Changeset 1796 for trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/3.3

Timestamp:

05/14/09 13:46:57 (15 years ago)

Author:

gkronber

Message:

Refactored GP evaluation to make it possible to use different evaluators to interpret function trees. #615 (Evaluation of HL3 function trees should be equivalent to evaluation in HL2)

Location:

trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/3.3

Files:

• AccuracyEvaluator.cs (modified) (2 diffs)
• ClassificationMeanSquaredErrorEvaluator.cs (modified) (1 diff)
• ConfusionMatrixEvaluator.cs (modified) (2 diffs)
• GPClassificationEvaluatorBase.cs (modified) (2 diffs)
• MulticlassOneVsOneAnalyzer.cs (modified) (4 diffs)

trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/3.3/AccuracyEvaluator.cs

@@ -43 +43 @@
     }
 
-    public override void Evaluate(IScope scope, BakedTreeEvaluator evaluator, Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end) {
+    public override void Evaluate(IScope scope, ITreeEvaluator evaluator, IFunctionTree tree, Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end) {
       DoubleData accuracy = GetVariableValue<DoubleData>("Accuracy", scope, false, false);
       if (accuracy == null) {
@@ -53 +53 @@
       int nCorrect = 0;
       for (int sample = start; sample < end; sample++) {
-        double est = evaluator.Evaluate(sample);
+        double est = evaluator.Evaluate(tree, sample);
         double origClass = dataset.GetValue(sample, targetVariable);
         double estClass = double.NaN;

trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/3.3/ClassificationMeanSquaredErrorEvaluator.cs

@@ -43 +43 @@
     }
 
-    public override void Evaluate(IScope scope, BakedTreeEvaluator evaluator, HeuristicLab.DataAnalysis.Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end) {
+    public override void Evaluate(IScope scope, ITreeEvaluator evaluator, IFunctionTree tree, 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 = evaluator.Evaluate(sample);
+        double estimated = evaluator.Evaluate(tree, sample);
         double original = dataset.GetValue(sample, targetVariable);
         if (!double.IsNaN(original) && !double.IsInfinity(original)) {

trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/3.3/ConfusionMatrixEvaluator.cs

@@ -41 +41 @@
     }
 
-    public override void Evaluate(IScope scope, BakedTreeEvaluator evaluator, HeuristicLab.DataAnalysis.Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end) {
+    public override void Evaluate(IScope scope, ITreeEvaluator evaluator, IFunctionTree tree, 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) {
@@ -50 +50 @@
       int nSamples = end - start;
       for (int sample = start; sample < end; sample++) {
-        double est = evaluator.Evaluate(sample);
+        double est = evaluator.Evaluate(tree, sample);
         double origClass = dataset.GetValue(sample, targetVariable);
         int estClassIndex = -1;

trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/3.3/GPClassificationEvaluatorBase.cs

@@ -37 +37 @@
     }
 
-    public override void Evaluate(IScope scope, BakedTreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
+    public override void Evaluate(IScope scope, ITreeEvaluator evaluator, IFunctionTree tree, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
 
       ItemList<DoubleData> classes = GetVariableValue<ItemList<DoubleData>>("TargetClassValues", scope, true);
@@ -48 +48 @@
       }
 
-      Evaluate(scope, evaluator, dataset, targetVariable, classesArr, thresholds, start, end);
+      Evaluate(scope, evaluator, tree, dataset, targetVariable, classesArr, thresholds, start, end);
     }
 
-    public abstract void Evaluate(IScope scope, BakedTreeEvaluator evaluator, Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end);
+    public abstract void Evaluate(IScope scope, ITreeEvaluator evaluator, IFunctionTree tree, Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end);
   }
 }

trunk/sources/HeuristicLab.GP.StructureIdentification.Classification/3.3/MulticlassOneVsOneAnalyzer.cs

@@ -35 +35 @@
     private const string TARGETVARIABLE = "TargetVariable";
     private const string TARGETCLASSVALUES = "TargetClassValues";
+    private const string TRAININGSAMPLESSTART = "TrainingSamplesStart";
+    private const string TRAININGSAMPLESEND = "TrainingSamplesEnd";
     private const string SAMPLESSTART = "SamplesStart";
     private const string SAMPLESEND = "SamplesEnd";
@@ -56 +58 @@
       AddVariableInfo(new VariableInfo(CLASSAVALUE, "The original class value of the class A in the subscope", typeof(DoubleData), VariableKind.In));
       AddVariableInfo(new VariableInfo(CLASSBVALUE, "The original class value of the class B in the subscope", typeof(DoubleData), VariableKind.In));
+      AddVariableInfo(new VariableInfo(TRAININGSAMPLESSTART, "The start of training samples in the original dataset", typeof(IntData), VariableKind.In));
+      AddVariableInfo(new VariableInfo(TRAININGSAMPLESEND, "The end of training samples in the original dataset", typeof(IntData), VariableKind.In));
       AddVariableInfo(new VariableInfo(SAMPLESSTART, "The start of samples in the original dataset", typeof(IntData), VariableKind.In));
       AddVariableInfo(new VariableInfo(SAMPLESEND, "The end of samples in the original dataset", typeof(IntData), VariableKind.In));
@@ -67 +71 @@
       Dataset dataset = GetVariableValue<Dataset>(DATASET, scope, true);
       int targetVariable = GetVariableValue<IntData>(TARGETVARIABLE, scope, true).Data;
+      int trainingSamplesStart = GetVariableValue<IntData>(TRAININGSAMPLESSTART, scope, true).Data;
+      int trainingSamplesEnd = GetVariableValue<IntData>(TRAININGSAMPLESEND, scope, true).Data;
       int samplesStart = GetVariableValue<IntData>(SAMPLESSTART, scope, true).Data;
       int samplesEnd = GetVariableValue<IntData>(SAMPLESEND, scope, true).Data;
@@ -79 +85 @@
 
         BakedTreeEvaluator evaluator = new BakedTreeEvaluator();
-        evaluator.ResetEvaluator(functionTree, dataset, targetVariable, samplesStart, samplesEnd, 1.0);
+        evaluator.ResetEvaluator(dataset, targetVariable, trainingSamplesStart, trainingSamplesEnd, 1.0);
 
         for(int i = 0; i < (samplesEnd - samplesStart); i++) {
-          double est = evaluator.Evaluate(i + samplesStart);
+          double est = evaluator.Evaluate(functionTree, i + samplesStart);
           if(est < 0.5) {
             CastVote(votes, i, classAValue, classValues);