Changeset 5894 for trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification

Timestamp:

03/30/11 18:04:03 (14 years ago)

Author:

gkronber

Message:

#1453: Added an ErrorState property to online evaluators to indicate if the result value is valid or if there has been an error in the calculation. Adapted all classes that use one of the online evaluators to check this property.

Location:

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4

Files:

• MultiObjective/SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs (modified) (2 diffs)
• MultiObjective/SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs (modified) (1 diff)
• SingleObjective/SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator.cs (modified) (1 diff)
• SingleObjective/SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator.cs (modified) (1 diff)
• SingleObjective/SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator.cs (modified) (2 diffs)
• SymbolicDiscriminantFunctionClassificationModel.cs (modified) (1 diff)

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs

@@ -58 +58 @@
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
       IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
-      double mse = OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues);
+      OnlineEvaluatorError errorState;
+      double mse = OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues, out errorState);
+      if (errorState != OnlineEvaluatorError.None) mse = double.NaN;
       return new double[2] { mse, solution.Length };
     }
@@ -72 +74 @@
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
       EstimationLimitsParameter.ExecutionContext = null;
-      EvaluatedNodesParameter.ExecutionContext = null; 
+      EvaluatedNodesParameter.ExecutionContext = null;
 
       return quality;

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs

@@ -37 +37 @@
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
-      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues);
-      return new double[] { double.IsNaN(r2) ? 0.0 : r2, solution.Length };
+      OnlineEvaluatorError errorState;
+      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues, out errorState);
+      if (errorState != OnlineEvaluatorError.None) r2 = 0.0;
+      return new double[] { r2, solution.Length };
+
     }
 

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveBoundedMeanSquaredErrorEvaluator.cs

@@ -63 +63 @@
       IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
       IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
-      OnlineMeanSquaredErrorEvaluator mseEvaluator = new OnlineMeanSquaredErrorEvaluator();
       double errorSum = 0.0;
       int n = 0;

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator.cs

@@ -58 +58 @@
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
       IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
-      return OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues);
+      OnlineEvaluatorError errorState;
+      double mse = OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues, out errorState);
+      if (errorState != OnlineEvaluatorError.None) return double.NaN;
+      else return mse;
     }
 

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator.cs

@@ -58 +58 @@
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
-      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues);
-      return double.IsNaN(r2) ? 0.0 : r2;
+      OnlineEvaluatorError errorState;
+      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues, out errorState);
+      if (errorState != OnlineEvaluatorError.None) return 0.0;
+      else return r2;
     }
 
@@ -72 +74 @@
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
       EstimationLimitsParameter.ExecutionContext = null;
-      EvaluatedNodesParameter.ExecutionContext = null; 
+      EvaluatedNodesParameter.ExecutionContext = null;
 
       return r2;

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicDiscriminantFunctionClassificationModel.cs

@@ -124 +124 @@
       double alpha;
       double beta;
-      OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out alpha, out beta);
+      OnlineEvaluatorError errorState;
+      OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out alpha, out beta, out errorState);
+      if (errorState != OnlineEvaluatorError.None) return;
 
       ConstantTreeNode alphaTreeNode = null;