move feature name assignment to right spot

src/glum/_glm.py

@@ -228,6 +228,20 @@ def _check_offset(
     return offset
 
 
+def _name_categorical_variables(
+    categories: tuple[str], column_name: str, drop_first: bool
+):
+    new_names = [
+        f"{column_name}__{category}" for category in categories[int(drop_first) :]
+    ]
+    if len(new_names) == 0:
+        raise ValueError(
+            f"Categorical column: {column_name}, contains only one category. "
+            + "This should be dropped from the feature matrix."
+        )
+    return new_names
+
+
 def _parse_formula(
     formula: FormulaSpec, include_intercept: bool = True
 ) -> tuple[Optional[Formula], Formula]:
@@ -2696,16 +2710,6 @@ def _set_up_and_check_fit_args(
                 self.term_names_ = list(
                     chain.from_iterable(
                         [term] * len(cols) for term, _, cols in X.model_spec.structure
-
-            if any(X.dtypes == "category"):
-                self.feature_names_ = list(
-                    chain.from_iterable(
-                        _name_categorical_variables(
-                            dtype.categories, column, getattr(self, "drop_first", False)
-                        )
-                        if isinstance(dtype, pd.CategoricalDtype)
-                        else [column]
-                        for column, dtype in zip(X.columns, X.dtypes)
                     )
                 )
 
@@ -2715,6 +2719,17 @@ def _set_up_and_check_fit_args(
                 self.feature_dtypes_ = X.dtypes.to_dict()
 
                 if any(X.dtypes == "category"):
+
+                    self.feature_names_ = list(
+                        chain.from_iterable(
+                            _name_categorical_variables(
+                                dtype.categories, column, getattr(self, "drop_first", False)
+                            )
+                            if isinstance(dtype, pd.CategoricalDtype)
+                            else [column]
+                            for column, dtype in zip(X.columns, X.dtypes)
+                        )
+                    )
 
                     def _expand_categorical_penalties(penalty, X, drop_first):
                         """