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Commit e1e5836

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adrinjalaliadrinjalali
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MNT apply black to codebase
1 parent 1022ad8 commit e1e5836
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‎sklearn/__check_build/__init__.py

Copy file name to clipboardExpand all lines: sklearn/__check_build/__init__.py
+2-5Lines changed: 2 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -28,8 +28,7 @@ def raise_build_error(e):
2828
dir_content.append(filename.ljust(26))
2929
else:
3030
dir_content.append(filename + "\n")
31-
raise ImportError(
32-
"""%s
31+
raise ImportError("""%s
3332
___________________________________________________________________________
3433
Contents of %s:
3534
%s
@@ -39,9 +38,7 @@ def raise_build_error(e):
3938
If you have installed scikit-learn from source, please do not forget
4039
to build the package before using it: run `python setup.py install` or
4140
`make` in the source directory.
42-
%s"""
43-
% (e, local_dir, "".join(dir_content).strip(), msg)
44-
)
41+
%s""" % (e, local_dir, "".join(dir_content).strip(), msg))
4542

4643

4744
try:

‎sklearn/_build_utils/__init__.py

Copy file name to clipboardExpand all lines: sklearn/_build_utils/__init__.py
-1Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -97,7 +97,6 @@ def gen_from_templates(templates):
9797
os.path.exists(outfile)
9898
and os.stat(template).st_mtime < os.stat(outfile).st_mtime
9999
):
100-
101100
with open(template, "r") as f:
102101
tmpl = f.read()
103102

‎sklearn/_build_utils/openmp_helpers.py

Copy file name to clipboardExpand all lines: sklearn/_build_utils/openmp_helpers.py
+4-8Lines changed: 4 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -38,17 +38,15 @@ def check_openmp_support():
3838
# Pyodide doesn't support OpenMP
3939
return False
4040

41-
code = textwrap.dedent(
42-
"""\
41+
code = textwrap.dedent("""\
4342
#include <omp.h>
4443
#include <stdio.h>
4544
int main(void) {
4645
#pragma omp parallel
4746
printf("nthreads=%d\\n", omp_get_num_threads());
4847
return 0;
4948
}
50-
"""
51-
)
49+
""")
5250

5351
extra_preargs = os.getenv("LDFLAGS", None)
5452
if extra_preargs is not None:
@@ -96,8 +94,7 @@ def check_openmp_support():
9694
"Failed to build scikit-learn with OpenMP support"
9795
) from openmp_exception
9896
else:
99-
message = textwrap.dedent(
100-
"""
97+
message = textwrap.dedent("""
10198
10299
***********
103100
* WARNING *
@@ -120,8 +117,7 @@ def check_openmp_support():
120117
parallelism.
121118
122119
***
123-
"""
124-
)
120+
""")
125121
warnings.warn(message)
126122

127123
return openmp_supported

‎sklearn/_build_utils/pre_build_helpers.py

Copy file name to clipboardExpand all lines: sklearn/_build_utils/pre_build_helpers.py
+2-4Lines changed: 2 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -64,12 +64,10 @@ def basic_check_build():
6464
# The following check won't work in pyodide
6565
return
6666

67-
code = textwrap.dedent(
68-
"""\
67+
code = textwrap.dedent("""\
6968
#include <stdio.h>
7069
int main(void) {
7170
return 0;
7271
}
73-
"""
74-
)
72+
""")
7573
compile_test_program(code)

‎sklearn/base.py

Copy file name to clipboardExpand all lines: sklearn/base.py
+7-5Lines changed: 7 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -239,9 +239,11 @@ def set_params(self, **params):
239239
and self.__module__.startswith("sklearn.")
240240
):
241241
warnings.warn(
242-
f"Parameter 'base_estimator' of {self.__class__.__name__} is"
243-
" deprecated in favor of 'estimator'. See"
244-
f" {self.__class__.__name__}'s docstring for more details.",
242+
(
243+
f"Parameter 'base_estimator' of {self.__class__.__name__} is"
244+
" deprecated in favor of 'estimator'. See"
245+
f" {self.__class__.__name__}'s docstring for more details."
246+
),
245247
FutureWarning,
246248
stacklevel=2,
247249
)
@@ -1035,8 +1037,8 @@ class _UnstableArchMixin:
10351037

10361038
def _more_tags(self):
10371039
return {
1038-
"non_deterministic": (
1039-
_IS_32BIT or platform.machine().startswith(("ppc", "powerpc"))
1040+
"non_deterministic": _IS_32BIT or platform.machine().startswith(
1041+
("ppc", "powerpc")
10401042
)
10411043
}
10421044

