10BC0 MNT Update black to 23.3.0 (#26110) · scikit-learn/scikit-learn@893d5ac · GitHub
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MNT Update black to 23.3.0 (#26110)
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‎.pre-commit-config.yaml‎

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@@ -6,7 +6,7 @@ repos:
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- id: end-of-file-fixer
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- id: trailing-whitespace
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- repo: https://github.com/psf/black
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rev: 22.3.0
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rev: 23.3.0
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hooks:
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- id: black
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- repo: https://github.com/pycqa/flake8

‎benchmarks/bench_20newsgroups.py‎

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# Data
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-e", "--estimators", nargs="+", required=True, choices=ESTIMATORS

‎benchmarks/bench_feature_expansions.py‎

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fig, axes = plt.subplots(nrows=len(densities), ncols=1, figsize=(8, 10))
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for density, ax in zip(densities, axes):
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ax.plot(
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dimensionalities,
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csr_times[density] / trials,

‎benchmarks/bench_glm.py‎

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if __name__ == "__main__":
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import matplotlib.pyplot as plt
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n_iter = 40
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dimensions = 500 * np.arange(1, n_iter + 1)
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for i in range(n_iter):
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print("Iteration %s of %s" % (i, n_iter))
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n_samples, n_features = 10 * i + 3, 10 * i + 3

‎benchmarks/bench_isolation_forest.py‎

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# Loop over all datasets for fitting and scoring the estimator:
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for dat in datasets:
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# Loading and vectorizing the data:
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print("====== %s ======" % dat)
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print("--- Fetching data...")

‎benchmarks/bench_kernel_pca_solvers_time_vs_n_components.py‎

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r_time = np.empty((len(n_compo_range), n_iter)) * np.nan
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# loop
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for j, n_components in enumerate(n_compo_range):
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n_components = int(n_components)
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print("Performing kPCA with n_components = %i" % n_components)
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‎benchmarks/bench_kernel_pca_solvers_time_vs_n_samples.py‎

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# loop
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for j, n_samples in enumerate(n_samples_range):
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n_samples = int(n_samples)
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print("Performing kPCA with n_samples = %i" % n_samples)
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‎benchmarks/bench_mnist.py‎

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)
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print("-" * 60)
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for name in sorted(args["classifiers"], key=error.get):
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print(
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"{0: <23} {1: >10.2f}s {2: >10.2f}s {3: >12.4f}".format(
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name, train_time[name], test_time[name], error[name]

‎benchmarks/bench_online_ocsvm.py‎

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results_online = np.empty((len(datasets), n_axis + 5))
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for dat, dataset_name in enumerate(datasets):
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print(dataset_name)
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# Loading datasets
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gamma = 1 / n_features # OCSVM default pa A2B2 rameter
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for random_state in random_states:
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print("random state: %s" % random_state)
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X, y = shuffle(X, y, random_state=random_state)

‎benchmarks/bench_plot_fastkmeans.py‎

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def compute_bench(samples_range, features_range):
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it = 0
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results = defaultdict(lambda: [])
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chunk = 100

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