--- /home/runner/work/scikit-learn/scikit-learn/examples/classification/plot_classifier_comparison.py	2024-12-12 19:45:14.925881+00:00
+++ /home/runner/work/scikit-learn/scikit-learn/examples/classification/plot_classifier_comparison.py	2024-12-12 19:45:23.184305+00:00
@@ -16,10 +16,11 @@
 The plots show training points in solid colors and testing points
 semi-transparent. The lower right shows the classification accuracy on the test
 set.
 
 """
+
 """
 This script compares the performance of multiple classification algorithms on
 synthetic datasets. It visualizes the decision boundaries of classifiers such as
 Logistic Regression, SVM, Decision Tree, Random Forest, and Gradient Boosting. 
 Use this as a reference to understand how different classifiers perform on simple data.
@@ -116,11 +117,13 @@
     cm_bright = ListedColormap(["#FF0000", "#0000FF"])
     ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
     if ds_cnt == 0:
         ax.set_title("Input data")
     ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
-    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k")
+    ax.scatter(
+        X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k"
+    )
     ax.set_xlim(x_min, x_max)
     ax.set_ylim(y_min, y_max)
     ax.set_xticks(())
     ax.set_yticks(())
     i += 1
@@ -135,12 +138,21 @@
 
         DecisionBoundaryDisplay.from_estimator(
             clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5
         )
 
-        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
-        ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, edgecolors="k", alpha=0.6)
+        ax.scatter(
+            X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k"
+        )
+        ax.scatter(
+            X_test[:, 0],
+            X_test[:, 1],
+            c=y_test,
+            cmap=cm_bright,
+            edgecolors="k",
+            alpha=0.6,
+        )
 
         ax.set_xlim(x_min, x_max)
         ax.set_ylim(y_min, y_max)
         ax.set_xticks(())
         ax.set_yticks(())
would reformat /home/runner/work/scikit-learn/scikit-learn/examples/classification/plot_classifier_comparison.py

Oh no! 💥 💔 💥
1 file would be reformatted, 921 files would be left unchanged.

examples/classification/plot_classifier_comparison.py:24:79: W291 Trailing whitespace
   |
22 | This script compares the performance of multiple classification algorithms on
23 | synthetic datasets. It visualizes the decision boundaries of classifiers such as
24 | Logistic Regression, SVM, Decision Tree, Random Forest, and Gradient Boosting. 
   |                                                                               ^ W291
25 | Use this as a reference to understand how different classifiers perform on simple data.
26 | """
   |
   = help: Remove trailing whitespace

examples/classification/plot_classifier_comparison.py:31:1: I001 [*] Import block is un-sorted or un-formatted
   |
29 |   # SPDX-License-Identifier: BSD-3-Clause
30 |   
31 | / import matplotlib.pyplot as plt
32 | | import numpy as np
33 | | from matplotlib.colors import ListedColormap
34 | | 
35 | | from sklearn.datasets import make_circles, make_classification, make_moons
36 | | from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
37 | | from sklearn.ensemble import (
38 | |     AdaBoostClassifier,
39 | |     RandomForestClassifier,
40 | |     GradientBoostingClassifier,  # Gradient Boosting added
41 | | )
42 | | from sklearn.gaussian_process import GaussianProcessClassifier
43 | | from sklearn.gaussian_process.kernels import RBF
44 | | from sklearn.inspection import DecisionBoundaryDisplay
45 | | from sklearn.model_selection import train_test_split
46 | | from sklearn.naive_bayes import GaussianNB
47 | | from sklearn.neighbors import KNeighborsClassifier
48 | | from sklearn.neural_network import MLPClassifier
49 | | from sklearn.pipeline import make_pipeline
50 | | from sklearn.preprocessing import StandardScaler
51 | | from sklearn.svm import SVC
52 | | from sklearn.tree import DecisionTreeClassifier
53 | | 
54 | | # Classifier descriptions
   | |_^ I001
55 |   names = [
56 |       "Nearest Neighbors",
   |
   = help: Organize imports

examples/classification/plot_classifier_comparison.py:121:89: E501 Line too long (95 > 88)
    |
119 |         ax.set_title("Input data")
120 |     ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
121 |     ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k")
    |                                                                                         ^^^^^^^ E501
122 |     ax.set_xlim(x_min, x_max)
123 |     ax.set_ylim(y_min, y_max)
    |

examples/classification/plot_classifier_comparison.py:140:89: E501 Line too long (91 > 88)
    |
138 |         )
139 | 
140 |         ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
    |                                                                                         ^^^ E501
141 |         ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, edgecolors="k", alpha=0.6)
    |

examples/classification/plot_classifier_comparison.py:141:89: E501 Line too long (99 > 88)
    |
140 |         ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
141 |         ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, edgecolors="k", alpha=0.6)
    |                                                                                         ^^^^^^^^^^^ E501
142 | 
143 |         ax.set_xlim(x_min, x_max)
    |

Found 5 errors.
[*] 1 fixable with the `--fix` option (1 hidden fix can be enabled with the `--unsafe-fixes` option).