8000 MNT `ruff format` · scikit-learn/scikit-learn@d3bfa00 · GitHub
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DimitriPapadopoulosDimitriPapadopoulos
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MNT ruff format 
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‎benchmarks/bench_hist_gradient_boosting_adult.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -46,7 +46,7 @@ def predict(est, data_test, target_test):
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toc = time()
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roc_auc = roc_auc_score(target_test, predicted_proba_test[:, 1])
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acc = accuracy_score(target_test, predicted_test)
49-
print(f"predicted in {toc - tic:.3f}s, ROC AUC: {roc_auc:.4f}, ACC: {acc :.4f}")
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print(f"predicted in {toc - tic:.3f}s, ROC AUC: {roc_auc:.4f}, ACC: {acc:.4f}")
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data = fetch_openml(data_id=179, as_frame=True) # adult dataset

‎benchmarks/bench_hist_gradient_boosting_higgsboson.py

Lines changed: 1 addition & 1 deletion
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@@ -74,7 +74,7 @@ def predict(est, data_test, target_test):
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toc = time()
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roc_auc = roc_auc_score(target_test, predicted_proba_test[:, 1])
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acc = accuracy_score(target_test, predicted_test)
77-
print(f"predicted in {toc - tic:.3f}s, ROC AUC: {roc_auc:.4f}, ACC: {acc :.4f}")
77+
print(f"predicted in {toc - tic:.3f}s, ROC AUC: {roc_auc:.4f}, ACC: {acc:.4f}")
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df = load_data()

‎build_tools/get_comment.py

Lines changed: 1 addition & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -55,9 +55,7 @@ def get_step_message(log, start, end, title, message, details):
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if end not in log:
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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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f"{message}\n\n"
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f"-----------------------------------------------\n### {title}\n\n{message}\n\n"
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)
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if details:
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res += (

‎examples/applications/plot_species_distribution_modeling.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -109,7 +109,7 @@ def create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):
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def plot_species_distribution(
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species=("bradypus_variegatus_0", "microryzomys_minutus_0")
112+
species=("bradypus_variegatus_0", "microryzomys_minutus_0"),
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):
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"""
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Plot the species distribution.

‎examples/applications/plot_time_series_lagged_features.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -265,7 +265,7 @@ def consolidate_scores(cv_results, scores, metric):
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time = cv_results["fit_time"]
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scores["fit_time"].append(f"{time.mean():.2f} ± {time.std():.2f} s")
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268-
scores["loss"].append(f"quantile {int(quantile*100)}")
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scores["loss"].append(f"quantile {int(quantile * 100)}")
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for key, value in cv_results.items():
270270
if key.startswith("test_"):
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metric = key.split("test_")[1]

‎examples/applications/plot_topics_extraction_with_nmf_lda.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -50,7 +50,7 @@ def plot_top_words(model, feature_names, n_top_words, title):
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ax = axes[topic_idx]
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ax.barh(top_features, weights, height=0.7)
53-
ax.set_title(f"Topic {topic_idx +1}", fontdict={"fontsize": 30})
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ax.set_title(f"Topic {topic_idx + 1}", fontdict={"fontsize": 30})
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ax.tick_params(axis="both", which="major", labelsize=20)
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for i in "top right left".split():
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ax.spines[i].set_visible(False)

‎examples/linear_model/plot_tweedie_regression_insurance_claims.py

Lines changed: 3 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -606,8 +606,9 @@ def score_estimator(
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"predicted, frequency*severity model": np.sum(
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exposure * glm_freq.predict(X) * glm_sev.predict(X)
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),
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"predicted, tweedie, power=%.2f"
610-
% glm_pure_premium.power: np.sum(exposure * glm_pure_premium.predict(X)),
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"predicted, tweedie, power=%.2f" % glm_pure_premium.power: np.sum(
610+
exposure * glm_pure_premium.predict(X)
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),
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}
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)
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‎examples/manifold/plot_lle_digits.py

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@@ -10,7 +10,6 @@
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# Authors: The scikit-learn developers
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# SPDX-License-Identifier: BSD-3-Clause
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# %%
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# Load digits dataset
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# -------------------

‎examples/manifold/plot_manifold_sphere.py

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Original file line numberDiff line numberDiff line change
@@ -50,7 +50,7 @@
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t = random_state.rand(n_samples) * np.pi
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# Sever the poles from the sphere.
53-
indices = (t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8)))
53+
indices = (t < (np.pi - (np.pi / 8))) & (t > (np.pi / 8))
5454
colors = p[indices]
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x, y, z = (
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np.sin(t[indices]) * np.cos(p[indices]),

‎examples/model_selection/plot_likelihood_ratios.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -40,7 +40,7 @@ class proportion than the target application.
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from sklearn.datasets import make_classification
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X, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)
43-
print(f"Percentage of people carrying the disease: {100*y.mean():.2f}%")
43+
print(f"Percentage of people carrying the disease: {100 * y.mean():.2f}%")
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4545
# %%
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# A machine learning model is built to diagnose if a person with some given

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