Changeset 18103 for branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator.cs

Timestamp:

12/02/21 17:23:10 (2 years ago)

Author:

dpiringe

Message:

#3136

• refactor the evaluation logic of NMSESingleObjectiveConstraintsEvaluator
• refactor the new method Evaluate for PearsonRSquaredAverageSimilarityEvaluator
• change the parameter order of some evaluate/calculate methods

File:

• branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator.cs (modified) (4 diffs)

branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator.cs

@@ -43 +43 @@
 
     public override IOperation InstrumentedApply() {
-      var solution = SymbolicExpressionTreeParameter.ActualValue;
+      var tree = SymbolicExpressionTreeParameter.ActualValue;
       IEnumerable<int> rows = GenerateRowsToEvaluate();
 
-      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
+      double quality = Calculate(tree, 
+        ProblemDataParameter.ActualValue, rows, 
+        SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, 
+        ApplyLinearScalingParameter.ActualValue.Value,
+        EstimationLimitsParameter.ActualValue.Lower, 
+        EstimationLimitsParameter.ActualValue.Upper);
       QualityParameter.ActualValue = new DoubleValue(quality);
 
@@ -52 +57 @@
     }
 
-    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
-      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
+    public static double Calculate(
+      ISymbolicExpressionTree tree, 
+      IRegressionProblemData problemData, 
+      IEnumerable<int> rows,
+      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, 
+      bool applyLinearScaling,
+      double lowerEstimationLimit, 
+      double upperEstimationLimit) {
+      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
       IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
       OnlineCalculatorError errorState;
@@ -76 +88 @@
       ApplyLinearScalingParameter.ExecutionContext = context;
 
-      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
+      double mse = Calculate(
+        tree, problemData, rows, 
+        SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, 
+        ApplyLinearScalingParameter.ActualValue.Value,
+        EstimationLimitsParameter.ActualValue.Lower, 
+        EstimationLimitsParameter.ActualValue.Upper);
 
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
@@ -85 +102 @@
     }
 
-    public override double Evaluate(IRegressionProblemData problemData,
-      ISymbolicExpressionTree solution,
+    public override double Evaluate(
+      ISymbolicExpressionTree tree,
+      IRegressionProblemData problemData,
+      IEnumerable<int> rows,
       ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
-      IEnumerable<int> rows = null,
       bool applyLinearScaling = true,
       double lowerEstimationLimit = double.MinValue,
       double upperEstimationLimit = double.MaxValue) {
-      return Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows ?? problemData.TrainingIndices, applyLinearScaling);
+      return Calculate(tree, problemData, rows, interpreter, applyLinearScaling, lowerEstimationLimit, upperEstimationLimit);
     }
   }