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#### What does this implement/fix? Explain your changes.
* Adds links to glossary where relevant
* `y_true` docstring only talks about the binary case - specifically detail binary data and multiclass data requirements
* Fix typo multilabel -> multiclass
#### AI usage disclosure
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RANSACRegressor may call estimator.score on inlier subsets that lead to
non-finite scores (e.g. R² with fewer than two samples), resulting in
warnings and undefined score comparisons.
This PR skips iterations with non-finite scores and adds a regression
test to ensure such cases are handled safely without emitting warnings.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33049 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33049/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33049/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33049/events | https://github.com/scikit-learn/scikit-learn/pull/33049 | 3,802,710,642 | PR_kwDOAAzd1s68ndC- | 33,049 | DOC clarify that Lasso.path() defaults to Elastic Net path | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 0 | 2026-01-12T04:19:59 | 2026-01-16T20:43:32 | 2026-01-16T20:43:32 | null | NONE | null | null | null | null | ## Reference Issues/PRs
Fixes #17181
## What does this implement/fix?
This PR clarifies the documentation for `Lasso.path()` to address the
confusion around the default `l1_ratio=0.5` parameter.
### Problem
The `Lasso.path()` method is inherited from `ElasticNet` and defaults to
`l1_ratio=0.5`. This is inconsistent with the `Lasso` estimator itself,
which optimizes with an implicit `l1_ratio=1.0` (pure L1 penalty).
### Changes
- Added a `.. note::` in the `Lasso` class docstring explaining that
`path()` is inherited from `ElasticNet` with `l1_ratio=0.5` default
- Updated `lasso_path` description in "See Also" to recommend it for
computing true Lasso paths | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33048 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33048/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33048/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33048/events | https://github.com/scikit-learn/scikit-learn/pull/33048 | 3,802,665,315 | PR_kwDOAAzd1s68nTaz | 33,048 | Fix ValueError in KNeighborsClassifier with brute force and p=1 | {
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] | open | false | [] | null | 3 | 2026-01-12T03:51:39 | 2026-01-25T20:17:39 | null | null | NONE | null | null | null | null | #### Reference Issues/PRs
Fixes #33034
#### What does this implement/fix? Explain your changes.
When using `KNeighborsClassifier` with `algorithm="brute"` and `p=1` (Manhattan distance), the code path utilizes `ArgKminClassMode`. Previously, `self.classes_` (which can contain strings) was passed directly to the `unique_Y_labels` argument of the Cython method, causing a `ValueError` when it attempted to interpret strings as integers.
I have updated the call to pass `np.arange(len(self.classes_), dtype=np.intp)` instead, which provides the correct integer indices expected by the underlying Cython implementation.
#### Any other comments?
Added a regression test case in `sklearn/neighbors/tests/test_neighbors.py`. | null | {
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- Test Collection Failure
- test_loader[load_digits-data_shape5-target_shape5-10-True-filenames5]
- test_loader[loader_func6-data_shape6-target_shape6-10-True-filenames6]
- test_toy_dataset_frame_dtype[load_digits-float64-int]
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- test_load_fake_lfw_pairs
- test_imports_strategies
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- test_MDS
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- test_fit_transform[kmeans-warn-expected1-None]
- test_fit_transform[kmeans-warn-expected8-sample_weight8]
- test_fit_transform[kmeans-warn-expected9-sample_weight9]
- test_fit_transform_n_bins_array[kmeans-warn-expected1-None]
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- test_kbinsdiscretizer_no_mutating_sample_weight[kmeans]
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- test_inverse_transform[ordinal-kmeans-expected_inv1-warn]
- test_inverse_transform[onehot-kmeans-expected_inv1-warn]
- test_inverse_transform[onehot-dense-kmeans-expected_inv1-warn]
- test_transform_outside_fit_range[kmeans]
- test_redundant_bins[kmeans-expected_bin_edges1-warn]
- test_kbinsdiscretizer_subsample[70-kmeans]
- test_check_accuracy_on_digits
- test_function_param_validation[sklearn.cluster.cluster_optics_dbscan]
- test_function_param_validation[sklearn.cluster.compute_optics_graph]
- test_function_param_validation[sklearn.cluster.estimate_bandwidth]
