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] | closed | false | [] | null | 1 | 2025-12-06T11:33:57 | 2025-12-06T14:02:38 | 2025-12-06T14:02:37 | null | NONE | null | null | null | null | #### Reference Issues/PRs
N/A
#### What does this implement/fix? Explain your changes.
This PR adds a basic example showing how to use `train_test_split` with
`LogisticRegression`. It demonstrates splitting the data, training the model,
and evaluating accuracy. The goal is to help beginners understand the standard
machine learning workflow more clearly.
#### AI usage disclosure
I used AI assistance for:
- [ ] Code generation
- [ ] Test/benchmark generation
- [x] Documentation (including examples)
- [ ] Research and understanding
#### Any other comments?
Let me know if the example should be placed elsewhere or formatted differently.
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] | closed | false | [] | null | 5 | 2025-12-06T07:17:54 | 2025-12-18T10:16:26 | 2025-12-18T09:04:02 | null | MEMBER | null | null | null | null | Noticed thanks to @MarcoGorelli https://github.com/scikit-learn/scikit-learn/pull/32813#pullrequestreview-3519391603
Sorry @AnneBeyer it would have been something you could have done indeed, but I only noticed your comment once I already did the work and was about to open the PR :sweat_smile:. Feel free to review my PR, in case you can spot something I missed.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32848 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32848/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32848/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32848/events | https://github.com/scikit-learn/scikit-learn/issues/32848 | 3,699,756,415 | I_kwDOAAzd1s7chc1_ | 32,848 | nan_euclidean_distances producing distance matrix not symmetrical due to floating point precision | {
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} | ### Describe the bug
nan_euclidean_distances is producing asymmetrical matrix when input matrix contains nan values. This, in turn, causes errors when checked for symmetry, for example by scipy.spatial.distance.squareform.
### Steps/Code to Reproduce
```Python
mat = np.random.rand(10, 20)
dist = nan_euclidean_distances(mat)
(dist == dist.T).all()
# output np.True_
mat = np.random.rand(10, 20)
missing = np.random.choice(mat.size, 50, replace=False)
mat.ravel()[missing] = np.nan
dist = nan_euclidean_distances(mat)
(dist == dist.T).all()
# output np.False_
(dist.round(5) == dist.T.round(5)).all()
# output np.True_
```
### Expected Results
Function should produce symmetrical matrix.
### Actual Results
Produces asymmetrical matrix.
### Versions
```shell
System:
python: 3.13.7 | packaged by conda-forge | (main, Sep 3 2025, 14:33:26) [Clang 19.1.7 ]
executable: /mambaforge-pypy3/envs/jupyterlab/bin/python
machine: macOS-15.7-x86_64-i386-64bit-Mach-O
Python dependencies:
sklearn: 1.7.2
pip: 25.2
setuptools: 80.9.0
numpy: 2.3.3
scipy: 1.16.2
Cython: None
pandas: 2.3.3
matplotlib: 3.10.6
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libopenblas
filepath: /mambaforge-pypy3/envs/jupyterlab/lib/libopenblasp-r0.3.30.dylib
version: 0.3.30
threading_layer: openmp
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 16
prefix: libomp
filepath: /mambaforge-pypy3/envs/jupyterlab/lib/libomp.dylib
version: None
``` | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32847 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32847/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32847/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32847/events | https://github.com/scikit-learn/scikit-learn/pull/32847 | 3,699,544,659 | PR_kwDOAAzd1s63U6WM | 32,847 | added version and tag validation functions | {
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-it will ensure thar every dependency version follows a valid pattern. (like 1.24.1)
2.added sanity check for known tags:
-it will validates that every tag (e.g., build, install, docs, tests) exists in the allowed list.
3.added helper get_min_version:
-this will make the module cleaner, avoids repeated code, and allows other scripts to import and use it directly.
4. added comments , improved structure/readability
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#### AI usage disclosure
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32845 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32845/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32845/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32845/events | https://github.com/scikit-learn/scikit-learn/pull/32845 | 3,699,266,395 | PR_kwDOAAzd1s63T-Aq | 32,845 | ENH/FIX: make coordinate descent solver converge for ridge regression | {
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"node_id": "MDU6... | closed | false | [] | null | 4 | 2025-12-05T14:49:52 | 2025-12-31T15:16:45 | 2025-12-31T14:44:08 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Warnings for `alpha=0` / `l1_ratio=0` where introduced in #620. A note that Enet with dual gap as stopping criterion does not converge was mentioned in https://github.com/scikit-learn/scikit-learn/pull/19620#issue-822947581.
#### What does this implement/fix? Explain your changes.
This PR makes the coordinate descent solver of `Lasso` and `ElasticNet` etc. converge for Ridge regression.
##### Details
What prevents convergence of the CD solver in the main branch is that the dual gap is not valid for `alpha=0` / `l1_ratio=0`. Fortunately, there is an alternative dual gap available, see #22836.
#### Any other comments?
- There are some surprising results, see the new `test_enet_ols_consistency`.
- `test_coordinate_descent.py` now passes without warnings (`pytest -We sklearn/linear_model/tests/test_coordinate_descent.py`). | {
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] | open | false | [] | null | 0 | 2025-12-05T09:36:46 | 2026-02-19T15:54:29 | null | null | CONTRIBUTOR | null | null | null | null | <!--
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Related to the ongoing discussion on managing RandomState objects : #26148
#### What does this implement/fix? Explain your changes.
The documentation's description of what happens when cloning an estimator initialized with a `RandomState` instance was not reflecting the current behaviour, stating the `RandomState` object is shared between clones while it is actually deepcopied. I updated this description to match the current behaviour, updating also the recommendations for CV and adding a warning to indicate that this is not the intended behaviour and it is worked on by maintainers.
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"... | closed | false | [] | null | 1 | 2025-12-05T08:51:48 | 2026-01-28T20:23:50 | 2026-01-28T17:14:33 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
closes #32076
supersedes #32239
**superseded by #33089**
#### What does this implement/fix? Explain your changes.
- adds a `groups` param to `TargetEncoder.fit_transform` that then internally picks a suitable non-overlapping cross-validation strategy (in its cross-fitting).
- adds more input options for `cv` init param (as discussed here https://github.com/scikit-learn/scikit-learn/issues/32076#issuecomment-3307697377)
- exposes cv_ attribute
- turns `TargetEncoder` into a metadata router that routes `groups` to the internal splitter
#### AI usage disclosure
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- [x] Research and understanding
#### Any other comments?
I also fixed some minor documentation issues across cross-validation, sorry I couldn't hold back. | {
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Aftermath of #32014 and #32811
#### What does this implement/fix? Explain your changes.
In current main, screening is not activated for `MultiTaskElasticNet` and `MultiTaskLasso` because they do not call `enet_path` but `enet_coordinate_descent_multi_task` directly.
#### Any other comments?
This does **not** change/fix the findings in https://github.com/scikit-learn/scikit-learn/pull/32811#pullrequestreview-3530261027. | {
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PR https://github.com/scikit-learn/scikit-learn/pull/30763 was listed already in version 1.7 [release highlights](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_7_0.html#release-highlights-for-scikit-learn-1-7:~:text=estimator%E2%80%99s%20HTML%20representation-,%23,-The%20HTML%20representation) and it was going to be added to release 1.8. For more info, please see https://github.com/scikit-learn/scikit-learn/pull/32809#issuecomment-3612869819 | {
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"node_id": "MDU6TGFiZWwyNjU5OD... | closed | false | [] | null | 3 | 2025-12-04T15:29:28 | 2025-12-05T15:14:14 | 2025-12-05T14:09:16 | null | MEMBER | null | null | null | null | This is a fix for #32837.
While investigating the issue above, I realized that we needed unittest for array API support for `_safe_indexing`.
I am not yet sure if this problem already existing in 1.7 or not. If it was I will add a changelog entry. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32839 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32839/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32839/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32839/events | https://github.com/scikit-learn/scikit-learn/pull/32839 | 3,694,818,273 | PR_kwDOAAzd1s63E6V- | 32,839 | MAINT Remove CSS template substitution in estimators' HTML Display | {
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#32834
#### What does this implement/fix? Explain your changes.
Maybe the template/substitution can be avoided.
The css doesn't change for each estimator, so why do we need a dynamic selector?
Edit: Removed `#$id` to `estimator.css` to avoid the dynamic behaviour. Added a selector called `css-selector` instead.
On `estimator.py` I'm adding a class with the same name - `css-selector`.
That way we don't need to substitute the id number, that I believe it's not needed.
Worked locally with vscode and jupyter notebook.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32838 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32838/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32838/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32838/events | https://github.com/scikit-learn/scikit-learn/pull/32838 | 3,694,728,312 | PR_kwDOAAzd1s63EmOT | 32,838 | FIX Make `get_namespace` handle pandas dataframe input | {
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} | 6 | 2025-12-04T13:38:00 | 2025-12-05T17:07:49 | 2025-12-05T16:59:36 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Closes #32836
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#### What does this implement/fix? Explain your changes.
A pandas DataFrame or Series does not have a namespace associated to it in the array API. As such we should filter it out, like we do for sparse arrays, before trying to determine the namespace. This means that for `get_namespace(a_df)` (with array API enabled) we get the NumPy compat namespace back as `xp`. I think this makes sense.
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#### Any other comments?
How do we solve the problem that we can't import `_is_pandas_df_or_series` from `sklearn.utils.validation` as it causes a circular import. Duplicating it feels a bit weird, but I can't think of a great alternative. We could use `hasattr(array, "iloc")` to detect "pandas-ness"?
Before discussing how we best detect "pandas-ness" we should agree that filtering `DataFrame` and `Series`out (like sparse arrays) is the right approach.
cc @ogrisel | {
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} | ### Describe the bug
This might be related to #32836 (or not).
I think we need a common test to check that enabling array API support does not affect estimators (without array API support) when called on regular NumPy (or other valid non-array inputs).
This bug might be a regression.
### Steps/Code to Reproduce
```python
# %%
import os
os.environ["SCIPY_ARRAY_API"] = "1"
# %%
import sklearn
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.datasets import load_iris
X_iris, y_iris = load_iris(return_X_y=True)
with sklearn.config_context(array_api_dispatch=True):
KBinsDiscretizer(
n_bins=3,
encode="onehot-dense",
strategy="uniform",
quantile_method="averaged_inverted_cdf",
).fit_transform(X_iris)
```
### Expected Results
No error.
