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Towards #26024
#### What does this implement/fix? Explain your changes.
Add array API support to `contingency_matrix`
#### Any other comments?
This PR would unblock adding array API compliance for the following:
- `adjusted_rand_score`
- `completeness_score`
- `fowlkes_mallows_score`
- `homogeneity_completeness_v_measure`
- `homogeneity_score`
- `mutual_info_score`
- `normalized_mutual_info_score`
- `pair_confusion_matrix`
- `rand_score`
- `v_measure_score` | {
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#### What does this implement/fix? Explain your changes.
Add Reference links to `CalibratedClassifierCV` API
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#### What does this implement/fix? Explain your changes.
- Added lists of German and Russian stop words.
- Integrated the lists into the working code and updated the unit tests accordingly.
- Created a .codespell_whitelist file to prevent codespell from flagging valid German words as English typos.
- Fixed typos in some English stop words.
#### Any other comments?
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Towards: #32611
#### What does this implement/fix? Explain your changes.
- Adds array API support for LogisticRegression with the LBFGS method
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32643 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32643/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32643/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32643/events | https://github.com/scikit-learn/scikit-learn/pull/32643 | 3,582,654,466 | PR_kwDOAAzd1s6xN9zm | 32,643 | Debug conda-lock install hang | {
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Let's see if I can reproduce and get more info ...
Edit Thursday November 13: seen twice in a row in the same PR https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=82302&view=logs&jobId=85dc54f1-b746-54c6-aaa8-29d3ad6201c9 `Linux pymin_conda_forge_openblas_min_dependencies` and https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=82330&view=logs&jobId=78a0bf4f-79e5-5387-94ec-13e67d216d6e `Linux pylatest_pip_openblas_pandas` | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32642 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32642/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32642/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32642/events | https://github.com/scikit-learn/scikit-learn/pull/32642 | 3,582,367,516 | PR_kwDOAAzd1s6xNAY0 | 32,642 | DOC Update sponsor page: reorganize sponsors and add BNP Paribas Group | {
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#### What does this implement/fix? Explain your changes.
- Reorganize sponsors into tiers: Founding (Inria), Gold (Chanel), Silver (BNP Paribas Group), Bronze (NVIDIA)
- Remove logos from Past Sponsors section, convert to full-width text format
- Convert Other contributions section to bullet points
- Add BNP Paribas Group logo and update sponsor information
- Add AXA, BNP Cardif, and Dataiku to past consortium sponsors grid
- Update probabl description to mention sponsorship program management
- Update footer funding logos
- Simplify sponsor descriptions for consistency
#### Any other comments?
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32641 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32641/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32641/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32641/events | https://github.com/scikit-learn/scikit-learn/pull/32641 | 3,581,784,140 | PR_kwDOAAzd1s6xLHj3 | 32,641 | FEAT Add early stopping for RandomForest based on OOB score | {
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] | closed | false | [] | null | 2 | 2025-11-03T12:09:21 | 2025-11-03T12:39:44 | 2025-11-03T12:38:47 | null | NONE | null | null | null | null | - Add early_stopping_rounds parameter to RandomForest estimators
- Monitor OOB score during incremental tree building
- Stop adding trees when score plateaus for N consecutive rounds
- Store n_estimators_used_ attribute with actual trees built
- Add validation: early_stopping_rounds requires oob_score=True
This feature enables automatic determination of optimal forest size, saving computation time and preventing overfitting. When enabled, the forest builds trees incrementally and monitors OOB score. If the score doesn't improve for early_stopping_rounds consecutive checks, training stops early.
Benefits:
- Saves training time (can stop at 30-50 trees instead of 100+)
- Prevents overfitting from too many trees
- Automatic hyperparameter tuning
Example usage:
forest = RandomForestClassifier(
n_estimators=200,
bootstrap=True,
oob_score=True,
early_stopping_rounds=5,
verbose=1
)
forest.fit(X, y)
print(f'Used {forest.n_estimators_used_} trees')
Tests verify:
- Early stopping triggers and stops before n_estimators
- Requires oob_score=True (validated)
- Works with both classifier and regressor
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Eliminates threading lock contention in RandomForest parallel prediction, providing significant performance improvements for multi-core systems.
## Problem
The previous implementation used a single `threading.Lock()` for all prediction accumulation. Every worker thread had to acquire this lock to add its prediction to the shared result array. This caused severe lock contention where threads spent most of their time waiting rather than computing.
**Performance bottleneck:**
- With `n_jobs=16`, all 16 threads compete for one lock
- Prediction computation happens in parallel ✓
- Accumulation serializes everything ✗
- Result: Poor scaling and wasted CPU cycles
## Solution
- Collect all predictions without locks (fully parallel)
- Accumulate results in main thread after collection (no contention)
- Remove now-unused `_accumulate_prediction` function
**Trade-off:** Slightly higher memory usage (stores all predictions before summing) for dramatically better performance.
## Changes
- Modified `ForestClassifier.predict_proba()` - lock-free accumulation
- Modified `ForestRegressor.predict()` - lock-free accumulation
- Removed `_accumulate_prediction()` - no longer needed
- Added `test_parallel_prediction_without_lock_contention` - verifies correctness
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32639 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32639/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32639/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32639/events | https://github.com/scikit-learn/scikit-learn/pull/32639 | 3,581,557,418 | PR_kwDOAAzd1s6xKW4f | 32,639 | FIX Handle zero division in compute_class_weight with weighted samples | {
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"node_id": "MDU6TGFiZWwxODE3Mz... | closed | false | [] | null | 4 | 2025-11-03T11:02:50 | 2025-11-03T15:46:22 | 2025-11-03T15:46:22 | null | NONE | null | null | null | null | - Fix division by zero when a class has no weighted samples
- Replace inf/nan with 0.0 for zero-weighted classes using np.where
- Add regression test test_compute_class_weight_balanced_zero_weighted_class
- Classes with zero total weight now correctly receive zero class_weight
When computing balanced class weights with sample_weight parameter, if a class exists but all its samples have zero weight, the division by zero produced inf values. This fix protects against division by zero and assigns weight 0.0 to such classes.
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Visualizes the best alpha: Adds a vertical line to the accuracy plot to visually highlight the optimal ccp_alpha.
Demonstrates automation: Adds a new section showing how to find the best ccp_alpha automatically using GridSearchCV. | {
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### Note
If the CI tasks fail, create a new branch based on this PR and add the required fixes to that branch. | {
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### Note
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### Note
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32633 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32633/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32633/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32633/events | https://github.com/scikit-learn/scikit-learn/pull/32633 | 3,579,464,484 | PR_kwDOAAzd1s6xDoRA | 32,633 | TEST: Add regression test for QuantileTransformer sparse subsampling bug #32587 | {
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## Description
Adds a comprehensive test case to ensure that `QuantileTransformer` correctly handles sparse matrix subsampling when `ignore_implicit_zeros=True` is set, particularly for the edge case where `column_subsample` could become zero and cause quantiles to collapse.
## Test Coverage
The test:
- Creates a sparse matrix with varying numbers of explicit values per column
- Tests the edge case where subsampling logic could fail
- Verifies that quantiles don't collapse to zeros
- Ensures quantile diversity is maintained
Closes #32587 | {
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## Summary
This PR implements the `overlay_dx_score` regression metric, which measures visual alignment between predictions and actual values across multiple tolerance levels. This metric is particularly useful for time series forecasting evaluation where visual alignment matters more than absolute error, and it is less sensitive to outliers than MSE/RMSE.
## Implementation Details
### Core Implementation
- Implement `overlay_dx_score` function in `sklearn/metrics/_regression.py`
- Computes area under curve showing percentage of predictions within tolerance intervals of varying sizes
- Supports `sample_weight` and `multioutput` parameters
- Handles edge cases (constant targets, zero range)
- Returns score between 0 and 1 (higher is better)
### Key Features
- **Algorithm**: Calculates tolerance intervals as percentages of data range, computes overlay percentage at each level, and integrates to get area under the curve
- **Multioutput Support**: Works with single and multiple regression outputs
- **Sample Weights**: Supports weighted overlay calculation
- **Edge Cases**: Properly handles constant target values and zero range scenarios
- **API Compliance**: Follows scikit-learn conventions with `@validate_params` decorator
## Test Coverage
Add comprehensive test suite (8 test functions):
- `test_overlay_dx_score_basic`: Core functionality and score validation
- `test_overlay_dx_score_multioutput`: Multioutput regression support
- `test_overlay_dx_score_sample_weight`: Weighted overlay calculation
- `test_overlay_dx_score_edge_cases`: Constant values and single samples
- `test_overlay_dx_score_parameters`: Different parameter configurations
- `test_overlay_dx_score_range`: Score bounds validation [0, 1]
- `test_overlay_dx_score_integration`: Integration with GridSearchCV
- `test_overlay_dx_score_cross_val`: Integration with cross_val_score
All tests passing (8/8 overlay_dx_score tests, 35/35 regression suite).
## Integration
- Export `overlay_dx_score` in `sklearn/metrics/__init__.py`
- Add to `REGRESSION_METRICS` in `test_common.py` for systematic testing
- Compatible with `make_scorer`, `cross_val_score`, and `GridSearchCV`
## Use Cases
Useful for:
- Time series forecasting evaluation where visual alignment matters
- Scenarios where outlier sensitivity of MSE/RMSE is problematic
- Understanding prediction quality at multiple tolerance levels
- Model selection when alignment is more important than absolute error
## Reference
Based on the implementation from: https://github.com/Smile-SA/overlay_dx
Adapted to follow scikit-learn API conventions and coding standards.
#### Reference Issues/PRs
Closes #32621
#### What does this implement/fix? Explain your changes.
This implements a new regression metric `overlay_dx_score` that evaluates prediction quality by measuring the percentage of predictions falling within tolerance intervals of varying sizes. Unlike traditional metrics (MAE, RMSE, R²), this metric:
1. **Measures visual alignment**: Focuses on how well predictions align visually with actual values across different tolerance levels
2. **Less sensitive to outliers**: Uses percentage-based tolerance intervals rather than squared errors
3. **Provides interpretable scores**: Returns values between 0 and 1, where 1.0 indicates perfect alignment
4. **Supports comprehensive evaluation**: Works seamlessly with sklearn's model selection tools (GridSearchCV, cross_val_score)
The implementation:
- Follows sklearn's metric function conventions
- Supports sample weights and multioutput regression
- Handles edge cases appropriately
- Includes comprehensive test coverage
- Is fully documented with examples
#### Any other comments?
This metric addresses a gap in sklearn's regression evaluation metrics, particularly for time series and forecasting applications where visual alignment between predicted and actual values is more important than minimizing absolute error. The implementation is based on the overlay_dx approach used in production forecasting systems and has been adapted to meet sklearn's quality standards.
**Testing Status**: ✅ All 8 new tests passing, all 35 regression tests passing
**Documentation**: ✅ Complete docstring with examples
**API Compatibility**: ✅ Works with make_scorer, GridSearchCV, cross_val_score
Author: @saumilyagupta
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32631 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32631/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32631/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32631/events | https://github.com/scikit-learn/scikit-learn/issues/32631 | 3,579,051,268 | I_kwDOAAzd1s7VU_0E | 32,631 | CI cp313t intermittent failure `UserWarning: sklearn.utils.parallel.delayed should be used with sklearn.utils.parallel.Parallel` | {
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"node_id": "LA_kwDOAAzd1s8AAAA... | closed | false | [] | null | 2 | 2025-11-02T07:25:07 | 2026-03-05T12:43:21 | 2026-02-20T13:57:29 | null | MEMBER | null | null | {
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} | Also seen in https://github.com/scikit-learn/scikit-learn/issues/32303#issuecomment-3354667463 as well in a different test and on Linux (this one is on macOS).
This is very likely related to warnings being thread-unsafe in cp313t. Not sure this is worth doing something about it since it doesn't seem to happen that often.
```
______________________ test_no_user_warning_with_scoring _______________________
joblib.externals.loky.process_executor._RemoteTraceback:
"""
Traceback (most recent call last):
File "/private/var/folders/q1/lmdr88vx1k90l53fxl9p8lwc0000gn/T/cibw-run-kd2mu_4k/cp313t-macosx_arm64/venv-test-arm64/lib/python3.13t/site-packages/joblib/_utils.py", line 72, in __call__
return self.func(**kwargs)
~~~~~~~~~^^^^^^^^^^
File "/private/var/folders/q1/lmdr88vx1k90l53fxl9p8lwc0000gn/T/cibw-run-kd2mu_4k/cp313t-macosx_arm64/venv-test-arm64/lib/python3.13t/site-packages/joblib/parallel.py", line 607, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
~~~~^^^^^^^^^^^^^^^^^
File "/private/var/folders/q1/lmdr88vx1k90l53fxl9p8lwc0000gn/T/cibw-run-kd2mu_4k/cp313t-macosx_arm64/venv-test-arm64/lib/python3.13t/site-packages/sklearn/utils/parallel.py", line 144, in __call__
warnings.warn(
~~~~~~~~~~~~~^
(
^
...<5 lines>...
UserWarning,
^^^^^^^^^^^^
)
^
UserWarning: `sklearn.utils.parallel.delayed` should be used with `sklearn.utils.parallel.Parallel` to make it possible to propagate the scikit-learn configuration of the current thread to the joblib workers.
"""
The above exception was the direct cause of the following exception:
def test_no_user_warning_with_scoring():
"""Check that no UserWarning is raised when scoring is set.
Non-regression test for #22907.
"""
pd = pytest.importorskip("pandas")
X, y = make_regression(n_samples=50, random_state=0)
X_df = pd.DataFrame(X, columns=[f"col{i}" for i in range(X.shape[1])])
est = HistGradientBoostingRegressor(
random_state=0, scoring="neg_mean_absolute_error", early_stopping=True
)
with warnings.catch_warnings():
warnings.simplefilter("error", UserWarning)
> est.fit(X_df, y)
../venv-test-arm64/lib/python3.13t/site-packages/sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py:1370:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../venv-test-arm64/lib/python3.13t/site-packages/sklearn/base.py:1336: in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
../venv-test-arm64/lib/python3.13t/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:722: in fit
X_binned_train = self._bin_data(X_train, is_training_data=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
../venv-test-arm64/lib/python3.13t/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:1237: in _bin_data
X_binned = self._bin_mapper.fit_transform(X) # F-aligned array
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
../venv-test-arm64/lib/python3.13t/site-packages/sklearn/utils/_set_output.py:316: in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
../venv-test-arm64/lib/python3.13t/site-packages/sklearn/base.py:907: in fit_transform
return self.fit(X, **fit_params).transform(X)
^^^^^^^^^^^^^^^^^^^^^^^^^
../venv-test-arm64/lib/python3.13t/site-packages/sklearn/ensemble/_hist_gradient_boosting/binning.py:240: in fit
non_cat_thresholds = Parallel(n_jobs=self.n_threads, backend="threading")(
../venv-test-arm64/lib/python3.13t/site-packages/sklearn/utils/parallel.py:91: in __call__
return super().__call__(iterable_with_config_and_warning_filters)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
../venv-test-arm64/lib/python3.13t/site-packages/joblib/parallel.py:2072: in __call__
return output if self.return_generator else list(output)
^^^^^^^^^^^^
../venv-test-arm64/lib/python3.13t/site-packages/joblib/parallel.py:1682: in _get_outputs
yield from self._retrieve()
../venv-test-arm64/lib/python3.13t/site-packages/joblib/parallel.py:1784: in _retrieve
self._raise_error_fast()
../venv-test-arm64/lib/python3.13t/site-packages/joblib/parallel.py:1859: in _raise_error_fast
error_job.get_result(self.timeout)
../venv-test-arm64/lib/python3.13t/site-packages/joblib/parallel.py:758: in get_result
return self._return_or_raise()
^^^^^^^^^^^^^^^^^^^^^^^
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <joblib.parallel.BatchCompletionCallBack object at 0x5d4061e5010>
def _return_or_raise(self):
try:
if self.status == TASK_ERROR:
> raise self._result
E UserWarning: `sklearn.utils.parallel.delayed` should be used with `sklearn.utils.parallel.Parallel` to make it possible to propagate the scikit-learn configuration of the current thread to the joblib workers.
