Creating K-Subjects out evaluations

moabb/evaluations/base.py

@@ -66,6 +66,7 @@ def __init__(
         return_epochs=False,
         return_raws=False,
         mne_labels=False,
+        n_splits=None,
         save_model=False,
         cache_config=None,
     ):
@@ -77,6 +78,7 @@ def __init__(
         self.return_epochs = return_epochs
         self.return_raws = return_raws
         self.mne_labels = mne_labels
+        self.n_splits = n_splits
         self.save_model = save_model
         self.cache_config = cache_config
         # check paradigm

moabb/evaluations/evaluations.py

@@ -8,6 +8,7 @@
 from sklearn.base import clone
 from sklearn.metrics import get_scorer
 from sklearn.model_selection import (
+    GroupKFold,
     LeaveOneGroupOut,
     StratifiedKFold,
     StratifiedShuffleSplit,
@@ -632,6 +633,9 @@ class CrossSubjectEvaluation(BaseEvaluation):
         use MNE raw to train pipelines.
     mne_labels: bool, default=False
         if returning MNE epoch, use original dataset label if True
+    n_splits: int, default=None
+        Number of splits for cross-validation. If None, the number of splits
+        is equal to the number of subjects.
     """
 
     # flake8: noqa: C901
@@ -675,7 +679,12 @@ def evaluate(
         scorer = get_scorer(self.paradigm.scoring)
 
         # perform leave one subject out CV
-        cv = LeaveOneGroupOut()
+        if self.n_splits is None:
+            cv = LeaveOneGroupOut()
+        else:
+            cv = GroupKFold(n_splits=self.n_splits)
+            n_subjects = self.n_splits
+
         inner_cv = StratifiedKFold(3, shuffle=True, random_state=self.random_state)
 
         # Implement Grid Search