### Actual Results
```python
Traceback (most recent call last):
Cell In[5], line 14
).fit_transform(X_iris)
File ~/code/scikit-learn/sklearn/utils/_set_output.py:316 in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File ~/code/scikit-learn/sklearn/base.py:907 in fit_transform
return self.fit(X, **fit_params).transform(X)
File ~/code/scikit-learn/sklearn/base.py:1336 in wrapper
return fit_method(estimator, *args, **kwargs)
File ~/code/scikit-learn/sklearn/preprocessing/_discretization.py:414 in fit
self._encoder.fit(np.zeros((1, len(self.n_bins_))))
File ~/code/scikit-learn/sklearn/base.py:1336 in wrapper
return fit_method(estimator, *args, **kwargs)
File ~/code/scikit-learn/sklearn/preprocessing/_encoders.py:999 in fit
self._fit(
File ~/code/scikit-learn/sklearn/preprocessing/_encoders.py:86 in _fit
X_list, n_samples, n_features = self._check_X(
File ~/code/scikit-learn/sklearn/preprocessing/_encoders.py:68 in _check_X
Xi = _safe_indexing(X, indices=i, axis=1)
File ~/code/scikit-learn/sklearn/utils/_indexing.py:357 in _safe_indexing
return _array_indexing(X, indices, indices_dtype, axis=axis)
File ~/code/scikit-learn/sklearn/utils/_indexing.py:39 in _array_indexing
key = move_to(key, xp=xp, device=device_)
File ~/code/scikit-learn/sklearn/utils/_array_api.py:508 in move_to
xp_array, _, device_array = get_namespace_and_device(array)
File ~/code/scikit-learn/sklearn/utils/_array_api.py:453 in get_namespace_and_device
xp, is_array_api = get_namespace(*array_list, **skip_remove_kwargs)
File ~/code/scikit-learn/sklearn/utils/_array_api.py:404 in get_namespace
namespace, is_array_api_compliant = array_api_compat.get_namespace(*arrays), True
File ~/code/scikit-learn/sklearn/externals/array_api_compat/common/_helpers.py:646 in array_namespace
raise TypeError("Unrecognized array input")
TypeError: Unrecognized array input
```
### Versions
```shell
System:
python: 3.13.7 | packaged by conda-forge | (main, Sep 3 2025, 14:24:46) [Clang 19.1.7 ]
executable: /Users/ogrisel/miniforge3/envs/dev/bin/python
machine: macOS-15.6.1-arm64-arm-64bit-Mach-O
Python dependencies:
sklearn: 1.9.dev0
pip: 25.2
setuptools: 80.9.0
numpy: 2.3.3
scipy: 1.16.3
Cython: 3.1.4
pandas: 3.0.0.dev0+2566.g2bb3fef887
matplotlib: 3.10.6
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 10
prefix: libopenblas
filepath: /Users/ogrisel/miniforge3/envs/dev/lib/libopenblas.0.dylib
version: 0.3.30
threading_layer: openmp
architecture: VORTEX
user_api: openmp
internal_api: openmp
num_threads: 10
prefix: libomp
filepath: /Users/ogrisel/miniforge3/envs/dev/lib/libomp.dylib
version: None
``` | {
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} | ### Describe the bug
Enabling the array API dispatch has a negative side effect on code that usually accepts non-array inputs such as pandas dataframes:
### Steps/Code to Reproduce
```python
# %%
import os
os.environ["SCIPY_ARRAY_API"] = "1"
# %%
import sklearn
from sklearn.datasets import fetch_openml
from sklearn.preprocessing import OneHotEncoder
adult = fetch_openml(name="adult", version=2, as_frame=True)
X, y = adult.data, adult.target
with sklearn.config_context(array_api_dispatch=True):
OneHotEncoder().fit(
X.select_dtypes(include=["category", "object"])
)
```
### Expected Results
The enabling of array API should not affect the estimator behavior on dataframe inputs.
### Actual Results
```python
Traceback (most recent call last):
Cell In[11], [line 11](vscode-notebook-cell:?execution_count=11&line=11)
OneHotEncoder().fit(
File ~/code/scikit-learn/sklearn/base.py:1336 in wrapper
return fit_method(estimator, *args, **kwargs)
File ~/code/scikit-learn/sklearn/preprocessing/_encoders.py:999 in fit
self._fit(
File ~/code/scikit-learn/sklearn/preprocessing/_encoders.py:86 in _fit
X_list, n_samples, n_features = self._check_X(
File ~/code/scikit-learn/sklearn/preprocessing/_encoders.py:69 in _check_X
Xi = check_array(
File ~/code/scikit-learn/sklearn/utils/validation.py:858 in check_array
xp, is_array_api_compliant = get_namespace(array)
File ~/code/scikit-learn/sklearn/utils/_array_api.py:404 in get_namespace
namespace, is_array_api_compliant = array_api_compat.get_namespace(*arrays), True
File ~/code/scikit-learn/sklearn/externals/array_api_compat/common/_helpers.py:643 in array_namespace
raise TypeError(f"{type(x).__name__} is not a supported array type")
TypeError: Series is not a supported array type
```
### Versions
```shell
System:
python: 3.13.7 | packaged by conda-forge | (main, Sep 3 2025, 14:24:46) [Clang 19.1.7 ]
executable: /Users/ogrisel/miniforge3/envs/dev/bin/python
machine: macOS-15.6.1-arm64-arm-64bit-Mach-O
Python dependencies:
sklearn: 1.9.dev0
pip: 25.2
setuptools: 80.9.0
numpy: 2.3.3
scipy: 1.16.3
Cython: 3.1.4
pandas: 3.0.0.dev0+2566.g2bb3fef887
matplotlib: 3.10.6
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 10
prefix: libopenblas
filepath: /Users/ogrisel/miniforge3/envs/dev/lib/libopenblas.0.dylib
version: 0.3.30
threading_layer: openmp
architecture: VORTEX
user_api: openmp
internal_api: openmp
num_threads: 10
prefix: libomp
filepath: /Users/ogrisel/miniforge3/envs/dev/lib/libomp.dylib
version: None
``` | {
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} | 1 | 2025-12-04T08:45:34 | 2025-12-04T11:13:57 | 2025-12-04T11:01:21 | null | MEMBER | null | null | null | null | Fix #32833.
Tested locally this works fine. Let's see if CI has something to say about it. Full disclosure: I don't remember whether the JupyterLite in CircleCI artifacts is functional or not ...
Quickly looking at the Pyodide [0.28](https://blog.pyodide.org/posts/0.28-release/) and [0.29](https://blog.pyodide.org/posts/0.29-release/) blog posts, I don't think there is anything unexpected that should break a lot of notebooks but I may try a few just to double-check. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32834 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32834/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32834/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32834/events | https://github.com/scikit-learn/scikit-learn/issues/32834 | 3,693,379,885 | I_kwDOAAzd1s7cJIEt | 32,834 | RFC: Potential improvement of HTML Display's css logic | {
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Every time that a cell in a jupyter notebook (or similar) uses the HTML display method, a new CSS block is added to the DOM. This happens in [`estimator_html_repr`](https://github.com/scikit-learn/scikit-learn/blob/4a10d0ed8d85e6ed24a647bd28a65c0c64b101ef/sklearn/utils/_repr_html/estimator.py#L448C5-L472C66).
But CSS is static and the styling rules don't change between estimators. That means that the `#$` in the css file [`estimator.css`](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/utils/_repr_html/estimator.css) is not needed I think. ~~And we could cache the css file so it's loaded and added to the DOM only once.~~
This way we improve performance by avoiding:
~~- DOM bloat with many duplicate CSS rules.~~
~~- Browser memory waste.~~
~~- Less parsing overhead by the browser (browser processing several times identical styles).~~
- Repeated template substitution through string processing in `estimator.py`.
~~The same thing happens with the javascript script, it is added to the DOM with each cell that uses a HTML display. But the script could be added just once at the bottom of the DOM.~~
Note: the container id in `estimator.py` is still needed as it is used by javascript - It's used to construct the fully qualified name of parameters. But I would add a new class name (to this file) because we still need a CSS selector in `estimator.css`.
I have done local tests that seem to work. If I am missing something about the purpose of multiple CSS blocks, I would love to know.
WDYT?
@Charlie-XIAO @glemaitre (or anybody)
Edit: crossing out points above, but keeping the last one.
<details>
<summary> Screenshot showing issue</summary>
One can see that for each estimator, there is styling and a script.
<img width="1344" height="964" alt="Image" src="https://github.com/user-attachments/assets/ea30c328-42c2-4679-9d72-74a925dd0434"/>
</details> | {
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} | 1 | 2025-12-03T16:01:03 | 2025-12-04T15:58:24 | 2025-12-04T11:01:23 | null | MEMBER | null | null | {
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} | can reproduce opening a new notebook in https://scikit-learn.org/dev/lite/lab/index.html
Maybe related to merging https://github.com/scikit-learn/scikit-learn/pull/32824, maybe something else in the world that changed.
Noticed thanks to https://github.com/lesteve/test-scikit-learn-jupyterlite
Error in browser console mentions `micropip`:
```
Uncaught (in promise) Traceback (most recent call last):
File "/lib/python312.zip/_pyodide/_base.py", line 597, in eval_code_async
await CodeRunner(
File "/lib/python312.zip/_pyodide/_base.py", line 411, in run_async
coroutine = eval(self.code, globals, locals)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<exec>", line 2, in <module>
File "/lib/python3.12/site-packages/piplite/__init__.py", line 3, in <module>
from .piplite import install
File "/lib/python3.12/site-packages/piplite/piplite.py", line 17, in <module>
from micropip.package_index import ProjectInfo, CompatibilityLayer
ImportError: cannot import name 'CompatibilityLayer' from 'micropip.package_index' (/lib/python3.12/site-packages/micropip/package_index.py)
```
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Addresses this comment : https://github.com/scikit-learn/scikit-learn/pull/32797#issuecomment-3589914268
#### What does this implement/fix? Explain your changes.
Instead of opening a single issue if any of the tests in the matrix failed, now independent issues for each matrix entry are generated.
Also allow the update tracking issue action to trigger on manually triggered actions.
#### AI usage disclosure
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I used AI assistance for:
- [x] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
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#### Any other comments?
It is a bit more complex to achieve than what I anticipated. I could not do it using a job running after all the matrix jobs of unit-tests, as github does not allow to access all outptuts of a matrix of jobs, so only a global status of the whole matrix ("success" if all jobs succeeded, "failure" if at least one failed) can be obtained in another job.
Thus I had to use an extra step in the unit-tests job, but using another workflow (i.e. `uses: ./.github/workflows/update_tracking_issue.yml`) cannot be done as a step, only as a job, so I had to re-implement it, creating some code duplication. But it also allowed to tweak the CI log URL to point to the log of the specific job rather than the whole workflow. And since github does not expose the job id necessary for this URL, I also had to add a step to get this id using the REST API.
I tested it on my fork, for example it created and commented https://github.com/FrancoisPgm/scikit-learn/issues/23
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} | 4 | 2025-12-03T12:44:10 | 2025-12-04T06:34:44 | 2025-12-04T06:34:27 | null | MEMBER | null | null | null | null | Looking at the changelog and release highlights #30763 is not mentioned. Since this is the one that adds parameters to HTML repr, this is a bit of a shame.
For completeness:
- this way is a bit hacky but works fine: you add manually a `:pr:` with the right PR and the second PR is based on the number inside the filename. I noticed @lorentzenchr did it and that's actually a good idea!
- I think towncrier has a way to do something similar where you need to add the exact same changelog text in two different files and they will be grouped in the same changelog entry (they need to be in the same category e.g. enhancement otherwise it's not grouped). IMO this is too complicated so let's do it the simple manual way. | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 1 | 2025-12-03T04:27:45 | 2025-12-03T09:13:56 | 2025-12-03T09:13:56 | null | CONTRIBUTOR | null | null | null | null | <!--
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Towards #31593 and closes #32150
#### What does this implement/fix? Explain your changes.
Adds the LaTeX documentation for [https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html) instead of rendering the formula as
`||y - Xw||^2_2 + alpha * ||w||^2_2`
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I used AI assistance for:
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Fixes #15389, follow-up of #32747.
#### What does this implement/fix? Explain your changes.
This PR enables `LogisticRegressionCV` to correctly work when the CV folds do not contain all classes and the `scoring` argument is explicitly passed.