- test_function_param_validation[sklearn.cluster.kmeans_plusplus]
- test_function_param_validation[sklearn.cluster.cluster_optics_xi]
- test_function_param_validation[sklearn.covariance.ledoit_wolf_shrinkage]
- test_function_param_validation[sklearn.covariance.shrunk_covariance]
- test_unsorted_indices[csr_matrix]
- test_function_param_validation[sklearn.decomposition.sparse_encode]
- test_function_param_validation[sklearn.feature_selection.chi2]
- test_unsorted_indices[csr_array]
- test_function_param_validation[sklearn.feature_selection.f_classif]
- test_function_param_validation[sklearn.feature_selection.f_regression]
- test_function_param_validation[sklearn.feature_selection.mutual_info_classif]
- test_function_param_validation[sklearn.feature_selection.mutual_info_regression]
- test_function_param_validation[sklearn.feature_selection.r_regression]
- test_function_param_validation[sklearn.inspection.partial_dependence]
- test_function_param_validation[sklearn.inspection.permutation_importance]
- test_function_param_validation[sklearn.linear_model.enet_path]
- test_function_param_validation[sklearn.linear_model.lars_path]
- test_function_param_validation[sklearn.linear_model.lars_path_gram]
- test_function_param_validation[sklearn.linear_model.lasso_path]
- test_function_param_validation[sklearn.linear_model.orthogonal_mp]
- test_function_param_validation[sklearn.linear_model.orthogonal_mp_gram]
- test_function_param_validation[sklearn.linear_model.ridge_regression]
- test_function_param_validation[sklearn.manifold.locally_linear_embedding]
- test_function_param_validation[sklearn.manifold.smacof]
- test_function_param_validation[sklearn.manifold.spectral_embedding]
- test_function_param_validation[sklearn.manifold.trustworthiness]
- test_weight[csr_matrix]
- test_weight[csr_array]
- test_class_wrapper_param_validation[sklearn.cluster.dbscan-sklearn.cluster.DBSCAN]
- test_class_wrapper_param_validation[sklearn.cluster.k_means-sklearn.cluster.KMeans]
- test_class_wrapper_param_validation[sklearn.cluster.mean_shift-sklearn.cluster.MeanShift]
- test_class_wrapper_param_validation[sklearn.cluster.spectral_clustering-sklearn.cluster.SpectralClustering]
- test_class_wrapper_param_validation[sklearn.covariance.graphical_lasso-sklearn.covariance.GraphicalLasso]
- test_class_wrapper_param_validation[sklearn.covariance.ledoit_wolf-sklearn.covariance.LedoitWolf]
- test_class_wrapper_param_validation[sklearn.covariance.oas-sklearn.covariance.OAS]
- test_class_wrapper_param_validation[sklearn.decomposition.dict_learning-sklearn.decomposition.DictionaryLearning]
- test_class_wrapper_param_validation[sklearn.decomposition.dict_learning_online-sklearn.decomposition.MiniBatchDictionaryLearning]
- test_class_wrapper_param_validation[sklearn.decomposition.fastica-sklearn.decomposition.FastICA]
- test_class_wrapper_param_validation[sklearn.decomposition.non_negative_factorization-sklearn.decomposition.NMF]
- test_weight
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- test_import_skl
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compares StandardScaler and MinMaxScaler on a simple 2D synthetic dataset.
The example helps new users understand how feature scaling affects the
geometry of data, which is important for models such as KMeans, KNN,
and SVM.
Includes:
- Synthetic dataset generation
- Side-by-side comparison plots
- Clear explanatory comments
- Educational summary section
No functional or API changes.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33045 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33045/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33045/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33045/events | https://github.com/scikit-learn/scikit-learn/issues/33045 | 3,800,736,300 | I_kwDOAAzd1s7iiqIs | 33,045 | Mixed string/numeric when input is list for classification metrics | {
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] | open | false | [] | null | 4 | 2026-01-11T04:47:04 | 2026-02-02T05:27:07 | null | null | MEMBER | null | null | {
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> This is not a good first (or second) issue to contribute. If you are interested in contributing to scikit-learn please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particular the section [Issues for new contributors](https://scikit-learn.org/dev/developers/contributing.html#new-contributors).
Noticed while working on #32755
Classification metrics (`CLASSIFICATION_METRICS` from `test_common.py`, they are all defined in `sklearn/metrics/_classification.py`, but not all metrics in `_classification.py` fall in this list) vary in how they handle mixed string and numeric inputs (e.g., `y_true` is string and `y_pred` is numeric).
