Changeset 8206 for branches/GP-MoveOperators/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective

Timestamp:

07/03/12 16:46:35 (12 years ago)

Author:

gkronber

Message:

#1847: merged r8084:8205 from trunk into GP move operators branch

Location:

branches/GP-MoveOperators

Files:

• . (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveProblem.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer.cs (modified) (2 diffs)

branches/GP-MoveOperators/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveProblem.cs

@@ -48 +48 @@
     [StorableConstructor]
     protected SymbolicClassificationSingleObjectiveProblem(bool deserializing) : base(deserializing) { }
-    protected SymbolicClassificationSingleObjectiveProblem(SymbolicClassificationSingleObjectiveProblem original, Cloner cloner) : base(original, cloner) { }
+    protected SymbolicClassificationSingleObjectiveProblem(SymbolicClassificationSingleObjectiveProblem original, Cloner cloner)
+      : base(original, cloner) {
+      RegisterEventHandlers();
+    }
     public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicClassificationSingleObjectiveProblem(this, cloner); }
 
@@ -60 +63 @@
       MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
 
-      SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols();
-
+      RegisterEventHandlers();
       ConfigureGrammarSymbols();
       InitializeOperators();
       UpdateEstimationLimits();
+    }
+
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      RegisterEventHandlers();
+      // compatibility
+      bool changed = false;
+      if (!Operators.OfType<SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer>().Any()) {
+        Operators.Add(new SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer());
+        changed = true;
+      }
+      if (!Operators.OfType<SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer>().Any()) {
+        Operators.Add(new SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer());
+        changed = true;
+      }
+      if (changed) ParameterizeOperators();
+    }
+
+    private void RegisterEventHandlers() {
+      SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols();
     }
 
@@ -82 +104 @@
 
     private void UpdateEstimationLimits() {
-      if (ProblemData.TrainingIndizes.Any()) {
-        var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes).ToList();
+      if (ProblemData.TrainingIndices.Any()) {
+        var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
         var mean = targetValues.Average();
         var range = targetValues.Max() - targetValues.Min();

branches/GP-MoveOperators/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer.cs

@@ -33 +33 @@
   [Item("SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer", "An operator that collects the training Pareto-best symbolic classification solutions for single objective symbolic classification problems.")]
   [StorableClass]
-  public sealed class SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingParetoBestSolutionAnalyzer<ISymbolicClassificationSolution>,
-  ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
-    private const string ProblemDataParameterName = "ProblemData";
-    private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
-    private const string EstimationLimitsParameterName = "EstimationLimits";
+  public sealed class SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingParetoBestSolutionAnalyzer<IClassificationProblemData, ISymbolicClassificationSolution> {
     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
     #region parameter properties
-    public ILookupParameter<IClassificationProblemData> ProblemDataParameter {
-      get { return (ILookupParameter<IClassificationProblemData>)Parameters[ProblemDataParameterName]; }
-    }
-    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
-      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
-    }
-    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
-      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
-    }
     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
       get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
@@ -65 +52 @@
     public SymbolicClassificationSingleObjectiveTrainingParetoBestSolutionAnalyzer()
       : base() {
-      Parameters.Add(new LookupParameter<IClassificationProblemData>(ProblemDataParameterName, "The problem data for the symbolic classification solution."));
-      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
-      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic classification model."));
       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic classification solution should be linearly scaled.", new BoolValue(false)));
     }

branches/GP-MoveOperators/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer.cs

@@ -33 +33 @@
   [Item("SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer", "An operator that collects the validation Pareto-best symbolic classification solutions for single objective symbolic classification problems.")]
   [StorableClass]
-  public sealed class SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationParetoBestSolutionAnalyzer<ISymbolicClassificationSolution, ISymbolicClassificationSingleObjectiveEvaluator, IClassificationProblemData>,
-  ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
-    private const string EstimationLimitsParameterName = "EstimationLimits";
+  public sealed class SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationParetoBestSolutionAnalyzer<ISymbolicClassificationSolution, ISymbolicClassificationSingleObjectiveEvaluator, IClassificationProblemData> {
     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
     #region parameter properties
-    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
-      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
-    }
     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
       get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
@@ -57 +52 @@
     public SymbolicClassificationSingleObjectiveValidationParetoBestSolutionAnalyzer()
       : base() {
-      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic classification model."));
       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic classification solution should be linearly scaled.", new BoolValue(false)));
     }