Drive splitters with stock scikit-learn CVs via groups=

docs/source/whats_new.rst

@@ -39,6 +39,7 @@ Enhancements
 - Re-enable auto-execution of the Riemannian Artifact Rejection tutorial (``examples/advanced_examples/plot_riemannian_artifact_rejection.py``) now that pyRiemann 0.11 is on PyPI with per-potato metrics and ``method_combination`` support on ``PotatoField`` (by `Bruno Aristimunha`_)
 - Use NEMAR as the default download source for datasets with an assigned ``nemar_id``, while preserving existing dataset-specific downloaders as a fallback (by `Bruno Aristimunha`_).
 - Add :class:`moabb.datasets.preprocessing.EuclideanAlignment`, a trial-level Euclidean Alignment transformer (He & Wu 2020; Junqueira et al. 2024) that whitens each trial by the inverse square root of the Euclidean mean covariance to remove per-domain covariance shift before a (deep) model sees the data. Inductive and leakage-free by default (``fit`` learns the reference from training trials, ``transform`` re-applies it to unseen trials); ``fit_transform`` gives the transductive, per-recording form. Accepts an :class:`mne.BaseEpochs` or an ``(n_trials, n_channels, n_times)`` ndarray, uses a shrinkage covariance estimator (``"lwf"``) for robustness, and adds no new dependency (``pyriemann >= 0.11`` is already required). Distinct from :class:`pyriemann.transfer.TLCenter`, which recenters covariance *matrices* (:gh:`1108` by `Bruno Aristimunha`_).
+- Drive cross-validation folds with any stock scikit-learn cross-validator passed as ``cv_class``, controlled by a ``groups`` argument — a metadata column name, a list of column names (compound key, e.g. ``["subject", "session"]``), or a callable ``metadata -> array`` — together with callable ``cv_kwargs`` resolved against the metadata (e.g. ``cv_class=PredefinedSplit`` with a ``test_fold`` callable to target a single fold). ``groups`` is exposed on :class:`moabb.evaluations.WithinSessionEvaluation`, :class:`moabb.evaluations.WithinSubjectEvaluation`, :class:`moabb.evaluations.CrossSessionEvaluation` and :class:`moabb.evaluations.CrossSubjectEvaluation` and threaded to their splitters; each splitter keeps its default grouping (``"subject"`` / ``"session"`` / labels) when ``groups`` is ``None``. :class:`moabb.evaluations.splitters.CrossDatasetSplitter` gains ``groups`` (its ``group_column`` argument is now a deprecated alias) (:gh:`1104` by `Bruno Aristimunha`_).
 
 API changes
 ~~~~~~~~~~~

moabb/evaluations/base.py

@@ -301,6 +301,13 @@ class BaseEvaluation(ABC):
     cv_kwargs : dict or None
         Keyword arguments passed to cv_class when constructing the splitter.
         Defaults to ``None``.
+    groups : str, list of str, callable, or None
+        What defines the cross-validation folds, forwarded to the evaluation's
+        splitter as its ``groups`` argument: a metadata column name, a list of
+        column names combined into a compound key, or a callable
+        ``metadata -> array``. When ``None`` (the default), the splitter's own
+        default grouping applies (e.g. ``"subject"`` / ``"session"`` / labels).
+        Defaults to ``None``.
     save_model : bool
         Save model after training, for each fold of cross-validation if needed.
         Defaults to ``False``.
@@ -359,6 +366,7 @@ def __init__(
         n_splits: Optional[int] = None,
         cv_class: Optional[type] = None,
         cv_kwargs: Optional[dict] = None,
+        groups=None,
         save_model: bool = False,
         cache_config: Optional["CacheConfig"] = None,
         optuna: bool = False,
@@ -376,6 +384,7 @@ def __init__(
         self.n_splits = n_splits
         self.cv_class = cv_class
         self.cv_kwargs = {} if cv_kwargs is None else cv_kwargs
+        self.groups = groups
         self.save_model = save_model
         self.cache_config = cache_config
         self.optuna = optuna

moabb/evaluations/evaluations.py

@@ -83,6 +83,8 @@ class WithinSessionEvaluation(BaseEvaluation):
     def _create_splitter(self):
         """Create the WithinSessionSplitter for parallel evaluation."""
         cv_class, cv_kwargs = self._resolve_cv(StratifiedKFold)
+        if self.groups is not None:
+            cv_kwargs = {**cv_kwargs, "groups": self.groups}
         return WithinSessionSplitter(
             n_folds=self.n_splits or 5,
             shuffle=True,
@@ -296,6 +298,8 @@ class CrossSessionEvaluation(BaseEvaluation):
     def _create_splitter(self):
         """Create the CrossSessionSplitter for parallel evaluation."""
         cv_class, cv_kwargs = self._resolve_cv(LeaveOneGroupOut)
+        if self.groups is not None:
+            cv_kwargs = {**cv_kwargs, "groups": self.groups}
         return CrossSessionSplitter(
             cv_class=cv_class, random_state=self.random_state, **cv_kwargs
         )
@@ -476,6 +480,8 @@ def _create_splitter(self):
             default_kwargs = {"n_splits": self.n_splits}
 
         cv_class, cv_kwargs = self._resolve_cv(default_class, default_kwargs)
+        if self.groups is not None:
+            cv_kwargs = {**cv_kwargs, "groups": self.groups}
         return CrossSubjectSplitter(
             cv_class=cv_class, random_state=self.random_state, **cv_kwargs
         )
@@ -657,6 +663,8 @@ class WithinSubjectEvaluation(BaseEvaluation):
     def _create_splitter(self):
         """Create the WithinSubjectSplitter for parallel evaluation."""
         cv_class, cv_kwargs = self._resolve_cv(StratifiedKFold)
+        if self.groups is not None:
+            cv_kwargs = {**cv_kwargs, "groups": self.groups}
         return WithinSubjectSplitter(
             n_folds=self.n_splits or 5,
             shuffle=True,