RMT generation: only split on relevant variables

1 files changed, 19 insertions, 3 deletions

diff --git a/lib/parameters.py b/lib/parameters.py
index a619103..faa2e96 100644
--- a/lib/parameters.py
+++ b/lib/parameters.py
@@ -1006,7 +1006,7 @@ class ModelAttribute:
         :param loss_ignore_scalar: Ignore scalar parameters when computing the loss for split candidates. Only sensible if with_function_leaves is enabled.
         :param threshold: Return a StaticFunction leaf node if std(data) < threshold. Default 100.
 
-        :returns: SplitFunction or StaticFunction
+        :returns: ModelFunction
         """
 
         param_count = len(self.param_names) + self.arg_count
@@ -1015,6 +1015,14 @@ class ModelAttribute:
             # sf.value_error["std"] = np.std(data)
 
         loss = list()
+
+        ffs_feasible = False
+        by_param = partition_by_param(data, parameters)
+        distinct_values_by_param_index = distinct_param_values(
+            parameters
+        )  # required, "unique_values" in for loop is insufficient for std_by_param foo
+        std_static = np.std(data)
+        std_lut = np.mean([np.std(v) for v in by_param.values()])
         for param_index in range(param_count):
 
             if param_index in self.ignore_param:
@@ -1034,6 +1042,13 @@ class ModelAttribute:
                 loss.append(np.inf)
                 continue
 
+            std_by_param = _mean_std_by_param(
+                by_param, distinct_values_by_param_index, param_index
+            )
+            if not _depends_on_param(None, std_by_param, std_lut):
+                loss.append(np.inf)
+                continue
+
             if (
                 with_function_leaves
                 and self.param_type[param_index] == ParamType.SCALAR
@@ -1041,6 +1056,7 @@ class ModelAttribute:
             ):
                 # param can be modeled as a function. Do not split on it.
                 loss.append(np.inf)
+                ffs_feasible = True
                 continue
 
             child_indexes = list()
@@ -1075,13 +1091,13 @@ class ModelAttribute:
                     children.extend((np.array(child_data) - np.mean(child_data)) ** 2)
 
             if np.any(np.isnan(children)):
-                loss.append(np.inf)  #
+                loss.append(np.inf)
             else:
                 loss.append(np.sum(children))
 
         if np.all(np.isinf(loss)):
             # all children have the same configuration. We shouldn't get here due to the threshold check above...
-            if with_function_leaves:
+            if ffs_feasible:
                 # try generating a function. if it fails, model_function is a StaticFunction.
                 ma = ModelAttribute(
                     "tmp",