Changeset 18103 for branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective

Timestamp:

12/02/21 17:23:10 (3 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

Location:

branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective

Files:

• PearsonRSquaredAverageSimilarityEvaluator.cs (modified) (3 diffs)
• PearsonRSquaredNestedTreeSizeEvaluator.cs (modified) (1 diff)
• PearsonRSquaredNumberOfVariablesEvaluator.cs (modified) (1 diff)
• PearsonRSquaredTreeComplexityEvaluator.cs (modified) (1 diff)
• SymbolicRegressionMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs (modified) (3 diffs)
• SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs (modified) (1 diff)

branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredAverageSimilarityEvaluator.cs

@@ -70 +70 @@
     public override IOperation InstrumentedApply() {
       IEnumerable<int> rows = GenerateRowsToEvaluate();
-      var solution = SymbolicExpressionTreeParameter.ActualValue;
+      var tree = SymbolicExpressionTreeParameter.ActualValue;
       var problemData = ProblemDataParameter.ActualValue;
       var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
@@ -77 +77 @@
 
       if (UseConstantOptimization) {
-        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper);
+        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper);
       }
 
-      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling);
+      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(
+        tree, problemData, rows, interpreter, applyLinearScaling,
+        estimationLimits.Lower, estimationLimits.Upper);
 
       if (DecimalPlaces >= 0)
@@ -107 +109 @@
       var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
 
-      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling);
+      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(
+        tree, problemData, rows, 
+        SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
+        applyLinearScaling,
+        estimationLimits.Lower, estimationLimits.Upper);
 
       lock (locker) {

branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNestedTreeSizeEvaluator.cs

@@ -63 +63 @@
     }
 
-    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
-      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
+    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
+      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(
+         tree, problemData, rows, 
+         interpreter, applyLinearScaling, 
+         lowerEstimationLimit, upperEstimationLimit);
       if (decimalPlaces >= 0)
         r2 = Math.Round(r2, decimalPlaces);
-      return new double[2] { r2, solution.IterateNodesPostfix().Sum(n => n.GetLength()) }; // sum of the length of the whole sub-tree for each node 
+      return new double[2] { r2, tree.IterateNodesPostfix().Sum(n => n.GetLength()) }; // sum of the length of the whole sub-tree for each node 
     }
 

branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNumberOfVariablesEvaluator.cs

@@ -62 +62 @@
     }
 
-    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
-      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
+    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
+      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(
+        tree, problemData, rows, interpreter, applyLinearScaling,
+        lowerEstimationLimit, upperEstimationLimit);
       if (decimalPlaces >= 0)
         r2 = Math.Round(r2, decimalPlaces);
-      return new double[2] { r2, solution.IterateNodesPostfix().OfType<IVariableTreeNode>().Count() }; // count the number of variables
+      return new double[2] { r2, tree.IterateNodesPostfix().OfType<IVariableTreeNode>().Count() }; // count the number of variables
     }
 

branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredTreeComplexityEvaluator.cs

@@ -61 +61 @@
     }
 
-    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
-      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
+    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
+      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(
+         tree, problemData, rows, 
+         interpreter, applyLinearScaling, 
+         lowerEstimationLimit, upperEstimationLimit);
       if (decimalPlaces >= 0)
         r2 = Math.Round(r2, decimalPlaces);
-      return new double[2] { r2, SymbolicDataAnalysisModelComplexityCalculator.CalculateComplexity(solution) };
+      return new double[2] { r2, SymbolicDataAnalysisModelComplexityCalculator.CalculateComplexity(tree) };
     }
 

branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs

@@ -47 +47 @@
     public override IOperation InstrumentedApply() {
       IEnumerable<int> rows = GenerateRowsToEvaluate();
-      var solution = SymbolicExpressionTreeParameter.ActualValue;
+      var tree = SymbolicExpressionTreeParameter.ActualValue;
       var problemData = ProblemDataParameter.ActualValue;
       var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
@@ -54 +54 @@
 
       if (UseConstantOptimization) {
-        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper);
+        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper);
       }
 
-      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces);
+      double[] qualities = Calculate(
+        tree, ProblemDataParameter.ActualValue, 
+        rows, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, 
+        ApplyLinearScalingParameter.ActualValue.Value, 
+        EstimationLimitsParameter.ActualValue.Lower, 
+        EstimationLimitsParameter.ActualValue.Upper, 
+        DecimalPlaces);
       QualitiesParameter.ActualValue = new DoubleArray(qualities);
       return base.InstrumentedApply();
     }
 
-    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
-      var mse = SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(interpreter, solution, lowerEstimationLimit,
-        upperEstimationLimit, problemData, rows, applyLinearScaling);
+    public static double[] Calculate(
+      ISymbolicExpressionTree tree,
+      IRegressionProblemData problemData, 
+      IEnumerable<int> rows,
+      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
+      bool applyLinearScaling,
+      double lowerEstimationLimit, double upperEstimationLimit,
+      int decimalPlaces) {
+      var mse = SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(
+        tree, problemData, rows, 
+        interpreter, applyLinearScaling, 
+        lowerEstimationLimit,
+        upperEstimationLimit);
 
       if (decimalPlaces >= 0)
         mse = Math.Round(mse, decimalPlaces);
 
-      return new double[2] { mse, solution.Length };
+      return new double[2] { mse, tree.Length };
     }
 
@@ -77 +93 @@
       ApplyLinearScalingParameter.ExecutionContext = context;
 
-      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces);
+      double[] quality = Calculate(
+        tree, problemData, rows, 
+        SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, 
+        ApplyLinearScalingParameter.ActualValue.Value, 
+        EstimationLimitsParameter.ActualValue.Lower, 
+        EstimationLimitsParameter.ActualValue.Upper, DecimalPlaces);
 
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;

branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs

@@ -61 +61 @@
     }
 
-    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
-      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
+    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
+      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(
+        tree, problemData, rows, interpreter, applyLinearScaling,
+        lowerEstimationLimit, upperEstimationLimit);
       if (decimalPlaces >= 0)
         r2 = Math.Round(r2, decimalPlaces);
-      return new double[2] { r2, solution.Length };
+      return new double[2] { r2, tree.Length };
     }