Changeset 15861 for branches/2886_SymRegGrammarEnumeration/HeuristicLab.Algorithms.DataAnalysis.SymRegGrammarEnumeration/Analysis

Timestamp:

03/23/18 18:57:36 (6 years ago)

Author:

lkammere

Message:

#2886: Make constant optimization toggleable in algorithm.

File:

• branches/2886_SymRegGrammarEnumeration/HeuristicLab.Algorithms.DataAnalysis.SymRegGrammarEnumeration/Analysis/RSquaredEvaluator.cs (modified) (2 diffs)

branches/2886_SymRegGrammarEnumeration/HeuristicLab.Algorithms.DataAnalysis.SymRegGrammarEnumeration/Analysis/RSquaredEvaluator.cs

@@ -19 +19 @@
     private readonly ISymbolicDataAnalysisExpressionTreeInterpreter expressionTreeLinearInterpreter = new SymbolicDataAnalysisExpressionTreeLinearInterpreter();
 
+    public bool OptimizeConstants { get; set; }
+
     public RSquaredEvaluator() { }
 
-    protected RSquaredEvaluator(RSquaredEvaluator original, Cloner cloner) { }
+    protected RSquaredEvaluator(RSquaredEvaluator original, Cloner cloner) {
+      this.OptimizeConstants = original.OptimizeConstants;
+    }
 
     public override IDeepCloneable Clone(Cloner cloner) {
@@ -72 +76 @@
       // TODO: Restarts
 
-      double r2 = SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(expressionTreeLinearInterpreter,
-        tree,
-        problemData,
-        problemData.TrainingIndices,
-        applyLinearScaling: false,
-        maxIterations: 50,
-        updateVariableWeights: true,
-        updateConstantsInTree: true);
-
-      foreach (var symbolicExpressionTreeNode in tree.IterateNodesPostfix()) {
-        ConstantTreeNode constTreeNode = symbolicExpressionTreeNode as ConstantTreeNode;
-        if (constTreeNode != null && constTreeNode.Value.IsAlmost(0.0)) {
-          constTreeNode.Value = 0.0;
-        }
-      }
+      double r2;
 
       SymbolicRegressionModel model = new SymbolicRegressionModel(
-        problemData.TargetVariable,
-        tree,
-        expressionTreeLinearInterpreter);
+          problemData.TargetVariable,
+          tree,
+          expressionTreeLinearInterpreter);
 
-      //var target = problemData.TargetVariableTrainingValues;
-      //var estVals = model.GetEstimatedValues(problemData.Dataset, problemData.TrainingIndices);
-      //OnlineCalculatorError error;
-      //double r2 = OnlinePearsonsRCalculator.Calculate(target, estVals, out error);
-      //if (error != OnlineCalculatorError.None) r2 = 0.0;
+      if (OptimizeConstants) {
+        r2 = SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(expressionTreeLinearInterpreter,
+          tree,
+          problemData,
+          problemData.TrainingIndices,
+          applyLinearScaling: false,
+          maxIterations: 50,
+          updateVariableWeights: true,
+          updateConstantsInTree: true);
+
+        foreach (var symbolicExpressionTreeNode in tree.IterateNodesPostfix()) {
+          ConstantTreeNode constTreeNode = symbolicExpressionTreeNode as ConstantTreeNode;
+          if (constTreeNode != null && constTreeNode.Value.IsAlmost(0.0)) {
+            constTreeNode.Value = 0.0;
+          }
+        }
+
+
+      } else {
+        var target = problemData.TargetVariableTrainingValues;
+        var estVals = model.GetEstimatedValues(problemData.Dataset, problemData.TrainingIndices);
+        OnlineCalculatorError error;
+        r2 = OnlinePearsonsRCalculator.Calculate(target, estVals, out error);
+        if (error != OnlineCalculatorError.None) r2 = 0.0;
+      }
 
       var bestR2Result = (DoubleValue)algorithm.Results[BestTrainingQualityResultName].Value;