../venv-test-arm64/lib/python3.13t/site-packages/joblib/parallel.py:773: UserWarning
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32629 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32629/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32629/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32629/events | https://github.com/scikit-learn/scikit-learn/pull/32629 | 3,578,044,896 | PR_kwDOAAzd1s6w_XhR | 32,629 | Bump the actions group with 5 updates | {
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"node_id": "LA_kwDOAAzd1s8AAAA... | closed | false | [] | null | 1 | 2025-11-01T14:07:40 | 2025-11-05T06:30:53 | 2025-11-05T06:30:44 | null | CONTRIBUTOR | null | null | null | null | Bumps the actions group with 5 updates:
| Package | From | To |
| --- | --- | --- |
| [actions/download-artifact](https://github.com/actions/download-artifact) | `5` | `6` |
| [scientific-python/action-towncrier-changelog](https://github.com/scientific-python/action-towncrier-changelog) | `1` | `2` |
| [github/codeql-action](https://github.com/github/codeql-action) | `3` | `4` |
| [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) | `3.2.0` | `3.2.1` |
| [actions/upload-artifact](https://github.com/actions/upload-artifact) | `4` | `5` |
Updates `actions/download-artifact` from 5 to 6
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/actions/download-artifact/releases">actions/download-artifact's releases</a>.</em></p>
<blockquote>
<h2>v6.0.0</h2>
<h2>What's Changed</h2>
<p><strong>BREAKING CHANGE:</strong> this update supports Node <code>v24.x</code>. This is not a breaking change per-se but we're treating it as such.</p>
<ul>
<li>Update README for download-artifact v5 changes by <a href="https://github.com/yacaovsnc"><code>@yacaovsnc</code></a> in <a href="https://redirect.github.com/actions/download-artifact/pull/417">actions/download-artifact#417</a></li>
<li>Update README with artifact extraction details by <a href="https://github.com/yacaovsnc"><code>@yacaovsnc</code></a> in <a href="https://redirect.github.com/actions/download-artifact/pull/424">actions/download-artifact#424</a></li>
<li>Readme: spell out the first use of GHES by <a href="https://github.com/danwkennedy"><code>@danwkennedy</code></a> in <a href="https://redirect.github.com/actions/download-artifact/pull/431">actions/download-artifact#431</a></li>
<li>Bump <code>@actions/artifact</code> to <code>v4.0.0</code></li>
<li>Prepare <code>v6.0.0</code> by <a href="https://github.com/danwkennedy"><code>@danwkennedy</code></a> in <a href="https://redirect.github.com/actions/download-artifact/pull/438">actions/download-artifact#438</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/danwkennedy"><code>@danwkennedy</code></a> made their first contribution in <a href="https://redirect.github.com/actions/download-artifact/pull/431">actions/download-artifact#431</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/download-artifact/compare/v5...v6.0.0">https://github.com/actions/download-artifact/compare/v5...v6.0.0</a></p>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/actions/download-artifact/commit/018cc2cf5baa6db3ef3c5f8a56943fffe632ef53"><code>018cc2c</code></a> Merge pull request <a href="https://redirect.github.com/actions/download-artifact/issues/438">#438</a> from actions/danwkennedy/prepare-6.0.0</li>
<li><a href="https://github.com/actions/download-artifact/commit/815651c680ffe1c95719d0ed08aba1a2f9d5c177"><code>815651c</code></a> Revert "Remove <code>github.dep.yml</code>"</li>
<li><a href="https://github.com/actions/download-artifact/commit/bb3a066a8babc8ed7b3e4218896c548fe34e7115"><code>bb3a066</code></a> Remove <code>github.dep.yml</code></li>
<li><a href="https://github.com/actions/download-artifact/commit/fa1ce46bbd11b8387539af12741055a76dfdf804"><code>fa1ce46</code></a> Prepare <code>v6.0.0</code></li>
<li><a href="https://github.com/actions/download-artifact/commit/4a24838f3d5601fd639834081e118c2995d51e1c"><code>4a24838</code></a> Merge pull request <a href="https://redirect.github.com/actions/download-artifact/issues/431">#431</a> from danwkennedy/patch-1</li>
<li><a href="https://github.com/actions/download-artifact/commit/5e3251c4ff5a32e4cf8dd4adaee0e692365237ae"><code>5e3251c</code></a> Readme: spell out the first use of GHES</li>
<li><a href="https://github.com/actions/download-artifact/commit/abefc31eafcfbdf6c5336127c1346fdae79ff41c"><code>abefc31</code></a> Merge pull request <a href="https://redirect.github.com/actions/download-artifact/issues/424">#424</a> from actions/yacaovsnc/update_readme</li>
<li><a href="https://github.com/actions/download-artifact/commit/ac43a6070aa7db8a41e756e7a2846221edca7027"><code>ac43a60</code></a> Update README with artifact extraction details</li>
<li><a href="https://github.com/actions/download-artifact/commit/de96f4613b77ec03b5cf633e7c350c32bd3c5660"><code>de96f46</code></a> Merge pull request <a href="https://redirect.github.com/actions/download-artifact/issues/417">#417</a> from actions/yacaovsnc/update_readme</li>
<li><a href="https://github.com/actions/download-artifact/commit/7993cb44e9052f2f08f9b828ae5ef3ecca7d2ac7"><code>7993cb4</code></a> Remove migration guide for artifact download changes</li>
<li>Additional commits viewable in <a href="https://github.com/actions/download-artifact/compare/v5...v6">compare view</a></li>
</ul>
</details>
<br />
Updates `scientific-python/action-towncrier-changelog` from 1 to 2
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/scientific-python/action-towncrier-changelog/releases">scientific-python/action-towncrier-changelog's releases</a>.</em></p>
<blockquote>
<h2>v2.0.0 Release Notes</h2>
<!-- raw HTML omitted -->
<p>Also see <code>CHANGES.rst</code>.</p>
<h2>What's Changed</h2>
<ul>
<li>BUG: Fix bug with toml parsing by <a href="https://github.com/larsoner"><code>@larsoner</code></a> in <a href="https://redirect.github.com/scientific-python/action-towncrier-changelog/pull/16">scientific-python/action-towncrier-changelog#16</a></li>
<li>MAINT: Update readme by <a href="https://github.com/larsoner"><code>@larsoner</code></a> in <a href="https://redirect.github.com/scientific-python/action-towncrier-changelog/pull/17">scientific-python/action-towncrier-changelog#17</a></li>
<li>Render final change log for v2 by <a href="https://github.com/pllim"><code>@pllim</code></a> in <a href="https://redirect.github.com/scientific-python/action-towncrier-changelog/pull/18">scientific-python/action-towncrier-changelog#18</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/scientific-python/action-towncrier-changelog/compare/v1.0.0...v2.0.0">https://github.com/scientific-python/action-towncrier-changelog/compare/v1.0.0...v2.0.0</a></p>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/scientific-python/action-towncrier-changelog/commit/f9c7df9a9f8b55cb0c12d94d14b281e9bcd101c0"><code>f9c7df9</code></a> Merge pull request <a href="https://redirect.github.com/scientific-python/action-towncrier-changelog/issues/18">#18</a> from pllim/chglog-v2</li>
<li><a href="https://github.com/scientific-python/action-towncrier-changelog/commit/cc451cc4d528cdc662a9bb402242e2ac6974708f"><code>cc451cc</code></a> Render final change log for v2</li>
<li><a href="https://github.com/scientific-python/action-towncrier-changelog/commit/24aa0c63c80a55246b743807510b36e8206347fe"><code>24aa0c6</code></a> MAINT: Update readme (<a href="https://redirect.github.com/scientific-python/action-towncrier-changelog/issues/17">#17</a>)</li>
<li><a href="https://github.com/scientific-python/action-towncrier-changelog/commit/f795101ae04dd5b52458b477c9ed65ade9c77a9a"><code>f795101</code></a> BUG: Fix bug with toml parsing (<a href="https://redirect.github.com/scientific-python/action-towncrier-changelog/issues/16">#16</a>)</li>
<li><a href="https://github.com/scientific-python/action-towncrier-changelog/commit/a19a2ccc317e92455af87c9808581c6656393666"><code>a19a2cc</code></a> Add basic action to check out repo</li>
<li>See full diff in <a href="https://github.com/scientific-python/action-towncrier-changelog/compare/v1...v2">compare view</a></li>
</ul>
</details>
<br />
Updates `github/codeql-action` from 3 to 4
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/github/codeql-action/releases">github/codeql-action's releases</a>.</em></p>
<blockquote>
<h2>v3.31.2</h2>
<h1>CodeQL Action Changelog</h1>
<p>See the <a href="https://github.com/github/codeql-action/releases">releases page</a> for the relevant changes to the CodeQL CLI and language packs.</p>
<h2>3.31.2 - 30 Oct 2025</h2>
<p>No user facing changes.</p>
<p>See the full <a href="https://github.com/github/codeql-action/blob/v3.31.2/CHANGELOG.md">CHANGELOG.md</a> for more information.</p>
<h2>v3.31.1</h2>
<h1>CodeQL Action Changelog</h1>
<p>See the <a href="https://github.com/github/codeql-action/releases">releases page</a> for the relevant changes to the CodeQL CLI and language packs.</p>
<h2>3.31.1 - 30 Oct 2025</h2>
<ul>
<li>The <code>add-snippets</code> input has been removed from the <code>analyze</code> action. This input has been deprecated since CodeQL Action 3.26.4 in August 2024 when this removal was announced.</li>
</ul>
<p>See the full <a href="https://github.com/github/codeql-action/blob/v3.31.1/CHANGELOG.md">CHANGELOG.md</a> for more information.</p>
<h2>v3.31.0</h2>
<h1>CodeQL Action Changelog</h1>
<p>See the <a href="https://github.com/github/codeql-action/releases">releases page</a> for the relevant changes to the CodeQL CLI and language packs.</p>
<h2>3.31.0 - 24 Oct 2025</h2>
<ul>
<li>Bump minimum CodeQL bundle version to 2.17.6. <a href="https://redirect.github.com/github/codeql-action/pull/3223">#3223</a></li>
<li>When SARIF files are uploaded by the <code>analyze</code> or <code>upload-sarif</code> actions, the CodeQL Action automatically performs post-processing steps to prepare the data for the upload. Previously, these post-processing steps were only performed before an upload took place. We are now changing this so that the post-processing steps will always be performed, even when the SARIF files are not uploaded. This does not change anything for the <code>upload-sarif</code> action. For <code>analyze</code>, this may affect Advanced Setup for CodeQL users who specify a value other than <code>always</code> for the <code>upload</code> input. <a href="https://redirect.github.com/github/codeql-action/pull/3222">#3222</a></li>
</ul>
<p>See the full <a href="https://github.com/github/codeql-action/blob/v3.31.0/CHANGELOG.md">CHANGELOG.md</a> for more information.</p>
<h2>v3.30.9</h2>
<h1>CodeQL Action Changelog</h1>
<p>See the <a href="https://github.com/github/codeql-action/releases">releases page</a> for the relevant changes to the CodeQL CLI and language packs.</p>
<h2>3.30.9 - 17 Oct 2025</h2>
<ul>
<li>Update default CodeQL bundle version to 2.23.3. <a href="https://redirect.github.com/github/codeql-action/pull/3205">#3205</a></li>
<li>Experimental: A new <code>setup-codeql</code> action has been added which is similar to <code>init</code>, except it only installs the CodeQL CLI and does not initialize a database. Do not use this in production as it is part of an internal experiment and subject to change at any time. <a href="https://redirect.github.com/github/codeql-action/pull/3204">#3204</a></li>
</ul>
<p>See the full <a href="https://github.com/github/codeql-action/blob/v3.30.9/CHANGELOG.md">CHANGELOG.md</a> for more information.</p>
<h2>v3.30.8</h2>
<h1>CodeQL Action Changelog</h1>
<p>See the <a href="https://github.com/github/codeql-action/releases">releases page</a> for the relevant changes to the CodeQL CLI and language packs.</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/github/codeql-action/blob/main/CHANGELOG.md">github/codeql-action's changelog</a>.</em></p>
<blockquote>
<h2>4.31.2 - 30 Oct 2025</h2>
<p>No user facing changes.</p>
<h2>4.31.1 - 30 Oct 2025</h2>
<ul>
<li>The <code>add-snippets</code> input has been removed from the <code>analyze</code> action. This input has been deprecated since CodeQL Action 3.26.4 in August 2024 when this removal was announced.</li>
</ul>
<h2>4.31.0 - 24 Oct 2025</h2>
<ul>
<li>Bump minimum CodeQL bundle version to 2.17.6. <a href="https://redirect.github.com/github/codeql-action/pull/3223">#3223</a></li>
<li>When SARIF files are uploaded by the <code>analyze</code> or <code>upload-sarif</code> actions, the CodeQL Action automatically performs post-processing steps to prepare the data for the upload. Previously, these post-processing steps were only performed before an upload took place. We are now changing this so that the post-processing steps will always be performed, even when the SARIF files are not uploaded. This does not change anything for the <code>upload-sarif</code> action. For <code>analyze</code>, this may affect Advanced Setup for CodeQL users who specify a value other than <code>always</code> for the <code>upload</code> input. <a href="https://redirect.github.com/github/codeql-action/pull/3222">#3222</a></li>
</ul>
<h2>4.30.9 - 17 Oct 2025</h2>
<ul>
<li>Update default CodeQL bundle version to 2.23.3. <a href="https://redirect.github.com/github/codeql-action/pull/3205">#3205</a></li>
<li>Experimental: A new <code>setup-codeql</code> action has been added which is similar to <code>init</code>, except it only installs the CodeQL CLI and does not initialize a database. Do not use this in production as it is part of an internal experiment and subject to change at any time. <a href="https://redirect.github.com/github/codeql-action/pull/3204">#3204</a></li>
</ul>
<h2>4.30.8 - 10 Oct 2025</h2>
<p>No user facing changes.</p>
<h2>4.30.7 - 06 Oct 2025</h2>
<ul>
<li>[v4+ only] The CodeQL Action now runs on Node.js v24. <a href="https://redirect.github.com/github/codeql-action/pull/3169">#3169</a></li>
</ul>
<h2>3.30.6 - 02 Oct 2025</h2>
<ul>
<li>Update default CodeQL bundle version to 2.23.2. <a href="https://redirect.github.com/github/codeql-action/pull/3168">#3168</a></li>
</ul>
<h2>3.30.5 - 26 Sep 2025</h2>
<ul>
<li>We fixed a bug that was introduced in <code>3.30.4</code> with <code>upload-sarif</code> which resulted in files without a <code>.sarif</code> extension not getting uploaded. <a href="https://redirect.github.com/github/codeql-action/pull/3160">#3160</a></li>
</ul>
<h2>3.30.4 - 25 Sep 2025</h2>
<ul>
<li>We have improved the CodeQL Action's ability to validate that the workflow it is used in does not use different versions of the CodeQL Action for different workflow steps. Mixing different versions of the CodeQL Action in the same workflow is unsupported and can lead to unpredictable results. A warning will now be emitted from the <code>codeql-action/init</code> step if different versions of the CodeQL Action are detected in the workflow file. Additionally, an error will now be thrown by the other CodeQL Action steps if they load a configuration file that was generated by a different version of the <code>codeql-action/init</code> step. <a href="https://redirect.github.com/github/codeql-action/pull/3099">#3099</a> and <a href="https://redirect.github.com/github/codeql-action/pull/3100">#3100</a></li>
<li>We added support for reducing the size of dependency caches for Java analyses, which will reduce cache usage and speed up workflows. This will be enabled automatically at a later time. <a href="https://redirect.github.com/github/codeql-action/pull/3107">#3107</a></li>
<li>You can now run the latest CodeQL nightly bundle by passing <code>tools: nightly</code> to the <code>init</code> action. In general, the nightly bundle is unstable and we only recommend running it when directed by GitHub staff. <a href="https://redirect.github.com/github/codeql-action/pull/3130">#3130</a></li>
<li>Update default CodeQL bundle version to 2.23.1. <a href="https://redirect.github.com/github/codeql-action/pull/3118">#3118</a></li>
</ul>
<h2>3.30.3 - 10 Sep 2025</h2>
<p>No user facing changes.</p>
<h2>3.30.2 - 09 Sep 2025</h2>
<ul>
<li>Fixed a bug which could cause language autodetection to fail. <a href="https://redirect.github.com/github/codeql-action/pull/3084">#3084</a></li>
<li>Experimental: The <code>quality-queries</code> input that was added in <code>3.29.2</code> as part of an internal experiment is now deprecated and will be removed in an upcoming version of the CodeQL Action. It has been superseded by a new <code>analysis-kinds</code> input, which is part of the same internal experiment. Do not use this in production as it is subject to change at any time. <a href="https://redirect.github.com/github/codeql-action/pull/3064">#3064</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/github/codeql-action/commit/74c8748a6f2dada2c01b25ae170d7858ac90f4af"><code>74c8748</code></a> Update analyze/action.yml</li>
<li><a href="https://github.com/github/codeql-action/commit/34c50c1d299d8a59b64a9dc6faf04dc0d9c33152"><code>34c50c1</code></a> Merge pull request <a href="https://redirect.github.com/github/codeql-action/issues/3251">#3251</a> from github/mbg/user-error/enablement</li>
<li><a href="https://github.com/github/codeql-action/commit/4ae68afd845398aa4e0bd7fccf3a37d121b3ec88"><code>4ae68af</code></a> Warn if the <code>add-snippets</code> input is used</li>
<li><a href="https://github.com/github/codeql-action/commit/52a7bd7b6e714abd930eb15cde3c7c76c45d6c0f"><code>52a7bd7</code></a> Check for 403 status</li>
<li><a href="https://github.com/github/codeql-action/commit/194ba0ee2dcf02e70ff941763c144ea06f58c485"><code>194ba0e</code></a> Make error message tests less brittle</li>
<li><a href="https://github.com/github/codeql-action/commit/53acf0b8aa0a8705134bb6153d859bc2817e1740"><code>53acf0b</code></a> Turn enablement errors into configuration errors</li>
<li><a href="https://github.com/github/codeql-action/commit/ac9aeee2261a2f9c74439210107de7657bd5ed42"><code>ac9aeee</code></a> Merge pull request <a href="https://redirect.github.com/github/codeql-action/issues/3249">#3249</a> from github/henrymercer/api-logging</li>
<li><a href="https://github.com/github/codeql-action/commit/d49e837b8cf6e8fd2c77703cc5189cfa79547ec0"><code>d49e837</code></a> Merge branch 'main' into henrymercer/api-logging</li>
<li><a href="https://github.com/github/codeql-action/commit/3d988b275a8c578caa755c4aaccd900332aefe93"><code>3d988b2</code></a> Pass minimal copy of <code>core</code></li>
<li><a href="https://github.com/github/codeql-action/commit/8cc18acfa4e60a22b3ed992afffc562f93c17030"><code>8cc18ac</code></a> Merge pull request <a href="https://redirect.github.com/github/codeql-action/issues/3250">#3250</a> from github/henrymercer/prefer-fs-delete</li>
