ran black for linting check

Author: SamuelCarliles3

.github/scripts/label_title_regex.py

@@ -1,5 +1,6 @@
 """Labels PRs based on title. Must be run in a github action with the
 pull_request_target event."""
+
 import json
 import os
 import re

asv_benchmarks/benchmarks/ensemble.py

@@ -2,7 +2,7 @@
     GradientBoostingClassifier,
     HistGradientBoostingClassifier,
     RandomForestClassifier,
-    RandomForestRegressor
+    RandomForestRegressor,
 )
 
 from .common import Benchmark, Estimator, Predictor
@@ -11,7 +11,7 @@
     _20newsgroups_lowdim_dataset,
     _synth_classification_dataset,
     _synth_regression_dataset,
-    _synth_regression_sparse_dataset
+    _synth_regression_sparse_dataset,
 )
 from .utils import make_gen_classif_scorers, make_gen_reg_scorers
 

benchmarks/bench_glm.py

@@ -4,6 +4,7 @@
 Data comes from a random square matrix.
 
 """
+
 from datetime import datetime
 
 import numpy as np

benchmarks/bench_glmnet.py

@@ -16,6 +16,7 @@
 
 In both cases, only 10% of the features are informative.
 """
+
 import gc
 from time import time
 

benchmarks/bench_isotonic.py

@@ -10,6 +10,7 @@
 This allows the scaling of the algorithm with the problem size to be
 visualized and understood.
 """
+
 import argparse
 import gc
 from datetime import datetime

benchmarks/bench_kernel_pca_solvers_time_vs_n_components.py

@@ -35,6 +35,7 @@
 You can also set `arpack_all=True` to activate arpack solver for large number
 of components (this takes more time).
 """
+
 # Authors: Sylvain MARIE, Schneider Electric
 
 import time

benchmarks/bench_kernel_pca_solvers_time_vs_n_samples.py

@@ -37,6 +37,7 @@
 Solvers comparison benchmark: time vs n_components", where this time the number
 of examples is fixed, and the desired number of components varies.
 """
+
 # Author: Sylvain MARIE, Schneider Electric
 
 import time

benchmarks/bench_lasso.py

@@ -11,6 +11,7 @@
 
 In both cases, only 10% of the features are informative.
 """
+
 import gc
 from time import time
 

benchmarks/bench_plot_lasso_path.py

@@ -2,6 +2,7 @@
 
 The input data is mostly low rank but is a fat infinite tail.
 """
+
 import gc
 import sys
 from collections import defaultdict

benchmarks/bench_plot_neighbors.py

@@ -1,6 +1,7 @@
 """
 Plot the scaling of the nearest neighbors algorithms with k, D, and N
 """
+
 from time import time
 
 import matplotlib.pyplot as plt