From 42e6b7b3827cd9c570faf9ef47c7927f11751755 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 29 Nov 2024 10:39:54 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

---
 moabb/evaluations/metasplitters.py |  7 +++++--
 moabb/evaluations/splitters.py     | 19 ++++++++++++-------
 2 files changed, 17 insertions(+), 9 deletions(-)

diff --git a/moabb/evaluations/metasplitters.py b/moabb/evaluations/metasplitters.py
index a3b0ae75b..5e95fb626 100644
--- a/moabb/evaluations/metasplitters.py
+++ b/moabb/evaluations/metasplitters.py
@@ -2,6 +2,7 @@
 
 from moabb.evaluations.utils import sort_group
 
+
 class PseudoOnlineSplit(BaseCrossValidator):
     """Pseudo-online split for evaluation test data.
 
@@ -69,7 +70,9 @@ def split(self, indices, y, metadata=None):
                         break  # Take the fist run as calibration
                 else:
                     if self.calib_size is None:
-                        raise ValueError('Data contains just one run. Need to provide calibration size.')
+                        raise ValueError(
+                            "Data contains just one run. Need to provide calibration size."
+                        )
                     # Take first #calib_size samples as calibration
                     calib_size = self.calib_size
                     calib_ix = group[:calib_size].index
@@ -79,6 +82,6 @@ def split(self, indices, y, metadata=None):
 
         else:
             if self.calib_size is None:
-                raise ValueError('Need to provide calibration size.')
+                raise ValueError("Need to provide calibration size.")
             calib_size = self.calib_size
             yield list(indices[:calib_size]), list(indices[calib_size:])
diff --git a/moabb/evaluations/splitters.py b/moabb/evaluations/splitters.py
index fe72ba037..a0b507d51 100644
--- a/moabb/evaluations/splitters.py
+++ b/moabb/evaluations/splitters.py
@@ -1,4 +1,3 @@
-import copy
 
 from sklearn.model_selection import BaseCrossValidator, StratifiedKFold
 from sklearn.utils import check_random_state
@@ -71,12 +70,12 @@ class WithinSessionSplitter(BaseCrossValidator):
 
     def __init__(
         self,
-        cv = StratifiedKFold,
-        custom_cv = False,
+        cv=StratifiedKFold,
+        custom_cv=False,
         n_folds: int = 5,
         random_state: int = 42,
         shuffle: bool = True,
-        calib_size: int = None
+        calib_size: int = None,
     ):
         self.n_folds = n_folds
         self.shuffle = shuffle
@@ -87,7 +86,11 @@ def __init__(
 
     def get_n_splits(self, metadata):
         num_sessions_subjects = metadata.groupby(["subject", "session"]).ngroups
-        return self.cv.get_n_splits(metadata) if self.custom_cv else self.n_folds * num_sessions_subjects
+        return (
+            self.cv.get_n_splits(metadata)
+            if self.custom_cv
+            else self.n_folds * num_sessions_subjects
+        )
 
     def split(self, y, metadata, **kwargs):
         all_index = metadata.index.values
@@ -115,7 +118,9 @@ def split(self, y, metadata, **kwargs):
                 # Handle custom splitter
                 if isinstance(self.cv(), PseudoOnlineSplit):
                     splitter = self.cv(calib_size=self.calib_size)
-                    for calib_ix, test_ix in splitter.split(indices, group_y, subject_metadata[session_mask]):
+                    for calib_ix, test_ix in splitter.split(
+                        indices, group_y, subject_metadata[session_mask]
+                    ):
                         yield calib_ix, test_ix
                 else:
                     # Handle standard CV like StratifiedKFold
@@ -125,4 +130,4 @@ def split(self, y, metadata, **kwargs):
                         random_state=self.random_state.randint(0, 2**10),
                     )
                     for train_ix, test_ix in splitter.split(indices, group_y):
-                        yield indices[train_ix], indices[test_ix]
\ No newline at end of file
+                        yield indices[train_ix], indices[test_ix]