Changeset 5720 for branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer.cs

Timestamp:

03/16/11 17:13:33 (14 years ago)

Author:

gkronber

Message:

#1418 Added upper and lower estimation bounds for symbolic classification and regression.

File:

• branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer.cs (modified) (5 diffs)

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer.cs

@@ -38 +38 @@
   [StorableClass]
   public sealed class SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveTrainingBestSolutionAnalyzer<ISymbolicClassificationSolution>,
-    ISymbolicDataAnalysisInterpreterOperator {
+    ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator {
     private const string ProblemDataParameterName = "ProblemData";
     private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
+    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
+    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
     #region parameter properties
     public ILookupParameter<IClassificationProblemData> ProblemDataParameter {
@@ -47 +49 @@
     public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
       get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
+    }
+    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
+      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
+    }
+
+    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
+      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
     }
     #endregion
@@ -56 +65 @@
       get { return SymbolicDataAnalysisTreeInterpreterParameter.ActualValue; }
     }
+    public DoubleValue UpperEstimationLimit {
+      get { return UpperEstimationLimitParameter.ActualValue; }
+    }
+    public DoubleValue LowerEstimationLimit {
+      get { return LowerEstimationLimitParameter.ActualValue; }
+    }
     #endregion
+
     [StorableConstructor]
     private SymbolicClassificationSingleObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
@@ -64 +80 @@
       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<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit for the estimated values produced by the symbolic classification model."));
+      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit for the estimated values produced by the symbolic classification model."));
     }
     public override IDeepCloneable Clone(Cloner cloner) {
@@ -72 +90 @@
       double[] classValues;
       double[] thresholds;
-      var estimatedValues = SymbolicDataAnalysisTreeInterpreter.GetSymbolicExpressionTreeValues(bestTree, ProblemData.Dataset, ProblemData.TrainingIndizes);
+      var estimatedValues = SymbolicDataAnalysisTreeInterpreter.GetSymbolicExpressionTreeValues(bestTree, ProblemData.Dataset, ProblemData.TrainingIndizes)
+        .LimitToRange(LowerEstimationLimit.Value, UpperEstimationLimit.Value);
       var targetValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes);
       AccuracyMaximizationThresholdCalculator.CalculateThresholds(ProblemData, estimatedValues, targetValues, out classValues, out thresholds);
-      var model = new SymbolicDiscriminantFunctionClassificationModel(bestTree, SymbolicDataAnalysisTreeInterpreter, classValues, thresholds);
+      var model = new SymbolicDiscriminantFunctionClassificationModel(bestTree, SymbolicDataAnalysisTreeInterpreter, classValues, thresholds, LowerEstimationLimit.Value, UpperEstimationLimit.Value);
       return new SymbolicDiscriminantFunctionClassificationSolution(model, ProblemData);
     }