10000 MNT Apply ruff/flake8-implicit-str-concat rules (ISC) (#30695) · scikit-learn/scikit-learn@5cdbbf1 · GitHub
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DimitriPapadopoulosCharlie-XIAO
DimitriPapadopoulos
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Charlie-XIAO
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MNT Apply ruff/flake8-implicit-str-concat rules (ISC) (#30695)
Co-authored-by: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com>
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-62
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26 files changed

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‎build_tools/get_comment.py‎

Lines changed: 2 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -56,9 +56,8 @@ def get_step_message(log, start, end, title, message, details):
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return ""
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res = (
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"-----------------------------------------------\n"
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+ f"### {title}\n\n"
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+ message
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+ "\n\n"
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f"### {title}\n\n"
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f"{message}\n\n"
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)
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if details:
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res += (

‎examples/classification/plot_lda.py‎

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Original file line numberDiff line numberDiff line change
@@ -98,10 +98,10 @@ def generate_data(n_samples, n_features):
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plt.legend(loc="lower left")
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plt.ylim((0.65, 1.0))
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plt.suptitle(
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"LDA (Linear Discriminant Analysis) vs. "
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+ "\n"
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+ "LDA with Ledoit Wolf vs. "
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+ "\n"
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+ "LDA with OAS (1 discriminative feature)"
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"LDA (Linear Discriminant Analysis) vs."
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"\n"
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"LDA with Ledoit Wolf vs."
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"\n"
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"LDA with OAS (1 discriminative feature)"
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)
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plt.show()

‎examples/cluster/plot_cluster_comparison.py‎

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Original file line numberDiff line numberDiff line change
@@ -224,14 +224,14 @@
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warnings.filterwarnings(
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"ignore",
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message="the number of connected components of the "
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+ "connectivity matrix is [0-9]{1,2}"
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+ " > 1. Completing it to avoid stopping the tree early.",
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"connectivity matrix is [0-9]{1,2}"
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" > 1. Completing it to avoid stopping the tree early.",
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category=UserWarning,
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)
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warnings.filterwarnings(
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"ignore",
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message="Graph is not fully connected, spectral embedding"
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+ " may not work as expected.",
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" may not work as expected.",
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category=UserWarning,
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)
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algorithm.fit(X)

‎examples/cluster/plot_linkage_comparison.py‎

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Original file line numberDiff line numberDiff line change
@@ -123,8 +123,8 @@
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warnings.filterwarnings(
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"ignore",
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message="the number of connected components of the "
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+ "connectivity matrix is [0-9]{1,2}"
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+ " > 1. Completing it to avoid stopping the tree early.",
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"connectivity matrix is [0-9]{1,2}"
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" > 1. Completing it to avoid stopping the tree early.",
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category=UserWarning,
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)
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algorithm.fit(X)

‎examples/kernel_approximation/plot_scalable_poly_kernels.py‎

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Original file line numberDiff line numberDiff line change
@@ -143,7 +143,7 @@
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}
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print(
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f"Linear SVM score on {n_components} PolynomialCountSketch "
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+ f"features: {ps_lsvm_score:.2f}%"
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f"features: {ps_lsvm_score:.2f}%"
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)
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# %%

‎examples/mixture/plot_concentration_prior.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -103,7 +103,7 @@ def plot_results(ax1, ax2, estimator, X, y, title, plot_title=False):
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# mean_precision_prior= 0.8 to minimize the influence of the prior
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estimators = [
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(
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"Finite mixture with a Dirichlet distribution\nprior and " r"$\gamma_0=$",
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"Finite mixture with a Dirichlet distribution\n" r"prior and $\gamma_0=$",
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BayesianGaussianMixture(
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weight_concentration_prior_type="dirichlet_distribution",
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n_components=2 * n_components,
@@ -116,7 +116,7 @@ def plot_results(ax1, ax2, estimator, X, y, title, plot_title=False):
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[0.001, 1, 1000],
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),
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(
119-
"Infinite mixture with a Dirichlet process\n prior and" r"$\gamma_0=$",
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"Infinite mixture with a Dirichlet process\n" r"prior and $\gamma_0=$",
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BayesianGaussianMixture(
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weight_concentration_prior_type="dirichlet_process",
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n_components=2 * n_components,

‎maint_tools/sort_whats_new.py‎

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Original file line numberDiff line numberDiff line change
@@ -23,7 +23,7 @@ def entry_sort_key(s):
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for entry in re.split("\n(?=- )", text.strip()):
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modules = re.findall(
26-
r":(?:func|meth|mod|class):" r"`(?:[^<`]*<|~)?(?:sklearn.)?([a-z]\w+)", entry
26+
r":(?:func|meth|mod|class):`(?:[^<`]*<|~)?(?:sklearn.)?([a-z]\w+)", entry
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)
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modules = set(modules)
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if len(modules) > 1:

‎sklearn/base.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -110,8 +110,8 @@ def _clone_parametrized(estimator, *, safe=True):
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if isinstance(estimator, type):
111111
raise TypeError(
112112
"Cannot clone object. "
113-
+ "You should provide an instance of "
114-
+ "scikit-learn estimator instead of a class."
113+
"You should provide an instance of "
114+
"scikit-learn estimator instead of a class."
115115
)
116116
else:
117117
raise TypeError(

‎sklearn/compose/tests/test_target.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -36,7 +36,7 @@ def test_transform_target_regressor_error():
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)
3737
with pytest.raises(
3838
TypeError,
39-
match=r"fit\(\) got an unexpected " "keyword argument 'sample_weight'",
39+
match=r"fit\(\) got an unexpected keyword argument 'sample_weight'",
4040
):
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regr.fit(X, y, sample_weight=sample_weight)
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‎sklearn/datasets/_twenty_newsgroups.py‎

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Original file line numberDiff line numberDiff line change
@@ -115,7 +115,7 @@ def strip_newsgroup_header(text):
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116116

117117
_QUOTE_RE = re.compile(
118-
r"(writes in|writes:|wrote:|says:|said:" r"|^In article|^Quoted from|^\||^>)"
118+
r"(writes in|writes:|wrote:|says:|said:|^In article|^Quoted from|^\||^>)"
119119
)
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