* `precision_recall_fscore_support` and friends (`precision_score`, `recall_score`, `f1_score`, `fbeta_score` and related `jaccard_score`) - raises `ValueError`:
* via `_check_set_wise_labels`, which calls [`unique_labels`](https://github.com/scikit-learn/scikit-learn/blob/66200f149ba6c64a3a93dd73e37bdeea87bb5db8/sklearn/utils/multiclass.py#L41), which does not allow "mix of string and integer labels“
* note that these metrics do take a `pos_label` parameter, so theoretically when `y_true` is string, we could use `y_true == pos_label` to convert to [1,0]. This would not make sense if `y_pred` was string.
* `confusion_matrix` (and metrics that use it - `balanced_accuracy_score`, `cohen_kappa_score`) - raises `ValueError` ONLY when `labels=None`:
* `labels=None`: calls [`unique_labels`](https://github.com/scikit-learn/scikit-learn/blob/66200f149ba6c64a3a93dd73e37bdeea87bb5db8/sklearn/utils/multiclass.py#L41) like above
* `labels` is set - AFAICT no error but the confusion matrix would only contain 0. e.g., if `y_true` is string and `labels` is set to all possible unique values in `y_true`, we would do the following conversion:
https://github.com/scikit-learn/scikit-learn/blob/66200f149ba6c64a3a93dd73e37bdeea87bb5db8/sklearn/metrics/_classification.py#L584-L587
Since none of the string `labels` are present in numeric `y_pred`, `y_pred` would be converted to array that consists only of `n_labels + 1` values
* `multilabel_confusion_matrix` - always raises raises `ValueError` (again via `unique_labels`):
* unlike `confusion_matrix`, even when `labels` is given, we call `unique_labels(y_true, y_pred)` to get all labels present, and if any are not provided in `labels` they are added.
The following all DO NOT error:
* `accuracy_score` - no error but result would always be 0 (as `score = y_true == y_pred` would always be 0). Note not relevant for multilabel cases as input needs to be label indicator matrix.
* `hamming_loss` - no error but result would always be 1. Again not relevant for multilabel.
* `zero_one_loss` - no error but result would always be 1 (when `normalize=False`). Again not relevant for multilabel.
* `matthews_corrcoef` - no error but result would always be 0. Note that we transform `y_true` and `y_pred` using `LabelEncoder` fit on `y_true` and `y_pred` concat (meaning `y_true` and `y_pred` will be numbers, but not contain any numbers in common), before we pass to `confusion_matrix`.
For these classification metrics which require `y_pred` to be thresholded predictions, I don't think it makes sense to have mixed string and numeri (e.g., how would you match ['apple', 'orange', 'apple] to [2,3,2] ?). Indeed, for the metrics that do not error, the result is the 'worst' value.
For metrics that accept `pos_label` the way forward is less clear. Note that for several continuous classification metrics we do use something like `y_true == pos_label`, thus allowing `y_true` to be string.
Note that I will open a separate issue around this for continuous classification metrics (i.e. those in `_ranking.py`).
cc @ogrisel
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"url": "ht... | closed | false | [] | null | 5 | 2026-01-11T03:55:51 | 2026-01-12T15:54:59 | 2026-01-12T14:28:57 | null | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
I need to use a significantly tighter value of `tol` to get the same quality of convergence from `enet_path` since #31882.
The point of running `enet_path` with a large number of alphas $\alpha_1 > \cdots > \alpha_n$ is that the resulting betas $\beta_1, \cdots, \beta_n$ are very close together, hence $\beta_k$ is a good initial guess for $\beta_{k+1}$.
In the example below, I expect to get 100 different betas, but I get just 20. The new code doesn't update beta at each step. It waits until beta becomes a "bad guess" for the next beta. Doesn't seem like a helpful optimization.
### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.linear_model import enet_path
def main():
height = 10_000
width = 10
rng = np.random.default_rng(0)
X = rng.uniform(-1, 1, size=(height, width))
beta_true = rng.uniform(-100, 100, size=width)
y = X @ beta_true
_alphas, coefs, _dual_gaps = enet_path(X, y, eps=1e-9, l1_ratio=1.0) # type: ignore
u = np.unique(coefs, axis=1)
n_unique_coefs = u.shape[1]
print(n_unique_coefs)
diff = beta_true - coefs[:, -1]
diffnorm = np.linalg.norm(diff)
print(diffnorm)
```
### Expected Results
1.7.2:
```
100
2.8294111246854234e-07
```
### Actual Results
1.8.0:
```
20
0.43002957697539734
```
### Versions
```shell
System:
python: 3.13.10 (main, Dec 2 2025, 19:42:11) [MSC v.1944 64 bit (AMD64)]
executable: C:\Users\jhopfens\.uv_python\p313\Scripts\python.exe
machine: Windows-11-10.0.26100-SP0
Python dependencies:
sklearn: 1.8.0
pip: None
setuptools: 80.9.0
numpy: 2.4.1
scipy: 1.16.3
Cython: None
pandas: 2.3.3
matplotlib: 3.10.8
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: openmp
internal_api: openmp
num_threads: 12
prefix: vcomp
filepath: C:\Users\jhopfens\.uv_python\p313\Lib\site-packages\sklearn\.libs\vcomp140.dll
version: None
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: C:\Users\jhopfens\.uv_python\p313\Lib\site-packages\numpy.libs\libscipy_openblas64_-9e3e5a4229c1ca39f10dc82bba9e2b2b.dll
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: C:\Users\jhopfens\.uv_python\p313\Lib\site-packages\scipy.libs\libscipy_openblas-48c358d105077551cc9cc3ba79387ed5.dll
version: 0.3.29.dev
threading_layer: pthreads
architecture: Haswell
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- Test Collection Failure | {
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} | ### Describe the workflow you want to enable
`partial_fit` for `RobustScaler`, `QuantileTransformer`, and likely others
### Describe your proposed solution
It seems like functionality for approximating quantiles using t-digest or something will do most of the heavy lifting.
### Describe alternatives you've considered, if relevant
Stuck with sampling from the dataset, fitting to it, and hoping it's sufficiently representative of the whole dataset.
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"node_id": "LA_k... | closed | false | [] | null | 0 | 2026-01-10T18:38:49 | 2026-02-13T17:25:59 | 2026-02-13T17:25:59 | null | CONTRIBUTOR | null | null | null | null | <!--
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#### Reference Issues/PRs
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#### What does this implement/fix? Explain your changes.
Add a test case to catch the unnecessary copy made on dense X, y input with sample weight when `copy=False`.
Then remove the unnecessary copy to pass the new test case.
#### AI usage disclosure
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I used AI assistance for: nothing
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33040 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33040/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33040/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33040/events | https://github.com/scikit-learn/scikit-learn/issues/33040 | 3,800,162,719 | I_kwDOAAzd1s7igeGf | 33,040 | confusion_matrix crashes when sample_weight is an empty array | {
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} | ### Describe the bug
When calling confusion_matrix with non-empty y_true and y_pred but an empty
sample_weight array, the function crashes during internal processing.
This appears to be caused by inconsistent handling of empty sample_weight
compared to all-zero sample_weight, which currently raises a ValueError.
### Steps/Code to Reproduce
from sklearn.metrics import confusion_matrix
y_true = [0, 1]
y_pred = [0, 1]
confusion_matrix(y_true, y_pred, sample_weight=[])
### Expected Results
Either
A clear ValueError indicating invalid sample_weight, OR
A documented and consistent behavior matching all-zero sample_weight.
In any case, the function should not crash internally.
### Actual Results
The function crashes with an internal error related to array length mismatch
during confusion matrix construction.
### Versions
System:
python: 3.12.10 (tags/v3.12.10:0cc8128, Apr 8 2025, 12:21:36) [MSC v.1943 64 bit (AMD64)]
executable: C:\Users\HP\venv-sklearn\Scripts\python.exe
machine: Windows-11-10.0.26200-SP0
Python dependencies:
sklearn: 1.9.dev0
pip: 25.3
setuptools: 80.9.0
numpy: 2.4.0
scipy: 1.16.3
Cython: 3.2.4
pandas: None
matplotlib: None
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33039 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33039/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33039/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33039/events | https://github.com/scikit-learn/scikit-learn/pull/33039 | 3,800,128,659 | PR_kwDOAAzd1s68faE6 | 33,039 | FIX: fix deprecation of "friedman_mse" criterion in forests | {
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"node_id": "MDU6TGFiZWwy... | closed | false | [] | null | 0 | 2026-01-10T17:47:13 | 2026-01-15T22:29:03 | 2026-01-15T22:29:03 | null | CONTRIBUTOR | null | null | null | null | Fixing my own mess :sweat_smile:
#### Reference Issues/PRs
Related to issue #32700.
Follow-up from PR #32708: While writing the PR #32708, I didn't anticipated that tree-related parameters will be validated before being passed down to trees (and it's where the deprecation warning is raised, and where the value of the criterion is modified from "friedman_mse" to "squared_error").
#### What does this implement/fix? Explain your changes.
The bug is explained above.