#### Any other comments?
I don't like my own solution (too much code), but it seems to work.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32827 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32827/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32827/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32827/events | https://github.com/scikit-learn/scikit-learn/pull/32827 | 3,681,973,006 | PR_kwDOAAzd1s62ZhCf | 32,827 | FIX: move_to should handle BufferError and ValueError to support PyTorch 2.9 with array API enabled | {
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} | 4 | 2025-12-01T17:05:33 | 2025-12-04T05:30:08 | 2025-12-02T10:03:18 | null | MEMBER | null | null | null | null | This fixes `move_to` (introduced in #31829) to make it work with PyTorch 2.9 and NumPy 2.3:
- we need to catch `BufferError` as per the [`__dlpack__` spec](https://data-apis.org/array-api/latest/API_specification/generated/array_api.array.__dlpack__.html#:~:text=dl_device,-(Optional)
- we also need to catch `ValuError` (at least for now) because of https://github.com/numpy/numpy/issues/30341.
I tested locally, and this fixes our test failures with PyTorch 2.9.1 (see https://github.com/scikit-learn/scikit-learn/pull/32824).
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32826 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32826/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32826/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32826/events | https://github.com/scikit-learn/scikit-learn/pull/32826 | 3,681,830,919 | PR_kwDOAAzd1s62ZBzN | 32,826 | TST add tests for pyproject minimum dependency checks | {
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Related to https://github.com/scikit-learn/scikit-learn/pull/32790#issuecomment-3589272892
#### What does this implement/fix? Explain your changes.
This adds a test using a minimal pyproject sample, to test that the tests actually work with upper bounds.
I had to restructure the code a bit in order to use a test string instead of the toml file.
Let me know if this is what you had in mind @lesteve.
#### Any other comments?
I noticed that there are also no tests testing the mismatch conditions of package versions. Adding this would be fairly straight forward now, but I'm not sure if these files are actually touched manually, and if this would make sense?
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32825 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32825/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32825/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32825/events | https://github.com/scikit-learn/scikit-learn/pull/32825 | 3,681,750,104 | PR_kwDOAAzd1s62YwOT | 32,825 | Add new default max_samples=None in Bagging estimators | {
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} | 17 | 2025-12-01T16:15:28 | 2025-12-05T13:23:07 | 2025-12-05T10:30:43 | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Fixes #32805. Follow-up to #31414.
#### What does this implement/fix? Explain your changes.
We add a new option `max_samples=None` in Bagging estimators, with the meaning of "draw exactly n_samples", and we make it the new default. This ensures that the number of bootstrap samples used (a crucial hyperparameter in bagging) will be the same across the 1.7 -> 1.8 transition, and makes the changes introduced in #31414 less disruptive when fitting with sample_weight:
- in 1.7 and before, the default max_samples=1.0 was drawing `n_samples` (discarding sample_weight)
- in #31414, the default max_samples=1.0 draws `sample_weight.sum()`. This can potentially lead to catastrophic fits if for some reason the `sample_weight` are very small or normalized to one for example. We do raise an error in that case, but the changes may still be drastic and surprising to users in less extreme cases.
- here we add the new default `max_samples=None`, which always draws `n_samples` as in 1.7.
cc @cakedev0 @ogrisel @lesteve
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#### What does this implement/fix? Explain your changes.
Correct grammar issues about a/an usage.
I searched for patterns such as `" a np"` and `" a nd"` in the GitHub codebase, because they are most likely to contain grammatical mistakes. Then I manually reviewed which ones needed to be changed. These patterns are not exhaustive, so a few issues of ("a" vs "an") still remain.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32819 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32819/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32819/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32819/events | https://github.com/scikit-learn/scikit-learn/pull/32819 | 3,677,735,430 | PR_kwDOAAzd1s62LIPQ | 32,819 | DOC Fix indentation in _logistic.py docstrings | {
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```
/home/circleci/project/sklearn/linear_model/_logistic.py:docstring of sklearn.linear_model._logistic.LogisticRegression:127: ERROR: Unexpected indentation. [docutils]
/home/circleci/project/sklearn/linear_model/_logistic.py:docstring of sklearn.linear_model._logistic.LogisticRegressionCV:104: ERROR: Unexpected indentation. [docutils]
```
[HTML doc](https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html)
<img width="765" height="267" alt="image" src="https://github.com/user-attachments/assets/e322f049-1110-4a37-9571-d3a1af48e5b1" />
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32818 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32818/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32818/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32818/events | https://github.com/scikit-learn/scikit-learn/pull/32818 | 3,677,523,110 | PR_kwDOAAzd1s62KfET | 32,818 | DOC Fix changelog fragment filename | {
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None
#### What does this implement/fix? Explain your changes.
See this in this section of release notes 1.8.0rc1: https://scikit-learn.org/1.8/whats_new/v1.8.html#free-threaded-cpython-3-14-support
<img width="759" height="97" alt="image" src="https://github.com/user-attachments/assets/f52c7124-af09-49e0-bbdb-4c1a9c6ae1d9" />
points to https://github.com/scikit-learn/scikit-learn/pull/custom-top-level-32079
instead of https://github.com/scikit-learn/scikit-learn/pull/32079
I believe renaming the file should be enough, no?
#### Any other comments?
Not tested the doc build locally (could not set the env properly and it's Sunday), but from the doc in the whats_new and folder, I am confident this should fix it (Famous last words).
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 4 | 2025-11-29T10:10:00 | 2025-12-15T02:46:12 | 2025-12-15T02:46:12 | null | NONE | null | null | null | null | This pull request fixes two minor documentation formatting issues in `_logistic.py`:
- Added missing spaces in the tuple `{'l2','none'}` → `{'l2', 'none'}`.
- Fixed spacing in the l1_ratio condition `0 <l1_ratio <1` → `0 < l1_ratio < 1`.
These changes improve readability and maintain consistency with the scikit-learn
documentation style guide.
No functional code changes.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32814 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32814/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32814/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32814/events | https://github.com/scikit-learn/scikit-learn/issues/32814 | 3,675,535,545 | I_kwDOAAzd1s7bFDi5 | 32,814 | Potential improvements for changelog/website situation during RC -> final transition | {
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"node_id": "MDU6TGFiZWwyNzEzNj... | open | false | [] | null | 0 | 2025-11-28T17:27:58 | 2025-11-30T17:38:23 | null | null | MEMBER | null | null | {
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} | So it's quite confusing and I don't know how easy/hard this is to improve the situation, it mostly looks like this is an unintended side-effect of our release process + towncrier setup. Since this is a 2-week transition period, nobody has yet found the time to figure out what possible improvements we could do.
Previously mentioned in https://github.com/scikit-learn/scikit-learn/issues/32807 and https://github.com/scikit-learn/scikit-learn/pull/31469#issuecomment-2931890657 and probably other places.
Let's try to summarize the issues:
- the changelog is not easily findable on the website. stable or dev website "ongoing development scikit-learn changelog 1.8" (bottom left) both point to https://scikit-learn.org/dev/whats_new/v1.8.html#version-1-8-0 which is empty. This is probably the easiest thing for now: copy the 1.8 changelog from `1.8.X` branch to `main` so that the 1.8 changelog is correct on the `dev` website.
- towncrier setup: during the rc -> final period, we still have the fragments in doc/whats_new/upcoming changes in both `main` and `1.8.X`. For the final, we generate the changelog with towncrier (without `--keep`), which removes the fragments. Then we forward-port this change in `main`. IIRC this was taken from another project, for example [astropy does that](https://docs.astropy.org/en/latest/development/maintainers/releasing.html#rendering-the-changelog) as well.
- slightly annoying but less crucial: during the transition period, the 1.9 changelog on dev website has all the changes that will go in 1.8 plus some that will go in 1.9
cc @jeremiedbb in case you remember if you put more info about the situation somewhere.
> [!WARNING]
> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particular the section [Issues for new contributors](https://scikit-learn.org/dev/developers/contributing.html#new-contributors).
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32813 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32813/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32813/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32813/events | https://github.com/scikit-learn/scikit-learn/pull/32813 | 3,675,239,020 | PR_kwDOAAzd1s62DbhK | 32,813 | MNT: Fix `pre-commit run --all-files` | {
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"node_id": "MDU6... | closed | false | [] | null | 2 | 2025-11-28T15:17:30 | 2026-01-05T15:18:50 | 2026-01-05T15:18:49 | null | CONTRIBUTOR | null | null | null | null | Currently `pre-commit run --all-files` fails, which damages the contributor experience
I know better than to get into discussions related to https://github.com/scikit-learn/scikit-learn/issues/32500, so I've left that alone, but this at least ensures that running `pre-commit` in a current PR doesn't result in unrelated changes
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} | 4 | 2025-11-28T15:04:41 | 2025-12-01T20:45:45 | 2025-11-29T04:17:01 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
#32747
#### What does this implement/fix? Explain your changes.
Tiny fix of whatsnew entry.
#### Any other comments?
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} | 7 | 2025-11-28T14:40:10 | 2025-12-02T13:58:58 | 2025-12-02T13:52:41 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Aftermath of #32014.
#### What does this implement/fix? Explain your changes.
This fixes a tiny code bug. I think it does (almost never) influence fitting an estimator.
#### Any other comments?
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Worked on it together with @lesteve.
Part of #32434. | {
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} | MEMBER | null | null | null | null | Topics I have thought of by looking at the [changelog](https://scikit-learn.org/1.8/whats_new/v1.8.html), in no particular order
- [x] array API support, do we want to add a non-executed snippet with PyTorch (and maybe CalibratedClassifierCV with temperature scaling)?
- [x] free-threaded do something similar as 1.6 release highlights
- [x] temperature scaling with CalibratedClassifierCV https://github.com/scikit-learn/scikit-learn/pull/31068
- [x] linear models improvements (suggested by @GaelVaroquaux)
- [x] HTML repr improvements https://github.com/scikit-learn/scikit-learn/pull/30763 and https://github.com/scikit-learn/scikit-learn/pull/31564
- [x] tree regressor with MAE https://github.com/scikit-learn/scikit-learn/pull/32100 and other bug-fixes
- [x] ClassicalMDS is shown as "Major Feature", so probably needs a release highlights entry https://github.com/scikit-learn/scikit-learn/pull/31322
Other things I am not sure whether they are release highlights noteworthy, TSNE with PCA initialization, MDS with different metrics, QuadraticDiscriminantAnalysis improvements.
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"node_id": "LA_kwDOAAzd1s7eWo1... | closed | false | [] | null | 2 | 2025-11-28T04:07:48 | 2025-11-29T04:17:52 | 2025-11-29T04:17:52 | null | MEMBER | null | null | null | null | Using the default logging level makes it a lot less likely for the hang to happen, see https://github.com/scikit-learn/scikit-learn/pull/32643#issuecomment-3587845466 for the summary and the debug PR for more info if you are curious. cc @ogrisel who nudged me towards not giving up my investigation :wink:.
The change was done in https://github.com/scikit-learn/scikit-learn/pull/32596 to reduce the verbosity of the command, but actually as noted in https://github.com/scikit-learn/scikit-learn/pull/32596#issuecomment-3455837859, the most noisy command is `conda create` that prints a lot of empty newlines, the `conda-lock install` is OKish [^1]. The PR was merged on October 29 and the debug PR showed I started to notice enough to open a debug PR on November 3.
[^1]: one of the most verbose build with plenty of pip packages is pylatest pandas that has ~30 lines see [build log](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=82973&view=logs&j=78a0bf4f-79e5-5387-94ec-13e67d216d6e&t=75a90307-b084-59e7-ba24-7f7161804495&l=188)
More info:
- this only happened for some reason in conda environments with pip packages, e.g. doc, doc-min-dependencies, pylatest_pip_openblas_pandas, pymin_conda_forge_openblas_min_dependencies, pylatest_pip_scipy_dev.
