Changeset 8915 for branches/HeuristicLab.TreeSimplifier/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer.cs

Timestamp:

11/15/12 16:47:25 (11 years ago)

Author:

mkommend

Message:

#1763: merged changes from trunk into the tree simplifier branch.

File:

• branches/HeuristicLab.TreeSimplifier/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer.cs (modified) (5 diffs)

branches/HeuristicLab.TreeSimplifier/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer.cs

@@ -22 +22 @@
 using HeuristicLab.Common;
 using HeuristicLab.Core;
-using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Parameters;
@@ -34 +33 @@
   [StorableClass]
   public sealed class SymbolicClassificationMultiObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisMultiObjectiveTrainingBestSolutionAnalyzer<ISymbolicClassificationSolution>,
-    ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
+    ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator, ISymbolicClassificationModelCreatorOperator {
     private const string ProblemDataParameterName = "ProblemData";
+    private const string ModelCreatorParameterName = "ModelCreator";
     private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
     private const string EstimationLimitsParameterName = "EstimationLimits";
-    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
 
     #region parameter properties
     public ILookupParameter<IClassificationProblemData> ProblemDataParameter {
       get { return (ILookupParameter<IClassificationProblemData>)Parameters[ProblemDataParameterName]; }
+    }
+    public IValueLookupParameter<ISymbolicClassificationModelCreator> ModelCreatorParameter {
+      get { return (IValueLookupParameter<ISymbolicClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
+    }
+    ILookupParameter<ISymbolicClassificationModelCreator> ISymbolicClassificationModelCreatorOperator.ModelCreatorParameter {
+      get { return ModelCreatorParameter; }
     }
     public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
@@ -49 +54 @@
     public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
       get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
-    }
-    public IValueParameter<BoolValue> ApplyLinearScalingParameter {
-      get { return (IValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
-    }
-    #endregion
-
-    #region properties
-    public BoolValue ApplyLinearScaling {
-      get { return ApplyLinearScalingParameter.Value; }
     }
     #endregion
@@ -67 +63 @@
       : base() {
       Parameters.Add(new LookupParameter<IClassificationProblemData>(ProblemDataParameterName, "The problem data for the symbolic classification solution."));
+      Parameters.Add(new ValueLookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, ""));
       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)));
     }
     public override IDeepCloneable Clone(Cloner cloner) {
@@ -75 +71 @@
     }
 
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      // BackwardsCompatibility3.4
+      #region Backwards compatible code, remove with 3.5
+      if (!Parameters.ContainsKey(ModelCreatorParameterName))
+        Parameters.Add(new ValueLookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, ""));
+      #endregion
+    }
+
     protected override ISymbolicClassificationSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
-      var model = new SymbolicDiscriminantFunctionClassificationModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
-      if (ApplyLinearScaling.Value) {
-        SymbolicDiscriminantFunctionClassificationModel.Scale(model, ProblemDataParameter.ActualValue);
-      }
-      return new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
+      var model = ModelCreatorParameter.ActualValue.CreateSymbolicClassificationModel((ISymbolicExpressionTree)bestTree.Clone(), SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
+      if (ApplyLinearScalingParameter.ActualValue.Value) SymbolicClassificationModel.Scale(model, ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TargetVariable);
+
+      model.RecalculateModelParameters(ProblemDataParameter.ActualValue, ProblemDataParameter.ActualValue.TrainingIndices);
+      return model.CreateClassificationSolution((IClassificationProblemData)ProblemDataParameter.ActualValue.Clone());
     }
   }