The proposed fix: copy the warning & criterion-value-rewrite in `RandomForestRegressor` and `ExtraTreesRegressor`. I think it's fine to repeat this small piece of code, it will not live too long in the code base.
#### AI usage: no
#### TODO?
More tests? | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33038 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33038/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33038/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33038/events | https://github.com/scikit-learn/scikit-learn/pull/33038 | 3,800,092,919 | PR_kwDOAAzd1s68fTDm | 33,038 | Fix float32 instability in LinearRegression lstsq cutoff | {
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"node_id": "LA_k... | closed | false | [] | null | 5 | 2026-01-10T17:19:37 | 2026-01-27T16:21:05 | 2026-01-27T15:51:58 | null | NONE | null | null | null | null | This PR fixes numerical instability in LinearRegression when fitting float32 data.
When X and y are float32, the default cutoff value
cond = max(X.shape) * np.finfo(X.dtype).eps
is too small and causes LAPACK to treat the matrix as rank-deficient, producing incorrect coefficients for large problems.
This change sets cond=None for float32 inputs, allowing LAPACK to choose a stable internal cutoff while keeping the existing behavior for float64.
This resolves issue #33032.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33037 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33037/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33037/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33037/events | https://github.com/scikit-learn/scikit-learn/pull/33037 | 3,799,717,040 | PR_kwDOAAzd1s68eNiT | 33,037 | Clarify that group-based splitters do not perform class-label stratification | {
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] | open | false | [] | null | 0 | 2026-01-10T13:24:48 | 2026-01-10T13:24:57 | null | null | NONE | null | null | null | null | #### What does this implement/fix? Explain your changes.
This PR clarifies terminology in several group-based cross-validation splitters by replacing uses of “stratification” that actually refer to domain-specific grouping. It also adds an explicit note explaining that these splitters do not perform class-label stratification with respect to the target variable ``y``. “Grouping” refers to making splits based on ``groups``, while “stratification” refers to making splits based on ``y``.
While the term “stratification” could be used for group CV (“strata” = “subgroups”), the current use of this term in scikit-learn documentation is confusing, because ``StratifiedKFold`` is only about classes of ``y``. I was confused myself when reading the scikit-learn documentation some time ago, and I have recently seen a post on LinkedIn where this ambiguity in the scikit-learn docs likely contributed to someone else’s confusion about what the different scikit-learn splitters do.
This is a documentation-only change; no behavior or APIs are modified.
#### AI usage disclosure
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I used AI assistance for:
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [x] Research and understanding | null | {
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] | closed | false | [] | null | 1 | 2026-01-10T10:57:12 | 2026-01-10T16:00:02 | 2026-01-10T16:00:02 | null | NONE | null | null | null | null | This PR adds a new example under examples/preprocessing that visually
compares StandardScaler and MinMaxScaler on a simple 2D synthetic dataset.
The example helps new users understand how feature scaling affects the
geometry of data, which is important for models such as KMeans, KNN,
and SVM.
Includes:
- Synthetic dataset generation
- Side-by-side comparison plots
- Clear explanatory comments
- Educational summary section
No functional or API changes.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33035 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33035/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33035/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33035/events | https://github.com/scikit-learn/scikit-learn/pull/33035 | 3,799,228,789 | PR_kwDOAAzd1s68crKX | 33,035 | DOC: Add references for Agglomerative Clustering | {
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"node_id": "MDU6TGFiZWwyMzk5... | closed | false | [] | null | 1 | 2026-01-10T06:52:30 | 2026-01-10T10:40:54 | 2026-01-10T10:40:50 | null | NONE | null | null | null | null | Adds academic references to the Hierarchical (Agglomerative) Clustering
documentation to improve theoretical background and usability.
Fixes #9756
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33034 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33034/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33034/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33034/events | https://github.com/scikit-learn/scikit-learn/issues/33034 | 3,798,564,117 | I_kwDOAAzd1s7iaX0V | 33,034 | `KNeighborsClassifier` fails on non-numeric labels with `ValueError` on certain parameters combination | {
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} | ### Describe the bug
When used with `algorithm="brute"` (or `algorithm="auto"` and the heuristic suggests brute force) and `p = 1`, `KNeighborsClassifier.predict` crashes when y labels are non-integers.
Note that this only crashes for this specific param combination, and `p=2`, for example, works fine with non-integer labels.
