CLN appease ruff/linting

Author: lorentzenchr

tests/python_package_test/test_sklearn.py

@@ -22,6 +22,7 @@
 from sklearn.utils.validation import check_is_fitted
 
 import lightgbm as lgb
+from lightgbm.basic import LGBMDeprecationWarning
 from lightgbm.compat import (
     DASK_INSTALLED,
     DATATABLE_INSTALLED,
@@ -31,7 +32,6 @@
     pd_DataFrame,
     pd_Series,
 )
-from lightgbm.basic import LGBMDeprecationWarning
 
 from .utils import (
     assert_silent,
@@ -2049,9 +2049,7 @@ def test_classifier_fit_detects_classes_every_time():
 def test_eval_set_deprecation():
     """Test use of eval_set raises deprecation warning."""
     X, y = make_synthetic_regression(n_samples=10)
-    X_train, X_test, y_train, y_test = train_test_split(
-        X, y, test_size=0.5, random_state=42
-    )
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
     gbm = lgb.LGBMRegressor()
     msg = "The argument 'eval_set' is deprecated.*"
     with pytest.warns(LGBMDeprecationWarning, match=msg):
@@ -2061,10 +2059,8 @@ def test_eval_set_deprecation():
 def test_eval_X_eval_y_eval_set_equivalence():
     """Test that eval_X and eval_y are equivalent to eval_set."""
     X, y = make_synthetic_regression()
-    X_train, X_test, y_train, y_test = train_test_split(
-        X, y, test_size=0.25, random_state=42
-    )
-    cbs=[lgb.early_stopping(2)]
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
+    cbs = [lgb.early_stopping(2)]
     gbm1 = lgb.LGBMRegressor()
     gbm1.fit(X_train, y_train, eval_set=(X_test, y_test), callbacks=cbs)
     gbm2 = lgb.LGBMRegressor()
@@ -2073,8 +2069,8 @@ def test_eval_X_eval_y_eval_set_equivalence():
 
     # 2 evaluation sets
     n = X_test.shape[0]
-    X_test1, X_test2 = X_test[:n//2], X_test[n//2:]
-    y_test1, y_test2 = y_test[:n//2], y_test[n//2:]
+    X_test1, X_test2 = X_test[: n // 2], X_test[n // 2 :]
+    y_test1, y_test2 = y_test[: n // 2], y_test[n // 2 :]
     gbm1 = lgb.LGBMRegressor()
     gbm1.fit(X_train, y_train, eval_set=[(X_test1, y_test1), (X_test2, y_test2)], callbacks=cbs)
     gbm2 = lgb.LGBMRegressor()