- I am going to wild-guess that this is some kind of dead-lock having to do with how conda-lock captures stdout/stderr and logs it again. I am not planning to understand it further :sweat_smile:. The conda-lock code has some mention of dead-lock https://github.com/conda/conda-lock/blob/61a623471e6468d70a037f145de150913e8a8ead/conda_lock/invoke_conda.py#L124-L127. Also this PR to fix what looks like a somewhat similar bug https://github.com/conda/conda-lock/pull/581.
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"node_id": "LA_kwDOAAzd1s7Ybv1... | closed | false | [] | null | 6 | 2025-11-27T21:08:56 | 2025-12-01T09:27:24 | 2025-11-30T08:07:30 | null | CONTRIBUTOR | null | null | {
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https://scikit-learn.org/dev/whats_new/v1.9.html
https://scikit-learn.org/dev/whats_new/v1.8.html
Maybe this is normal but I got there trying to figure out why 1.8.0rc1 was breaking some of our tests: so I thought I would open an issue as others may get the same problem trying to get to the changelog.
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] | open | false | [] | null | 2 | 2025-11-27T19:19:22 | 2025-12-08T15:32:26 | null | null | NONE | null | null | null | null | <!--
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#### Reference Issues/PRs
N/A (I'm happy to put up an issue if desired)
#### What does this implement/fix? Explain your changes.
This PR adds support for polars dataframes in `make_column_selector`, which will allow people using `ColumnTransformer`s with polars dataframes to take advantage of column selection by type and pattern.
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## Testing/Validation
I have tested out the example I added to the docstring with the changes in this branch:
```python
import polars as pl
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import make_column_transformer, make_column_selector
X_polars = pl.DataFrame({
'city': ['London', 'London', 'Paris', 'Sallisaw'],
'rating': [5, 3, 4, 5]
})
ct_polars = make_column_transformer(
(StandardScaler(),
make_column_selector(dtype_include=[int, pl.Float64])),
(OneHotEncoder(),
make_column_selector(dtype_include=str)))
print(ct_polars.fit_transform(X_polars))
```
Result
```
uv run python example.py
[[ 0.90453403 1. 0. 0. ]
[-1.50755672 1. 0. 0. ]
[-0.30151134 0. 1. 0. ]
[ 0.90453403 0. 0. 1. ]]
```
I've also tested a more substantial example but cannot easily share results here. | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32805 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32805/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32805/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32805/events | https://github.com/scikit-learn/scikit-learn/issues/32805 | 3,672,588,695 | I_kwDOAAzd1s7a50GX | 32,805 | RFC: `Bagging` estimators: avoid changing `max_samples` default behavior in 1.8 | {
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} | As stated in the change log of PR #31414
> `max_samples` is now interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]` when passed as a float.
Because `max_samples` default to `1.0`, this is a change of the default behavior. I expressed concerns about this change in this thread https://github.com/scikit-learn/scikit-learn/pull/31529#discussion_r2541938811 for a different PR but the same kind of change (on random forests).
It seems @antoinebaker, the author of both PRs, agrees with me that having `max_samples=None` as default (and `None` is then re-interpreted as `X.shape[0]`) sounds safer/less surprising for users. (I'll let you confirm or not @antoinebaker ^^).
So **I propose to not merge the PR/commit #31414 in v1.8** (is this even possible? 😅 @lesteve)
Or, merge a PR to change the default before v1.8? (not sure this is possible either).
Why I think this is "urgent"? Because we don't want to change default twice in a row (those are backward incompatible changes: same code produces different results).
What are your thoughts? @antoinebaker @ogrisel
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#### Reference Issues/PRs
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Following the discussion on #32751
#### What does this implement/fix? Explain your changes.
The linter bot no longer adds a comment when the linting succeeds, it only comments when the linting fails and removes its comment when the linting is fixed.
The code in `build_tools/get_comment.py` is also refactored to use the pyGithub package instead of http requests.
#### Any other comments?
Tested on my fork in https://github.com/FrancoisPgm/scikit-learn/pull/5, although it might be tough to see it's working as the comments are now removed.
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"node_id": "MDU6TGFiZWwxOD... | open | false | [] | null | 1 | 2025-11-27T15:28:00 | 2025-11-27T16:11:03 | null | null | CONTRIBUTOR | null | null | null | null | <!--
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#### Reference Issues/PRs
<!-- Add links if this PR addresses or closes any issues -->
#### What does this implement/fix?
This PR introduces a set of small, targeted performance improvements across several utility modules and the mean shift clustering implementation. The changes are internal-only and do not modify any public APIs.
**Summary of changes:**
- **class_weight**
Replaced repeated list-based membership checks with set operations for O(1) lookups. Also simplified dictionary access using `dict.get()` and cleaned up the construction of the error message by using `sorted()` directly.
- **_encode**
Added a `get()` method to `_NaNCounter` to support default value lookup and replaced `with suppress(KeyError)` patterns with direct `counter.get(key, 0)` calls. This removes exception-handling overhead in tight loops.
- **mean_shift**
Reworked the binning step by replacing a Python-level loop with a vectorized `numpy.unique()` call. This avoids manual accumulation with a `defaultdict` and removes the unused import.
- **graph utilities**
Updated the BFS queue in `single_source_shortest_path_length()` to use a `deque` instead of a list, as I feel it to be the correct data structure for the pattern and has more appropriate semantics on top of better algorithmic complexity for some standard operations if we add in future such as `popleft()`. Updated `.update()` to `.extend()` for consistency with the `deque` API.
#### Any other comments?
All modified files pass syntax checks, and the behavior remains consistent with previous versions. These optimizations aim to reduce overhead in code paths that operate on large datasets by improving lookup performance, avoiding unnecessary exceptions, taking advantage of vectorized numpy operations, and using more appropriate data structures for queue operations.
Thanks for reviewing!
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This was suggested during a face to face conversation. Instead of using the `__init__` signature order, place the non-default parameters first. Note - the non-default parameters are the ones with orange font color.
For example (see `C` and `max_iter`):
<img width="200" height="225" alt="Screenshot 2025-11-27 at 16 13 22" src="https://github.com/user-attachments/assets/16afeefc-5f50-4ce5-b06d-88db8eb3d812" />
Order them like this:
<img width="200" height="225" alt="Screenshot 2025-11-27 at 16 17 13" src="https://github.com/user-attachments/assets/861d53fe-0c31-48e3-a6ec-2bae9bc95fb6" />
#### What does this implement/fix? Explain your changes.
#### Any other comments?
@glemaitre and @ArturoAmorQ told me about this. :)
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https://github.com/scikit-learn/scikit-learn/pull/32549#issuecomment-3428908083
#### What does this implement/fix? Explain your changes.
This PR cleans up double calls to `check_array(y_pred, ...)` in `brier_score_loss`, `d2_brier_score` and `d2_log_loss_score`, that had been introduced probably by accident over the years.
In addition, I have seen that the call to `check_array(transformed_labels)` is probably unnecessary. I have checked `_one_hot_encoding_multiclass_target()` and there is no possibility of the return value not being a proper array, I think.
#### Details ####
Since, `y_pred` needs to be checked early in these scoring functions, I have decided to remove the second call to `check_array` from `_validate_multiclass_probabilistic_prediction` which is called later from of the functions. As an additional argument, `_validate_binary_probabilistic_prediction` doesn't check arrays either and the solution I have chosen is cleaner.
This required to add the check to `log_loss`, which previously had only relied on `_validate_multiclass_probabilistic_prediction` for checking `y_pred`. | {
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Fixes a typo in the 1.8 changelog to display the following item properly:
<img width="783" height="84" alt="image" src="https://github.com/user-attachments/assets/7d42d663-7bac-4229-874b-ee370b371f12" />
cc @lesteve | {
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"node_id": "MDU6TGFiZWwxODE1ND... | closed | false | [] | null | 2 | 2025-11-26T20:37:32 | 2025-11-26T21:33:10 | 2025-11-26T21:32:55 | null | NONE | null | null | null | null | ## Fixes #32631
The root cause is in sklearn/utils/parallel.py where the delayed function checks if it's being used with sklearn's Parallel and raises a warning if not. In cp313t, this warning mechanism is thread-unsafe. We need to modify the warning handling in the delayed function to be thread-safe by using a lock or by suppressing the warning in threaded contexts. The minimal fix is to catch and handle the warning more gracefully in threaded environments, or use thread-local storage to track whether the warning has been issued.
### AI Analysis
This is a thread-safety issue in Python 3.13t (free-threading) where warnings.warn() is being called from joblib workers without proper thread-safe handling. The issue occurs when sklearn.utils.parallel.delayed is used with joblib's Parallel instead of sklearn's Parallel wrapper, causing UserWarnings to be raised in a thread-unsafe manner during parallel execution.
### Changes Made
- modify: `sklearn/utils/parallel.py`
### AI Model Used
claude-sonnet-4-5-20250929
### Confidence Score
80%
---
*This PR was automatically generated by Kodin* | {
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Towards #32434
#### What does this implement/fix? Explain your changes.
Adding a scheduled and manual trigger for the `unit-tests` action, for both of these a random seed is used for `SKLEARN_TESTS_GLOBAL_RANDOM_SEED`. The `update-tracker` action is used to automatically create an issue if a scheduled test fails.
#### Any other comments?
When testing on my fork, the request to get the list of issues for the `update-tracker` to check if there already is an open issue for the CI failure returned an error saying the request should contain either `is:issue` or `is:pull-request`, so I updated `maint_tools/update_tracking_issue.py` but I assume it is working rn on the main repo, so maybe the issue was specific to my fork somehow and this update can be ignored.
~~Right now the `pylatest_conda_forge_arm` build explicitely uses `SKLEARN_TESTS_GLOBAL_RANDOM_SEED: 5` which should override the use of a random value in the scheduled runs. I don't know if that's the expected behaviour or if we want to use 5 in non-scheduled runs and the random value in the scheduled ones.~~
EDIT: actually I misunderstood how the env variables are managed in the jobs, so the random value does override the non-default fixed 5 one.
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"node_id": "LA_kwDOAAz... | closed | false | [] | null | 3 | 2025-11-26T15:06:39 | 2025-12-12T02:42:23 | 2025-12-12T02:42:23 | null | NONE | null | null | null | null | - Implements online centroid update using weighted running batch means
- Raises ValueError for metric='manhattan'
- Enables incremental fit without storing full history
- Adds tests ensuring equivalence to full-batch fit
Fixes scikit-learn#12952
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#### What does this implement/fix? Explain your changes.
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I triggered a full doc build, just in case.
cc @jeremiedbb | {
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Not 100% sure about timing, I guess this can be merged roughly at the same time as #32766 is merged?
I followed the [maintainer doc](https://scikit-learn.org/dev/developers/maintainer.html) and drew inspiration from https://github.com/scikit-learn/scikit-learn/pull/31336/files
cc @jeremiedbb | {
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"node_id": "MDU6TGFiZWwyNj... | open | false | [] | null | 4 | 2025-11-26T04:19:31 | 2025-11-27T01:23:03 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Ref: https://github.com/scikit-learn/scikit-learn/pull/32422/files#r2548690179
#### What does this implement/fix? Explain your changes.
Moves the array API tests for continuous metrics added in #32422 to `test_common.py`, so they can be re-used for other continuous metrics (e.g., #32626)
#### Any other comments?
cc @OmarManzoor
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] | closed | false | [] | null | 5 | 2025-11-26T03:24:17 | 2025-12-06T10:39:07 | 2025-12-02T15:14:54 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Follow up to #32715, specifically see https://github.com/scikit-learn/scikit-learn/pull/32715#discussion_r2538148882
#### What does this implement/fix? Explain your changes.
* Removes the line "and everyone is welcome to contribute"
* Make it more clear that this is not a good place to start for people new contributors
#### Any other comments?
I understand that this change may be a bit controversial and I am happy to discuss and iterate, this is a first attempt only. I've tried to keep the changes minimal.