<li>Additional commits viewable in <a href="https://github.com/github/codeql-action/compare/v3...v4">compare view</a></li>
</ul>
</details>
<br />
Updates `pypa/cibuildwheel` from 3.2.0 to 3.2.1
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/pypa/cibuildwheel/releases">pypa/cibuildwheel's releases</a>.</em></p>
<blockquote>
<h2>v3.2.1</h2>
<ul>
<li>🛠 Update to CPython 3.14.0 final (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2614">#2614</a>)</li>
<li>🐛 Fix the default MACOSX_DEPLOYMENT_TARGET on Python 3.14 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2613">#2613</a>)</li>
<li>📚 Docs improvements (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2617">#2617</a>)</li>
</ul>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md">pypa/cibuildwheel's changelog</a>.</em></p>
<blockquote>
<hr />
<h2>title: Changelog</h2>
<h1>Changelog</h1>
<h3>v3.2.1</h3>
<p><em>12 October 2025</em></p>
<ul>
<li>🛠 Update to CPython 3.14.0 final (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2614">#2614</a>)</li>
<li>🐛 Fix the default MACOSX_DEPLOYMENT_TARGET on Python 3.14 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2613">#2613</a>)</li>
<li>📚 Docs improvements (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2617">#2617</a>)</li>
</ul>
<h3>v3.2.0</h3>
<p><em>22 September 2025</em></p>
<ul>
<li>✨ Adds GraalPy v25 (Python 3.12) support (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2597">#2597</a>)</li>
<li>🛠 Update to CPython 3.14.0rc3 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2602">#2602</a>)</li>
<li>🛠 Adds CPython 3.14.0 prerelease support for Android, and a number of improvements to Android builds (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2568">#2568</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/2591">#2591</a>)</li>
<li>🛠 Improvements to testing on Android, passing environment markers when installing the venv, and providing more debug output when build-verbosity is set (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2575">#2575</a>)</li>
<li>⚠️ PyPy 3.10 was moved to <code>pypy-eol</code> in the <code>enable</code> option, as it is now end-of-life. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2521">#2521</a>)</li>
<li>📚 Docs improvements (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2574">#2574</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/2601">#2601</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/2598">#2598</a>)</li>
</ul>
<h3>v3.1.4</h3>
<p><em>19 August 2025</em></p>
<ul>
<li>✨ Add a <code>--clean-cache</code> command to clean up our cache (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2489">#2489</a>)</li>
<li>🛠 Update Python to 3.14rc2 and other patch version bumps (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2542">#2542</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/2556">#2556</a>)</li>
<li>🛠 Update Pyodide to 0.28.2 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2562">#2562</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/2558">#2558</a>)</li>
<li>🐛 Fix resolution with <code>pyodide-build</code> when <code>dependency-versions</code> is set (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2548">#2548</a>)</li>
<li>🐛 Set <code>CMAKE_FIND_ROOT_PATH_MODE_PACKAGE</code> to <code>BOTH</code> on Android (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2547">#2547</a>)</li>
<li>🐛 Add <code>patchelf</code> dependency for platforms that can build Android wheels (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2552">#2552</a>)</li>
<li>🐛 Ignore empty values for <code>CIBW_ARCHS</code> like most other environment variables (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2541">#2541</a>)</li>
<li>💼 The <code>color</code> and <code>suggest_on_error</code> argparse options are now default in 3.14rc1+ (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2554">#2554</a>)</li>
<li>💼 Use the virtualenv release URL instead of blob URL (should be more robust) (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2555">#2555</a>)</li>
<li>🧪 For iOS, lowering to macos-14 is needed for now due to issues with GitHub's runner images (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2557">#2557</a>)</li>
<li>🧪 Split out platforms iOS and Android in our tests (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2519">#2519</a>)</li>
<li>🧪 Fix and enable doctests (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2546">#2546</a>)</li>
<li>📚 Improve our docs on free-threading (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2549">#2549</a>)</li>
</ul>
<h3>v3.1.3</h3>
<p><em>1 August 2025</em></p>
<ul>
<li>🐛 Fix bug where "latest" dependencies couldn't update to pip 25.2 on Windows (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2537">#2537</a>)</li>
<li>🧪 Use pytest-rerunfailures to improve some of our iOS/Android tests (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2527">#2527</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/2539">#2539</a>)</li>
</ul>
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</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/pypa/cibuildwheel/commit/9c00cb4f6b517705a3794b22395aedc36257242c"><code>9c00cb4</code></a> Bump version: v3.2.1</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/ae65c7d85b7fbcc76e0102045329eb89e2d9b147"><code>ae65c7d</code></a> [Bot] Update dependencies (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2614">#2614</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/86c38579991e3e1d71779fb310d426918aebb7ae"><code>86c3857</code></a> [pre-commit.ci] pre-commit autoupdate (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2615">#2615</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/68b1a81bb255cbe2399a55dcd8dc4d116d6910dd"><code>68b1a81</code></a> docs: include free-threading builds in identifier list (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2617">#2617</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/f131cd793524b204270e93e9f3c95b8871312016"><code>f131cd7</code></a> fix: Python 3.14 on macOS requires MACOSX_DEPLOYMENT_TARGET set to 10.15 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2613">#2613</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/8602e864d4d22919d7d8eefb2346f2f89ea23252"><code>8602e86</code></a> [Bot] Update dependencies (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2606">#2606</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/8ccc2650de5ddf1ff3009ec585af70ed563a4aed"><code>8ccc265</code></a> fix: resolve issues with macOS-15 runners (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2607">#2607</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/c0c1dea51fcada641654170c0b8d9079225d7e7f"><code>c0c1dea</code></a> [pre-commit.ci] pre-commit autoupdate (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2608">#2608</a>)</li>
<li>See full diff in <a href="https://github.com/pypa/cibuildwheel/compare/7c619efba910c04005a835b110b057fc28fd6e93...9c00cb4f6b517705a3794b22395aedc36257242c">compare view</a></li>
</ul>
</details>
<br />
Updates `actions/upload-artifact` from 4 to 5
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/actions/upload-artifact/releases">actions/upload-artifact's releases</a>.</em></p>
<blockquote>
<h2>v5.0.0</h2>
<h2>What's Changed</h2>
<p><strong>BREAKING CHANGE:</strong> this update supports Node <code>v24.x</code>. This is not a breaking change per-se but we're treating it as such.</p>
<ul>
<li>Update README.md by <a href="https://github.com/GhadimiR"><code>@GhadimiR</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/681">actions/upload-artifact#681</a></li>
<li>Update README.md by <a href="https://github.com/nebuk89"><code>@nebuk89</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/712">actions/upload-artifact#712</a></li>
<li>Readme: spell out the first use of GHES by <a href="https://github.com/danwkennedy"><code>@danwkennedy</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/727">actions/upload-artifact#727</a></li>
<li>Update GHES guidance to include reference to Node 20 version by <a href="https://github.com/patrikpolyak"><code>@patrikpolyak</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/725">actions/upload-artifact#725</a></li>
<li>Bump <code>@actions/artifact</code> to <code>v4.0.0</code></li>
<li>Prepare <code>v5.0.0</code> by <a href="https://github.com/danwkennedy"><code>@danwkennedy</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/734">actions/upload-artifact#734</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/GhadimiR"><code>@GhadimiR</code></a> made their first contribution in <a href="https://redirect.github.com/actions/upload-artifact/pull/681">actions/upload-artifact#681</a></li>
<li><a href="https://github.com/nebuk89"><code>@nebuk89</code></a> made their first contribution in <a href="https://redirect.github.com/actions/upload-artifact/pull/712">actions/upload-artifact#712</a></li>
<li><a href="https://github.com/danwkennedy"><code>@danwkennedy</code></a> made their first contribution in <a href="https://redirect.github.com/actions/upload-artifact/pull/727">actions/upload-artifact#727</a></li>
<li><a href="https://github.com/patrikpolyak"><code>@patrikpolyak</code></a> made their first contribution in <a href="https://redirect.github.com/actions/upload-artifact/pull/725">actions/upload-artifact#725</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/upload-artifact/compare/v4...v5.0.0">https://github.com/actions/upload-artifact/compare/v4...v5.0.0</a></p>
<h2>v4.6.2</h2>
<h2>What's Changed</h2>
<ul>
<li>Update to use artifact 2.3.2 package & prepare for new upload-artifact release by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/685">actions/upload-artifact#685</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> made their first contribution in <a href="https://redirect.github.com/actions/upload-artifact/pull/685">actions/upload-artifact#685</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/upload-artifact/compare/v4...v4.6.2">https://github.com/actions/upload-artifact/compare/v4...v4.6.2</a></p>
<h2>v4.6.1</h2>
<h2>What's Changed</h2>
<ul>
<li>Update to use artifact 2.2.2 package by <a href="https://github.com/yacaovsnc"><code>@yacaovsnc</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/673">actions/upload-artifact#673</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/upload-artifact/compare/v4...v4.6.1">https://github.com/actions/upload-artifact/compare/v4...v4.6.1</a></p>
<h2>v4.6.0</h2>
<h2>What's Changed</h2>
<ul>
<li>Expose env vars to control concurrency and timeout by <a href="https://github.com/yacaovsnc"><code>@yacaovsnc</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/662">actions/upload-artifact#662</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/upload-artifact/compare/v4...v4.6.0">https://github.com/actions/upload-artifact/compare/v4...v4.6.0</a></p>
<h2>v4.5.0</h2>
<h2>What's Changed</h2>
<ul>
<li>fix: deprecated <code>Node.js</code> version in action by <a href="https://github.com/hamirmahal"><code>@hamirmahal</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/578">actions/upload-artifact#578</a></li>
<li>Add new <code>artifact-digest</code> output by <a href="https://github.com/bdehamer"><code>@bdehamer</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/656">actions/upload-artifact#656</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/hamirmahal"><code>@hamirmahal</code></a> made their first contribution in <a href="https://redirect.github.com/actions/upload-artifact/pull/578">actions/upload-artifact#578</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/actions/upload-artifact/commit/330a01c490aca151604b8cf639adc76d48f6c5d4"><code>330a01c</code></a> Merge pull request <a href="https://redirect.github.com/actions/upload-artifact/issues/734">#734</a> from actions/danwkennedy/prepare-5.0.0</li>
<li><a href="https://github.com/actions/upload-artifact/commit/03f282445299bbefc96171af272a984663b63a26"><code>03f2824</code></a> Update <code>github.dep.yml</code></li>
<li><a href="https://github.com/actions/upload-artifact/commit/905a1ecb5915b264cbc519e4eb415b5d82916018"><code>905a1ec</code></a> Prepare <code>v5.0.0</code></li>
<li><a href="https://github.com/actions/upload-artifact/commit/2d9f9cdfa99fedaddba68e9b5b5c281eca26cc63"><code>2d9f9cd</code></a> Merge pull request <a href="https://redirect.github.com/actions/upload-artifact/issues/725">#725</a> from patrikpolyak/patch-1</li>
<li><a href="https://github.com/actions/upload-artifact/commit/9687587dec67f2a8bc69104e183d311c42af6d6f"><code>9687587</code></a> Merge branch 'main' into patch-1</li>
<li><a href="https://github.com/actions/upload-artifact/commit/2848b2cda0e5190984587ec6bb1f36730ca78d50"><code>2848b2c</code></a> Merge pull request <a href="https://redirect.github.com/actions/upload-artifact/issues/727">#727</a> from danwkennedy/patch-1</li>
<li><a href="https://github.com/actions/upload-artifact/commit/9b511775fd9ce8c5710b38eea671f856de0e70a7"><code>9b51177</code></a> Spell out the first use of GHES</li>
<li><a href="https://github.com/actions/upload-artifact/commit/cd231ca1eda77976a84805c4194a1954f56b0727"><code>cd231ca</code></a> Update GHES guidance to include reference to Node 20 version</li>
<li><a href="https://github.com/actions/upload-artifact/commit/de65e23aa2b7e23d713bb51fbfcb6d502f8667d8"><code>de65e23</code></a> Merge pull request <a href="https://redirect.github.com/actions/upload-artifact/issues/712">#712</a> from actions/nebuk89-patch-1</li>
<li><a href="https://github.com/actions/upload-artifact/commit/8747d8cd7632611ad6060b528f3e0f654c98869c"><code>8747d8c</code></a> Update README.md</li>
<li>Additional commits viewable in <a href="https://github.com/actions/upload-artifact/compare/v4...v5">compare view</a></li>
</ul>
</details>
<br />
Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`.
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<details>
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You can trigger Dependabot actions by commenting on this PR:
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The test ensures that:
When global_random_seed is set, the results are consistent across various random seeds.
Consistent clustering results are produced by all pertinent random seeds.
Future modifications to the clustering module's random seed handling are supported by the test.
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Towards #26024
#### What does this implement/fix? Explain your changes.
Add array API support to `coverage_error`
#### Any other comments? | null | {
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#### What does this implement/fix? Explain your changes.
In the Post-tuning the decision threshold for cost-sensitive learning example,
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https://scikit-learn.org/dev/auto_examples/model_selection/plot_cost_sensitive_learning.html
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### What does this implement/fix?
This pull request adds a small "My Contribution" section to the README for open-source learning purposes.
It does not change the library’s code or functionality — just improves documentation clarity and demonstrates the contribution process.
### Summary of changes
- Added a brief contribution note under the "Citation" section.
- Included author information and GitHub link.
### Motivation
This contribution was made as part of my open-source learning journey, exploring popular Python libraries such as Matplotlib and Scikit-learn.
### Author
🧠 **Himani Devadiga**
🔗 [GitHub Profile](https://github.com/Himani-s-Devadiga)
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Small follow-up from https://github.com/scikit-learn/scikit-learn/pull/32509#issuecomment-3455944984
#### What does this implement/fix? Explain your changes.
Changes the tag name for the new development setup section from `install_bleeding_edge` to the more descriptive `setup_development_environment`.
The older release notes (v1.5 and v1.6) also contained this link, I replaced it with a reference to the meson backend section, as this is not mentioned explicitly (except for a note) in the setup process. Let me know if that is ok or if it should still point to the setup (or not be replaced at all?).
@lesteve
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32621 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32621/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32621/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32621/events | https://github.com/scikit-learn/scikit-learn/issues/32621 | 3,574,903,601 | I_kwDOAAzd1s7VFLMx | 32,621 | ENH: Add overlay_dx_score metric for regression evaluation | {
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"node_id": "MDU6TGFiZWwxODE3Mz... | closed | false | [] | null | 6 | 2025-10-31T13:11:11 | 2025-11-06T16:04:14 | 2025-11-06T16:04:14 | null | NONE | null | null | {
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} | ### Describe the workflow you want to enable
## Current Problem
When evaluating regression models, especially for time series forecasting, existing metrics (MAE, RMSE, R²) don't capture how well predictions align visually with actual values across different tolerance levels.
## Desired Workflow
```python
from sklearn.metrics import overlay_dx_score
from sklearn.model_selection import cross_val_score
# Simple evaluation
score = overlay_dx_score(y_true, y_pred)
# Cross-validation with custom scorer
from sklearn.metrics import make_scorer
scorer = make_scorer(overlay_dx_score)
scores = cross_val_score(model, X, y, scoring=scorer, cv=5)
# GridSearchCV
from sklearn.model_selection import GridSearchCV
grid = GridSearchCV(model, param_grid, scoring=scorer)
grid.fit(X, y)
```
## Use Cases
1. Time series forecasting evaluation where visual alignment matters
2. Scenarios where outlier sensitivity of MSE/RMSE is problematic
3. Understanding prediction quality at multiple tolerance levels
4. Model selection when alignment is more important than absolute error
## Proposed Implementation
Add `overlay_dx_score()` function to `sklearn.metrics`.
### Function Signature
```python
def overlay_dx_score(y_true, y_pred, *,
sample_weight=None,
multioutput='uniform_average',
max_percentage=100.0,
min_percentage=0.1,
step=0.1):
"""
Overlay DX metric for evaluating regression predictions.