‎sklearn/calibration.py

Copy file name to clipboardExpand all lines: sklearn/calibration.py
+4-2Lines changed: 4 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -314,8 +314,10 @@ def fit(self, X, y, sample_weight=None, **fit_params):
314314
"`estimator` since `base_estimator` is deprecated."
315315
)
316316
warnings.warn(
317-
"`base_estimator` was renamed to `estimator` in version 1.2 and "
318-
"will be removed in 1.4.",
317+
(
318+
"`base_estimator` was renamed to `estimator` in version 1.2 and "
319+
"will be removed in 1.4."
320+
),
319321
FutureWarning,
320322
)
321323
estimator = self.base_estimator

‎sklearn/cluster/_affinity_propagation.py

Copy file name to clipboardExpand all lines: sklearn/cluster/_affinity_propagation.py
+13-8Lines changed: 13 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -141,8 +141,10 @@ def _affinity_propagation(
141141
if K > 0:
142142
if never_converged:
143143
warnings.warn(
144-
"Affinity propagation did not converge, this model "
145-
"may return degenerate cluster centers and labels.",
144+
(
145+
"Affinity propagation did not converge, this model "
146+
"may return degenerate cluster centers and labels."
147+
),
146148
ConvergenceWarning,
147149
)
148150
c = np.argmax(S[:, I], axis=1)
@@ -161,8 +163,10 @@ def _affinity_propagation(
161163
labels = np.searchsorted(cluster_centers_indices, labels)
162164
else:
163165
warnings.warn(
164-
"Affinity propagation did not converge and this model "
165-
"will not have any cluster centers.",
166+
(
167+
"Affinity propagation did not converge and this model "
168+
"will not have any cluster centers."
169+
),
166170
ConvergenceWarning,
167171
)
168172
labels = np.array([-1] * n_samples)
@@ -453,7 +457,6 @@ def __init__(
453457
verbose=False,
454458
random_state=None,
455459
):
456-
457460
self.damping = damping
458461
self.max_iter = max_iter
459462
self.convergence_iter = convergence_iter
@@ -557,9 +560,11 @@ def predict(self, X):
557560
return pairwise_distances_argmin(X, self.cluster_centers_)
558561
else:
559562
warnings.warn(
560-
"This model does not have any cluster centers "
561-
"because affinity propagation did not converge. "
562-
"Labeling every sample as '-1'.",
563+
(
564+
"This model does not have any cluster centers "
565+
"because affinity propagation did not converge. "
566+
"Labeling every sample as '-1'."
567+
),
563568
ConvergenceWarning,
564569
)
565570
return np.array([-1] * X.shape[0])

‎sklearn/cluster/_agglomerative.py

Copy file name to clipboardExpand all lines: sklearn/cluster/_agglomerative.py
+20-15Lines changed: 20 additions & 15 deletions
Original file line numberDiff line numberDiff line change
@@ -275,12 +275,14 @@ def ward_tree(X, *, connectivity=None, n_clusters=None, return_distance=False):
275275

276276
if n_clusters is not None:
277277
warnings.warn(
278-
"Partial build of the tree is implemented "
279-
"only for structured clustering (i.e. with "
280-
"explicit connectivity). The algorithm "
281-
"will build the full tree and only "
282-
"retain the lower branches required "
283-
"for the specified number of clusters",
278+
(
279+
"Partial build of the tree is implemented "
280+
"only for structured clustering (i.e. with "
281+
"explicit connectivity). The algorithm "
282+
"will build the full tree and only "
283+
"retain the lower branches required "
284+
"for the specified number of clusters"
285+
),
284286
stacklevel=2,
285287
)
286288
X = np.require(X, requirements="W")
@@ -507,12 +509,14 @@ def linkage_tree(
507509

508510
if n_clusters is not None:
509511
warnings.warn(
510-
"Partial build of the tree is implemented "
511-
"only for structured clustering (i.e. with "
512-
"explicit connectivity). The algorithm "
513-
"will build the full tree and only "
514-
"retain the lower branches required "
515-
"for the specified number of clusters",
512+
(
513+
"Partial build of the tree is implemented "
514+
"only for structured clustering (i.e. with "
515+
"explicit connectivity). The algorithm "
516+
"will build the full tree and only "
517+
"retain the lower branches required "
518+
"for the specified number of clusters"
519+
),
516520
stacklevel=2,
517521
)
518522