(NB I think the changes suggested in https://github.com/scikit-learn/scikit-learn/pull/32715#discussion_r2538249454 to the "Our community, our values" section can be made in a follow up PR)
cc @reshamas @marenwestermann @lesteve @AnneBeyer who reviewed #32715
And @betatim @adrinjalali who may be interested
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Sorry all, small typo fixes from #32715, noticed when I checked the rendered docs. This just adds `"` around label names.
I had a look at `contributing.rst` and we seem to use `"` over `'` so I have as well to conform.
#### What does this implement/fix? Explain your changes.
#### Any other comments?
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32790 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32790/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32790/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32790/events | https://github.com/scikit-learn/scikit-learn/pull/32790 | 3,664,185,924 | PR_kwDOAAzd1s61egmN | 32,790 | TST Fix test_min_dependencies_readme.py with upper bounds | {
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"node_id": "... | closed | false | [] | null | 4 | 2025-11-25T18:42:21 | 2025-11-28T13:02:03 | 2025-11-26T03:48:06 | null | MEMBER | null | null | null | null | Noticed when setting upper bounds in the 1.8 release candidate PR in https://github.com/scikit-learn/scikit-learn/pull/32766
I think the previous test was not testing anything which is probably why we never noticed see https://github.com/scikit-learn/scikit-learn/pull/31656/files#r2334147276 ...
cc @jeremiedbb | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32789 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32789/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32789/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32789/events | https://github.com/scikit-learn/scikit-learn/pull/32789 | 3,662,874,171 | PR_kwDOAAzd1s61aIER | 32,789 | FIX Prevent segfault in TSNE with PCA init on constant data | {
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"node_id": ... | open | false | [] | null | 7 | 2025-11-25T12:28:38 | 2026-01-08T13:36:10 | null | null | NONE | null | null | null | null | #### Reference Issue
Fixes #28368 (ref: #32788 which is a duplicate issue)
#### What does this implement/fix? Explain your changes.
This PR fixes a bug where `TSNE` with `init='pca'` produces `NaN` values (leading to potential segmentation faults in the C++ extension) when the input data has zero variance (constant values).
**The Issue:**
In `_t_sne.py`, the PCA initialization step attempts to normalize the first principal component by dividing by its standard deviation: `X_embedded / np.std(...)`. When the input data is constant, the standard deviation is `0.0`, resulting in `NaN` propagation.
**The Fix:**
I added a safe-guard check for the standard deviation.
- If `std < 1e-12` (effectively zero), the division is skipped, and the embedding is only scaled by the default factor (`1e-4`).
- Otherwise, the standard normalization logic is applied.
**Verification:**
Added a regression test `test_tsne_pca_init_constant_data` in `sklearn/manifold/tests/test_t_sne.py` which asserts that fitting on a constant matrix (`np.ones`) produces finite values and does not crash.
#### Other comments
The fix uses a tolerance check (`< 1e-12`) rather than strict equality (`== 0`) to handle floating-point nuances, ensuring robustness for near-constant data. | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32788 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32788/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32788/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32788/events | https://github.com/scikit-learn/scikit-learn/issues/32788 | 3,662,056,785 | I_kwDOAAzd1s7aRo1R | 32,788 | TSNE segfaults when numbers turn to NaN | {
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} | ### Describe the bug
When running `TSNE.fit_transform`, if numbers produced during the initialization somehow turned to NaN, it will segfault the process instead of throwing a python exception.
### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.manifold import TSNE
X = np.ones((10,5))
TSNE(init="pca", perplexity=5).fit_transform(X)
```
### Expected Results
Should not segfault.
### Actual Results
Segfaults.
### Versions
```shell
System:
python: 3.12.6 | packaged by conda-forge | (main, Sep 22 2024, 14:16:49) [GCC 13.3.0]
executable: /home/david/miniforge3/bin/python
machine: Linux-6.12.57+deb12-amd64-x86_64-with-glibc2.36
Python dependencies:
sklearn: 1.7.1
pip: 24.2
setuptools: 74.1.2
numpy: 2.0.1
scipy: 1.14.1
Cython: 3.1.0
pandas: 2.2.3
matplotlib: 3.9.2
joblib: 1.4.2
threadpoolctl: 3.5.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 20
prefix: libscipy_openblas
filepath: /home/david/.local/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-99b71e71.so
version: 0.3.27
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: mkl
num_threads: 14
prefix: libmkl_rt
filepath: /home/david/miniforge3/lib/libmkl_rt.so.2
version: 2023.2-Product
threading_layer: gnu
user_api: openmp
internal_api: openmp
num_threads: 20
prefix: libgomp
filepath: /home/david/miniforge3/lib/libgomp.so.1.0.0
version: None
``` | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32787 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32787/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32787/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32787/events | https://github.com/scikit-learn/scikit-learn/pull/32787 | 3,661,810,321 | PR_kwDOAAzd1s61WnU8 | 32,787 | ENH: Add _LinearPredictMixin and update BaseSGDRegressor; fix numpy w… | {
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https://github.com/scikit-learn/scikit-learn/issues/31315
This PR introduces a small private `_LinearPredictMixin` and wires it into `BaseSGDRegressor` to remove duplicated linear-prediction code while keeping behavior identical.
## Motivation
- `SGDClassifier` uses helper mixins and `SGDRegressor` duplicated prediction logic.
- A private mixin is preferred to reduce duplication and align patterns across classifiers/regressors.
## Changes
1. **Added** `_LinearPredictMixin` to `sklearn/linear_model/_base.py` providing `_linear_predict` and `_decision_function`.
2. **Updated** `BaseSGDRegressor` to inherit `_LinearPredictMixin` so SGD regressors use the shared helper.
3. **Fixed** small NumPy compatibility issues in `sklearn/utils/fixes.py` to satisfy mypy and maintain compatibility across NumPy versions.
## Tests
- Local targeted runs:
- `test_sgd.py` — 264 passed
- `test_logistic.py` — 261 passed, 3 skipped
- `test_common.py` — 113 passed, 11 skipped (xfailed as expected)
- Full test run (local): `2516 passed, 193 skipped, 41 xfailed, 11 xpassed`
## Notes for reviewers
- No public API change.
- Private-only cleanup.
- Behavior and semantics preserved.
- Let me know if you prefer the mixin in a different module, but `_base.py` felt like the most consistent place with existing helpers. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32786 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32786/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32786/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32786/events | https://github.com/scikit-learn/scikit-learn/pull/32786 | 3,660,836,330 | PR_kwDOAAzd1s61TWcm | 32,786 | DOC Add explanation about tie-breaking in decision and extra trees | {
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#### Reference Issues/PRs
Fixes #32752.
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#### What does this implement/fix? Explain your changes.
Fixes missing documentation about tie-breaking in decision and extra
trees when there are equal improvements or missing values with equal improvements.
#### Any other comments?
Documentation-only change. No impact on code or behavior.
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#### Reference Issues/PRs
Fixes LinearSVR in #16298.
#### What does this implement/fix? Explain your changes.
I have found that the solver selector referenced in this mr was wrong and selecting l1 instead of l2 loss.
First lets look at the LinearSVR and to be more specific the loss type at line 588. It is clearly shown that is 'l2'
<img width="1866" height="1472" alt="Screenshot from 2025-11-24 23-41-40" src="https://github.com/user-attachments/assets/ba0b84e5-e9d3-4af2-acd4-72da76142c57" />
If we dig deeper we can see that the selector or the _get_liblinear_solver_type function clearly returns in the original code the number 13 for epsilon_insensitive: l2
<img width="1866" height="1472" alt="Screenshot from 2025-11-24 23-43-22" src="https://github.com/user-attachments/assets/9cf97ab6-4760-49a7-99a7-912197c0fdcb" />
But if we look further at the cpp implementation this will select for a l1 loss not l2 loss
<img width="2811" height="1149" alt="Screenshot from 2025-11-24 23-44-41" src="https://github.com/user-attachments/assets/39eeba06-420d-4774-8823-aab922d05d35" />
Without going in any more details this will lead for a bound for the update loss to be selected represented by the sample weights and the regularizing term for weights lambda to be zero. With my implementation the l2 loss is selected and there is no bound on the loss update ,the sample weights goes to the regularizing factor lambda and the test(mentioned in the 16298 the one with the notebook ) passes with flying colors.
#### Any other comments?
Earlier I have tried to set C to 0.001 and it worked but idk why exactly. My mentor mentioned that there is a possibility for
numerical instability and I am investigating this possibility | {
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} | ### Describe the workflow you want to enable
LinearRegression is the "Hello World" of machine learning, yet it currently lacks native Array API support, forcing users to move data to the CPU for training. This creates a significant gap in scikit-learn's interoperability story with the PyTorch, JAX, and CuPy ecosystems.
Currently, the fit method in sklearn/linear_model/_base.py relies on:
scipy.optimize.nnls when positive=True.
scipy.linalg.lstsq for standard cases.
While lstsq has potential dispatch paths via SCIPY_ARRAY_API, the nnls dependency effectively blocks GPU support for constrained regression because scipy.optimize does not support Array API inputs.
This proposal aims to enable the following workflow without any CPU roundtrips:
```import torch
from sklearn.linear_model import LinearRegression
X_gpu = torch.rand(100, 10, device='cuda')
y_gpu = torch.rand(100, device='cuda')
# Should run entirely on GPU
reg = LinearRegression(positive=True)
reg.fit(X_gpu, y_gpu)
```
### Describe your proposed solution
**Implement _array_api_nnls**: Create a custom Non-Negative Least Squares solver in sklearn/utils/_array_api.py using strictly xp namespace primitives (e.g., a Projected Gradient Descent or Coordinate Descent approach). This removes the hard dependency on SciPy's Fortran implementation for the positive=True case.
**Refactor LinearRegression.fit**:
Detect Array API inputs.
Route positive=True cases to the new _array_api_nnls solver.
Route standard cases to xp.linalg.lstsq (if available) or leverage the SCIPY_ARRAY_API dispatch mechanism for lstsq, ensuring no implicit host memory transfers.
**Technical Debt Cleanup**: Refactor _preprocess_data in sklearn/linear_model/_base.py to remove the X_scale variable (marked as a "historic artifact" in the source code) to prevent unnecessary device memory allocation.
**Tagging & Testing:**
Update __sklearn_tags__ to set tags.array_api_support = True.
Add comprehensive tests in sklearn/linear_model/tests/test_base.py validating execution on PyTorch (CPU/MPS/CUDA) and CuPy devices.
### Describe alternatives you've considered, if relevant
Wait for SciPy nnls support: We could wait for scipy.optimize.nnls to support the Array API standard. However, this timeline is uncertain, and LinearRegression is too fundamental to leave unsupported.
Partial Support: We could support positive=False only (via lstsq) and raise an error for positive=True on Array API inputs. This was rejected as it creates a confusing user experience and fragment feature support.
CPU Fallback: Silently converting inputs to NumPy, fitting on CPU, and converting back. This was rejected as it defeats the primary purpose of Array API support (performance and keeping data on-device).
### Additional context
Requires implementing a numerical optimization solver restricted to the Array API surface (no NumPy fallbacks allowed).
