Changeset 12378 for branches/GBT/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4

Timestamp:

05/04/15 13:08:49 (10 years ago)

Author:

gkronber

Message:

Merged revision(s) 12333-12365 from trunk/sources:
#2373: Corrected typo in RandomBinaryVectorCreator by implementing an after-deserialization-hook.

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#2359: Refactored pruning operators and analyzers.

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#2359: Removed commented code from pruning analyzer.

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#2378: Vertex.cs: Fixed bug in Label setter.
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#2359: The changes in r12358 look fine to me. Added total number of pruned nodes in the analyzer's data table. Removed unused parameter names in the SymbolicDataAnalysisSingleObjectivePruningAnalyzer.
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#2345: Fixed x-axis maximum in error characteristics curve.

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Location:

branches/GBT

Files:

• . (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Classification (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicClassificationPruningAnalyzer.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicClassificationPruningOperator.cs (modified) (3 diffs)

branches/GBT/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicClassificationPruningAnalyzer.cs

@@ -29 +29 @@
   [StorableClass]
   public sealed class SymbolicClassificationPruningAnalyzer : SymbolicDataAnalysisSingleObjectivePruningAnalyzer {
-    private const string ImpactValuesCalculatorParameterName = "ImpactValuesCalculator";
     private const string PruningOperatorParameterName = "PruningOperator";
-    private SymbolicClassificationPruningAnalyzer(SymbolicClassificationPruningAnalyzer original, Cloner cloner)
-      : base(original, cloner) {
+    public IValueParameter<SymbolicClassificationPruningOperator> PruningOperatorParameter {
+      get { return (IValueParameter<SymbolicClassificationPruningOperator>)Parameters[PruningOperatorParameterName]; }
     }
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new SymbolicClassificationPruningAnalyzer(this, cloner);
+
+    protected override SymbolicDataAnalysisExpressionPruningOperator PruningOperator {
+      get { return PruningOperatorParameter.Value; }
     }
+
+    private SymbolicClassificationPruningAnalyzer(SymbolicClassificationPruningAnalyzer original, Cloner cloner) : base(original, cloner) { }
+    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicClassificationPruningAnalyzer(this, cloner); }
 
     [StorableConstructor]
@@ -42 +45 @@
 
     public SymbolicClassificationPruningAnalyzer() {
-      Parameters.Add(new ValueParameter<SymbolicDataAnalysisSolutionImpactValuesCalculator>(ImpactValuesCalculatorParameterName, "The impact values calculator", new SymbolicClassificationSolutionImpactValuesCalculator()));
       Parameters.Add(new ValueParameter<SymbolicDataAnalysisExpressionPruningOperator>(PruningOperatorParameterName, "The operator used to prune trees", new SymbolicClassificationPruningOperator(new SymbolicClassificationSolutionImpactValuesCalculator())));
     }

branches/GBT/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicClassificationPruningOperator.cs

@@ -42 +42 @@
     #endregion
 
-    protected SymbolicClassificationPruningOperator(SymbolicClassificationPruningOperator original, Cloner cloner)
-      : base(original, cloner) {
-    }
-
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new SymbolicClassificationPruningOperator(this, cloner);
-    }
+    protected SymbolicClassificationPruningOperator(SymbolicClassificationPruningOperator original, Cloner cloner) : base(original, cloner) { }
+    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicClassificationPruningOperator(this, cloner); }
 
     [StorableConstructor]
@@ -68 +63 @@
     protected override double Evaluate(IDataAnalysisModel model) {
       var classificationModel = (IClassificationModel)model;
-      var classificationProblemData = (IClassificationProblemData)ProblemData;
-      var trainingIndices = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
+      var classificationProblemData = (IClassificationProblemData)ProblemDataParameter.ActualValue;
+      var rows = Enumerable.Range(FitnessCalculationPartitionParameter.ActualValue.Start, FitnessCalculationPartitionParameter.ActualValue.Size);
 
-      return Evaluate(classificationModel, classificationProblemData, trainingIndices);
+      return Evaluate(classificationModel, classificationProblemData, rows);
     }
 
@@ -100 +95 @@
         impactValuesCalculator.CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, quality);
 
-        if (pruneOnlyZeroImpactNodes) {
-          if (!impactValue.IsAlmost(0.0)) continue;
-        } else if (nodeImpactThreshold < impactValue) {
-          continue;
-        }
+        if (pruneOnlyZeroImpactNodes && !impactValue.IsAlmost(0.0)) continue;
+        if (!pruneOnlyZeroImpactNodes && impactValue > nodeImpactThreshold) continue;
 
         var constantNode = (ConstantTreeNode)node.Grammar.GetSymbol("Constant").CreateTreeNode();