Issue

Fixes #23

Reproduction

import numpy as np
from sklearn.preprocessing import KBinsDiscretizer
X = np.array([0, 0.5, 2, 3, 9, 10]).reshape(-1, 1)
est = KBinsDiscretizer(n_bins=5, strategy='kmeans', encode='ordinal')
Xt = est.fit_transform(X)

Observed failure (pre-fix):

Traceback (most recent call last):
  File "<stdin>", line 24, in <module>
  File "/root/.local/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 157, in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/.local/lib/python3.11/site-packages/sklearn/base.py", line 916, in fit_transform
    return self.fit(X, **fit_params).transform(X)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/.local/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 157, in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/scikit-learn/sklearn/preprocessing/_discretization.py", line 255, in transform
    Xt[:, jj] = np.digitize(Xt[:, jj] + eps, bin_edges[jj][1:])
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/.local/lib/python3.11/site-packages/numpy/lib/function_base.py", line 5725, in digitize
    raise ValueError("bins must be monotonically increasing or decreasing")
ValueError: bins must be monotonically increasing or decreasing

Fix

• Sort the 1D k-means cluster centers before deriving interior midpoints
• Rebuild bin_edges_ from [col_min, interior midpoints, col_max] to guarantee monotonic edges for strategy='kmeans'
• Add regression coverage for the 1D failure and for monotonic bin edges across multi-feature inputs

Tests

• `PATH=/root/.local/bin:$PATH LD_LIBRARY_PATH=/root/.local/lib/python3.11/site-packages/scikit_learn.libs:/nix/store/qipd93x9gjyiygqk673rd2ssnf8y7jj0-gcc-14.3.0-lib/lib:/nix/store/gh2dd8vimringn726ndall19gbm77prj-openblas-0.3.30/lib:/nix/store/y6zrr7dfg051mz4dpjvaldy4g9cy6wmq-lapack-3/lib:/nix/store/4wdz42ns29ys6fm1xak68bnp51nxhd2s-zlib-1.3.1/lib PYTHONPATH=/root/.local/lib/python3.11/site-packages python3 - <<'PY'
  import numpy as np
  np.int = int
  import importlib.util
  import importlib
  import sys
  import tempfile
  from pathlib import Path

site_packages = '/root/.local/lib/python3.11/site-packages'
repo_root = '/workspace/scikit-learn'

sys.path.insert(0, site_packages)

import sklearn
import sklearn.preprocessing

sys.modules.pop('sklearn.preprocessing._discretization', None)

spec = importlib.util.spec_from_file_location(
'sklearn.preprocessing._discretization',
f'{repo_root}/sklearn/preprocessing/_discretization.py')
module = importlib.util.module_from_spec(spec)
sys.modules['sklearn.preprocessing._discretization'] = module
spec.loader.exec_module(module)

importlib.reload(sklearn.preprocessing)

sys.modules.pop('sklearn.utils._unittest_backport', None)
unittest_spec = importlib.util.spec_from_file_location(
'sklearn.utils._unittest_backport',
f'{repo_root}/sklearn/utils/_unittest_backport.py')
unittest_module = importlib.util.module_from_spec(unittest_spec)
sys.modules['sklearn.utils._unittest_backport'] = unittest_module
unittest_spec.loader.exec_module(unittest_module)

sys.modules.pop('sklearn.utils.testing', None)
testing_spec = importlib.util.spec_from_file_location(
'sklearn.utils.testing',
f'{repo_root}/sklearn/utils/testing.py')
testing_module = importlib.util.module_from_spec(testing_spec)
sys.modules['sklearn.utils.testing'] = testing_module
testing_spec.loader.exec_module(testing_module)

from pytest import main

with tempfile.TemporaryDirectory() as tmpdir:
test_path = Path(tmpdir) / 'test_discretization_local.py'
original = Path(repo_root) / 'sklearn' / 'preprocessing' / 'tests' / 'test_discretization.py'
test_path.write_text(original.read_text(encoding='utf-8'), encoding='utf-8')
sys.exit(main(['-q', str(test_path), '-k', 'kmeans']))
PY`

• 10 passed, 26 deselected
• PATH=/root/.local/bin:$PATH flake8 sklearn/preprocessing/_discretization.py sklearn/preprocessing/tests/test_discretization.py