This happens because `self.classes_` is passed to `unique_Y_labels` here:
https://github.com/scikit-learn/scikit-learn/blob/66200f149ba6c64a3a93dd73e37bdeea87bb5db8/sklearn/neighbors/_classification.py#L358
And the [docs](https://github.com/scikit-learn/scikit-learn/blob/66200f149ba6c64a3a93dd73e37bdeea87bb5db8/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py#L514-L516) for `ArgKminClassMode.compute` say:
```
unique_Y_labels : ndarray
An array containing all unique indices contained in the
corresponding `Y_labels` array.
```
while `self.classes_` contains actual values, not indices.
### Steps/Code to Reproduce
```python
import random
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
# generate random data
rng = np.random.default_rng()
x = rng.normal(size=(5, 3))
y = random.choices(['foo', 'bar'], k=5)
kn = KNeighborsClassifier(algorithm='brute', p=1)
kn = kn.fit(x, y)
kn.predict(rng.normal(size=(2, 3)))
```
### Expected Results
No error.
### Actual Results
```python-traceback
Traceback (most recent call last):
File "<string>", line 16, in <module>
File "/home/stas/Documents/Work/polymerize/ml_dev/.venv/lib/python3.11/site-packages/sklearn/neighbors/_classification.py", line 266, in predict
probabilities = self.predict_proba(X)
^^^^^^^^^^^^^^^^^^^^^
File "/home/stas/Documents/Work/polymerize/ml_dev/.venv/lib/python3.11/site-packages/sklearn/neighbors/_classification.py", line 352, in predict_proba
probabilities = ArgKminClassMode.compute(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/stas/Documents/Work/polymerize/ml_dev/.venv/lib/python3.11/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py", line 589, in compute
unique_Y_labels=np.array(unique_Y_labels, dtype=np.intp),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: invalid literal for int() with base 10: np.str_('bar')
```
### Versions
```shell
System:
python: 3.11.14 (main, Dec 9 2025, 19:02:23) [Clang 21.1.4 ]
executable: /home/stas/Documents/Work/polymerize/ml_dev/.venv/bin/python
machine: Linux-6.14.0-37-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.8.0
pip: None
setuptools: None
numpy: 2.4.0
scipy: 1.16.3
Cython: None
pandas: 2.3.1
matplotlib: 3.10.8
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 4
prefix: libscipy_openblas
filepath: /home/stas/Documents/Work/polymerize/ml_dev/.venv/lib/python3.11/site-packages/numpy.libs/libscipy_openblas64_-fdde5778.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 4
prefix: libscipy_openblas
filepath: /home/stas/Documents/Work/polymerize/ml_dev/.venv/lib/python3.11/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: 4
prefix: libgomp
filepath: /home/stas/Documents/Work/polymerize/ml_dev/.venv/lib/python3.11/site-packages/scikit_learn.libs/libgomp-e985bcbb.so.1.0.0
version: None
``` | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33033 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33033/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33033/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33033/events | https://github.com/scikit-learn/scikit-learn/issues/33033 | 3,798,440,098 | I_kwDOAAzd1s7iZ5ii | 33,033 | RFC Question global random seed testing | {
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"url"... | closed | false | [] | null | 10 | 2026-01-09T21:49:04 | 2026-02-18T15:43:22 | 2026-02-18T15:43:22 | null | MEMBER | null | null | {
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} | As a developer, I more and more doubt the added value of our global random seed test setup. Ideally, I do not want to bear the mental load of:
- running tests locally with all random seeds (`SKLEARN_TESTS_GLOBAL_RANDOM_SEED="all"`)
- add a commit with message `[all random seeds]` just to trigger CI/CD to run with all seeds.
Recently, CI/CD often fails because of runs with all random seeds. This not only annoys but actually slows down development. It seems to be mainly @lesteve how takes care of those.
**What is the actual benefit of it? Which bugs have we found this way?**
On top, we have the open issue #22827, which seems like a never ending story. I mostly don't see the added value of the changed tests.
See discussion in #33021:
> by @lorentzenchr
> General comment: As a developer, I find our test setup too complicated. Even when running tests with all rng seeds locally, one still needs a commit to trigger the CD pipeline with all seeds.
I would prefer to not need to think about test random seeds at all.
> by @lesteve
> Personally I am less and less convinced that our global_random_seed setup is worth its maintenance cost. I do understand why it exists, i.e. catch numerical instability or avoid that a test pass only because we chose a lucky random seed. At the same time this setup has a maintenance cost and I am not sure we have actually seen any of the benefit. Maybe I don't remember the few cases where it allowed us to notice a numerical instability or maybe it's the kind of "silent" usefulness that makes you write a more robust test in the first place and/or catch numerical instability bugs when you write the test.
