[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -1,16 +1,16 @@
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v6.0.0
     hooks:
     -   id: check-yaml
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
--   repo: https://github.com/psf/black
-    rev: 23.1.0
+-   repo: https://github.com/psf/black-pre-commit-mirror
+    rev: 26.3.1
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/flake8
-    rev: 6.0.0
+    rev: 7.3.0
     hooks:
     -   id: flake8
         types: [file, python]

examples/cluster/plot_clustering.py

@@ -14,6 +14,7 @@
 parameterized, detects the outliers as a class of
 their own.
 """
+
 print(__doc__)
 
 import time

examples/cluster/plot_commonnn_data_sets.py

@@ -19,7 +19,6 @@
 from sklearn import datasets
 from sklearn.preprocessing import StandardScaler
 
-
 print(__doc__)
 
 

examples/cluster/plot_kmedoids_digits.py

@@ -5,6 +5,7 @@
 In this example we compare different pairwise distance
 metrics for K-Medoids.
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 

examples/eigenpro/plot_eigenpro_synthetic.py

@@ -11,6 +11,7 @@
 the number of features exceeds 500, it begins to outperform
 SVM and shows more stability.
 """
+
 print(__doc__)
 
 import matplotlib

examples/kernel_approximation/plot_kernel_approximation.py

@@ -41,6 +41,7 @@
 in :ref:`kernel_approximation`.
 
 """
+
 print(__doc__)
 
 # Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>

examples/plot_clara_digits.py

@@ -5,6 +5,7 @@
 In this example we compare different computation time of K-Medoids and CLARA on
 the handwritten digits data.
 """
+
 import numpy as np
 import matplotlib.pyplot as plt
 import time

examples/plot_commonnn.py

@@ -10,6 +10,7 @@
 <commonnn>`.
 
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -18,7 +19,6 @@
 from sklearn.datasets import make_blobs
 from sklearn.preprocessing import StandardScaler
 
-
 print(__doc__)
 
 # #############################################################################

examples/plot_kmedoids.py

@@ -9,13 +9,13 @@
 dataset.  Read more in the :ref:`User Guide <_k_medoids>`.
 
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 
 from sklearn_extra.cluster import KMedoids
 from sklearn.datasets import make_blobs
 
-
 print(__doc__)
 
 # #############################################################################

examples/plot_robust_classification_toy.py

@@ -6,14 +6,14 @@
 In this example we compare the RobustWeightedClassifier using SGDClassifier
 for classification with the vanilla SGDClassifier with various losses.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 from sklearn_extra.robust import RobustWeightedClassifier
 from sklearn.linear_model import SGDClassifier
 from sklearn.datasets import make_blobs
 from sklearn.utils import shuffle
 
-
 rng = np.random.RandomState(42)
 
 # Sample two Gaussian blobs

examples/plot_robust_regression_toy.py

@@ -6,6 +6,7 @@
 In this example we compare the RobustWeightedRegressor
 with various robust regression algorithms from scikit-learn.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 

examples/robust/plot_robust_classification_diabete.py

@@ -8,6 +8,7 @@
 We only compare the estimator with SGDClassifier as there is no robust
 classification estimator in scikit-learn.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 from sklearn_extra.robust import RobustWeightedClassifier

examples/robust/plot_robust_regression_california_houses.py

@@ -16,6 +16,7 @@
 robustness in RobustWeightedRegressor can change and this is not so
 straightforward when using RANSAC and TheilSenRegressor.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 from sklearn_extra.robust import RobustWeightedRegressor

sklearn_extra/cluster/_commonnn.py

@@ -1,6 +1,5 @@
 # -*- coding: utf-8 -*-
-"""Density-Based Common-Nearest-Neighbors Clustering
-"""
+"""Density-Based Common-Nearest-Neighbors Clustering"""
 
 # Author: Jan-Oliver Joswig <jan.joswig@fu-berlin.de>
 #

sklearn_extra/cluster/tests/test_commonnn.py

@@ -1,5 +1,4 @@
-"""Tests for common-nearest neighbour clustering
-"""
+"""Tests for common-nearest neighbour clustering"""
 
 import pickle
 
@@ -21,7 +20,6 @@
 from sklearn.cluster.tests.common import generate_clustered_data
 from sklearn.metrics.pairwise import pairwise_distances
 
-
 # TODO Tests where adapted from sklearn.cluster.tests.test_dbscan
 #     of scikit-learn version 0.24.dev0.
 #     To make sklearn_extra.cluster._commonnn compatible with

sklearn_extra/cluster/tests/test_k_medoids.py

@@ -1,4 +1,5 @@
 """Testing for K-Medoids"""
+
 import warnings
 import numpy as np
 from unittest import mock
@@ -15,7 +16,6 @@
 from sklearn.cluster import KMeans
 from sklearn.datasets import make_blobs
 
-
 seed = 0
 X = np.random.RandomState(seed).rand(100, 5)
 

sklearn_extra/kernel_approximation/__init__.py

@@ -1,4 +1,3 @@
 from ._fastfood import Fastfood
 
-
 __all__ = ["Fastfood"]

sklearn_extra/kernel_approximation/test_fastfood.py

@@ -6,7 +6,6 @@
 
 from sklearn_extra.kernel_approximation import Fastfood
 
-
 # generate data
 rng = np.random.RandomState(0)
 X = rng.random_sample(size=(300, 50))

sklearn_extra/robust/robust_weighted_estimator.py

@@ -32,7 +32,6 @@
 from .mean_estimators import median_of_means_blocked, block_mom, huber
 from ._robust_weighted_estimator_helper import _kmeans_loss
 
-
 # cython implementation of loss functions, copied from scikit-learn with light
 # modifications.
 from ._robust_weighted_estimator_helper import (
@@ -45,7 +44,6 @@
     SquaredHinge,
 )
 
-
 LOSS_FUNCTIONS = {
     "hinge": (Hinge,),
     "log_loss": (Log,),

sklearn_extra/robust/tests/test_mean_estimators.py

@@ -3,7 +3,6 @@
 
 from sklearn_extra.robust.mean_estimators import median_of_means, huber
 
-
 rng = np.random.RandomState(42)
 
 sample = rng.normal(size=100)

sklearn_extra/tests/test_common.py

@@ -10,7 +10,6 @@
     RobustWeightedKMeans,
 )
 
-
 ALL_ESTIMATORS = [
     Fastfood,
     KMedoids,