Changeset 18104

Timestamp:

12/03/21 13:46:20 (3 years ago)

Author:

dpiringe

Message:

#3136

• overrode the method GetActualValue in ValueLookupParameter to get the default value when the execution context is null
• reverted the linear scaling logic for NMSESingleObjectiveConstraintsEvaluator
• in SymbolicRegressionConstantOptimizationEvaluator: removed the usage of GenerateRowsToEvaluate because it uses lookup parameters
• set the value of RelativeNumberOfEvaluatedSamplesParameter for SymbolicRegressionConstantOptimizationEvaluator in StructuredSymbolicRegressionSingleObjectiveProblem if Maximization = true and the SymbolicRegressionConstantOptimizationEvaluator is configured as evaluator
• added the SubFunctionSymbol in TreeToAutoDiffTermConverter

Location:

branches/3136_Structural_GP

Files:

• HeuristicLab.Parameters/3.3/ValueLookupParameter.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/NMSESingleObjectiveConstraintsEvaluator.cs (modified) (4 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.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/SingleObjective/StructuredSymbolicRegressionSingleObjectiveProblem.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Converters/TreeToAutoDiffTermConverter.cs (modified) (2 diffs)

branches/3136_Structural_GP/HeuristicLab.Parameters/3.3/ValueLookupParameter.cs

@@ -151 +151 @@
     }
 
+    protected override IItem GetActualValue() {
+      if (ExecutionContext == null)
+        return Value;
+      return base.GetActualValue();
+    }
+
     public override string ToString() {
       if (Value != null)

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

@@ -141 +141 @@
                             .GetSubtree(0); //Offset
       var scaling = offset.GetSubtree(0);
-
+      
       //Check if tree contains offset and scaling nodes
       if (!(offset.Symbol is Addition) || !(scaling.Symbol is Multiplication))
@@ -155 +155 @@
 
       var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
-      OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out var alpha, out var beta,
-        out var errorState);
+      OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out var alpha, out var beta, out var errorState);
       if (errorState == OnlineCalculatorError.None) {
         //Set alpha and beta to the scaling nodes from ia grammar
@@ -249 +248 @@
           interpreter, tree, 
           problemData, rows,
-          applyLinearScaling: false, // OptimizeConstants deletes the scaling terms -> wrong estimations
+          applyLinearScaling: false, // Tree already contains scaling terms
           ConstantOptimizationIterations, 
           updateVariableWeights: true,
@@ -255 +254 @@
           upperEstimationLimit);
       
-      if (applyLinearScaling) // extra scaling terms, which are included in tree
-        CalcLinearScalingTerms(tree, problemData, rows, interpreter);
-
+      else if (applyLinearScaling) // extra scaling terms, which are included in tree
+        CalcLinearScalingTerms(tree, problemData, rows, interpreter); 
+      
       return Calculate(
         tree, problemData,

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

@@ -202 +202 @@
       var propability = random.NextDouble();
       if (propability < ConstantOptimizationProbability.Value) {
-        IEnumerable<int> constantOptimizationRows = GenerateRowsToEvaluate(ConstantOptimizationRowsPercentage.Value);
         quality = OptimizeConstants(
           interpreter, tree,
-          problemData, constantOptimizationRows,
+          problemData, rows,
           applyLinearScaling,
           ConstantOptimizationIterations.Value,

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

@@ -59 +59 @@
     }
 
-    //TODO: refactor like evaluate method
     public static double Calculate(
       ISymbolicExpressionTree tree, 

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

@@ -194 +194 @@
       individual[SymbolicExpressionTreeName] = tree;
 
-      if(TreeEvaluatorParameter.Value is SymbolicRegressionConstantOptimizationEvaluator)
-        TreeEvaluatorParameter.Value.RandomParameter.Value = random;
+      // dpiringe: needed when Maximization = true
+      if (TreeEvaluatorParameter.Value is SymbolicRegressionConstantOptimizationEvaluator constantOptEvaluator) {
+        constantOptEvaluator.RandomParameter.Value = random;
+        constantOptEvaluator.RelativeNumberOfEvaluatedSamplesParameter.Value = 
+          (PercentValue)constantOptEvaluator.ConstantOptimizationRowsPercentage.Clone();
+      }
 
       return TreeEvaluatorParameter.Value.Evaluate(

branches/3136_Structural_GP/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Converters/TreeToAutoDiffTermConverter.cs

@@ -302 +302 @@
           return t * alpha + beta;
         } else return ConvertToAutoDiff(node.GetSubtree(0));
+      }
+      if (node.Symbol is SubFunctionSymbol) {
+        return ConvertToAutoDiff(node.GetSubtree(0));
       }
       throw new ConversionException();
@@ -350 +353 @@
           !(n.Symbol is Cube) &&
           !(n.Symbol is CubeRoot) &&
-          !(n.Symbol is Power)
+          !(n.Symbol is Power) &&
+          !(n.Symbol is SubFunctionSymbol)
         select n).Any();
       return !containsUnknownSymbol;