>
> To be explicit about the maintenance costs:
>
> intermittent CI error tracking. I am probably the main person that seems to care about [sporadic automatically opened issues](https://github.com/scikit-learn/scikit-learn/issues?q=sort%3Aupdated-desc%20is%3Aissue%20is%3Aopen%20author%3Ascikit-learn-bot). This does take a bit of time (like a few hours a week maybe) and adds some noise in the CI. I would say global_random_seed are the main source of intermittent errors even if there are others, like conda-forge download error, or intermittent segmentation fault on free-threaded.
ad-hoc PR raising atol and/or rtol where nobody really has a super clear idea whether this is OK or not to raise it or it would need a deeper look and generally we merge anyway.
new global_random_seed issues keep appearing, the PR CI only checks a single random seed, and it's only afterwards that you notice issues in automatically opened issues where the global random seed is random on each daily schedule run.
>
> @lorentzenchr to answer your point I don't know how to make our test setup simpler with our current set of constraints so I am proposing to remove one constraint (without too much hope to be honest).
>
> In an ideal infinite-resources world (which we don't live in, remember 😉), I think we should have some kind of fuzzing-like tests that lives outside the main CI workflow, tweaks the random seeds, and reports the most problematic tests (maybe they fail for a high number of seeds, maybe the difference is big for values not near 0, or some better criterion). Just to be explicit, this ideal scenario is super unlikely to happen. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33032 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33032/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33032/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33032/events | https://github.com/scikit-learn/scikit-learn/issues/33032 | 3,798,238,248 | I_kwDOAAzd1s7iZIQo | 33,032 | BUG: LinearRegression is wrong on float32 and large n_samples (> 500k rows) due to `cond` param to scipy.linalg.lstsq | {
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} | ### Describe the bug
`LinearRegression` is wrong on `float32` with a large sample size (e.g. 500k rows) since sklearn 1.6.0. This is still the case on 1.8.0. See minimal repro below. The residual sum of squares is much larger than the solution found by `{scipy,numpy}.linalg.lstsq` on `float32`.
Using the minimal repro below, `git bisect` points to this commit from https://github.com/scikit-learn/scikit-learn/pull/30040: https://github.com/scikit-learn/scikit-learn/commit/bef9d1803d1f243e4d3f9e845fac356a4fc555a8
https://github.com/scikit-learn/scikit-learn/blob/66200f149ba6c64a3a93dd73e37bdeea87bb5db8/sklearn/linear_model/_base.py#L702-L703
- Indeed, changing L702 to `cond = None` fixes the bug.
- Doing so will revive issue #26164, so someone needs to make a call.
- I think issue 26164 is strictly less important than the correctness of linear regression (minimize residue).
- `cond` at L702 scales up linearly with the number of rows and eventually zeros out large enough singular values to throw off the solution completely.
- As a result, sklearn `LinearRegression` fails to scale to even 500k rows at `float32` precision below.
- Meanwhile, `scipy.linalg.lstsq` (the underlying solver) and `numpy.linalg.lstsq` don't have this issue with the internally defaulted `cond` / `rcond` values (see inline comments below).
