From 459041ed9827c52cadafbe62dcaf32cec239bf60 Mon Sep 17 00:00:00 2001
From: ilyushka <61294398+DRMPN@users.noreply.github.com>
Date: Thu, 8 Feb 2024 11:42:34 +0300
Subject: [PATCH] fix parentheses

---
 test/unit/data/test_data_split.py | 40 ++++++++++++++++---------------
 1 file changed, 21 insertions(+), 19 deletions(-)

diff --git a/test/unit/data/test_data_split.py b/test/unit/data/test_data_split.py
index f8a87f4d86..29e0367f6c 100644
--- a/test/unit/data/test_data_split.py
+++ b/test/unit/data/test_data_split.py
@@ -202,25 +202,27 @@ def test_multivariate_time_series_splitting_correct():
         assert np.allclose(test_series_data.target, np.array([16, 17, 18, 19]))
 
 
-@pytest.mark.parametrize(('datas_funs', 'cv_folds', 'shuffle', 'stratify'),
-                         [
-    # classification + stratify + shuffle + cv_folds
-    ([partial(get_tabular_classification_data, 100, 5)] * 3, 4, True, True),
-    # classification + shuffle + cv_folds
-    ([partial(get_tabular_classification_data, 100, 5)] * 3, 4, True, False),
-    # classification + cv_folds
-    ([partial(get_tabular_classification_data, 100, 5)] * 3, 4, False, False),
-    # classification + stratify + shuffle
-    ([partial(get_tabular_classification_data, 100, 5)] * 3, None, True, True),
-    # classification + shuffle
-    ([partial(get_tabular_classification_data, 100, 5)] * 3, None, True, False),
-    # classification
-    ([partial(get_tabular_classification_data, 100, 5)] * 3, None, False, False),
-    # timeseries + cv_folds
-    ([partial(get_ts_data_to_forecast, 10, 100)] * 3, 3, False, False),
-    # timeseries
-    ([partial(get_ts_data_to_forecast, 10, 100)] * 3, None, False, False),
-])
+@pytest.mark.parametrize(
+    ("datas_funs", "cv_folds", "shuffle", "stratify"),
+    [
+        # classification + stratify + shuffle + cv_folds
+        ([partial(get_tabular_classification_data, 100, 5)] * 3, 4, True, True),
+        # classification + shuffle + cv_folds
+        ([partial(get_tabular_classification_data, 100, 5)] * 3, 4, True, False),
+        # classification + cv_folds
+        ([partial(get_tabular_classification_data, 100, 5)] * 3, 4, False, False),
+        # classification + stratify + shuffle
+        ([partial(get_tabular_classification_data, 100, 5)] * 3, None, True, True),
+        # classification + shuffle
+        ([partial(get_tabular_classification_data, 100, 5)] * 3, None, True, False),
+        # classification
+        ([partial(get_tabular_classification_data, 100, 5)] * 3, None, False, False),
+        # timeseries + cv_folds
+        ([partial(get_ts_data_to_forecast, 10, 100)] * 3, 3, False, False),
+        # timeseries
+        ([partial(get_ts_data_to_forecast, 10, 100)] * 3, None, False, False),
+    ],
+)
 def test_multimodal_data_splitting_is_correct(datas_funs, cv_folds, shuffle, stratify):
     mdata = MultiModalData({f'data_{i}': data_fun() for i, data_fun in enumerate(datas_funs)})
     data_splitter = DataSourceSplitter(cv_folds=cv_folds, shuffle=shuffle, stratify=stratify)