Changeset 18086 for branches/2521_ProblemRefactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs

Timestamp:

11/19/21 16:07:45 (2 years ago)

Author:

mkommend

Message:

#2521: Merged trunk changes into branch.

File:

• branches/2521_ProblemRefactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs (modified) (5 diffs)

branches/2521_ProblemRefactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs

@@ -209 +209 @@
       bool updateConstantsInTree = true, Action<double[], double, object> iterationCallback = null, EvaluationsCounter counter = null) {
 
-      // numeric constants in the tree become variables for constant opt
-      // variables in the tree become parameters (fixed values) for constant opt
-      // for each parameter (variable in the original tree) we store the 
+      // Numeric constants in the tree become variables for parameter optimization.
+      // Variables in the tree become parameters (fixed values) for parameter optimization.
+      // For each parameter (variable in the original tree) we store the 
       // variable name, variable value (for factor vars) and lag as a DataForVariable object.
       // A dictionary is used to find parameters
@@ -221 +221 @@
       if (!TreeToAutoDiffTermConverter.TryConvertToAutoDiff(tree, updateVariableWeights, applyLinearScaling, out parameters, out initialConstants, out func, out func_grad))
         throw new NotSupportedException("Could not optimize constants of symbolic expression tree due to not supported symbols used in the tree.");
-      if (parameters.Count == 0) return 0.0; // gkronber: constant expressions always have a R² of 0.0 
+      if (parameters.Count == 0) return 0.0; // constant expressions always have a R² of 0.0 
       var parameterEntries = parameters.ToArray(); // order of entries must be the same for x
 
-      //extract inital constants
+      // extract inital constants
       double[] c;
       if (applyLinearScaling) {
@@ -270 +270 @@
       try {
         alglib.lsfitcreatefg(x, y, c, n, m, k, false, out state);
-        alglib.lsfitsetcond(state, 0.0, 0.0, maxIterations);
+        alglib.lsfitsetcond(state, 0.0, maxIterations);
         alglib.lsfitsetxrep(state, iterationCallback != null);
-        //alglib.lsfitsetgradientcheck(state, 0.001);
         alglib.lsfitfit(state, function_cx_1_func, function_cx_1_grad, xrep, rowEvaluationsCounter);
         alglib.lsfitresults(state, out retVal, out c, out rep);
@@ -285 +284 @@
 
       //retVal == -7  => constant optimization failed due to wrong gradient
-      if (retVal != -7) {
+      //          -8  => optimizer detected  NAN / INF  in  the target
+      //                 function and/ or gradient
+      if (retVal != -7 && retVal != -8) {
         if (applyLinearScaling) {
           var tmp = new double[c.Length - 2];
@@ -309 +310 @@
         VariableTreeNodeBase variableTreeNodeBase = node as VariableTreeNodeBase;
         FactorVariableTreeNode factorVarTreeNode = node as FactorVariableTreeNode;
-        if (constantTreeNode != null)
+        if (constantTreeNode != null) {
+          if (constantTreeNode.Parent.Symbol is Power 
+              && constantTreeNode.Parent.GetSubtree(1) == constantTreeNode) continue; // exponents in powers are not optimizated (see TreeToAutoDiffTermConverter)
           constantTreeNode.Value = constants[i++];
-        else if (updateVariableWeights && variableTreeNodeBase != null)
+        } else if (updateVariableWeights && variableTreeNodeBase != null)
           variableTreeNodeBase.Weight = constants[i++];
         else if (factorVarTreeNode != null) {