sklearn/tests/test_metaestimators_metadata_routing.py:1:1: I001 [*] Import block is un-sorted or un-formatted
   |
 1 | / import copy
 2 | | import re
 3 | |
 4 | | import numpy as np
 5 | | import pytest
 6 | |
 7 | | from ..base import (
 8 | |         BaseEstimator,
 9 | |         RegressorMixin
10 | | )
11 | |
12 | | from sklearn import config_context
13 | | from sklearn.base import BaseEstimator, is_classifier
14 | | from sklearn.calibration import CalibratedClassifierCV
15 | | from sklearn.compose import TransformedTargetRegressor
16 | | from sklearn.covariance import GraphicalLassoCV
17 | | from sklearn.ensemble import (
18 | |     AdaBoostClassifier,
19 | |     AdaBoostRegressor,
20 | |     BaggingClassifier,
21 | |     BaggingRegressor,
22 | | )
23 | | from sklearn.exceptions import UnsetMetadataPassedError
24 | | from sklearn.experimental import (
25 | |     enable_halving_search_cv,  # noqa: F401
26 | |     enable_iterative_imputer,  # noqa: F401
27 | | )
28 | | from sklearn.feature_selection import (
29 | |     RFE,
30 | |     RFECV,
31 | |     SelectFromModel,
32 | |     SequentialFeatureSelector,
33 | | )
34 | | from sklearn.impute import IterativeImputer
35 | | from sklearn.linear_model import (
36 | |     ElasticNetCV,
37 | |     LarsCV,
38 | |     LassoCV,
39 | |     LassoLarsCV,
40 | |     LogisticRegressionCV,
41 | |     MultiTaskElasticNetCV,
42 | |     MultiTaskLassoCV,
43 | |     OrthogonalMatchingPursuitCV,
44 | |     RANSACRegressor,
45 | |     RidgeClassifierCV,
46 | |     RidgeCV,
47 | | )
48 | | from sklearn.metrics._regression import mean_squared_error
49 | | from sklearn.metrics._scorer import make_scorer
50 | | from sklearn.model_selection import (
51 | |     FixedThresholdClassifier,
52 | |     GridSearchCV,
53 | |     GroupKFold,
54 | |     HalvingGridSearchCV,
55 | |     HalvingRandomSearchCV,
56 | |     RandomizedSearchCV,
57 | |     TunedThresholdClassifierCV,
58 | |     cross_validate,
59 | | )
60 | | from sklearn.multiclass import (
61 | |     OneVsOneClassifier,
62 | |     OneVsRestClassifier,
63 | |     OutputCodeClassifier,
64 | | )
65 | | from sklearn.multioutput import (
66 | |     ClassifierChain,
67 | |     MultiOutputClassifier,
68 | |     MultiOutputRegressor,
69 | |     RegressorChain,
70 | | )
71 | | from sklearn.preprocessing import FunctionTransformer
72 | | from sklearn.semi_supervised import SelfTrainingClassifier
73 | | from sklearn.tests.metadata_routing_common import (
74 | |     ConsumingClassifier,
75 | |     ConsumingRegressor,
76 | |     ConsumingScorer,
77 | |     ConsumingSplitter,
78 | |     NonConsumingClassifier,
79 | |     NonConsumingRegressor,
80 | |     _Registry,
81 | |     assert_request_is_empty,
82 | |     check_recorded_metadata,
83 | | )
84 | | from sklearn.utils.metadata_routing import MetadataRouter
   | |_________________________________________________________^ I001
85 |
86 |   rng = np.random.RandomState(42)
   |
   = help: Organize imports

sklearn/tests/test_metaestimators_metadata_routing.py:13:26: F811 Redefinition of unused `BaseEstimator` from line 8
   |
12 | from sklearn import config_context
13 | from sklearn.base import BaseEstimator, is_classifier
   |                          ^^^^^^^^^^^^^ F811
14 | from sklearn.calibration import CalibratedClassifierCV
15 | from sklearn.compose import TransformedTargetRegressor
   |
   = help: Remove definition: `BaseEstimator`

Found 2 errors.
[*] 1 fixable with the `--fix` option.

--- sklearn/tests/test_metaestimators_metadata_routing.py
+++ sklearn/tests/test_metaestimators_metadata_routing.py
@@ -4,10 +4,7 @@
 import numpy as np
 import pytest
 
-from ..base import (
-        BaseEstimator,
-        RegressorMixin
-)
+from ..base import BaseEstimator, RegressorMixin
 
 from sklearn import config_context
 from sklearn.base import BaseEstimator, is_classifier
@@ -933,12 +930,13 @@
         scoring=make_scorer(mean_squared_error, response_method="predict"),
     )
 
+
 class ValidateDimensionRegressor(BaseEstimator, RegressorMixin):
     def __init__(self, ndim):
         self.ndim = ndim
 
     def fit(self, X, y):
-        assert(y.ndim == self.ndim)
+        assert y.ndim == self.ndim
 
     def predict(self, X):
         pass

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