Measures alignment by computing area under curve showing percentage
of predictions within tolerance intervals of varying sizes.
Returns float between 0 and 1, higher is better.
"""
```
### How It Works
1. Define tolerance intervals as percentages of the data range
2. For each tolerance level, compute percentage of predictions within tolerance
3. Calculate area under the resulting curve
4. Normalize by maximum possible area
### Mathematical Formula
```
overlay_dx = A / A_max
```
where:
- A = area under the overlay percentage curve
- A_max = maximum possible area (max_percentage × 100)
### Key Features
- Supports `sample_weight` (sklearn standard)
- Supports `multioutput` regression
- Less sensitive to outliers than MSE/RMSE
- Provides interpretable score (0 to 1 range)
### Implementation Details
- Pure NumPy implementation, no external dependencies
- Vectorized operations for efficiency
- Follows sklearn API conventions
- Complete numpydoc documentation
- 95%+ test coverage ready
## References
- Repository: https://github.com/Smile-SA/overlay_dx
- Currently used internally for time series forecasting evaluation
- Compatible implementation exists: works with cross_val_score, GridSearchCV, Pipeline
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
_No response_ | {
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cc @lesteve @jeremiedbb | {
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"... | closed | false | [] | null | 1 | 2025-10-31T08:15:08 | 2025-10-31T10:27:48 | 2025-10-31T10:13:44 | null | CONTRIBUTOR | null | null | null | null | <!--
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Towards: #26024
#### What does this implement/fix? Explain your changes.
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#### Any other comments?
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<!-- Example: Fixes #1234. See also #3456. -->
None - this is a new example
#### What does this implement/fix?
Adds a new example demonstrating basic ColumnTransformer usage with:
- Preprocessing numeric features with StandardScaler
- Encoding categorical features with OneHotEncoder
- Building a complete pipeline with LogisticRegression
#### Any other comments?
This is my first contribution to scikit-learn. The example shows a common preprocessing pattern for mixed-type data.
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 2 | 2025-10-31T01:46:37 | 2025-10-31T05:22:30 | 2025-10-31T05:18:56 | null | MEMBER | null | null | null | null | <!--
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#### Reference Issues/PRs
Fix comment about scipy's array API supporting quantile - this was added in 1.16 NOT 1.6 (https://github.com/scipy/scipy/pull/22352)
https://github.com/scikit-learn/scikit-learn/pull/31406#discussion_r2476871034
cc @lesteve
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@lesteve
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### `scikit-learn` version and operating system
scikit-learn version: 1.5.2 (also affects 1.4.x)
Operating System: (e.g., macOS, Windows, Linux)
Python version: (e.g., 3.10)
### The bug
When using `StackingRegressor` (or `StackingClassifier`), passing a `TimeSeriesSplit` object to the `cv` parameter fails, raising a `ValueError: cross_val_predict only works for partitions`.
This check is fundamentally incompatible with `TimeSeriesSplit`, which correctly reserves the initial chunk of data for training and never uses it as a test set.
This bug makes it impossible to use `StackingRegressor` for time-series forecasting without resorting to a leaky workaround like `KFold(shuffle=False)`.
### Steps/Code to Reproduce
Here is a minimal reproducible example:
```python
import numpy as np
import sklearn
from sklearn.model_selection import TimeSeriesSplit, KFold
from sklearn.ensemble import StackingRegressor
from sklearn.linear_model import LinearRegression
print(f"scikit-learn version: {sklearn.__version__}")
# 1. Create dummy time-series data
X = np.random.rand(100, 2)
y = np.random.rand(100)
# 2. Define estimators
estimators = [
('lr1', LinearRegression()),
('lr2', LinearRegression())
]
# 3. Define the TimeSeriesSplit (the correct tool for this)
# n_splits=5 will reserve the first 1/6th of data for training only.
tscv = TimeSeriesSplit(n_splits=5)
# 4. Create the Stacker using TimeSeriesSplit
stacker_tscv = StackingRegressor(
estimators=estimators,
final_estimator=LinearRegression(),
cv=tscv # Pass the TimeSeriesSplit
)
# 5. This will FAIL
try:
print("\nAttempting to fit with TimeSeriesSplit...")
stacker_tscv.fit(X, y)
print("SUCCESS: TimeSeriesSplit worked.")
except ValueError as e:
print(f"FAILED with TimeSeriesSplit: {e}")
# 6. Show the "workaround" that succeeds (but leaks data)
print("\nAttempting to fit with KFold(shuffle=False)...")
kf_cv = KFold(n_splits=5, shuffle=False)
stacker_kf = StackingRegressor(
estimators=estimators,
final_estimator=LinearRegression(),
cv=kf_cv
)
try:
stacker_kf.fit(X, y)
print("SUCCESS: KFold(shuffle=False) worked.")
except Exception as e:
print(f"FAILED with KFold(shuffle=False): {e}")
```
### Impact
This bug effectively breaks **StackingRegressor** for all time-series applications. It forces users to choose between:
Downgrading to scikit-learn==1.3.2 (where this check did not exist?). Not entirely sure which version it got introduced!
Using KFold(shuffle=False) as the cv parameter, which introduces severe data leakage and leads to invalid models. | null | {
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Towards: https://github.com/scikit-learn/scikit-learn/issues/26024
**What does this implement/fix? Explain your changes:**
Add array API support for `sklearn.metrics.laplacian_kernel` | {
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"id": 5464... | closed | false | [] | null | 8 | 2025-10-30T10:57:15 | 2026-02-21T10:56:11 | 2026-02-21T10:56:11 | null | MEMBER | null | null | {
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} | Even if `scipy.optimize` does not support array API yet, we can probably work around it to perform the computation of the gradient using the input data array namespace and convert the gradient values to numpy prior to feeding them back to the solver at each iteration.
Assuming `n_samples >> n_features`, this is likely to be a worthwhile way to get a large speed-up on GPU devices for that estimator despite back and forth conversion to and from device for the parameters array.
To compute the gradient we perform `X @ coef + intercept` which leads to "raw predictions". Those raw predictions are fed along with `y` to the Cython loss. This will be an `(n_samples, n_classes)` array. For a first iteration, we can convert that to NumPy and continue using Cython. Alternatively, we can write pure Python array API capable version of the gradient method of the loss functions. We should start with log loss for which we can already refactor and move the `sklearn.utils._array_api._half_multinomial_loss` function (introduced for metrics) into the `sklearn._loss` package to add its gradient/Hessian counterparts.
We can leave the other losses as raising `NotImplementError` for now. | {
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https://scikit-learn.org/1.4/tutorial/statistical_inference/unsupervised_learning.html#connectivity-constrained-clustering) | {
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More info: https://circleci.com/changelog/introducing-a-faster-checkout-option/?utm_medium=email&_hsmi=387462139&utm_content=387460229&utm_source=hs_email
cc @lesteve @lucyleeow | {
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} | ### Describe the bug
Trying to install scikit-learn using uv pip install scikit-learn. Python 3.14t
Got an error while building
### Steps/Code to Reproduce
Use python 3.14t
Run uv pip install scikit-learn
### Expected Results
scikit-learn successfully installed
### Actual Results
Got an error
FAILED: [code=1] sklearn/utils/murmurhash.cp314t-win_amd64.pyd.p/meson-generated_murmurhash.c.obj
"clang" "-Isklearn\utils\murmurhash.cp314t-win_amd64.pyd.p" "-Isklearn\utils" "-I..\sklearn\utils" "-Isklearn"
"-IC:\Users\Egor\AppData\Local\Programs\Python\Python314\Include" "-fvisibility=hidden" "-fdiagnostics-color=always" "-DNDEBUG" "-D_FILE_OFFSET_BITS=64" "-Wall"
"-Winvalid-pch" "-std=c11" "-O3" "-Wno-unused-but-set-variable" "-Wno-unused-function" "-Wno-conversion" "-Wno-misleading-indentation" "-DPy_GIL_DISABLED" -MD -MQ
sklearn/utils/murmurhash.cp314t-win_amd64.pyd.p/meson-generated_murmurhash.c.obj -MF "sklearn\utils\murmurhash.cp314t-win_amd64.pyd.p\meson-generated_murmurhash.c.obj.d"
-o sklearn/utils/murmurhash.cp314t-win_amd64.pyd.p/meson-generated_murmurhash.c.obj "-c" sklearn/utils/murmurhash.cp314t-win_amd64.pyd.p/murmurhash.c
In file included from sklearn/utils/murmurhash.cp314t-win_amd64.pyd.p/murmurhash.c:1129:
..\sklearn\utils\src/MurmurHash3.h:16:23: error: typedef redefinition with different types ('unsigned long' vs 'unsigned int')
16 | typedef unsigned long uint32_t;
| ^
sklearn/utils/murmurhash.cp314t-win_amd64.pyd.p/murmurhash.c:468:39: note: previous definition is here
468 | typedef unsigned __int32 uint32_t;
| ^
1 error generated.
### Versions
```shell
scikit-learn 1.7.2
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Similar to https://github.com/scikit-learn/scikit-learn/pull/32605
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Follow-up on https://github.com/scikit-learn/scikit-learn/pull/32555
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Towards: #26024
#### What does this implement/fix? Explain your changes.
- Add array API support for `sklearn.metrics.balanced_accuracy_score`
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] | closed | false | [] | null | 2 | 2025-10-29T03:54:44 | 2025-10-29T15:01:19 | 2025-10-29T13:45:10 | null | MEMBER | null | null | null | null | Follow numpy which has done it in March 2025 see https://github.com/numpy/numpy/pull/28436. I could add a changelog similar to numpy:
```
Wheels for linux systems will use the ``manylinux_2_28`` tag, which means
dropping support for redhat7/centos7, amazonlinux2, debian9, ubuntu18.04, and
other pre-glibc2.28 operating system versions.
```
For some unclear reason, this seems to fix https://github.com/scikit-learn/scikit-learn/issues/32599.
cc @ogrisel.
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#### Reference Issues/PRs
Noticed while reviewing #32540
#### What does this implement/fix? Explain your changes.
* Make classes API reference links
* Fix small typos
#### Any other comments?
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Added enhancement mentioned in issue 32109
#### What does this implement/fix? Explain your changes.
Added specific variable for the inner solver
#### Any other comments?
None
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Towards #26024
#### What does this implement/fix? Explain your changes.
Add array API support to `calinski_harabasz_score`
#### Any other comments? | {
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} | last successful on October 22 [build log](https://github.com/scikit-learn/scikit-learn/actions/runs/18704782622/job/53340764569)
first failing on October 23 [build log](https://github.com/scikit-learn/scikit-learn/actions/runs/18736923722/job/53445538260)
Unclear what the root cause is for now (identical versions it seems, the Python 3.11 build has a different Python bugfix version but I think this is a red herring since Python 3.12 and Python 3.13 have the same bugfix version) ...
Failing tests:
```
FAILED inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[0.5-2]
FAILED inspection/tests/test_permutation_importance.py::test_permutation_importance_equivalence_array_dataframe[1.0-2]
```
```
________ test_permutation_importance_equivalence_array_dataframe[0.5-2] ________
joblib.externals.loky.process_executor._RemoteTraceback:
"""
Traceback (most recent call last):
File "/tmp/tmp.q3sHOyewIE/venv/lib/python3.13/site-packages/joblib/externals/loky/process_executor.py", line 453, in _process_worker
call_item = call_queue.get(block=True, timeout=timeout)
File "/opt/_internal/cpython-3.13.8/lib/python3.13/multiprocessing/queues.py", line 120, in get
return _ForkingPickler.loads(res)
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/tmp/tmp.q3sHOyewIE/venv/lib/python3.13/site-packages/pandas/core/arrays/categorical.py", line 1726, in __setstate__
return super().__setstate__(state)
~~~~~~~~~~~~~~~~~~~~^^^^^^^
File "pandas/_libs/arrays.pyx", line 85, in pandas._libs.arrays.NDArrayBacked.__setstate__
File "pandas/_libs/arrays.pyx", line 103, in pandas._libs.arrays.NDArrayBacked.__setstate__
NotImplementedError: (CategoricalDtype(categories=[0.0, 1.0, 2.0], ordered=False, categories_dtype=float64), array([1, 0, 2, 1, 0, 2, 2, 1, 0, 1, 2, 1, 2, 0, 0, 1, 1, 0, 1, 2, 2, 0,
1, 2, 2, 0, 2, 1, 2, 0, 0, 2, 2, 2, 2, 2, 1, 1, 1, 1, 0, 1, 1, 0,
1, 0, 1, 1, 1, 2, 1, 0, 1, 0, 2, 0, 1, 1, 2, 1, 1, 1, 0, 0, 0, 2,
0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 1, 0, 0, 0, 1, 2, 0, 2,
1, 0, 0, 1, 1, 2, 2, 2, 1, 2, 2, 2], dtype=int8))
"""
The above exception was the direct cause of the following exception:
n_jobs = 2, max_samples = 0.5
@pytest.mark.parametrize("n_jobs", [None, 1, 2])
@pytest.mark.parametrize("max_samples", [0.5, 1.0])
def test_permutation_importance_equivalence_array_dataframe(n_jobs, max_samples):
# This test checks that the column shuffling logic has the same behavior
# both a dataframe and a simple numpy array.
pd = pytest.importorskip("pandas")
# regression test to make sure that sequential and parallel calls will
# output the same results.
X, y = make_regression(n_samples=100, n_features=5, random_state=0)
X_df = pd.DataFrame(X)
# Add a categorical feature that is statistically linked to y:
binner = KBinsDiscretizer(
n_bins=3,
encode="ordinal",
quantile_method="averaged_inverted_cdf",
)
cat_column = binner.fit_transform(y.reshape(-1, 1))
# Concatenate the extra column to the numpy array: integers will be
# cast to float values
X = np.hstack([X, cat_column])
assert X.dtype.kind == "f"
# Insert extra column as a non-numpy-native dtype:
cat_column = pd.Categorical(cat_column.ravel())
new_col_idx = len(X_df.columns)
X_df[new_col_idx] = cat_column
assert X_df[new_col_idx].dtype == cat_column.dtype
# Stich an arbitrary index to the dataframe:
X_df.index = np.arange(len(X_df)).astype(str)
rf = RandomForestRegressor(n_estimators=5, max_depth=3, random_state=0)
rf.fit(X, y)
n_repeats = 3
importance_array = permutation_importance(
rf,
X,
y,
n_repeats=n_repeats,
random_state=0,
n_jobs=n_jobs,
max_samples=max_samples,
)
# First check that the problem is structured enough and that the model is
# complex enough to not yield trivial, constant importances:
imp_min = importance_array["importances"].min()
imp_max = importance_array["importances"].max()
assert imp_max - imp_min > 0.3
# Now check that importances computed on dataframe matche the values
# of those computed on the array with the same data.
> importance_dataframe = permutation_importance(
rf,
X_df,
y,
n_repeats=n_repeats,
random_state=0,
n_jobs=n_jobs,
max_samples=max_samples,
)
../venv/lib/python3.13/site-packages/sklearn/inspection/tests/test_permutation_importance.py:357:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../venv/lib/python3.13/site-packages/sklearn/utils/_param_validation.py:218: in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
../venv/lib/python3.13/site-packages/sklearn/inspection/_permutation_importance.py:288: in permutation_importance
scores = Parallel(n_jobs=n_jobs)(
../venv/lib/python3.13/site-packages/sklearn/utils/parallel.py:91: in __call__
return super().__call__(iterable_with_config_and_warning_filters)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
../venv/lib/python3.13/site-packages/joblib/parallel.py:2072: in __call__
return output if self.return_generator else list(output)
^^^^^^^^^^^^
../venv/lib/python3.13/site-packages/joblib/parallel.py:1682: in _get_outputs
yield from self._retrieve()
../venv/lib/python3.13/site-packages/joblib/parallel.py:1784: in _retrieve
self._raise_error_fast()
../venv/lib/python3.13/site-packages/joblib/parallel.py:1859: in _raise_error_fast
error_job.get_result(self.timeout)
../venv/lib/python3.13/site-packages/joblib/parallel.py:758: in get_result
return self._return_or_raise()
^^^^^^^^^^^^^^^^^^^^^^^
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <joblib.parallel.BatchCompletionCallBack object at 0x7f8c44e4d6d0>
def _return_or_raise(self):
try:
if self.status == TASK_ERROR:
> raise self._result
E joblib.externals.loky.process_executor.BrokenProcessPool: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.
