scikit-learn · jeremiedbb · Mar 14, 2022 · Oct 6, 2021 · Mar 14, 2022
diff --git a/doc/conf.py b/doc/conf.py
@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-#
 # scikit-learn documentation build configuration file, created by
 # sphinx-quickstart on Fri Jan  8 09:13:42 2010.
 #

diff --git a/doc/sphinxext/sphinx_issues.py b/doc/sphinxext/sphinx_issues.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """A Sphinx extension for linking to your project's issue tracker.
 
 Copyright 2014 Steven Loria

diff --git a/sklearn/cluster/_dbscan.py b/sklearn/cluster/_dbscan.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 DBSCAN: Density-Based Spatial Clustering of Applications with Noise
 """

diff --git a/sklearn/cluster/_optics.py b/sklearn/cluster/_optics.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """Ordering Points To Identify the Clustering Structure (OPTICS)
 
 These routines execute the OPTICS algorithm, and implement various

diff --git a/sklearn/cluster/_spectral.py b/sklearn/cluster/_spectral.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """Algorithms for spectral clustering"""
 
 # Author: Gael Varoquaux <[email protected]>

diff --git a/sklearn/feature_extraction/tests/test_text.py b/sklearn/feature_extraction/tests/test_text.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 from collections.abc import Mapping
 import re
 

diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 # Authors: Olivier Grisel <[email protected]>
 #          Mathieu Blondel <[email protected]>
 #          Lars Buitinck

diff --git a/sklearn/feature_selection/_base.py b/sklearn/feature_selection/_base.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """Generic feature selection mixin"""
 
 # Authors: G. Varoquaux, A. Gramfort, L. Buitinck, J. Nothman

diff --git a/sklearn/gaussian_process/__init__.py b/sklearn/gaussian_process/__init__.py
@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 # Author: Jan Hendrik Metzen <[email protected]>
 #         Vincent Dubourg <[email protected]>
 #         (mostly translation, see implementation details)

diff --git a/sklearn/linear_model/_ransac.py b/sklearn/linear_model/_ransac.py
@@ -1,5 +1,3 @@
-# coding: utf-8
-
 # Author: Johannes Schönberger
 #
 # License: BSD 3 clause

diff --git a/sklearn/linear_model/_theil_sen.py b/sklearn/linear_model/_theil_sen.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 A Theil-Sen Estimator for Multiple Linear Regression Model
 """

diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 # Authors: Alexandre Gramfort <[email protected]>
 #          Mathieu Blondel <[email protected]>
 #          Robert Layton <[email protected]>

diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py
@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 """
 The :mod:`sklearn.naive_bayes` module implements Naive Bayes algorithms. These
 are supervised learning methods based on applying Bayes' theorem with strong

diff --git a/sklearn/neighbors/_nca.py b/sklearn/neighbors/_nca.py
@@ -1,4 +1,3 @@
-# coding: utf-8
 """
 Neighborhood Component Analysis
 """

diff --git a/sklearn/neighbors/_nearest_centroid.py b/sklearn/neighbors/_nearest_centroid.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Nearest Centroid Classification
 """

diff --git a/sklearn/neighbors/tests/test_nca.py b/sklearn/neighbors/tests/test_nca.py
@@ -1,4 +1,3 @@
-# coding: utf-8
 """
 Testing for Neighborhood Component Analysis module (sklearn.neighbors.nca)
 """

diff --git a/sklearn/preprocessing/_discretization.py b/sklearn/preprocessing/_discretization.py
@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 # Author: Henry Lin <[email protected]>
 #         Tom Dupré la Tour
 

diff --git a/sklearn/preprocessing/tests/test_encoders.py b/sklearn/preprocessing/tests/test_encoders.py
@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 import re
 
 import numpy as np

diff --git a/sklearn/random_projection.py b/sklearn/random_projection.py
@@ -1,4 +1,3 @@
-# -*- coding: utf8
 """Random Projection transformers.
 
 Random Projections are a simple and computationally efficient way to