FIX: Apply ruff formatting fixes

Yashp002 · Yashp002 · commit d04e52b11167 · 2025-08-30T12:51:34.000+05:30
diff --git a/benchmarks/bench_tsne_mnist.py b/benchmarks/bench_tsne_mnist.py
@@ -15,14 +15,14 @@
 
 import numpy as np
 from joblib import Memory
+from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
 
 from sklearn.datasets import fetch_openml
 from sklearn.decomposition import PCA
 from sklearn.manifold import TSNE
 from sklearn.neighbors import NearestNeighbors
 from sklearn.utils import check_array
 from sklearn.utils import shuffle as _shuffle
-from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
 
 LOG_DIR = "mnist_tsne_output"
 if not os.path.exists(LOG_DIR):
diff --git a/sklearn/metrics/_plot/precision_recall_curve.py b/sklearn/metrics/_plot/precision_recall_curve.py
@@ -47,7 +47,7 @@ class PrecisionRecallDisplay(_BinaryClassifierCurveDisplayMixin):
         .. versionadded:: 0.24
 
     prevalence_pos_label : float, default=None
-        The prevalence of the positive label. It is used for plotting the 
+        The prevalence of the positive label. It is used for plotting the
         prevalence baseline line (horizontal line representing the proportion
         of positive samples). If None, the baseline line will not be plotted
         even if `plot_chance_level` is set to True when plotting.
@@ -157,16 +157,16 @@ def plot(
             `estimator_name` if not `None`, otherwise no labeling is shown.
 
         plot_chance_level : bool, default=False
-            Whether to plot the prevalence baseline. The baseline is the prevalence 
-            of the positive label computed from the data passed during 
+            Whether to plot the prevalence baseline. The baseline is the prevalence
+            of the positive label computed from the data passed during
             :meth:`from_estimator` or :meth:`from_predictions` call.
 
 
             .. versionadded:: 1.3
 
         chance_level_kw : dict, default=None
-            Keyword arguments to be passed to matplotlib's `plot` for rendering 
-            the prevalence baseline line (horizontal line representing the 
+            Keyword arguments to be passed to matplotlib's `plot` for rendering
+            the prevalence baseline line (horizontal line representing the
             proportion of positive samples).
 
 
@@ -475,17 +475,17 @@ def from_predictions(
             Axes object to plot on. If `None`, a new figure and axes is created.
 
         plot_chance_level : bool, default=False
-            Whether to plot the prevalence baseline (horizontal reference line). 
-            The baseline represents the prevalence of the positive label computed 
-            from the data passed during :meth:`from_estimator` or 
+            Whether to plot the prevalence baseline (horizontal reference line).
+            The baseline represents the prevalence of the positive label computed
+            from the data passed during :meth:`from_estimator` or
             :meth:`from_predictions` call.
 
 
             .. versionadded:: 1.3
 
         chance_level_kw : dict, default=None
-            Keyword arguments to be passed to matplotlib's `plot` for rendering 
-            the prevalence baseline line (horizontal line representing the 
+            Keyword arguments to be passed to matplotlib's `plot` for rendering
+            the prevalence baseline line (horizontal line representing the
             proportion of positive samples).
 
 
diff --git a/sklearn/metrics/_plot/roc_curve.py b/sklearn/metrics/_plot/roc_curve.py
@@ -91,7 +91,7 @@ class RocCurveDisplay(_BinaryClassifierCurveDisplayMixin):
             This attribute can now be a list of Artists, for when multiple curves
             are plotted.
 
-    chance_level : matplotlib Artist or None  
+    chance_level : matplotlib Artist or None
          The random classifier baseline line representing chance performance.
          It is `None` if the baseline is not plotted.
 