../venv/lib/python3.13/site-packages/joblib/parallel.py:773: BrokenProcessPool
________ test_permutation_importance_equivalence_array_dataframe[1.0-2] ________
joblib.externals.loky.process_executor._RemoteTraceback:
"""
Traceback (most recent call last):
File "/tmp/tmp.q3sHOyewIE/venv/lib/python3.13/site-packages/joblib/externals/loky/process_executor.py", line 453, in _process_worker
call_item = call_queue.get(block=True, timeout=timeout)
File "/opt/_internal/cpython-3.13.8/lib/python3.13/multiprocessing/queues.py", line 120, in get
return _ForkingPickler.loads(res)
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/tmp/tmp.q3sHOyewIE/venv/lib/python3.13/site-packages/pandas/core/arrays/categorical.py", line 1726, in __setstate__
return super().__setstate__(state)
~~~~~~~~~~~~~~~~~~~~^^^^^^^
File "pandas/_libs/arrays.pyx", line 85, in pandas._libs.arrays.NDArrayBacked.__setstate__
File "pandas/_libs/arrays.pyx", line 103, in pandas._libs.arrays.NDArrayBacked.__setstate__
NotImplementedError: (CategoricalDtype(categories=[0.0, 1.0, 2.0], ordered=False, categories_dtype=float64), array([1, 0, 2, 1, 0, 2, 2, 1, 0, 1, 2, 1, 2, 0, 0, 1, 1, 0, 1, 2, 2, 0,
1, 2, 2, 0, 2, 1, 2, 0, 0, 2, 2, 2, 2, 2, 1, 1, 1, 1, 0, 1, 1, 0,
1, 0, 1, 1, 1, 2, 1, 0, 1, 0, 2, 0, 1, 1, 2, 1, 1, 1, 0, 0, 0, 2,
0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 1, 0, 0, 0, 1, 2, 0, 2,
1, 0, 0, 1, 1, 2, 2, 2, 1, 2, 2, 2], dtype=int8))
"""
The above exception was the direct cause of the following exception:
n_jobs = 2, max_samples = 1.0
@pytest.mark.parametrize("n_jobs", [None, 1, 2])
@pytest.mark.parametrize("max_samples", [0.5, 1.0])
def test_permutation_importance_equivalence_array_dataframe(n_jobs, max_samples):
# This test checks that the column shuffling logic has the same behavior
# both a dataframe and a simple numpy array.
pd = pytest.importorskip("pandas")
# regression test to make sure that sequential and parallel calls will
# output the same results.
X, y = make_regression(n_samples=100, n_features=5, random_state=0)
X_df = pd.DataFrame(X)
# Add a categorical feature that is statistically linked to y:
binner = KBinsDiscretizer(
n_bins=3,
encode="ordinal",
quantile_method="averaged_inverted_cdf",
)
cat_column = binner.fit_transform(y.reshape(-1, 1))
# Concatenate the extra column to the numpy array: integers will be
# cast to float values
X = np.hstack([X, cat_column])
assert X.dtype.kind == "f"
# Insert extra column as a non-numpy-native dtype:
cat_column = pd.Categorical(cat_column.ravel())
new_col_idx = len(X_df.columns)
X_df[new_col_idx] = cat_column
assert X_df[new_col_idx].dtype == cat_column.dtype
# Stich an arbitrary index to the dataframe:
X_df.index = np.arange(len(X_df)).astype(str)
rf = RandomForestRegressor(n_estimators=5, max_depth=3, random_state=0)
rf.fit(X, y)
n_repeats = 3
importance_array = permutation_importance(
rf,
X,
y,
n_repeats=n_repeats,
random_state=0,
n_jobs=n_jobs,
max_samples=max_samples,
)
# First check that the problem is structured enough and that the model is
# complex enough to not yield trivial, constant importances:
imp_min = importance_array["importances"].min()
imp_max = importance_array["importances"].max()
assert imp_max - imp_min > 0.3
# Now check that importances computed on dataframe matche the values
# of those computed on the array with the same data.
> importance_dataframe = permutation_importance(
rf,
X_df,
y,
n_repeats=n_repeats,
random_state=0,
n_jobs=n_jobs,
max_samples=max_samples,
)
../venv/lib/python3.13/site-packages/sklearn/inspection/tests/test_permutation_importance.py:357:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../venv/lib/python3.13/site-packages/sklearn/utils/_param_validation.py:218: in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
../venv/lib/python3.13/site-packages/sklearn/inspection/_permutation_importance.py:288: in permutation_importance
scores = Parallel(n_jobs=n_jobs)(
../venv/lib/python3.13/site-packages/sklearn/utils/parallel.py:91: in __call__
return super().__call__(iterable_with_config_and_warning_filters)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
../venv/lib/python3.13/site-packages/joblib/parallel.py:2072: in __call__
return output if self.return_generator else list(output)
^^^^^^^^^^^^
../venv/lib/python3.13/site-packages/joblib/parallel.py:1682: in _get_outputs
yield from self._retrieve()
../venv/lib/python3.13/site-packages/joblib/parallel.py:1784: in _retrieve
self._raise_error_fast()
../venv/lib/python3.13/site-packages/joblib/parallel.py:1859: in _raise_error_fast
error_job.get_result(self.timeout)
../venv/lib/python3.13/site-packages/joblib/parallel.py:758: in get_result
return self._return_or_raise()
^^^^^^^^^^^^^^^^^^^^^^^
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <joblib.parallel.BatchCompletionCallBack object at 0x7f8c44e4ef90>
def _return_or_raise(self):
try:
if self.status == TASK_ERROR:
> raise self._result
E joblib.externals.loky.process_executor.BrokenProcessPool: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.
../venv/lib/python3.13/site-packages/joblib/parallel.py:773: BrokenProcessPool
``` | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32598 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32598/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32598/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32598/events | https://github.com/scikit-learn/scikit-learn/pull/32598 | 3,561,633,932 | PR_kwDOAAzd1s6wJd1e | 32,598 | DOC: Add ColumnTransformer passthrough example with Pandas | {
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"node_id": "MDU6TGFiZWwyMzk5... | closed | false | [] | null | 1 | 2025-10-28T13:40:18 | 2025-10-28T17:54:36 | 2025-10-28T17:54:36 | null | NONE | null | null | null | null | Closes #32167
This PR adds a new example demonstrating how to use `ColumnTransformer` with
`remainder='passthrough'` when working with Pandas DataFrames.
- New example: `examples/compose/plot_column_transformer_passthrough.py`
- Demonstrates scaling, encoding, and leaving one column untouched
- Updates `doc/modules/compose.rst` to include the new example
- Verified that all tests and documentation build pass locally | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32597 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32597/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32597/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32597/events | https://github.com/scikit-learn/scikit-learn/pull/32597 | 3,560,917,071 | PR_kwDOAAzd1s6wHMWd | 32,597 | FEA Add array API support for `manhattan_distances` | {
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"node_id": "LA_kwDOAAzd1s8... | closed | false | [] | null | 2 | 2025-10-28T10:42:52 | 2025-10-29T06:01:06 | 2025-10-29T05:13:42 | null | CONTRIBUTOR | null | null | null | null | <!--
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#### Reference Issues/PRs
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Towards: #26024
#### What does this implement/fix? Explain your changes.
- Adds array API support for `sklearn.metrics.pairwise.manhattan_distances`
#### Any other comments?
CC: @ogrisel
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32596 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32596/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32596/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32596/events | https://github.com/scikit-learn/scikit-learn/pull/32596 | 3,560,775,051 | PR_kwDOAAzd1s6wGxgg | 32,596 | CI Make 'conda create' command less verbose' | {
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"node_id": "LA_kwDOAAzd1s7eWo1... | closed | false | [] | null | 3 | 2025-10-28T10:14:00 | 2025-10-29T04:10:11 | 2025-10-29T02:18:11 | null | MEMBER | null | null | null | null | `conda-lock install` is not super useful with plenty of empty lines in the CI log, see [this log](https://github.com/scikit-learn/scikit-learn/actions/runs/18863675724/job/53827210748#step:5:43) for example.
Let's see what setting the log level to WARNING does.
Noticed with @StefanieSenger and @DeaMariaLeon the other day. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32595 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32595/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32595/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32595/events | https://github.com/scikit-learn/scikit-learn/pull/32595 | 3,560,145,856 | PR_kwDOAAzd1s6wEpaf | 32,595 | DOC: Add [all random seeds] commit marker to documentation | {
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"node_id": "MDU6TGFiZWwyMzk5... | closed | false | [] | null | 1 | 2025-10-28T07:18:11 | 2025-10-29T00:10:12 | 2025-10-29T00:10:12 | null | NONE | null | null | null | null | This PR adds documentation for the [all random seeds] commit message marker.
**Changes**:
- Added [all random seeds] to the commit message markers table in the contributing guide with a link to issue #28959
- Added a Python code example showing how to write a test using the global_random_seed fixture in the parallelism documentation
Fixes #32551 | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32594 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32594/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32594/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32594/events | https://github.com/scikit-learn/scikit-learn/pull/32594 | 3,560,010,974 | PR_kwDOAAzd1s6wEMr5 | 32,594 | FIX: guard _accept_sample_weight in _MultimetricScorer and add regression test (Fixes #31599) | {
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**Fixes #31599**
### What does this implement/fix? Explain your changes.
**Bug before this change**
`_MultimetricScorer.__call__` iterates over the scorers in `self._scorers` and forwards `sample_weight` to each scorer if appropriate.
Before this change, the code unconditionally did something like:
```python
accepts = scorer._accept_sample_weight()
```
This assumes that every scorer in `self._scorers` is a `_BaseScorer` instance with an `_accept_sample_weight` attribute.
However, users can (and do) pass a mix of scorer types, for example:
- a normal `scorer` created via `make_scorer` (i.e. a `_BaseScorer` subclass), and
- a plain callable (function) that computes a custom score.
Plain callables do not define `_accept_sample_weight`. In that case, calling `_accept_sample_weight()` raised:
```python
AttributeError: 'function' object has no attribute '_accept_sample_weight'
```
This means that evaluating multiple metrics at once with `sample_weight=...` could crash at runtime if any of those metrics was provided as a bare function instead of a scorer object.
**Behavior after this change**
We now guard access to `_accept_sample_weight()`.
For each scorer in the multimetric scorer:
- If the scorer defines `_accept_sample_weight()`, we call it and (as before) forward `sample_weight` only if it returns `True`.
- If the scorer does not define `_accept_sample_weight` (e.g. it's just a plain callable), we treat it as not accepting `sample_weight` instead of raising.
In code terms (simplified for illustration):
```python
try:
accepts = scorer._accept_sample_weight()
except AttributeError:
accepts = False
if accepts:
routed_params[name]["sample_weight"] = sample_weight
```
This preserves the existing behavior for real scorer objects, while preventing the `AttributeError` for plain callables. The result is that mixed-metric evaluation with `sample_weight` no longer crashes.
**Tests**
This PR adds a non-regression test:
`test_multimetric_scorer_mixed_callable_no_attribute_error_with_sample_weight`
in `sklearn/metrics/tests/test_score_objects.py`.
The test does the following:
- Builds a dummy estimator.
- Builds one scorer that is a `_BaseScorer` (and therefore supports `_accept_sample_weight`) and another scorer that is just a plain function with no `_accept_sample_weight`.
- Constructs an instance of `_MultimetricScorer` combining both scorers.
- Calls that multimetric scorer with `sample_weight=...`.
The assertions are:
- No `AttributeError` (or other exception) is raised when calling with `sample_weight`.
- The returned result is a `dict` containing numeric scores for both metrics.
The test also imports `_BaseScorer` from `sklearn.metrics._scorer` so we can explicitly build the mixed scorer setup.
**User impact**
- Before: valid code using a mix of scorer styles could raise at runtime when passing `sample_weight`.
- After: that code now runs without error.
- Behavior for scorers that already support `sample_weight` is unchanged.
- Behavior for scorers that do not support `sample_weight` is also unchanged (they simply do not receive `sample_weight`), but we no longer crash in that case.
This is therefore a robustness / correctness fix to `_MultimetricScorer.__call__`, plus a regression test to prevent regressions in the future.
### Any other comments?
- This change should be backward compatible.
- Because this avoids a runtime crash in user code, it may warrant a short changelog entry under bug fixes in `doc/whats_new/upcoming_changes/`. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32593 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32593/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32593/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32593/events | https://github.com/scikit-learn/scikit-learn/pull/32593 | 3,559,926,718 | PR_kwDOAAzd1s6wD62H | 32,593 | DOC: Explain 'friedman_mse' regression criterion and when to use it (#32204) | {
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Fixes #32204.
#### What does this implement/fix? Explain your changes.
This PR expands the "Regression criteria" subsection in `doc/modules/tree.rst` to document the `friedman_mse` criterion alongside the existing criteria (`squared_error`, Poisson deviance, and MAE).
Specifically:
- It explains that `criterion="friedman_mse"` scores a split using the improvement in squared error between the parent node and its children, rather than just the raw child node MSE.
- It clarifies that this criterion originated in gradient boosting regression trees and is the default in `GradientBoostingRegressor` and `HistGradientBoostingRegressor`.
- It notes when you would choose `friedman_mse` (often better split decisions in additive/boosting ensembles) and how it compares to `"squared_error"` for standalone trees / forests.
- It keeps the mathematical definitions and narrative for MSE, Poisson deviance, and MAE, and reorganizes the subsection so `friedman_mse` is described in the same place where users learn about the other regression criteria.
Motivation:
`friedman_mse` is exposed as a valid `criterion` in tree-based estimators, but prior to this PR the user guide did not explain what it is, why it exists, or when to use it. This made the parameter unclear for users. This PR addresses that gap in the documentation.
This PR is docs-only. It does not modify any estimator code, runtime behavior, defaults, or public API.
#### Any other comments?
No changelog entry is added because this is a documentation clarification with no behavioral or API change. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32592 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32592/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32592/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32592/events | https://github.com/scikit-learn/scikit-learn/pull/32592 | 3,559,628,454 | PR_kwDOAAzd1s6wC5l1 | 32,592 | FIX preprocessing: Fix OneHotEncoder handle_unknown='warn' behavior | {
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## Description
According to the [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html) `handle_unknown='warn'` in `OneHotEncoder` is supposed to behave identically to `handle_unknown='infrequent_if_exist'` (i.e., map unknown categories to the infrequent category) while also emitting a `UserWarning`.
This PR fixes a bug where `handle_unknown='warn'` was incorrectly behaving like `handle_unknown='ignore'`, causing unknown categories to be encoded as all zeros.
Additionally, the `UserWarning` itself was misleading. It incorrectly stated that unknown categories would be "encoded as all zeros" even when `handle_unknown='infrequent_if_exist'` was used.
## Changes Made
This PR addresses the bug in two ways:
1. **Corrected the Behavior:**
In `_BaseEncoder._map_infrequent_categories`, the logic that un-masks unknown values (to map them to the infrequent category) was updated to include `handle_unknown='warn'`. It previously only checked for `handle_unknown='infrequent_if_exist'`. This ensures the behavior of `'warn'` now matches `'infrequent_if_exist'`.
2. **Corrected the Warning Message:**
In `_BaseEncoder._transform`, the warning-generation logic was updated to be conditional.
* It now emits the correct message: `"...encoded as the infrequent category."` when `handle_unknown` is `'warn'` or `'infrequent_if_exist'`.
* It continues to emit the `"...encoded as all zeros"` message when `handle_unknown='ignore'`.
## Testing
* Added a new regression test, `test_onehotencoder_handle_unknown_warn_maps_to_infrequent`, to specifically verify that `'warn'` produces the same output as `'infrequent_if_exist'` and emits the new, correct warning.
* Updated several existing tests (like `test_ohe_handle_unknown_warn` and `test_ohe_drop_first_handle_unknown_ignore_warns`) that were failing because they were asserting the old, incorrect warning message. They now expect the new, correct warning message.
* All tests in `sklearn/preprocessing/tests/test_encoders.py` now pass.
## Checklist
- [x] A short, descriptive title has been added.
- [x] Tests have been added to cover all changes.
- [x] All existing tests still pass. | {
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Unable to find junit file. Please see link for details. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32590 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32590/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32590/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32590/events | https://github.com/scikit-learn/scikit-learn/issues/32590 | 3,558,997,774 | I_kwDOAAzd1s7UIf8O | 32,590 | Add shrinkage support with `solver="svd"` in `LinearDiscriminantAnalysis` and `QuadraticDiscriminantAnalysis` | {
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} | ### Describe the workflow you want to enable
Both Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) can be solved with the SVD solver. The SVD solver does not explicitly compute the covariance matrix, so covariance shrinkage is not supported when we set `solver="svd"`, as illustrated in the snippet below:
```python
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import numpy as np
# Data that has zero variance in one dimension and needs regularization
X = np.random.randn(9, 2)
y = np.array([1, 1, 1, 1, 1, 2, 2, 2, 2])
solver = "svd"
clf = LinearDiscriminantAnalysis(solver=solver, shrinkage=0.5)
clf.fit(X, y)
```
```python
File ~/Packages/scikit-learn/scikit-learn/sklearn/discriminant_analysis.py:700, in LinearDiscriminantAnalysis.fit(self, X, y)
698 if self.solver == "svd":
699 if self.shrinkage is not None:
--> 700 raise NotImplementedError("shrinkage not supported with 'svd' solver.")
701 if self.covariance_estimator is not None:
702 raise ValueError(
703 "covariance estimator "
704 "is not supported "
705 "with svd solver. Try another solver"
706 )
NotImplementedError: shrinkage not supported with 'svd' solver.
```
Similar behavior for QDA is implemented in PR #32108.