Relevant Files: sklearn/linear_model/_base.py, sklearn/utils/_array_api.py.
Prior Art: Estimators like Ridge and LinearDiscriminantAnalysis have already received Array API support, providing a pattern for the lstsq components of this work. | {
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Added class_weight to all bayes algorithms and updated the test file with corresponding tests for the weight_class added.
@adrinjalali @StefanieSenger | {
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This helper is kept private (_LinearPredictMixin) and only used by classifier mixin and the SGD base class.
We intentionally do not use it widely in LinearModel base class to avoid modifying the many non-SGD regressors.
MRO was carefully chosen to avoid conflicts.
**Summary :**
This PR reduces code duplication in linear models by introducing a shared _LinearPredictMixin, used by SGDRegressor and future linear estimators.
Previously, SGD implemented its own prediction logic (_predict_regressor, _predict_binary, etc.), which duplicated the existing _linear_predict logic used across many linear models.
**This PR:**
Introduces a lightweight LinearPredictMixin
Centralizes linear prediction logic (X @ coef + intercept_)
Replaces the duplicated prediction paths in SGDRegressor
Maintains full backward compatibility
Does not change public APIs
**What is changed :**
Adds-> sklearn/linear_model/_base.py::_LinearPredictMixin
Refactors -> sklearn/linear_model/_stochastic_gradient.py
Ensures all linear estimators using this path behave consistently
**Why this change is useful :**
Removes duplicated prediction logic across linear estimators
Prevents future inconsistencies in linear prediction behavior
Makes maintenance easier
Avoids silent drift between SGD and other linear models
**Tests :**
Ran the full scikit-learn test suite locally-> 2516 passed, 193 skipped, 41 xfailed, 11 xpassed
Verified SGD regression/classification predictions match previous behavior
Verified Log, Hinge, and regression losses remain unaffected
**Notes for reviewers :**
This PR intentionally avoids touching the following:
_decision_function
_predict_proba
Any classification-specific mixins
Any solver logic
Only prediction code paths were refactored to remove duplication.
Happy to adjust based on reviewer feedback! | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32781 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32781/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32781/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32781/events | https://github.com/scikit-learn/scikit-learn/issues/32781 | 3,660,180,932 | I_kwDOAAzd1s7aKe3E | 32,781 | Address sanitizer | {
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] | open | false | [] | null | 5 | 2025-11-24T19:09:48 | 2025-11-26T08:12:15 | null | null | NONE | null | null | {
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> This is not a good first issue to contribute. Great if you are interested to contribute to scikit-learn 🙏. Please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particular the section [Issues for new contributors](https://scikit-learn.org/dev/developers/contributing.html#new-contributors).
### Describe the workflow you want to enable
This was discussed a little on issue https://github.com/scikit-learn/scikit-learn/issues/32491 and I wanted to give a bit of context about how to do it (unfortunately I don't think I know your CI workflow well enough to do it myself).
Essentially, address sanitizer is a compiler option which looks for out of bounds reads and writes so should catch errors like reading beyond the end of an array. It slows things down so you definitely don't want to distribute wheels with it enabled, but it may help with testing. It only works if you actually exercise the code so you'll want to build and run the tests. It's probably easiest to do on Linux and with just one Python version - there's likely little benefit to doing it comprehensively.
Undefined behaviour sanitizer is related and can be enabled at the same time. However it probably has less value, and there's some minor bits of undefined behaviour in Cython which you'll have to ignore.
### Describe your proposed solution
Rough outline:
* pick a compiler - `clang` is probably the best but `gcc` or `msvc` will work if you have strong preferences.
* Compile and link with `-fsanitize=address` as a command-line option.
* Run the tests with `ASAN_OPTIONS=detect_leaks=0` set as an environmental variable - Cython definitely doesn't clean everything up on exit so you'll get lots of fairly useless false positives.
* You may need `LD_PRELOAD=$(gcc --print-file-name=libasan.so)` as an environmental variable too.
* Dependency: I don't think it's strictly necessary to build all your dependencies with address sanitizer, however:
* if you're linking to C++ libraries that use `std::string` and `std::vector` then it's a good idea to build them with it too (otherwise you have to manually disable some checks).
* I imagine you're doing a lot of your memory allocation in Numpy? In which case it'd probably be a good idea to build Numpy with it too. I'm not the right person to advise on that.
Hopefully that makes some sense - feel ping me if it doesn't work, or close if it isn't something you want to do.
### Additional context
[Numpy doc](https://numpy.org/devdocs/dev/development_advanced_debugging.html#compiler-sanitizers) about compiler sanitizers
[Numpy CI workflow](https://github.com/numpy/numpy/blob/main/.github/workflows/compiler_sanitizers.yml) | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32780 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32780/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32780/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32780/events | https://github.com/scikit-learn/scikit-learn/pull/32780 | 3,660,162,606 | PR_kwDOAAzd1s61RCuL | 32,780 | added class_weight to all bayesian algorithms with updated test file | {
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"node_id": "MDU6TGFiZW... | closed | false | [] | null | 1 | 2025-11-24T19:04:08 | 2025-11-24T19:44:35 | 2025-11-24T19:36:31 | null | NONE | null | null | null | null | issue #32763
I have added class_weight in all the bayesian algorithms in the naive_bayes.py, also updated new test for class_weight in naive_bayes_test.py .
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32779 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32779/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32779/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32779/events | https://github.com/scikit-learn/scikit-learn/pull/32779 | 3,660,143,843 | PR_kwDOAAzd1s61Q-oc | 32,779 | MAINT: Deduplicate linear prediction logic via shared mixin (_LinearPredictMixin) | {
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"node_id": "LA_k... | closed | false | [] | null | 1 | 2025-11-24T18:57:54 | 2025-11-24T19:51:05 | 2025-11-24T19:51:04 | null | NONE | null | null | null | null | ### Implementation detail
This helper is kept private (`_LinearPredictMixin`) and only used by classifier mixin and the SGD base class.
We intentionally do **not** use it widely in `LinearModel` base class to avoid modifying the many non-SGD regressors.
MRO was carefully chosen to avoid conflicts.
**Summary :**
This PR reduces code duplication in linear models by introducing a shared _LinearPredictMixin, used by SGDRegressor and future linear estimators.
Previously, SGD implemented its own prediction logic (_predict_regressor, _predict_binary, etc.), which duplicated the existing _linear_predict logic used across many linear models.
**This PR:**
Introduces a lightweight _LinearPredictMixin
Centralizes linear prediction logic (X @ coef_ + intercept_)
Replaces the duplicated prediction paths in SGDRegressor
Maintains full backward compatibility
Does not change public APIs
**What is changed :**
Adds-> sklearn/linear_model/_base.py::_LinearPredictMixin
Refactors -> sklearn/linear_model/_stochastic_gradient.py
Ensures all linear estimators using this path behave consistently
**Why this change is useful :**
Removes duplicated prediction logic across linear estimators
Prevents future inconsistencies in linear prediction behavior
Makes maintenance easier
Avoids silent drift between SGD and other linear models
**Tests :**
Ran the full scikit-learn test suite locally-> 2516 passed, 193 skipped, 41 xfailed, 11 xpassed
Verified SGD regression/classification predictions match previous behavior
Verified Log, Hinge, and regression losses remain unaffected
**Notes for reviewers :**
This PR intentionally avoids touching the following:
_decision_function
_predict_proba
Any classification-specific mixins
Any solver logic
Only prediction code paths were refactored to remove duplication.
Happy to adjust based on reviewer feedback! | {
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Follow up of https://github.com/scikit-learn/scikit-learn/pull/31856#discussion_r2393881653
#### What does this implement/fix? Explain your changes.
- Fixes the formulation of `alpha` in `SGDOneClassSVM` by using `alpha = nu` (instead of `alpha = nu / 2`)
#### Any other comments?
CC: @antoinebaker
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### Note
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Fixes #30389. Continues work from #32481.
#### What does this implement/fix? Explain your changes.
This PR refactors feature validation to centralize checks and expose useful validation utilities.
Refactoring: Replaces direct calls to private `_check_feature_names` and `_check_n_features` with `validate_data` in several estimators (`SelectFromModel`, `CategoricalNB`, `ColumnTransformer`, `SimpleImputer`, etc.). This ensures validation logic is consistent and centralized.
Public API: Renames `_check_feature_names` and `_check_n_features` to `check_feature_names` and `check_n_features` in sklearn.utils.validation and exports them in sklearn.utils. These are documented with `versionadded: 1.6`.
Backward Compatibility: Retains `_check_feature_names` and `_check_n_features` as aliases that issue a `FutureWarning` before calling the new public functions.
#### Any other comments?
Verified locally by building from source and running a script to check imports, warnings, and estimator behavior. Existing tests in `test_base.py` and `test_pipeline.py` were updated to use the new public names.
Added changelog entry in doc/whats_new/upcoming_changes/sklearn.utils/30389.api.rst
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None.
#### What does this implement/fix? Explain your changes.
Fixes a typo in the MacPorts installation instructions.
The package name was incorrectly documented as `py<XY>-scikits-learn`, while the actual MacPorts port is `py<XY>-scikit-learn`.
Updated the documentation to reflect the correct package name.
#### Any other comments?
Documentation-only change. No impact on code or behavior.
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- Added `class_weight` parameter accepting `None`, `'balanced'`, or a dict
- Converts class weights to sample weights internally using `compute_sample_weight()`
- Multiplies with user-provided `sample_weight` if both are specified
- Raises informative error when using `class_weight='balanced'` with `partial_fit`
- Updated docstrings for all 5 classifiers
- Added comprehensive tests
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Fix #32767.
#### What does this implement/fix? Explain your changes.
1. Make alpha_max and the alpha search range correct for `ElasticNetCV(positive=True)` and `LassoCV(positive=True)`.
2. Add test case to cover `positive=True`, and make sure the test data is sensitive to the `positive` parameter.
3. Drop a redundant `np.abs()` for a sum of squares in `_alpha_grid()`.
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"url"... | closed | false | [] | null | 8 | 2025-11-23T11:56:21 | 2025-12-31T15:19:54 | 2025-12-31T15:19:54 | null | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
`ElasticNetCV` internally computes `alpha_max` that is just big enough to push all coef to 0.
The existing code (in `_alpha_grid()`) does not take `positive` as an input, and is only correct when `positive=False`.
As a result, `ElasticNetCV(positive=True)` may use the wrong `alpha_max` to define the `alphas_` search grid. Sometimes all the alphas in the search grid will push all coef to 0 💥
This is an easy fix. I'll try to submit a PR...
### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.linear_model import ElasticNetCV, ElasticNet
X = np.array([[1, 1], [1, -1]])
beta = np.array([1, -1e3])
y = X @ beta
config = dict(fit_intercept=False, positive=True)
alpha_grid = ElasticNetCV(cv=2, **config).fit(X, y).alphas_
alpha_max = alpha_grid[0]
alpha_min = alpha_grid[-1]
print(f"{alpha_max=}, {alpha_min=}")
assert ElasticNet(alpha=alpha_max, **config).fit(X, y).coef_.max() == 0 # OK
assert ElasticNet(alpha=alpha_min, **config).fit(X, y).coef_.max() > 0, f"{alpha_min=} still gives all zero coef"
```
### Expected Results
```python
alpha_max=np.float64(2.0), alpha_min=np.float64(0.002)
```
### Actual Results
```python
alpha_max=np.float64(2000.0), alpha_min=np.float64(2.0)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
Cell In[1], line 17
14 print(f"{alpha_max=}, {alpha_min=}")
16 assert ElasticNet(alpha=alpha_max, **config).fit(X, y).coef_.max() == 0 # OK
---> 17 assert ElasticNet(alpha=alpha_min, **config).fit(X, y).coef_.max() > 0, f"{alpha_min=} still gives all zero coef"
AssertionError: alpha_min=np.float64(2.0) still gives all zero coef
```
### Versions
```shell
System:
python: 3.12.11 | packaged by conda-forge | (main, Jun 4 2025, 14:45:31) [GCC 13.3.0]
executable: /home/jli/miniforge3/envs/torch/bin/python3
machine: Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.7.2
pip: 25.1.1
setuptools: 80.9.0
numpy: 2.3.5
scipy: 1.16.3
Cython: None
pandas: 2.3.0
matplotlib: 3.10.3
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: /home/jli/miniforge3/envs/torch/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-fdde5778.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: /home/jli/miniforge3/envs/torch/lib/python3.12/site-packages/scipy.libs/libscipy_openblas-b75cc656.so
version: 0.3.29.dev
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 12
prefix: libgomp
filepath: /home/jli/miniforge3/envs/torch/lib/python3.12/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
version: None
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* [x] Cleanup the doc repo to free up space
* [x] Set the version number in the release branch
* [x] Set an upper bound on build dependencies in the release branch
* [x] Generate the changelog in the release branch
* [x] Check that the wheels for the release can be built successfully
* [x] Merge the PR with `[cd build]` commit message to upload wheels to the staging repo
* [x] Upload the wheels and source tarball to https://test.pypi.org. We hit the size limit of 10GB, I guess things need to be cleaned up, see [debug build log](https://github.com/scikit-learn/scikit-learn/actions/runs/19703967396/job/56446791578) `HTTP 400 Project size too large. Limit for project 'scikit-learn' total size is 10 GB.`
* [x] Create tag on the main repo
* [x] Confirm bot detected at https://github.com/conda-forge/scikit-learn-feedstock and wait for merge. Manual PR done https://github.com/conda-forge/scikit-learn-feedstock/pull/291.
* [x] Upload the wheels and source tarball to PyPI
* [x] Announce on mailing list and on social media platforms (LinkedIn, Bluesky, etc.)
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32763 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32763/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32763/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32763/events | https://github.com/scikit-learn/scikit-learn/issues/32763 | 3,654,569,919 | I_kwDOAAzd1s7Z1E-_ | 32,763 | Add class_weight support to Naive Bayes models (GaussianNB, MultinomialNB, BernoulliNB) | {
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} | ### Describe the workflow you want to enable
Naive Bayes classifiers (GaussianNB, MultinomialNB, BernoulliNB, ComplementNB, CategoricalNB) currently do not support the class_weight parameter, while almost all other scikit-learn classifiers do. This creates API inconsistency and requires users to manually compute and pass sample_weight for imbalanced datasets.
# Current Workaround
```py
from sklearn.naive_bayes import GaussianNB
from sklearn.utils.class_weight import compute_sample_weight
sample_weight = compute_sample_weight('balanced', y)
gnb = GaussianNB()
gnb.fit(X, y, sample_weight=sample_weight)
```
# Proposed Enhancement
```py
gnb = GaussianNB(class_weight='balanced')
gnb.fit(X, y)
```
### Describe your proposed solution
Add a class_weight parameter to all Naive Bayes classifiers that internally converts class weights to sample weights before calling the existing fit() logic. This leverages the fact that all NB classifiers already support sample_weight in their fitting methods.
```py
class GaussianNB(_BaseNB):
_parameter_constraints: dict = {
"priors": ["array-like", None],
"var_smoothing": [Interval(Real, 0, None, closed="left")],
"class_weight": ["dict", "balanced", None], # NEW
}
def __init__(self, *, priors=None, var_smoothing=1e-9, class_weight=None):
self.priors = priors
self.var_smoothing = var_smoothing
--> self.class_weight = class_weight
```
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
This is a improvement for API consistency. While the workaround exists, having uniform behavior across classifiers reduces confusion and improves discoverability, especially for beginners. | {
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"node_id": "MDU6TGFiZWwyNj... | closed | false | [] | null | 1 | 2025-11-22T00:05:26 | 2025-11-24T19:12:45 | 2025-11-24T11:42:42 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Ref: https://github.com/scikit-learn/scikit-learn/pull/30395#discussion_r2545517923
#### What does this implement/fix? Explain your changes.
This is just for completeness, testing multiclass and multilabel is probably adequate but this brings it in line with other metrics.
#### Any other comments?
cc @OmarManzoor
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Follow up on previously closed PR #31147 and issue [#30707](https://github.com/scikit-learn/scikit-learn/issues/30707)
#### What does this implement/fix? Explain your changes.
Adds sample weights within the QuantileTransformer under preprocessing using the _weighted_percentile function.
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Partially fixes #32725
#### What does this implement/fix? Explain your changes.
Use `global_random_seed` to avoid the test to randomly fail due to bad luck (only happens on some platforms like debian 32 bits, and something like once every 300-500 runs...). | {
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None
#### What does this implement/fix? Explain your changes.
Over time, `test_logistic.py` has grown to more than 2600 lines of code with duplicate tests.
This PR reorders some of the tests and removes some of the duplicate ones.
#### Any other comments?
In case of doubt, follow the single commits. I tried to keep them very clean. | {
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"node_id": "MDU6TGFi... | closed | false | [] | null | 3 | 2025-11-21T10:42:59 | 2025-11-27T18:00:57 | 2025-11-27T07:58:30 | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Fixes partially #32725
Close https://github.com/scikit-learn/scikit-learn/issues/32765
#### What does this implement/fix? Explain your changes.
very slightly decrease strictness in `test_mcd` to fix flakyness: 75 -> 74 | {
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Fixes part of #32725
#### What does this implement/fix? Explain your changes.
small `atol` increase to fix graphical lasso test flakyness
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"node_id": "MDU6TGFiZWwyNjU5ODYz... | closed | false | [] | null | 1 | 2025-11-21T09:44:26 | 2025-11-27T16:02:35 | 2025-11-27T16:02:35 | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Partially fixes #32725
#### What does this implement/fix? Explain your changes.
Just changed the `atol` to `1e-11` in some `assert_all_close` which was failing comparing 0 with very small values (~2e-12).
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"node_id": "MDU6TGFiZWwxOD... | closed | false | [] | null | 36 | 2025-11-21T06:12:15 | 2026-03-06T09:48:27 | 2026-03-06T09:47:39 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Follow up to [#31829](https://github.com/scikit-learn/scikit-learn/pull/31829)
Related to #31274 (and loosely #28668)
#### What does this implement/fix? Explain your changes.
Adds a common test to check that a metric supports. The idea is that we can just add metrics to this common test as we add support.
Had many questions during implementation, but thought it would still be nice to have a draft PR to look at.
* I don't check that the value output is as expected (i.e. the same as if I had used numpy inputs). I thought it wasn't necessary, as we check the accuracy elsewhere. It checks
* for float outputs, no error is raised
* for array outputs, no error is raised and the output array is of the correct namespace/device
* checks with different array namespaces for `y_true` and `y_pred` - sample weight is not used, and other kwargs (e.g., for continuous metrics cycling through "average": ("micro", "macro", "weighted")) are not checked
I'll write other items as comments on the code.
The other implementation I thought about was to include it in [`check_array_api_metric`](https://github.com/scikit-learn/scikit-learn/blob/883ad3209388e1a4244aa728674408a3bc88df09/sklearn/metrics/tests/test_common.py#L1939) by adding an optional `check_mixed_input` kwarg. This avoids the 'messy' conditionals at the start of the current `test_mixed_namespace_input_compliance` where we work out what type of input should go in. But this approach had other problems:
* we'd have to chain the parametrizations `yield_namespace_device_dtype_combinations` and the new mixed array combinations in `test_array_api_compliance`, and somehow skip the mixed input for metrics that don't yet support it
* to turn on mixed input checking we'd have to use `partial(check_array_api_*, check_mixed_input=True)`
* we'd need to add kwargs so `check_array_api_metric` to handle accepting 2 namespace/devices
#### Any other comments?
cc @ogrisel @betatim @lesteve
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"node_id": "MDU6TGFiZWwxODE1ND... | closed | false | [] | null | 8 | 2025-11-21T05:33:58 | 2025-11-25T09:04:37 | 2025-11-25T09:04:26 | null | NONE | null | null | null | null | Fixes #32491, a segmentation fault occurring in `sklearn/tree/_utils` on Windows ARM64 during decision tree training with the MAE criterion.
**Root Cause**
The crash was caused by an out-of-bounds memory access in `precompute_absolute_errors`.
The `WeightedFenwickTree.search` method is designed to return an index used to retrieve values from `sorted_y`. However, under certain conditions (likely due to floating-point precision issues or edge cases where `target_weight` \>= `total_weight`), search could return an index equal to `self.size` (i.e., `n`).
Since `sorted_y` is a 0-indexed array of size `n`, accessing `sorted_y[n]` resulted in an invalid read, triggering the crash.
**The Fix**
The fix involves clamping the indices returned by `WeightedFenwickTree.search` to ensure they never exceed `self.size - 1`. This change was applied in three specific locations within `_utils.pyx` to cover all exit paths of the function:
- **Standard Search Path**: When no exact match for the weight is found, `current_idx` is now clamped before returning.
- **Exact Match Path (Upper Bound)**: When an exact match is found, `current_idx` is clamped.
- **Exact Match Path (Lower Bound)**: The `prev_idx` (returned via pointer) is also clamped, as it is used to calculate the median in the exact match scenario.
This ensures that the returned rank is always a valid index for
the `sorted_y` array, preventing the segmentation fault.
<details>
<summary>Exception analysys via WinDbg:</summary>
```
*******************************************************************************
* *
* Exception Analysis *
* *
*******************************************************************************
KEY_VALUES_STRING: 1
Key : AV.Fault
Value: Read
Key : Analysis.CPU.mSec
Value: 796
Key : Analysis.Elapsed.mSec
Value: 1786
Key : Analysis.IO.Other.Mb
Value: 0
Key : Analysis.IO.Read.Mb
Value: 0
Key : Analysis.IO.Write.Mb
Value: 0
Key : Analysis.Init.CPU.mSec
Value: 3531
Key : Analysis.Init.Elapsed.mSec
Value: 43456
Key : Analysis.Memory.CommitPeak.Mb
Value: 129
Key : Failure.Bucket
Value: INVALID_POINTER_READ_c0000005__utils.cp314-win_arm64.pyd!Unknown
Key : Failure.Hash
Value: {94d1b53b-d1cb-71c1-a5eb-cc5cc4c74f4f}
Key : Timeline.OS.Boot.DeltaSec
Value: 1632856
Key : Timeline.Process.Start.DeltaSec
Value: 16
Key : WER.OS.Branch
Value: ge_release
Key : WER.OS.Version
Value: 10.0.26100.1
Key : WER.Process.Version
Value: 3.14.150.1013
FILE_IN_CAB: crash_41.dmp
NTGLOBALFLAG: 70
APPLICATION_VERIFIER_FLAGS: 0
CONTEXT: (.ecxr)
x0=000001a33a491a40 x1=ffffffffffffffff x2=0000000000000000 x3=0000000000000001
x4=0000000000000000 x5=0000000000000001 x6=0000000000000000 x7=0000000040004000
x8=000001a331a90000 x9=fffffffffffffff0 x10=000001a331a9c000 x11=00007ffd0db800c8
x12=000001a33b3b3040 x13=000001a33940e9e0 x14=0000000000000001 x15=0000000000000000
x16=0000af0515adfa72 x17=000001a33940e9c8 x18=0000000000000000 x19=000001a33a91dad0
x20=0000005d12cdb0d0 x21=0000000000000006 x22=0000000000000000 x23=00000000000003de
x24=00000000000003dd x25=000001a33a857980 x26=000001a33b1d9c30 x27=0000000000000000
x28=0000000000000001 fp=0000005d12cdb140 lr=00007ffd0719b9d8 sp=0000005d12cda8e0
pc=00007ffd0719b6c8 psr=60001040 -ZC- EL0
_utils_cp314_win_arm64!PyInit__utils+0x70b0:
00007ffd`0719b6c8 fc696900 ldr d0,[x8,x9]
Resetting default scope
EXCEPTION_RECORD: (.exr -1)
ExceptionAddress: 00007ffd0719b6c8 (_utils_cp314_win_arm64!PyInit__utils+0x00000000000070b0)
ExceptionCode: c0000005 (Access violation)
ExceptionFlags: 00000000
NumberParameters: 2
Parameter[0]: 0000000000000000
Parameter[1]: 000001a331a8fff0
Attempt to read from address 000001a331a8fff0
PROCESS_NAME: python.exe
READ_ADDRESS: 000001a331a8fff0
ERROR_CODE: (NTSTATUS) 0xc0000005 - The instruction at 0x%p referenced memory at 0x%p. The memory could not be %s.