@@ -541,7 +545,6 @@ def linkage_tree(
541545
and not callable(affinity)
542546
and affinity in METRIC_MAPPING
543547
):
544-
545548
# We need the fast cythonized metric from neighbors
546549
dist_metric = DistanceMetric.get_metric(affinity)
547550

@@ -995,8 +998,10 @@ def _fit(self, X):
995998
" 1.4. To avoid this error, only set the `metric` attribute."
996999
)
9971000
warnings.warn(
998-
"Attribute `affinity` was deprecated in version 1.2 and will be removed"
999-
" in 1.4. Use `metric` instead",
1001+
(
1002+
"Attribute `affinity` was deprecated in version 1.2 and will be"
1003+
" removed in 1.4. Use `metric` instead"
1004+
),
10001005
FutureWarning,
10011006
)
10021007
self._metric = self.affinity

‎sklearn/cluster/_bisect_k_means.py

Copy file name to clipboardExpand all lines: sklearn/cluster/_bisect_k_means.py
-1Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -226,7 +226,6 @@ def __init__(
226226
algorithm="lloyd",
227227
bisecting_strategy="biggest_inertia",
228228
):
229-
230229
super().__init__(
231230
n_clusters=n_clusters,
232231
init=init,

‎sklearn/cluster/_kmeans.py

Copy file name to clipboardExpand all lines: sklearn/cluster/_kmeans.py
+27-16Lines changed: 27 additions & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -888,9 +888,11 @@ def _check_params_vs_input(self, X, default_n_init=None):
888888
self._n_init = self.n_init
889889
if self._n_init == "warn":
890890
warnings.warn(
891-
"The default value of `n_init` will change from "
892-
f"{default_n_init} to 'auto' in 1.4. Set the value of `n_init`"
893-
" explicitly to suppress the warning",
891+
(
892+
"The default value of `n_init` will change from "
893+
f"{default_n_init} to 'auto' in 1.4. Set the value of `n_init`"
894+
" explicitly to suppress the warning"
895+
),
894896
FutureWarning,
895897
)
896898
self._n_init = default_n_init
@@ -902,9 +904,11 @@ def _check_params_vs_input(self, X, default_n_init=None):
902904

903905
if _is_arraylike_not_scalar(self.init) and self._n_init != 1:
904906
warnings.warn(
905-
"Explicit initial center position passed: performing only"
906-
f" one init in {self.__class__.__name__} instead of "
907-
f"n_init={self._n_init}.",
907+
(
908+
"Explicit initial center position passed: performing only"
909+
f" one init in {self.__class__.__name__} instead of "
910+
f"n_init={self._n_init}."
911+
),
908912
RuntimeWarning,
909913
stacklevel=2,
910914
)
@@ -1101,8 +1105,10 @@ def predict(self, X, sample_weight="deprecated"):
11011105
X = self._check_test_data(X)
11021106
if not (isinstance(sample_weight, str) and sample_weight == "deprecated"):
11031107
warnings.warn(
1104-
"'sample_weight' was deprecated in version 1.3 and "
1105-
"will be removed in 1.5.",
1108+
(
1109+
"'sample_weight' was deprecated in version 1.3 and "
1110+
"will be removed in 1.5."
1111+
),
11061112
FutureWarning,
11071113
)
11081114
sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
@@ -1415,15 +1421,19 @@ def _check_params_vs_input(self, X):
14151421
self._algorithm = self.algorithm
14161422
if self._algorithm in ("auto", "full"):
14171423
warnings.warn(
1418-
f"algorithm='{self._algorithm}' is deprecated, it will be "
1419-
"removed in 1.3. Using 'lloyd' instead.",
1424+
(
1425+
f"algorithm='{self._algorithm}' is deprecated, it will be "
1426+
"removed in 1.3. Using 'lloyd' instead."
1427+
),
14201428
FutureWarning,
14211429
)
14221430
self._algorithm = "lloyd"
14231431
if self._algorithm == "elkan" and self.n_clusters == 1:
14241432
warnings.warn(
1425-
"algorithm='elkan' doesn't make sense for a single "
1426-
"cluster. Using 'lloyd' instead.",
1433+
(
1434+
"algorithm='elkan' doesn't make sense for a single "
1435+
"cluster. Using 'lloyd' instead."
1436+
),
14271437
RuntimeWarning,
14281438
)
14291439
self._algorithm = "lloyd"
@@ -1907,7 +1917,6 @@ def __init__(
19071917
n_init="warn",
19081918
reassignment_ratio=0.01,
19091919
):
1910-
19111920
super().__init__(
19121921
n_clusters=n_clusters,
19131922
init=init,
@@ -1937,9 +1946,11 @@ def _check_params_vs_input(self, X):
19371946
self._init_size = 3 * self.n_clusters
19381947
elif self._init_size < self.n_clusters:
19391948
warnings.warn(
1940-
f"init_size={self._init_size} should be larger than "
1941-
f"n_clusters={self.n_clusters}. Setting it to "
1942-
"min(3*n_clusters, n_samples)",
1949+
(
1950+
f"init_size={self._init_size} should be larger than "
1951+
f"n_clusters={self.n_clusters}. Setting it to "
1952+
"min(3*n_clusters, n_samples)"
1953+
),
19431954
RuntimeWarning,
19441955
stacklevel=2,
19451956
)

