Control parallel column transformers for wrapping

docs/configuration.rst

@@ -47,3 +47,10 @@ Here you can find the comprehensive list of flags that can be set:
      - | Disable printing the list of expanded column names in ``X_types``.
        | If set to ``True``, the list of types of X will not be printed.
      - The user will not see the expanded ``X_types`` column names.
+   * - ``enable_parallel_column_transformers``
+     - | This flag enables parallel execution of column transformers by
+       | reverting to the default behaviour of scikit-learn
+       | (instead of using ``n_jobs=1``)
+       | If set to ``True``, the parameter will be set back to None.
+     - | Column transformers will be applied in parallel, using more resources.
+       | than expected.

julearn/config.py

@@ -14,6 +14,7 @@
 _global_config["disable_xtypes_check"] = False
 _global_config["disable_x_verbose"] = False
 _global_config["disable_xtypes_verbose"] = False
+_global_config["enable_parallel_column_transformers"] = False
 
 
 def set_config(key: str, value: Any) -> None:

julearn/transformers/ju_column_transformer.py

@@ -12,6 +12,7 @@
 from sklearn.utils.validation import check_is_fitted
 
 from ..base import ColumnTypesLike, JuTransformer, ensure_column_types
+from ..config import get_config
 from ..utils.logging import raise_error
 from ..utils.typing import DataLike, EstimatorLike
 
@@ -93,6 +94,9 @@ def _fit(
             [(self.name, self.transformer, self.apply_to.to_type_selector())],
             verbose_feature_names_out=verbose_feature_names_out,
             remainder="passthrough",
+            n_jobs=None
+            if get_config("enable_parallel_column_transformers")
+            else 1,
         )
         self.column_transformer_.fit(X, y, **fit_params)