EXCEPTION_CODE_STR: c0000005
EXCEPTION_PARAMETER1: 0000000000000000
EXCEPTION_PARAMETER2: 000001a331a8fff0
STACK_TEXT:
0000005d`12cda8e0 00007ffd`0719b9d8 : 000001a3`3940e380 00007ffd`1307ff74 3fe08072`24a5a584 3fd91a0f`f25f82e0 : _utils_cp314_win_arm64!PyInit__utils+0x70b0
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0000005d`12cdfc10 00007ffd`97738740 : 0000005d`12cdfc20 0d427ffd`97738740 0000005d`12cdfc30 3301fffd`9b4843b4 : python+0x143c
0000005d`12cdfc20 00007ffd`9b4843b4 : 0000005d`12cdfc30 3301fffd`9b4843b4 00000000`00000000 5e418000`00000000 : kernel32!BaseThreadInitThunk+0x40
0000005d`12cdfc30 00000000`00000000 : 00000000`00000000 5e418000`00000000 00000000`00000000 00000000`00000000 : ntdll!RtlUserThreadStart+0x44
STACK_COMMAND: ~0s; .ecxr ; kb
SYMBOL_NAME: _utils_cp314_win_arm64+70b0
MODULE_NAME: _utils_cp314_win_arm64
IMAGE_NAME: _utils.cp314-win_arm64.pyd
FAILURE_BUCKET_ID: INVALID_POINTER_READ_c0000005__utils.cp314-win_arm64.pyd!Unknown
OS_VERSION: 10.0.26100.1
BUILDLAB_STR: ge_release
OSPLATFORM_TYPE: arm64
OSNAME: Windows 10
FAILURE_ID_HASH: {94d1b53b-d1cb-71c1-a5eb-cc5cc4c74f4f}
Followup: MachineOwner
---------
0:000> lmvm _utils_cp314_win_arm64
Browse full module list
start end module name
00007ffd`07190000 00007ffd`071ac000 _utils_cp314_win_arm64 (export symbols) _utils.cp314-win_arm64.pyd
Loaded symbol image file: _utils.cp314-win_arm64.pyd
Image path: X:\GitHub\scikit-learn\build\cp314\sklearn\tree\_utils.cp314-win_arm64.pyd
Image name: _utils.cp314-win_arm64.pyd
Browse all global symbols functions data
Timestamp: Tue Oct 28 19:44:20 2025 (69017F84)
CheckSum: 0001B704
ImageSize: 0001C000
Translations: 0000.04b0 0000.04e4 0409.04b0 0409.04e4
```
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"url": "ht... | open | false | [] | null | 22 | 2025-11-20T16:32:34 | 2026-02-02T13:12:54 | null | null | NONE | null | null | {
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} | ### Describe the bug
I encountered the following bug while doing an outlier analysis on a large dataset:
To detect outliers in a dataset with known outliers, I followed these steps:
1) compute the covariance for a set of preselected numerical features with either `MinCovDet` or `EmpiricalCovariance` and extracted the inverse matrix VI using the `precision_` parameter.
2) I initialized the LocalOutlierFactor class with the metric `mahalanobis`, passed `VI`, and set n_jobs to 1
3) I used `fit_predict()`to get the outlier predictions
Everything looked as expected.
Later on, I tried speeding up the computation by adding a few cores to `n_jobs` and had to realise that no outliers were detected anymore. After some debugging, I found that:
1) outlier detection seems not to work when using multiple cores (all samples are marked as inliers)
2) this effect seems to be tied to `mahalanobis` distance, because the bug does not appear with minkowski (p=2) distance regardless of the number of cores.
One workaround seems to be to precompute the mahalanobis distance and passing it to the LOF, but this is again using only one core for the computation and is not feasible for large datasets due to the size of the full pairwise distance matrix.
### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.covariance import MinCovDet
from sklearn.neighbors import LocalOutlierFactor
from scipy.spatial.distance import cdist
import matplotlib.pyplot as plt
RNG = np.random.RandomState(42)
def make_data(n_inliers=200, n_outliers=10, n_features=10):
# Inliers: standard normal in 10D
X_in = RNG.normal(loc=0.0, scale=1.0, size=(n_inliers, n_features))
# Outliers: shifted by +8 in all dimensions
X_out = RNG.normal(loc=8.0, scale=1.0, size=(n_outliers, n_features))
X = np.vstack([X_in, X_out])
y_true = np.hstack([np.ones(n_inliers, dtype=int), -np.ones(n_outliers, dtype=int)])
return X, y_true
def robust_inverse_covariance(X, random_state=0):
# Fit robust covariance estimator
mcd = MinCovDet(random_state=random_state).fit(X)
cov = mcd.covariance_ # shape (n_features, n_features)
# Compute inverse covariance (VI) expected by 'mahalanobis' metric
VI = mcd.precision_
# ensure dtype=float64 and C-contiguous so it serializes safely across processes
VI = np.ascontiguousarray(VI, dtype=np.float64)
return VI, cov
def run_lof(X, VI, n_neighbors=20, n_jobs=1):
lof = LocalOutlierFactor(
n_neighbors=n_neighbors,
metric='mahalanobis',
metric_params={'VI': VI},
n_jobs=n_jobs
)
# fit_predict returns -1 for outliers and 1 for inliers
labels = lof.fit_predict(X)
return labels
def run_lof_precomputed(X, VI, n_neighbors=20, n_jobs=1):
# compute full pairwise Mahalanobis distance matrix in the main process
D = cdist(X, X, metric='mahalanobis', VI=VI)
lof = LocalOutlierFactor(
n_neighbors=n_neighbors,
metric='precomputed',
n_jobs=n_jobs
)
labels = lof.fit_predict(D)
return labels
def stats(labels):
unique, counts = np.unique(labels, return_counts=True)
return dict(zip(unique.tolist(), counts.tolist()))
# Executing example
## make data
X, y_true = make_data(n_inliers=200, n_outliers=10, n_features=10)
## plot data
plt.figure(figsize=(8, 6))
plt.scatter(X[:, 0], X[:, 1], c=y_true, cmap='coolwarm', edgecolor='k')
plt.title("Toy dataset with inliers (blue) and outliers (red)")
plt.xlabel("Feature 0")
plt.ylabel("Feature 1")
plt.show()
## Fit MinCovDet on the whole dataset
VI, cov = robust_inverse_covariance(X, random_state=42)
## Run LOF with a single core (n_jobs=1)
labels_n1 = run_lof(X, VI, n_neighbors=20, n_jobs=1)
print("n_jobs=1 unique counts:", stats(labels_n1))
## expected result: n_jobs=1 unique counts: {-1: 11, 1: 199}
## Run LOF allowing multiple cores (n_jobs=4)
labels_n4 = run_lof(X, VI, n_neighbors=20, n_jobs=4)
print("n_jobs=4 unique counts:", stats(labels_n4))
## exprected result: n_jobs=4 unique counts: {1: 210}
## fix attempt: precompute Mahalanobis with single core and LOF with multiple
labels_precomputed = run_lof_precomputed(X, VI, n_neighbors=20, n_jobs=4)
print("precomputed (n_jobs=4) unique counts:", stats(labels_precomputed))
## expected result: precomputed (n_jobs=4) unique counts: {-1: 11, 1: 199}
## Print outlier labels
pred_outliers = np.where(labels_n1 == -1)[0]
print("Indices detected as outliers by n_jobs=1", pred_outliers)
## expected output: Indices detected as outliers by n_jobs=1: [116 200 201 202 203 204 205 206 207 208 209]
y_true_outliers = np.where(y_true == -1)[0]
print("Indices of true outliers:", y_true_outliers)
## expected output: Indices of true outliers: [200 201 202 203 204 205 206 207 208 209]
```
### Expected Results
It shouldn't matter how many cores are passed to the 'n_jobs' parameters. The results should be comparable, or at least detect some outliers.
### Actual Results
Results for LOF should be comparable, but are wildly different based on changes to the 'n_jobs' parameter, with all other parameters staying the same:
Results for n_jobs=1:
n_jobs=1 unique counts: {-1: 11, 1: 199}
Results for n_jobs=4:
n_jobs=4 unique counts: {1: 210}
### Versions
```shell
Python dependencies:
sklearn: 1.7.2
pip: 25.2
setuptools: 80.9.0
numpy: 2.3.4
scipy: 1.15.2
Cython: None
pandas: 2.2.3
matplotlib: 3.10.6
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 5
prefix: libscipy_openblas
filepath: /home/USER/environments/conda_environments/MY_ENV/lib/python3.11/site-packages/numpy.libs/libscipy_openblas64_-8fb3d286.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 5
prefix: libopenblas
filepath: /home/USER/environments/conda_environments/MY_ENV/lib/libopenblasp-r0.3.30.so
version: 0.3.30
threading_layer: pthreads
architecture: Zen
user_api: openmp
internal_api: openmp
num_threads: 5
prefix: libgomp
filepath: /home/USER/environments/conda_environments/MY_ENV/lib/libgomp.so.1.0.0
version: None
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} | ### Describe the issue linked to the documentation
(IA generated, but read and pruned by a human ^^)
The user guide and API reference for decision trees and extra trees do not currently document what happens when there are multiple optimal splits during training. Specifically, when there are several splits with the same criterion value (i.e., splitting produces the same improvement/loss), the algorithm picks the first split it encounters. This is due to a strict ordering (`if current_proxy_improvement > best_proxy_improvement:`) in the code. For splits involving missing values, the first split with missing values assigned to the right child is considered.
This tie-breaking bias is confusing—recent discussions in PR #32274 highlighted that documentation only describes this for missing value scenarios, and not as a general rule.
**References:** PR #32274, Meta issue #23728.
### Suggest a potential alternative/fix
Add a concise section to the decision tree documentation summarizing the present tie-breaking logic: when multiple splits have the same criterion, the first encountered is always chosen (order matters). For missing values, explain that splits with missing to the right are evaluated first, so those are chosen by default in a tie.
Maybe mention relevant discussion in #23728, and clarify if/when unbiased/random tie-breaking might be considered as a future enhancement? | null | {
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"merged_at": "2025-11-24T06:44:20"
} | true |
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