Changeset 12669 for stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4

Timestamp:

07/08/15 09:52:09 (9 years ago)

Author:

gkronber

Message:

#2392: merged r12492 and r12641 from trunk to stable

Location:

stable

Files:

• . (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionPruningOperator.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionSolutionImpactValuesCalculator.cs (modified) (2 diffs)

stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs

@@ -57 +57 @@
       OnlineCalculatorError errorState;
 
-      double r2;
+      double r;
       if (applyLinearScaling) {
-        var r2Calculator = new OnlinePearsonsRSquaredCalculator();
-        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, r2Calculator, problemData.Dataset.Rows);
-        errorState = r2Calculator.ErrorState;
-        r2 = r2Calculator.RSquared;
+        var rCalculator = new OnlinePearsonsRCalculator();
+        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, rCalculator, problemData.Dataset.Rows);
+        errorState = rCalculator.ErrorState;
+        r = rCalculator.R;
       } else {
         IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
-        r2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
+        r = OnlinePearsonsRCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
       }
 
-      if (errorState != OnlineCalculatorError.None) r2 = double.NaN;
-      return new double[2] { r2, solution.Length };
+      if (errorState != OnlineCalculatorError.None) r = double.NaN;
+      return new double[2] { r*r, solution.Length };
     }
 

stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs

@@ -59 +59 @@
       OnlineCalculatorError errorState;
 
-      double r2;
+      double r;
       if (applyLinearScaling) {
-        var r2Calculator = new OnlinePearsonsRSquaredCalculator();
-        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, r2Calculator, problemData.Dataset.Rows);
-        errorState = r2Calculator.ErrorState;
-        r2 = r2Calculator.RSquared;
+        var rCalculator = new OnlinePearsonsRCalculator();
+        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, rCalculator, problemData.Dataset.Rows);
+        errorState = rCalculator.ErrorState;
+        r = rCalculator.R;
       } else {
         IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
-        r2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
+        r = OnlinePearsonsRCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
       }
       if (errorState != OnlineCalculatorError.None) return double.NaN;
-      return r2;
+      return r*r;
     }
 

stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionPruningOperator.cs

@@ -60 +60 @@
       var targetValues = ProblemData.Dataset.GetDoubleValues(regressionProblemData.TargetVariable, trainingIndices);
       OnlineCalculatorError errorState;
-      var quality = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, estimatedValues, out errorState);
+      var quality = OnlinePearsonsRCalculator.Calculate(targetValues, estimatedValues, out errorState);
       if (errorState != OnlineCalculatorError.None) return double.NaN;
-      return quality;
+      return quality*quality;
     }
   }

stable/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionSolutionImpactValuesCalculator.cs

@@ -66 +66 @@
       if (double.IsNaN(originalQuality)) {
         var originalValues = regressionModel.GetEstimatedValues(dataset, rows);
-        originalQuality = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, originalValues, out errorState);
+        originalQuality = OnlinePearsonsRCalculator.Calculate(targetValues, originalValues, out errorState);
         if (errorState != OnlineCalculatorError.None) originalQuality = 0.0;
       }
@@ -83 +83 @@
 
       var estimatedValues = tempModel.GetEstimatedValues(dataset, rows);
-      double newQuality = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, estimatedValues, out errorState);
+      double newQuality = OnlinePearsonsRCalculator.Calculate(targetValues, estimatedValues, out errorState);
       if (errorState != OnlineCalculatorError.None) newQuality = 0.0;
 
-      impactValue = originalQuality - newQuality;
+      impactValue = (originalQuality*originalQuality) - (newQuality*newQuality);
     }
   }