Changeset 5846

Timestamp:

03/28/11 17:27:00 (14 years ago)

Author:

gkronber

Message:

#1453: changed *RSquaredEvaluators to return 0.0 (worst possible value) if the result returned by the OnlinePearsonsRSquaredEvaluator is double.NaN

Location:

trunk/sources

Files:

• HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs (modified) (1 diff)

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

@@ -34 +34 @@
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
-      try {
-        double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(originalValues, estimatedValues);
-        return new double[2] { r2, solution.Length };
-      }
-      catch (ArgumentException) {
-        // if R² cannot be calcualted because of infinity or NaN values => return worst possible fitness value
-        return new double[2] { 0.0, solution.Length };
-      }
+      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues);
+      return new double[] { double.IsNaN(r2) ? 0.0 : r2, solution.Length };
     }
 

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

@@ -55 +55 @@
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
-      try {
-        return OnlinePearsonsRSquaredEvaluator.Calculate(originalValues, estimatedValues);
-      }
-      catch (ArgumentException) {
-        // if R² cannot be calculated because of NaN or ininity elements => return worst possible fitness value
-        return 0.0;
-      }
+      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues);
+      return double.IsNaN(r2) ? 0.0 : r2;
     }
 

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs

@@ -55 +55 @@
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
-      try {
-        double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(originalValues, estimatedValues);
-        return new double[2] { r2, solution.Length };
-      }
-      catch (ArgumentException) {
-        // if R² cannot be calcualted return worst possible fitness value
-        return new double[2] { 0.0, solution.Length };
-      }
+      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues);
+      return new double[] { double.IsNaN(r2) ? 0.0 : r2, solution.Length };
     }
 

trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs

@@ -55 +55 @@
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
-      try {
-        return OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues);
-      }
-      catch (ArgumentException) {
-        // if R² cannot be calculated because of NaN or ininity elements => return worst possible fitness value
-        return 0.0;
-      }
+      double r2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedValues, originalValues);
+      return double.IsNaN(r2) ? 0.0 : r2;
     }