However, it should be possible to implement shrinkage with the SVD solver that is mathematically equivalent to shrinkage with the other solvers, and it might be desirable to do so (https://github.com/scikit-learn/scikit-learn/pull/32108#discussion_r2454295132).
### Describe your proposed solution
While `solver="svd"` does not explicitly compute the covariance matrix, it relies on the singular values of the data matrix `X` of shape `(n_samples, n_features)`, denoted `S`. The i'th singular value `S[i]` is directly related to the i'th eigenvalue `lambda[i]` of the covariance matrix by the relation `lambda[i] = S[i]**2 / (n - 1)`, where `n` is the number of samples (see [here](https://github.com/scikit-learn/scikit-learn/blob/72d97bceec1af70d9b65bb49d12abdd5694e67f1/sklearn/discriminant_analysis.py#L1019) and [here](https://stats.stackexchange.com/questions/134282/relationship-between-svd-and-pca-how-to-use-svd-to-perform-pca)).
Shrinkage of the covariance matrix [as implemented](https://scikit-learn.org/stable/modules/generated/sklearn.covariance.ShrunkCovariance.html) by the parameter `shrinkage` is just a shrinking of the eigenvalues of the covariance towards the mean eigenvalue.
Given the above, support for the parameter `shrinkage` with `solver="svd"` can be implemented by appropriately shrinking the singular values `S` to match the shrinkage of the implicit eigenvalues. This should be pretty easy to implement.
One detail of this implementation is that when `n_samples < n_features`, then SVD only recovers `n_samples` number of singular values. Implicitly, the rest of the singular values are 0, corresponding to the rank-defficient covariance matrix. To implement shrinkage as suggested here, we would have to pad the vector `S` with zeros up to a length of `n_features` in this case.
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
_No response_ | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32589 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32589/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32589/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32589/events | https://github.com/scikit-learn/scikit-learn/issues/32589 | 3,558,688,905 | I_kwDOAAzd1s7UHUiJ | 32,589 | OneHotEncoder handle_unknown='warn' behaves like 'ignore' instead of 'infrequent_if_exist' | {
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} | ### Describe the bug
According to the [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html), `handle_unknown='warn'` should behave like `handle_unknown='infrequent_if_exist'` (mapping unknown categories to the infrequent category) while emitting a warning.
However, in practice, `handle_unknown='warn'` behaves like `handle_unknown='ignore'` (mapping unknown categories to all zeros).
Documentation states:
> 'warn' : When an unknown category is encountered during transform a warning is issued, and the encoding then proceeds as described for handle_unknown="infrequent_if_exist".
### Steps/Code to Reproduce
```python
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
# Training data with infrequent category
train_data = pd.DataFrame({
'building_type': ['restaurant']*3 + ['shop']*3 + ['snack']
})
# Test data with unknown category
test_data = pd.DataFrame({
'building_type': ['restaurant', 'snack', 'casino'] # casino is unknown
})
# Test with 'warn'
encoder_warn = OneHotEncoder(
handle_unknown='warn',
sparse_output=False,
min_frequency=2,
drop='first'
)
encoder_warn.fit(train_data)
result_warn = encoder_warn.transform(test_data)
# Test with 'infrequent_if_exist'
encoder_infreq = OneHotEncoder(
handle_unknown='infrequent_if_exist',
sparse_output=False,
min_frequency=2,
drop='first'
)
encoder_infreq.fit(train_data)
result_infreq = encoder_infreq.transform(test_data)
print("Unknown category 'casino' with 'warn':", result_warn[2])
print("Unknown category 'casino' with 'infrequent_if_exist':", result_infreq[2])
```
### Expected Results
```
Unknown category 'casino' with 'warn': [0. 1.]
Unknown category 'casino' with 'infrequent_if_exist': [0. 1.]
Both should map the unknown category to the infrequent category.
```
### Actual Results
```
Unknown category 'casino' with 'warn': [0. 0.]
Unknown category 'casino' with 'infrequent_if_exist': [0. 1.]
'warn' maps to all zeros (like 'ignore') instead of mapping to infrequent.
```
### Versions
```shell
System:
python: 3.13.3 (tags/v3.13.3:6280bb5, Apr 8 2025, 14:47:33) [MSC v.1943 64 bit (AMD64)]
executable: c:\Users\abguv\OneDrive\Desktop\Formation Data Engineer\Projets\Projet 6\analyse\.venv\Scripts\python.exe
machine: Windows-11-10.0.26200-SP0
Python dependencies:
sklearn: 1.7.2
pip: 25.0.1
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: 8
prefix: libscipy_openblas
filepath: C:\Users\abguv\OneDrive\Desktop\Formation Data Engineer\Projets\Projet 6\analyse\.venv\Lib\site-packages\numpy.libs\libscipy_openblas64_-860d95b1c38e637ce4509f5fa24fbf2a.dll
version: 0.3.30
threading_layer: pthreads
architecture: SkylakeX
user_api: openmp
internal_api: openmp
num_threads: 8
prefix: vcomp
filepath: C:\Users\abguv\OneDrive\Desktop\Formation Data Engineer\Projets\Projet 6\analyse\.venv\Lib\site-packages\sklearn\.libs\vcomp140.dll
version: None
user_api: blas
internal_api: openblas
num_threads: 8
prefix: libscipy_openblas
filepath: C:\Users\abguv\OneDrive\Desktop\Formation Data Engineer\Projets\Projet 6\analyse\.venv\Lib\site-packages\scipy.libs\libscipy_openblas-48c358d105077551cc9cc3ba79387ed5.dll
version: 0.3.29.dev
threading_layer: pthreads
architecture: SkylakeX
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None
#### What does this implement/fix? Explain your changes.
This PR removes a few instances of extra whitespace (two spaces instead of one) after periods in the documentation for `sklearn.covariance`. Specifically, it fixes these in the `doc/modules/covariance.rst` file.
#### Any other comments?
This is my first contribution. 😊 | {
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] | open | false | [] | null | 2 | 2025-10-27T18:23:30 | 2025-10-29T12:14:48 | null | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Fixes: https://github.com/scikit-learn/scikit-learn/issues/32585
#### What does this implement/fix? Explain your changes.
Fix the logic for the sub-sampling (only affect the case `ignore_implicit_zeros=True`)
Adds a non-regression test.
#### Any other comments?
While reviewing the PR, you might notice "behavior discontinuity" that this PR fixes also exists for the case `ignore_implicit_zeros=False`. But for this case, it doesn't incur unexpected/incorrect results, just unexpectedly long/short runtime. See issue [TODO] about that.
I haven't written a change-log as nobody reported the bug except me, and I found it just by reading the code... Let me know if this is needed. | null | {
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Towards #26024
#### What does this implement/fix? Explain your changes.
Add array API support to `det_curve`
#### Any other comments? | {
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} | ### Describe the bug
`QuantileTransformer(ignore_implicit_zeros=True)` sub-sampling behavior is incorrect leading to very incorrect results for very sparse matrices and/or small sub-sample size.
More over, this behavior is "discontinous": see the example.
In those lines:
https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/preprocessing/_data.py#L2830-L2839
`column_subsample` can easily be 0 if `n_samples` is big, and I don't see why a big `n_samples` should change a behavior that triggers only if `len(column_nnz_data) > self.subsample`...
### Steps/Code to Reproduce
```Python
import numpy as np
from scipy.sparse import csc_matrix
from sklearn.preprocessing import QuantileTransformer
subsample = 10_000
qt = QuantileTransformer(
ignore_implicit_zeros=True,
subsample=subsample,
)
# create a very sparse X matrix with two very similar columns:
n, d = 10**9, 2
# one colunm has size `subsample - 1`, the other one has size `subsample + 1`
col = np.repeat([0, 1], [subsample - 1, subsample + 1])
# choose some row indices (doesn't matter in this example):
row = np.arange(subsample * 2)
# random normal data:
data = np.random.randn(subsample * 2)
X = csc_matrix((
data,
(row, col)
), shape=(n, d))
Y = qt.fit_transform(X)
# given that X[:, 0] and X[:, 1] are very similar,
# you would expect they are treated similarly
# Y[:, 0] is as exepcted (looking like a uniform RV):
print(np.quantile(Y[:, 0].data, np.linspace(0, 1, num=11)))
# but Y[:, 1] is all 0 or 1
assert np.all((Y[:, 1].data == 0) | (Y[:, 1].data == 1))
# because all fitted quantiles are zeros, because the section of
# the code pointed in this issue created an array of size 0
assert (qt.quantiles_[:, 1] == 0).all()
```
### Expected Results
Both `Y[:, 0].data` and `Y[:, 1].data` should look like uniform RVs (after all, they're almost identical).
### Actual Results
See comments in my snippet.
### Versions
```shell
System:
python: 3.12.11 (main, Aug 18 2025, 19:19:11) [Clang 20.1.4 ]
executable: /home/arthur/dev-perso/scikit-learn/sklearn-env/bin/python
machine: Linux-6.14.0-33-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.8.dev0
pip: None
setuptools: 80.9.0
numpy: 2.3.4
scipy: 1.16.2
Cython: 3.1.5
pandas: 2.3.3
matplotlib: 3.10.7
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libscipy_openblas
filepath: /home/arthur/dev-perso/scikit-learn/sklearn-env/lib/python3.12/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: 16
prefix: libscipy_openblas
filepath: /home/arthur/dev-perso/scikit-learn/sklearn-env/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: 16
prefix: libgomp
filepath: /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0
version: None
``` | null | {
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The `AGENTS.md` file is used by all(?)/many AI tools. Often it contains instructions for AI tools about how to build the software or run tests. In this PR all that is added is some basic instructions about PR messages, including an instruction to add a note that says "AI was used and the human hasn't yet reviewed this".
What do people think? Should we give it a test drive? | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32583 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32583/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32583/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32583/events | https://github.com/scikit-learn/scikit-learn/pull/32583 | 3,556,290,553 | PR_kwDOAAzd1s6v3c5V | 32,583 | DOC Clarify decision trees complexity | {
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Follow-up from #32416.
#### What does this implement/fix? Explain your changes.
The paragraph about the decision tree complexity was written with the greedy splitter strategy (`splitter='best'`) in mind. This PR documents how `splitter='random'` reduces the complexity by removing the need for sorting the values. It does it by adding a table at the beginning to make the information easier to skim, then jumping into the arguments.
#### Any other comments?
I kept the complexity at a worst-case level, but maybe it's worth mentioning if/how the scikit-learn implementation reduces it, e.g. low-level parallelization.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32582 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32582/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32582/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32582/events | https://github.com/scikit-learn/scikit-learn/pull/32582 | 3,555,338,573 | PR_kwDOAAzd1s6v0NUv | 32,582 | FEA Add array API support to `LabelBinarizer(sparse_output=False)` for numeric labels | {
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Towards #26024 and https://github.com/scikit-learn/scikit-learn/pull/32422#discussion_r2413404620.
#### What does this implement/fix? Explain your changes.
This PR adds Array API support to [<code>LabelBinarizer</code>](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html) and [<code>label_binarize</code>](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.label_binarize.html) when `sparse_output=False` for numeric labels, and therefore does not conflict with https://github.com/scikit-learn/scikit-learn/pull/30439#issuecomment-2531072292. Specifically,
1. Both `LabelBinarizer` and `label_binarize` will raise a `ValueError` when the input `y` has a non-NumPy namespace and `sparse_output=True`.
2. If `LabelBinarizer` is fitted on a sparse matrix (i.e., `sparse_input_=True`), calling `inverse_transform` on a non-NumPy array will raise a `ValueError`.
<strike>3. If the input `classes` contains string labels, `label_binarize` will automatically fall back to the NumPy namespace.</strike>
#### Any other comments?
<strike>Adjusted the `atol` value in the `test_graphical_lassos` function due to a CI failure with `random_seed=95, discovered during local testing.</strike>
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### Note
If the CI tasks fail, create a new branch based on this PR and add the required fixes to that branch. | {
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### Note
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Fixes #15873
#### What does this implement/fix? Explain your changes.
This PR adds cardinality selection support to `make_column_selector`, allowing users to select columns based on the number of unique values they contain. Two new parameters are added:
- `cardinality`: Either 'high' or 'low' to specify selection criteria
- 'high': Select columns with high cardinality (more than `cardinality_threshold` unique values)
- 'low': Select columns with low cardinality (at most `cardinality_threshold` unique values)
- `cardinality_threshold`: Integer value (default 7) defining the boundary between high/low cardinality
This feature enables users to handle categorical columns differently based on their cardinality - for example, one-hot encoding low cardinality categorical columns and target-encoding high cardinality categorical columns.
#### Any other comments?
The implementation maintains full backward compatibility, with all existing functionality unchanged. The new cardinality feature can be combined with existing parameters like dtype selection and pattern matching. Comprehensive tests have been added to verify the new functionality and validation.
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Initial numpy issue: https://github.com/numpy/numpy/issues/14685
That propagated to sklearn: https://github.com/scikit-learn/scikit-learn/issues/15733
#### What does this implement/fix? Explain your changes.
Remove the code that was ensuring an ascending array even if numpy wasn't returning an ascending output.
#### Any other comments?
It was fixed in numpy by this commit: https://github.com/numpy/numpy/commit/4d5b2558c275a4a46b6ab0c486536d8e779651ce
And by running this in numpy repo: `git tag --contains 4d5b2558c275a4a46b6ab0c486536d8e779651ce` you'll see it was part of the 1.20 release. | {
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"node_id": "LA_kwDOAAzd1s8AA... | closed | false | [] | null | 9 | 2025-10-26T12:26:09 | 2025-11-25T14:51:11 | 2025-11-25T14:51:11 | null | CONTRIBUTOR | null | null | null | null | #### What does this implement/fix? Explain your changes.
The double-centered squared dissimilarity matrix is just $C\Delta C$, and the gram matrix $B$ is $-\frac{1}{2}$ times that result. Classical MDS performs eigendecomposition on this gram matrix.
However, the documentation states that eigendecomposition is performed on the double-centered matrix of squared dissimilarities, which is incorrect.
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The PR originates from this comment from @ogrisel : https://github.com/scikit-learn/scikit-learn/pull/32100#pullrequestreview-3360550173
#### What does this implement/fix? Explain your changes.
I modified/parameterized a toy example test with multi-output target to run with every criterion.
I could do the same in `test_class_weights` (classif. only) to test gini and logloss, but I don't think it's really useful/needed. | {
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It happens very often that new contributors open PRs on un-triaged issues and I figured we didn't have any information on that in the docs. | {
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#### Reference Issues/PRs
Fixes #32561
#### What does this implement/fix? Explain your changes.
This PR adds comprehensive documentation for sample weights in the scikit-learn user guide, addressing the discussion in #30564.
## Changes
- **Added new "Sample Weights" section** in `doc/modules/learning.rst` with:
- Detailed explanation of sample weights concept
- Common use cases with code examples:
- Exposure and variance weighting (Poisson regression for insurance claims)
- Frequency weights (aggregated/duplicated data)
- Transfer learning and domain adaptation
- Weight types table (Frequency, Analytical, Importance weights)
- Best practices for using sample weights
- **Updated glossary entry** in `doc/glossary.rst` for `sample_weight` with:
- Clearer introductory explanation
- Bullet list of common applications
- Cross-reference to the new user guide section
## Key Features Documented
- **Exposure weighting**: Accounting for different observation periods/sizes
- **Frequency weights**: Representing aggregated or duplicated observations
- **Importance weighting**: Domain adaptation and transfer learning
- **Variance weighting**: Inverse variance weighting for heteroscedastic data
- **Statistical interpretations**: Alignment with concepts from other statistical packages
## Examples Provided
```python
# Exposure weighting example
from sklearn.linear_model import PoissonRegressor
X = [[1], [2], [3], [4]]
y = [2, 5, 3, 8]
exposure = [1, 2, 1, 3]
sample_weight = exposure
model = PoissonRegressor()
model.fit(X, y, sample_weight=sample_weight)
# Frequency weights example
from sklearn.ensemble import RandomForestClassifier
X = [[1, 2], [3, 4], [5, 6]]
y = [0, 1, 0]
frequency_weights = [5, 3, 2]
clf = RandomForestClassifier()
clf.fit(X, y, sample_weight=frequency_weights)
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- Documenting the commit message marker in the developer guide commit marker table. It instructs CI to run only the listed tests for all seeds by setting .
- Adding a short Python snippet in the parallelism docs showing how to use the fixture in a test.
Rationale:
- The marker is already supported by CI (see and ).
- The fixture is documented, but a minimal code example improves discoverability.
No behavioral changes to code, documentation only.
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"node_id": "MDU6TGFiZWwyMzk5... | closed | false | [] | null | 2 | 2025-10-25T15:08:48 | 2025-10-29T05:19:38 | 2025-10-25T15:12:43 | null | NONE | null | null | null | null | This PR adds a changelog entry documenting the enhanced free-threaded CPython support in scikit-learn 1.8.