‎sklearn/cluster/_optics.py

Copy file name to clipboardExpand all lines: sklearn/cluster/_optics.py
+4-2Lines changed: 4 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -619,8 +619,10 @@ def compute_optics_graph(
619619
)
620620
if np.all(np.isinf(reachability_)):
621621
warnings.warn(
622-
"All reachability values are inf. Set a larger"
623-
" max_eps or all data will be considered outliers.",
622+
(
623+
"All reachability values are inf. Set a larger"
624+
" max_eps or all data will be considered outliers."
625+
),
624626
UserWarning,
625627
)
626628
return ordering, core_distances_, reachability_, predecessor_

‎sklearn/cluster/_spectral.py

Copy file name to clipboardExpand all lines: sklearn/cluster/_spectral.py
+4-2Lines changed: 4 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -778,6 +778,8 @@ def fit_predict(self, X, y=None):
778778

779779
def _more_tags(self):
780780
return {
781-
"pairwise": self.affinity
782-
in ["precomputed", "precomputed_nearest_neighbors"]
781+
"pairwise": self.affinity in [
782+
"precomputed",
783+
"precomputed_nearest_neighbors",
784+
]
783785
}

‎sklearn/cluster/tests/test_bicluster.py

Copy file name to clipboardExpand all lines: sklearn/cluster/tests/test_bicluster.py
-2Lines changed: 0 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -105,7 +105,6 @@ def test_spectral_biclustering(global_random_seed):
105105
for mat in (S, csr_matrix(S)):
106106
for param_name, param_values in non_default_params.items():
107107
for param_value in param_values:
108-
109108
model = SpectralBiclustering(
110109
n_clusters=3,
111110
n_init=3,
@@ -253,7 +252,6 @@ def test_spectralbiclustering_parameter_validation(params, type_err, err_msg):
253252

254253
@pytest.mark.parametrize("est", (SpectralBiclustering(), SpectralCoclustering()))
255254
def test_n_features_in_(est):
256-
257255
X, _, _ = make_biclusters((3, 3), 3, random_state=0)
258256

259257
assert not hasattr(est, "n_features_in_")

‎sklearn/compose/_target.py

Copy file name to clipboardExpand all lines: sklearn/compose/_target.py
+6-4Lines changed: 6 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -188,10 +188,12 @@ def _fit_transformer(self, y):
188188
y_sel_t = self.transformer_.transform(y_sel)
189189
if not np.allclose(y_sel, self.transformer_.inverse_transform(y_sel_t)):
190190
warnings.warn(
191-
"The provided functions or transformer are"
192-
" not strictly inverse of each other. If"
193-
" you are sure you want to proceed regardless"
194-
", set 'check_inverse=False'",
191+
(
192+
"The provided functions or transformer are"
193+
" not strictly inverse of each other. If"
194+
" you are sure you want to proceed regardless"
195+
", set 'check_inverse=False'"
196+
),
195197
UserWarning,
196198
)
197199

‎sklearn/compose/tests/test_column_transformer.py

Copy file name to clipboardExpand all lines: sklearn/compose/tests/test_column_transformer.py
-1Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -803,7 +803,6 @@ def test_column_transformer_get_feature_names():
803803

804804

805805
def test_column_transformer_special_strings():
806-
807806
# one 'drop' -> ignore
808807
X_array = np.array([[0.0, 1.0, 2.0], [2.0, 4.0, 6.0]]).T
809808
ct = ColumnTransformer([("trans1", Trans(), [0]), ("trans2", "drop", [1])])

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