOriginalValue(sample);
-        SetOriginalValue(sample, estimated);
+        double estimated = evaluator.Evaluate(sample);
+        double original = dataset.GetValue(targetVariable, sample);
         if(!double.IsNaN(original) && !double.IsInfinity(original)) {
           double error = estimated - original;
@@ -63 +54 @@
           // 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((IsEqual(original, classesArr[0]) && error < -1.0) ||
-            (IsEqual(original, classesArr[classesArr.Length - 1]) && error > 1.0)) {
+          if((IsEqual(original, classes[0]) && error < -1.0) ||
+            (IsEqual(original, classes[classes.Length - 1]) && error > 1.0)) {
             errorsSquaredSum += Math.Abs(error); // only add linear error below the smallest class or above the largest class
           } else {
@@ -76 +67 @@
         errorsSquaredSum = double.MaxValue;
       }
+
+      DoubleData mse = GetVariableValue<DoubleData>("MSE", scope, false, false);
+      if(mse == null) {
+        mse = new DoubleData();
+        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("MSE"), mse));
+      }
+
       mse.Data = errorsSquaredSum;
     }