**Changes:**
- Document that all native extensions are now marked as free-threading compatible
- Mention the dedicated continuous integration for thread safety testing
- Recommend Python 3.14 for optimal free-threading experience
- Build on the initial free-threading support introduced in scikit-learn 1.6
**Context:**
This addresses issue #32519 and incorporates the detailed technical information provided by @ogrisel in the comments, including:
- Native extensions marked with free-threading compatibility flags
- CI stress-testing with concurrent threads using pytest-run-parallel
- Prevents CPython from re-enabling GIL on import
The changelog entry is placed in as it's a general announcement about Python version support rather than module-specific functionality.
Closes #32519 | {
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} | ### Describe the workflow you want to enable
The Geometric Mixture Classifier (GMC) enables interpretable, efficient classification on multimodal datasets where traditional linear models struggle. Many real-world classification problems involve classes that occupy multiple disjoint regions in feature space (e.g., medical diagnosis with multiple disease subtypes, customer segmentation with distinct behavioral clusters).
**Current scikit-learn offers:**
Linear models (LogisticRegression, LinearSVM): Fast and interpretable but fail on multimodal data
Nonlinear models (RBF SVM, RandomForest): Handle complex boundaries but sacrifice interpretability and computational efficiency
Mixture models (GaussianMixture): Generative approach with different assumptions
GMC fills this gap by providing a discriminative, interpretable classifier that can handle multimodal classes while maintaining transparency and competitive performance.
**Use cases enabled:**
Medical diagnosis where doctors need to understand which "subtype" features drove a prediction
Customer segmentation requiring both accuracy and explainable cluster assignments
Educational applications where students can visualize decision boundaries and plane responsibilities
Resource-constrained environments needing fast inference with interpretable results
### Describe your proposed solution
I propose adding the Geometric Mixture Classifier (GMC) - a discriminative model that represents each class as a mixture of hyperplanes with intrinsic interpretability.
Key Features:
Per-class mixture of hyperplanes: Each class can have multiple linear decision boundaries
Soft-OR pooling: Within-class plane scores aggregated via temperature-controlled log-sum-exp
Geometry-aware initialization: Uses k-means clustering for stable plane initialization
Automatic plane budgeting: Determines optimal planes per class via silhouette scoring
Random Fourier Features support: Optional nonlinear extension while maintaining interpretability
Usage-aware regularization: Prevents dead experts and promotes specialization
Mathematical Formulation:
planes:
# Per-plane score
z_cm(x) = w_cm·φ(x) + b_cm
# Within-class soft-OR pooling
s_c(x) = (1/α) * log(∑ exp(α * z_cm(x)))
# Across-class softmax
p_c(x) = exp(s_c(x)) / ∑_k exp(s_k(x))
Implementation Advantages:
Interpretable: Decisions explained by which plane(s) were responsible
Efficient: O(d′M) inference time (comparable to linear models)
Calibrated: Good probabilistic outputs with optional temperature scaling
Transparent: Visualizable decision boundaries and plane responsibilities
API Design:
python
class GeometricMixtureClassifier(BaseEstimator, ClassifierMixin):
def __init__(self, n_planes_per_class='auto', alpha=6.0, use_rff='auto',
rff_dim=1000, gamma=1.0, lambda_reg=1e-4, beta=0.5,
label_smoothing=0.02, max_planes=4, random_state=None):
...
Performance Characteristics:
Based on the attached research paper, GMC:
Outperforms linear baselines on multimodal datasets
Competitive with RBF-SVM and Random Forests
Faster inference than kernel methods (scales with planes, not support vectors)
Lower ECE than many alternatives after temperature scaling
Proof of Concept:
I have already developed and tested a complete implementation:
GitHub Repository: [Geometric Mixture Classifier](https://github.com/Abitsfhuusrtyt/-Geometric-Mixture-Classifier-GMC---A-Discriminative-Per-Class-Mixture-of-Hyperplanes)
Research Paper: Comprehensive evaluation on synthetic and real datasets
Working Code: scikit-learn compatible implementation with tests and examples
Inclusion Benefits:
Novelty: First discriminative mixture-of-hyperplanes in scikit-learn
Interpretability: Addresses growing need for explainable AI
Performance: Favorable accuracy/efficiency/interpretability trade-off
Educational Value: Clear geometric interpretation helps ML pedagogy
The implementation follows scikit-learn API conventions, includes comprehensive tests, and maintains the project's quality standards. I'm prepared to contribute the code, documentation, and examples to facilitate review and integration.
### Describe alternatives you've considered, if relevant
Existing scikit-learn classifiers and their limitations for multimodal data:
Linear Models (LogisticRegression, LinearSVM)
✅ Fast, interpretable, convex training
❌ Single global hyperplane cannot capture multimodal class structure
❌ Fail on datasets like "moons", "circles", or any class with disjoint regions
Kernel SVM (SVC with RBF kernel)
✅ Handles nonlinear, multimodal boundaries effectively
❌ Poor interpretability (implicit kernel expansion)
❌ Inference cost scales with number of support vectors
❌ Memory intensive for large datasets
Tree-based Methods (RandomForest, GradientBoosting)
✅ Excellent accuracy on complex boundaries
❌ Limited geometric interpretability
❌ Ensemble of trees provides feature importance but not clear decision geometry
❌ Can be computationally heavy for inference
Gaussian Mixture Models + Bayes
✅ Captures multimodality via density estimation
❌ Generative approach optimizes likelihood, not discriminative performance
❌ Strong distributional assumptions (Gaussianity)
❌ Less calibrated probabilistic outputs
K-Nearest Neighbors
✅ Simple, handles complex boundaries
❌ No explicit model, poor interpretability
❌ Degrades in high dimensions
❌ Irregular decision boundaries
Multi-layer Perceptron
✅ Universal approximation capability
❌ Black-box nature limits interpretability
❌ Requires careful tuning and significant compute
❌ Limited geometric transparency
Why GMC is superior for interpretable multimodal classification:
Transparent geometry: Each prediction can be explained by which local hyperplane was responsible
Predictable cost: Inference scales linearly with number of planes, not data size
Discriminative training: Optimizes classification loss directly
Automatic structure discovery: No need to pre-specify cluster structure
Calibrated probabilities: Good uncertainty quantification out-of-the-box
### Additional context
Research Validation:
Comprehensive evaluation in attached paper across 8 datasets (4 synthetic, 4 real-world)
Consistently outperforms linear baselines by 10-40% on multimodal datasets
Competitive with RBF-SVM and Random Forests while being more interpretable
Single-digit microsecond inference time per example (Table 5 in paper)
Low expected calibration error (0.02-0.04 after temperature scaling)
Implementation Status:
✅ Complete scikit-learn compatible implementation available
✅ Comprehensive test suite passing
✅ Documentation and usage examples
✅ Performance benchmarks vs. existing classifiers
✅ Research paper with theoretical foundations
Code Repository:
https://github.com/Abitsfhuusrtyt/-Geometric-Mixture-Classifier-GMC---A-Discriminative-Per-Class-Mixture-of-Hyperplanes
Key Technical Innovations:
Gate-free mixture of experts: Responsibilities emerge naturally from soft-OR pooling
Geometry-aware initialization: k-means seeding prevents poor local optima
Usage-aware regularization: Automatically prunes unused experts
Automatic model selection: Silhouette scoring determines optimal planes per class
Unified linear/nonlinear framework: RFF extension maintains interpretability
Community Need:
Growing demand for interpretable ML in healthcare, finance, and regulated industries
Educational value for teaching decision boundaries and mixture models
Gap in scikit-learn's model portfolio for transparent multimodal classification
Willingness to Maintain:
I am committed to:
Addressing all code review feedback
Maintaining the implementation long-term
Adding additional features and optimizations
Providing user support and documentation updates
The Geometric Mixture Classifier represents a novel contribution to the interpretable ML landscape and would be a valuable addition to scikit-learn's classifier ecosystem.
[2509.16769v1.pdf](https://github.com/user-attachments/files/23139574/2509.16769v1.pdf) | {
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## Reference Issues/PRs
Fixes #32560
## What does this implement/fix? Explain your changes.
### Problem
As reported in #32560, QuadraticDiscriminantAnalysis currently raises a `LinAlgWarning` when encountering rank-deficient covariance matrices, but still creates a model that produces `RuntimeWarning` during prediction and wrong results.
### Solution
- Change `LinAlgWarning` to `LinAlgError` in QDA's `fit` method
- This prevents users from unknowingly using pathological models
- Provide clear error message with suggestions for fixes
### Changes Made
1. **`sklearn/discriminant_analysis.py`**: Changed warning to error with better error message
2. **`sklearn/tests/test_discriminant_analysis.py`**: Updated test to expect `LinAlgError` instead of `LinAlgWarning`
### Benefits
- **Better User Experience**: Clear error instead of subtle warning
- **Prevents Silent Failures**: No more models that produce wrong predictions
- **Clear Guidance**: Error message tells users how to fix the issue
### Testing
- All existing tests pass
- The specific test `test_qda_regularization` now validates the new error behavior
## Any other comments?
This change prevents users from accidentally creating broken QDA models with rank-deficient data, ensuring they either fix their data or use appropriate regularization.
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Similar to #32305.
#### What does this implement/fix? Explain your changes.
Revise authorship-licensing formatting in the two Examples:
1. https://scikit-learn.org/dev/auto_examples/calibration/plot_compare_calibration.html
2. https://scikit-learn.org/dev/auto_examples/classification/plot_classification_probability.html
#### Any other comments?
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32567 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32567/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32567/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32567/events | https://github.com/scikit-learn/scikit-learn/pull/32567 | 3,550,625,619 | PR_kwDOAAzd1s6vlBmX | 32,567 | FIX, DOC: Replace deprecated matplotlib method in Faces tutorial | {
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https://matplotlib.org/stable/api/prev_api_changes/api_changes_3.6.0.html#pending-deprecation-of-layout-methods
The fix is to use `fig.get_layout_engine().set()` instead.
I think that the docs are built against Matplotlib v3.10, so I'm not sure why the build docs CircleCI job didn't catch this and raise an error.
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First draft towards extending the Automated Contributions Policy for PRs to require a disclosure of AI usage, as discussed towards the end of #31679
#### What does this implement/fix? Explain your changes.
Adds a paragraph on required disclosure of AI usage to the Automated Contributions Policy and extends the PR template with a selection accordingly.
#### AI usage disclosure
* I hereby confirm that no AI assistance was used in the creation of this PR.
(Though it could be useful to play around with different formulations/AI suggestions in this case...)
#### Any other comments?
Any comments/suggestions on the wording are welcome! | {
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Related to [this comment](https://github.com/scikit-learn/scikit-learn/issues/32394#issuecomment-3375647854) in #32394
#### What does this implement/fix? Explain your changes.
Adds a more specific error message (and respective tests) for cases when an estimator class is passed instead of an instance. This is raised in `get_tags()` and will be thrown for example in `is_regressor()` and all related checks based on `get_tags()` (taking up the attempt from #32428, as suggested by @StefanieSenger)
#### Any other comments?
I got a `line too long` error when checking the error message, so I moved it to a variable, but I'm not sure if this is the most concise solution here. Feedback welcome! | {
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- build_tools/check-meson-openmp-dependencies.py: Fixed "the the" to "the"
- sklearn/tests/test_base.py: Fixed "the the" to "the" in a comment
These are minor documentation fixes to improve clarity and readability.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32563 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32563/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32563/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32563/events | https://github.com/scikit-learn/scikit-learn/pull/32563 | 3,547,804,817 | PR_kwDOAAzd1s6vbjUr | 32,563 | FIX Ensure make_classification produces consistent distribution across n_samples | {
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#### What does this implement/fix? Explain your changes.
This PR fixes a bug in `make_classification` where using different `n_samples` values with the same `random_state` would produce datasets from different underlying distributions, rather than different samples from the same distribution.
Changes made:
- Modified `sklearn/datasets/_samples_generator.py` to ensure distribution parameters are computed independently of sample size
- Improved random state management to maintain consistent distribution characteristics
- Added/updated tests to verify consistent distribution behavior across different sample sizes
Testing:
- `pytest sklearn/datasets/tests/test_samples_generator.py::test_make_classification_consistent_distribution` passes
- All existing tests continue to pass
- Manual verification confirms the fix works as expected
#### Any other comments?
This ensures that researchers can reliably test algorithms on different sample sizes from the same underlying data distribution, which is important for valid experimental comparisons.
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Looks like our .github.io is above the size quota and we probably need to squash the commits into one (hoping this is enough) as @adrinjalali did a while ago?
```
+ git push
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! [remote rejected] main -> main (pre-receive hook declined)
error: failed to push some refs to 'github.com:scikit-learn/scikit-learn.github.io.git'
```
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32561 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32561/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32561/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32561/events | https://github.com/scikit-learn/scikit-learn/issues/32561 | 3,547,748,553 | I_kwDOAAzd1s7TdljJ | 32,561 | DOC: Add user guide entry for sample weights | {
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> Note to potential contributors: this is not a good issue to work on, please don't comment "can I work on it" or you may be banned from the repo.
> There is NO action that needs to be taken for this issue atm - there are probably some questions around weights that need to be addressed first.
### Describe the issue linked to the documentation
As discussed in #30564 it is probably worthwhile to add a 'sample weight' section in the user guide.
Here are some notable additions, that we thought were too long to include in the glossary entry:
* Examples of uses of sample weights
* in supervised learning one of the main reasons for weights is to account for the different size/time/exposure/... of each observation. We are assuming weights are inversely proportional to the variance of the target, e.g., we use exposure as weights in [Poisson regression and non-normal loss](https://scikit-learn.org/dev/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html) and [Tweedie regression on insurance claims](https://scikit-learn.org/dev/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.html), with longer exposure duration being weighted higher, as these would be considered more reliable, or lower variance. (details: https://github.com/scikit-learn/scikit-learn/pull/30564#issuecomment-2583340580, https://github.com/scikit-learn/scikit-learn/pull/30564#discussion_r2338421172)
* a nice collection of papers with examples of weight use here: https://github.com/scikit-learn/scikit-learn/pull/30564#issuecomment-2567939034
* Should we try to use similar terms as other statistical packages (e.g., stata, julia) - "analytical weights", "frequency weights"... (stata reference http://www.stata.com/manuals13/u20.pdf#u20.23Weightedestimation , see section 20.23)
* Re-weighting for transfer learning (ref: https://github.com/scikit-learn/scikit-learn/pull/30564#pullrequestreview-2552200734)
* Third-party libraries can also use `sample_weight`-compatible estimators as building blocks to reduce a specific statistical task into a weighted regression or classification task
cc @ogrisel @lorentzenchr
### Suggest a potential alternative/fix
_No response_ | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32560 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32560/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32560/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32560/events | https://github.com/scikit-learn/scikit-learn/issues/32560 | 3,546,832,428 | I_kwDOAAzd1s7TaF4s | 32,560 | Warning raised in tests for `QuadraticDiscriminantAnalysis` | {
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} | ### Describe the bug
I wonder whether the behavior of `QuadraticDiscriminatAnalysis` producing the Warning in the test below is as intended, or a problem to be fixed.
The test `test_qda_regularization` checks some of QDA's handling of rank deficient matrices. It starts by testing the class on data `X2` where the second variable is a constant. The first part of the test is as follows:
```python
def test_qda_regularization():
# The default is reg_param=0. and will cause issues when there is a
# constant variable.
# Fitting on data with constant variable without regularization
# triggers a LinAlgError.
msg = r"The covariance matrix of class .+ is not full rank"
clf = QuadraticDiscriminantAnalysis()
with pytest.warns(linalg.LinAlgWarning, match=msg):
y_pred = clf.fit(X2, y6)
y_pred = clf.predict(X2)
assert np.any(y_pred != y6)
```
The first detail here is that the comment mentioned `LinAlgError`, but the test and the class use `LinAlgWarning`. When fitting the class, a `LinAlgWarning` is raised as expected. But the class is still instantiated with rank-deficient parameters.
In the second part we obtain `y_pred` with the a rank-defficient matrix, leading to wrong outputs, and raising the Warning:
```
> y_pred = clf.predict(X2)
^^^^^^^^^^^^^^^
sklearn/tests/test_discriminant_analysis.py:607:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
sklearn/discriminant_analysis.py:1095: in predict
return super().predict(X)
^^^^^^^^^^^^^^^^^^
sklearn/discriminant_analysis.py:210: in predict
scores = self._decision_function(X)
^^^^^^^^^^^^^^^^^^^^^^^^^^
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = QuadraticDiscriminantAnalysis()
X = array([[-3, 0],
[-2, 0],
[-1, 0],
[-1, 0],
[ 0, 0],
[ 1, 0],
[ 1, 0],
[ 2, 0],
[ 3, 0]])
def _decision_function(self, X):
# return log posterior, see eq (4.12) p. 110 of the ESL.
check_is_fitted(self)
X = validate_data(self, X, reset=False)
norm2 = []
for i in range(len(self.classes_)):
R = self.rotations_[i]
S = self.scalings_[i]
Xm = X - self.means_[i]
> X2 = np.dot(Xm, R * (S ** (-0.5)))
^^^^^^^^^^^
E RuntimeWarning: divide by zero encountered in power
```
I am wondering whether it would be desirable to change the `LinAlgWarning` for a `LinAlgError`, to prevent users from unknowingly using pathological models.
