STY Uses black's with target_version >= 3.7 (scikit-learn#20294)

Author: thomasjpfan

setup.py

@@ -280,7 +280,7 @@ def setup_package():
         python_requires=">=3.7",
         install_requires=min_deps.tag_to_packages["install"],
         package_data={"": ["*.pxd"]},
-        **extra_setuptools_args
+        **extra_setuptools_args,
     )
 
     commands = [arg for arg in sys.argv[1:] if not arg.startswith("-")]

sklearn/cluster/_affinity_propagation.py

@@ -42,7 +42,7 @@ def affinity_propagation(
     copy=True,
     verbose=False,
     return_n_iter=False,
-    random_state=None
+    random_state=None,
 ):
     """Perform Affinity Propagation Clustering of data.
 
@@ -385,7 +385,7 @@ def __init__(
         preference=None,
         affinity="euclidean",
         verbose=False,
-        random_state=None
+        random_state=None,
     ):
 
         self.damping = damping

sklearn/cluster/_agglomerative.py

@@ -853,7 +853,7 @@ def __init__(
         compute_full_tree="auto",
         linkage="ward",
         distance_threshold=None,
-        compute_distances=False
+        compute_distances=False,
     ):
         self.n_clusters = n_clusters
         self.distance_threshold = distance_threshold
@@ -953,7 +953,7 @@ def fit(self, X, y=None):
             connectivity=connectivity,
             n_clusters=n_clusters,
             return_distance=return_distance,
-            **kwargs
+            **kwargs,
         )
         (self.children_, self.n_connected_components_, self.n_leaves_, parents) = out[
             :4
@@ -1141,7 +1141,7 @@ def __init__(
         linkage="ward",
         pooling_func=np.mean,
         distance_threshold=None,
-        compute_distances=False
+        compute_distances=False,
     ):
         super().__init__(
             n_clusters=n_clusters,

sklearn/cluster/_bicluster.py

@@ -309,7 +309,7 @@ def __init__(
         mini_batch=False,
         init="k-means++",
         n_init=10,
-        random_state=None
+        random_state=None,
     ):
         super().__init__(
             n_clusters, svd_method, n_svd_vecs, mini_batch, init, n_init, random_state
@@ -461,7 +461,7 @@ def __init__(
         mini_batch=False,
         init="k-means++",
         n_init=10,
-        random_state=None
+        random_state=None,
     ):
         super().__init__(
             n_clusters, svd_method, n_svd_vecs, mini_batch, init, n_init, random_state

sklearn/cluster/_birch.py

@@ -463,7 +463,7 @@ def __init__(
         branching_factor=50,
         n_clusters=3,
         compute_labels=True,
-        copy=True
+        copy=True,
     ):
         self.threshold = threshold
         self.branching_factor = branching_factor

sklearn/cluster/_dbscan.py

@@ -31,7 +31,7 @@ def dbscan(
     leaf_size=30,
     p=2,
     sample_weight=None,
-    n_jobs=None
+    n_jobs=None,
 ):
     """Perform DBSCAN clustering from vector array or distance matrix.
 
@@ -301,7 +301,7 @@ def __init__(
         algorithm="auto",
         leaf_size=30,
         p=None,
-        n_jobs=None
+        n_jobs=None,
     ):
         self.eps = eps
         self.min_samples = min_samples

sklearn/cluster/_mean_shift.py

@@ -116,7 +116,7 @@ def mean_shift(
     min_bin_freq=1,
     cluster_all=True,
     max_iter=300,
-    n_jobs=None
+    n_jobs=None,
 ):
     """Perform mean shift clustering of data using a flat kernel.
 
@@ -380,7 +380,7 @@ def __init__(
         min_bin_freq=1,
         cluster_all=True,
         n_jobs=None,
-        max_iter=300
+        max_iter=300,
     ):
         self.bandwidth = bandwidth
         self.seeds = seeds

sklearn/compose/_target.py

@@ -122,7 +122,7 @@ def __init__(
         transformer=None,
         func=None,
         inverse_func=None,
-        check_inverse=True
+        check_inverse=True,
     ):
         self.regressor = regressor
         self.transformer = transformer

sklearn/covariance/_elliptic_envelope.py

@@ -132,7 +132,7 @@ def __init__(
         assume_centered=False,
         support_fraction=None,
         contamination=0.1,
-        random_state=None
+        random_state=None,
     ):
         super().__init__(
             store_precision=store_precision,

sklearn/covariance/_graph_lasso.py

@@ -87,7 +87,7 @@ def graphical_lasso(
     verbose=False,
     return_costs=False,
     eps=np.finfo(np.float64).eps,
-    return_n_iter=False
+    return_n_iter=False,
 ):
     """l1-penalized covariance estimator
 
@@ -408,7 +408,7 @@ def __init__(
         enet_tol=1e-4,
         max_iter=100,
         verbose=False,
-        assume_centered=False
+        assume_centered=False,
     ):
         super().__init__(assume_centered=assume_centered)
         self.alpha = alpha
@@ -758,7 +758,7 @@ def __init__(
         mode="cd",
         n_jobs=None,
         verbose=False,
-        assume_centered=False
+        assume_centered=False,
     ):
         super().__init__(
             mode=mode,