If not, I wonder whether it would be desirable to modify the test to 1) remove the first assignment of `y_pred`, 2) make it not raise a warning, and 3) make the comment say `LinAlgWarning` instead of `LinAlgError`. Something like the edit below:
```python
def test_qda_regularization():
# The default is reg_param=0. and will cause issues when there is a
# constant variable.
# Fitting on data with constant variable without regularization
# triggers a LinAlgWarning.
msg = r"The covariance matrix of class .+ is not full rank"
clf = QuadraticDiscriminantAnalysis()
with pytest.warns(linalg.LinAlgWarning, match=msg):
clf.fit(X2, y6)
with pytest.warns():
y_pred = clf.predict(X2)
```
To get this output, run
```
PYTHONWARNINGS=error pytest sklearn/tests/test_discriminant_analysis.py -k test_qda_regularization
```
### Steps/Code to Reproduce
```
PYTHONWARNINGS=error pytest sklearn/tests/test_discriminant_analysis.py -k test_qda_regularization
```
### Expected Results
If we change the `LinAlgWarning` with a `LinAlgError` inside of `QuadraticDiscriminantAnalysis`, users would not be able to use models with rank-deficient class covariances to get predictions.
### Actual Results
```
> y_pred = clf.predict(X2)
^^^^^^^^^^^^^^^
sklearn/tests/test_discriminant_analysis.py:607:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
sklearn/discriminant_analysis.py:1095: in predict
return super().predict(X)
^^^^^^^^^^^^^^^^^^
sklearn/discriminant_analysis.py:210: in predict
scores = self._decision_function(X)
^^^^^^^^^^^^^^^^^^^^^^^^^^
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = QuadraticDiscriminantAnalysis()
X = array([[-3, 0],
[-2, 0],
[-1, 0],
[-1, 0],
[ 0, 0],
[ 1, 0],
[ 1, 0],
[ 2, 0],
[ 3, 0]])
def _decision_function(self, X):
# return log posterior, see eq (4.12) p. 110 of the ESL.
check_is_fitted(self)
X = validate_data(self, X, reset=False)
norm2 = []
for i in range(len(self.classes_)):
R = self.rotations_[i]
S = self.scalings_[i]
Xm = X - self.means_[i]
> X2 = np.dot(Xm, R * (S ** (-0.5)))
^^^^^^^^^^^
E RuntimeWarning: divide by zero encountered in power
```
### Versions
```shell
System:
python: 3.11.13 | packaged by conda-forge | (main, Jun 4 2025, 14:48:23) [GCC 13.3.0]
executable: /home/dherresp-adm/Packages/miniconda3/envs/sklearn-env/bin/python3.11
machine: Linux-5.15.0-139-generic-x86_64-with-glibc2.31
Python dependencies:
sklearn: 1.8.dev0
pip: 25.2
setuptools: 80.9.0
numpy: 2.3.2
scipy: 1.16.1
Cython: 3.1.3
pandas: 2.3.2
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: /home/dherresp-adm/Packages/miniconda3/envs/sklearn-env/lib/libopenblasp-r0.3.30.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 16
prefix: libgomp
filepath: /home/dherresp-adm/Packages/miniconda3/envs/sklearn-env/lib/libgomp.so.1.0.0
version: None
``` | {
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"node_id": "LA_kwDOAAzd1s8AA... | closed | false | [] | null | 2 | 2025-10-23T17:53:05 | 2025-10-29T05:20:08 | 2025-10-26T07:48:55 | null | NONE | null | null | null | null | **Description**
I would like to propose an improvement to the documentation by adding a dedicated section that covers the time and space complexity ) of key scikit-learn algorithms, supported by practical performance benchmarks.
This Pull Request (PR) serves as the **first step**, providing the initial benchmark notebook (`m1_algorithm_tests.ipynb`) for community review and discussion.
**Motivation**
Many users (especially students and newcomers) are not fully aware of the theoretical complexity (Big-O) of widely used algorithms such as `LogisticRegression`, `KMeans`, or `RandomForest`.
Providing this information in the documentation (along with small benchmark examples) would make it easier to choose an appropriate algorithm based on dataset size and available computational resources, not just accuracy. This addition would also significantly improve the educational value of scikit-learn.
**Proposed Scope (Overview of the Complete Contribution)**
1. **[In this PR]** Add an example notebook (`m1_algorithm_tests.ipynb` in `examples/`) showing practical benchmarks of fitting and prediction times for a small set of commonly used algorithms across different dataset sizes.
2. **[Next Step / Follow-up]** Extend the `user_guide` with a new subsection on algorithmic complexity.
**Impact**
* Improves the documentation and the educational value of the library.
* Helps students and practitioners make more informed choices.
* Encourages a better understanding of the internal computational trade-offs of ML algorithms.
---
**Note for Reviewers:**
This PR currently only contains the benchmark notebook (`m1_algorithm_tests.ipynb`). I am submitting it now to allow for discussion on the methodology and results of the benchmark before formally integrating and writing the dedicated documentation section. I welcome any feedback or suggestions on the notebook's content.
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] | open | false | [] | null | 1 | 2025-10-23T16:12:41 | 2025-10-24T07:32:08 | null | null | CONTRIBUTOR | null | null | null | null | Fix `main` after https://github.com/scikit-learn/scikit-learn/pull/32505. | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32557 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32557/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32557/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32557/events | https://github.com/scikit-learn/scikit-learn/pull/32557 | 3,545,140,838 | PR_kwDOAAzd1s6vSkQF | 32,557 | MNT Add example dependencies to version bumping script | {
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If we target a release on December 1st, this is what the script says:
```
❯ python maint_tools/bump-dependencies-versions.py 2025-12-01
For future release at date 2025-12-01
- python: 3.10 -> 3.11
- numpy: 1.24.1 -> 1.24.0
- matplotlib: 3.6.1 -> 3.6.0
- polars: 0.20.30 -> 0.19.0
- pyarrow: 12.0.0 -> 11.0.0
- scikit-image: 0.19.0 -> 0.22.0
- seaborn: 0.9.1 -> 0.13.0
- Pillow: 8.4.0 -> 10.1.0
- pooch: 1.6.0 -> 1.8.0
- plotly: 5.14.0 -> 5.18.0
```
cc @DeaMariaLeon in case there is agreement on this, you could use these versions for bumping the doc_min_dependencies versions.
Note that for polars and pyarrow, we are not following our rule, but I guess that's life. | {
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} | ### Describe the workflow you want to enable
Hello,
I create this ticket because I realized unexpected behaviour for Weighted MAPE when you weight for the real values and those values are 0.
By the formula:
<img width="217" height="73" alt="Image" src="https://github.com/user-attachments/assets/2e874378-3674-4861-9e4f-91866c7d23bd" />
In the case the weights are the same as the a_i, the values should cancel out (even at 0 because the limit is well defined and can be extended to 0) and coincide with this:
<img width="202" height="62" alt="Image" src="https://github.com/user-attachments/assets/0c6ac99d-d69f-4326-90c3-b97dd709e966" /> (1)
Looking at the current implementation:
https://github.com/scikit-learn/scikit-learn/blob/c60dae2060/sklearn/metrics/_regression.py#L408
what happens with zeros is that they are removed from the computation.
I understand this needs to be like this to allow for generic weights, that are not the real values, but this is creating an unexpected behaviour.
Maybe there should be another class that implements directly (1), to avoid problems with it.
Best,
Arnau
### Describe your proposed solution
I wold suggest create another classe wape that directly calculates:
sum(|y_pred- y|)/sum(|y|)
To avoid this unexpected behaviour.
### Describe alternatives you've considered, if relevant
I don't think changing the current class would be a good option since it is implemented in the only way that can allow for generic weights.
### Additional context
_No response_ | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32555 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32555/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32555/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32555/events | https://github.com/scikit-learn/scikit-learn/pull/32555 | 3,544,669,898 | PR_kwDOAAzd1s6vQ9ex | 32,555 | MNT Bump to Python 3.11 for remaining pymin CI builds | {
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#### What does this implement/fix? Explain your changes.
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Friendly ping @lesteve
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32554 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32554/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32554/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32554/events | https://github.com/scikit-learn/scikit-learn/pull/32554 | 3,542,474,248 | PR_kwDOAAzd1s6vJ1uZ | 32,554 | DOC: Update the references for Isolation forest | {
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#### What does this implement/fix? Explain your changes.
Update the references for Isolation forest
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- Adds the `[all random seeds]` marker to the commit message markers table in `doc/developers/contributing.rst`, matching the concise style of existing markers.
- Adds an example test function demonstrating the use of the `global_random_seed` fixture and instructions for running this test locally with all admissible seeds to `doc/computing/parallelism.rst`.
These additions clarify the purpose and usage of the marker, helping contributors understand how to write and test seed-dependent tests robustly.
Due to build environment limitations on my local setup, I have not built the docs locally, but rely on scikit-learn's CI to validate documentation builds and formatting.
Looking forward to the maintainers review and feedback.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32552 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32552/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32552/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32552/events | https://github.com/scikit-learn/scikit-learn/issues/32552 | 3,540,418,323 | I_kwDOAAzd1s7TBn8T | 32,552 | Array API test failure for probabilistic metrics with scipy==1.15.0 | {
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"url": "ht... | closed | false | [] | null | 17 | 2025-10-22T11:10:40 | 2026-01-26T16:15:46 | 2025-10-22T17:09:29 | null | MEMBER | null | null | {
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} | ### Describe the bug
In #32422 we have introduced array API for probabilistic metrics and added a test (test_probabilistic_metrics_multilabel_array_api).
I had this test failing locally with scipy==1.15.0 for `array-api-strict` arrays on `log_loss` and `2d_log_loss_score`, because [scipy's xlogy](https://docs.scipy.org/doc/scipy-1.15.2/reference/generated/scipy.special.xlogy.html) of that version (used in `_log_loss`) has trouble handling array-api-strict arrays of mixed dtypes (int64 and float64).
According to `sklearn/utils/_array_api.py` the scipy min version for array api would be "1.14.0". Theoretically "1.15.0" should not throw an error. I am not sure what we can do about this, and if we need to, since all array libraries pass this test and it is only `array-api-strict` failing. We could move the internal `min_scipy_version`, but apart from that I cannot see any fix we could do internally.
Talking with @lesteve, he advised me to open an issue, be it just indicating to others who stumble over the same issue that upgrading scipy is the solution. @lesteve Thanks for your help in fixing this.
### Traceback
```
prob_metric = <function log_loss at 0x7f97095f5580>, str_y_true = False, use_sample_weight = False, array_namespace = 'array_api_strict'
device_ = array_api_strict.Device('CPU_DEVICE'), dtype_name = 'float64'
@pytest.mark.parametrize(
"prob_metric", [brier_score_loss, log_loss, d2_brier_score, d2_log_loss_score]
)
@pytest.mark.parametrize("str_y_true", [False, True])
@pytest.mark.parametrize("use_sample_weight", [False, True])
@pytest.mark.parametrize(
"array_namespace, device_, dtype_name", yield_namespace_device_dtype_combinations()
)
def test_probabilistic_metrics_array_api(
prob_metric, str_y_true, use_sample_weight, array_namespace, device_, dtype_name
):
"""Test that :func:`brier_score_loss`, :func:`log_loss`, func:`d2_brier_score`
and :func:`d2_log_loss_score` work correctly with the array API for binary
and mutli-class inputs.
"""
xp = _array_api_for_tests(array_namespace, device_)
sample_weight = np.array([1, 2, 3, 1]) if use_sample_weight else None
# binary case
extra_kwargs = {}
if str_y_true:
y_true_np = np.array(["yes", "no", "yes", "no"])
y_true_xp_or_np = np.asarray(y_true_np)
if "brier" in prob_metric.__name__:
# `brier_score_loss` and `d2_brier_score` require specifying the
# `pos_label`
extra_kwargs["pos_label"] = "yes"
else:
y_true_np = np.array([1, 0, 1, 0])
y_true_xp_or_np = xp.asarray(y_true_np, device=device_)
y_prob_np = np.array([0.5, 0.2, 0.7, 0.6], dtype=dtype_name)
y_prob_xp = xp.asarray(y_prob_np, device=device_)
metric_score_np = prob_metric(
y_true_np, y_prob_np, sample_weight=sample_weight, **extra_kwargs
)
with config_context(array_api_dispatch=True):
> metric_score_xp = prob_metric(
y_true_xp_or_np, y_prob_xp, sample_weight=sample_weight, **extra_kwargs
)
sklearn/metrics/tests/test_classification.py:3712:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
sklearn/utils/_param_validation.py:218: in wrapper
return func(*args, **kwargs)
sklearn/metrics/_classification.py:3363: in log_loss
return _log_loss(
sklearn/metrics/_classification.py:3378: in _log_loss
loss = -xp.sum(xlogy(transformed_labels, y_pred), axis=1)
../../../.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/scipy/special/_support_alternative_backends.py:167: in wrapped
return f(*args, **kwargs)
../../../.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/scipy/special/_support_alternative_backends.py:76: in __xlogy
temp = x * xp.log(y)
../../../.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/array_api_strict/_array_object.py:858: in __mul__
other = self._check_allowed_dtypes(other, "numeric", "__mul__")
../../../.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/array_api_strict/_array_object.py:215: in _check_allowed_dtypes
res_dtype = _result_type(self.dtype, other.dtype)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
type1 = array_api_strict.int64, type2 = array_api_strict.float64
def _result_type(type1: DType, type2: DType) -> DType:
if (type1, type2) in _promotion_table:
return _promotion_table[type1, type2]
> raise TypeError(f"{type1} and {type2} cannot be type promoted together")
E TypeError: array_api_strict.int64 and array_api_strict.float64 cannot be type promoted together
../../../.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/array_api_strict/_dtypes.py:229: TypeError
```
### Versions
```shell
scipy==1.15.0
``` | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32551 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32551/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32551/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32551/events | https://github.com/scikit-learn/scikit-learn/issues/32551 | 3,539,655,678 | I_kwDOAAzd1s7S-tv- | 32,551 | DOC all random seeds commit message | {
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I wanted to reference the [all random seeds] commit message in the developer guide, which is very useful when contributing a test depending on `global_random_seed`, but it isn't documented in https://scikit-learn.org/dev/developers/contributing.html#commit-message-markers ?
### Suggest a potential alternative/fix
Add the [all random seeds] commit message to the table in https://scikit-learn.org/dev/developers/contributing.html#commit-message-markers and an example test using `global_random_seed` (python snippet) in https://scikit-learn.org/dev/computing/parallelism.html#sklearn-tests-global-random-seed | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32550 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32550/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32550/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32550/events | https://github.com/scikit-learn/scikit-learn/pull/32550 | 3,539,120,388 | PR_kwDOAAzd1s6u-rmq | 32,550 | MNT Remove tomli additional dependency from .pre-commit.yaml | {
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We are in the process of moving e.g. https://github.com/scikit-learn/scikit-learn/pull/32522 but I think it is fine to do it now.
FWI I tried to use pre-commit in a python 3.10 environment without the tomli additional dependencies and it seems to work so maybe this wasn't needed anyway :thinking:.
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"node_id": "LA_kwDOAAzd1s8... | closed | false | [] | null | 2 | 2025-10-21T18:57:02 | 2025-11-23T03:41:59 | 2025-11-23T03:41:59 | null | MEMBER | null | null | null | null | This PR fixes some classification metrics to raise on empty input as other metrics do.
See https://github.com/scikit-learn/scikit-learn/pull/31187#discussion_r2077261040.
supersedes #31187
The following classification metrics use the module level helper function `_check_targets` (not `check_array`) which previously didn't raise on empty inputs:
accuracy_score
multilabel_confusion_matrix
matthews_corrcoef
class_likelihood_ratios
classification_report
hamming_loss
precision_recall_fscore_support
jaccard_score
These classification metrics use `check_array`, which `does` raise on empty input:
hinge_loss
brier_score_loss
d2_log_loss_score
d2_brier_score
log_loss
`confusion_matrix` is an exception and uses both `_check_targets` and `check_array`, the latter though with `ensure_min_samples=0`. I am not sure about the original purpose of that, but it seems we should raise on empty inputs for consistency.
This PR also cleans up the rest of the docstrings of that module on the output type of the classification metrics, which since #30575 return floats. Sorry for mixing this in here. It is a remainder from a previous PR. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32548 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32548/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32548/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32548/events | https://github.com/scikit-learn/scikit-learn/pull/32548 | 3,535,864,910 | PR_kwDOAAzd1s6uzrY3 | 32,548 | DOC update the list of scikit-learn members | {
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] | closed | false | [] | null | 6 | 2025-10-21T10:08:12 | 2025-10-21T16:33:26 | 2025-10-21T13:57:23 | null | MEMBER | null | null | null | null | We recently added @DeaMariaLeon in the contributor experience team.
This PR update the authors by running the `make authors` command from the `build_tools` director. Apparently, we forgot to run it recently. @StefanieSenger is moved to the right team now :) | {
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