### Steps/Code to Reproduce
```python3
import numpy as np
from scipy import linalg
from sklearn.linear_model import LinearRegression
np.random.seed(1337)
n_samples = 500_000
x1 = np.random.rand(n_samples)
x2 = 0.3 * x1 + 0.1 * np.random.rand(n_samples)
X = np.column_stack([x1, x2])
y = X @ [0.5, 2.0] + 0.1 * np.random.rand(n_samples)
assert X.dtype == np.float64 and y.dtype == np.float64
X32, y32 = X.astype(np.float32), y.astype(np.float32)
scipy_f64 = linalg.lstsq(X, y)[0]
scipy_f32 = linalg.lstsq(X32, y32)[0] # pdb -> cond defaults to np.float32(1.1920929e-07)
numpy_f64 = np.linalg.lstsq(X, y)[0]
numpy_f32 = np.linalg.lstsq(X32, y32)[0] # pdb -> rcond defaults to np.float64(1.1102230246251565e-10)
sklearn_f64 = LinearRegression(fit_intercept=False).fit(X, y).coef_
sklearn_f32 = LinearRegression(fit_intercept=False).fit(X32, y32).coef_ # pdb -> sklearn sets cond to np.float32(0.059604645)
print(f"scipy float64: {scipy_f64}, residue: {linalg.norm(y - X @ scipy_f64):.2f}")
print(f"scipy float32: {scipy_f32}, residue: {linalg.norm(y32 - X32 @ scipy_f32):.2f}")
print(f"numpy float64: {numpy_f64}, residue: {linalg.norm(y - X @ numpy_f64):.2f}")
print(f"numpy float32: {numpy_f32}, residue: {linalg.norm(y32 - X32 @ numpy_f32):.2f}")
print(f"sklearn float64: {sklearn_f64}, residue: {linalg.norm(y - X @ sklearn_f64):.2f}")
print(f"sklearn float32: {sklearn_f32}, residue: {linalg.norm(y32 - X32 @ sklearn_f32):.2f} <--------")
```
### Expected Results
```python3
scipy float64: [0.41391995 2.42901607], residue: 24.40
scipy float32: [0.4139197 2.429016 ], residue: 24.40
numpy float64: [0.41391995 2.42901607], residue: 24.40
numpy float32: [0.41391996 2.429016 ], residue: 24.40
sklearn float64: [0.41391995 2.42901607], residue: 24.40
sklearn float32: [0.4139197 2.429016 ], residue: 24.40 <--------
```
### Actual Results
```python3
scipy float64: [0.41391995 2.42901607], residue: 24.40
scipy float32: [0.4139197 2.429016 ], residue: 24.40
numpy float64: [0.41391995 2.42901607], residue: 24.40
numpy float32: [0.41391996 2.429016 ], residue: 24.40
sklearn float64: [0.41391995 2.42901607], residue: 24.40
sklearn float32: [1.1635013 0.43804753], residue: 59.06 <--------
```
### Root cause analysis
Why does sklearn set `cond` this way in the first place? [#30040](https://github.com/scikit-learn/scikit-learn/pull/30040) explains:
> The `scipy.linalg.lstsq` solver can fail the sample weight consistency test, especially for wide dataset (`n_features > n_samples`) after centering `X,y` (as done when `fit_intercept=True`).
>
> Setting the `cond` parameter (cut-off ratio on singular values) to the value recommended by `numpy.linalg.lstsq` documentation seems to fix the bug.
- Indeed, `numpy.linalg.lstsq` defaults to [`rcond = finfo(t).eps * max(n, m)`](https://github.com/numpy/numpy/blob/v2.4.0/numpy/linalg/_linalg.py#L2528-L2529)
- However, for float32 input, numpy sets `t` (calculation precision) to float64 and `result_t` (result precision) to float32 ([two lines above](https://github.com/numpy/numpy/blob/v2.4.0/numpy/linalg/_linalg.py#L2525)), so numpy does not have the bug described by the current issue.
- Calc type is always float64: [here](https://github.com/numpy/numpy/blob/v2.4.1/numpy/linalg/_linalg.py#L206-L210) and [there (`signature = 'ddd->ddid'`)](https://github.com/numpy/numpy/blob/v2.4.1/numpy/linalg/_linalg.py#L2525-L2531).
- This comes at a cost. float32 X will be cast into a float64 copy by numpy's [ufunc `signature` mechanism](https://numpy.org/doc/stable/reference/ufuncs.html):
- > Casting will be used as necessary. The actual DType of the input arrays is not considered unless signature is None for that array.
- sklearn's underlying solver is `scipy.linalg.lstsq`. For float32 input, it sets calculation precision to `float32` (no casting / copy), unlike numpy's `float64`.
- This does not interact well with `numpy's` default `rcond`, resulting in this bug.
- The above can be validated by pdb-ing into the corresponding lines in the minimal repro above.
### Suggested fix
First add the above minimal repro as a new test case.
Then add `float32` to #30040's test case `test_linear_regression_sample_weight_consistency` with this patch:
<details>
<summary>Click to expand patch!</summary>
```diff
diff --git a/sklearn/linear_model/tests/test_base.py b/sklearn/linear_model/tests/test_base.py
index a1b7c79e6f..9d4982f52e 100644
--- a/sklearn/linear_model/tests/test_base.py
+++ b/sklearn/linear_model/tests/test_base.py
@@ -762,8 +762,9 @@ def test_fused_types_make_dataset(csr_container):
],
)
@pytest.mark.parametrize("fit_intercept", [False, True])
+@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_linear_regression_sample_weight_consistency(
- X_shape, sparse_container, fit_intercept, global_random_seed
+ X_shape, sparse_container, fit_intercept, dtype, global_random_seed
):
"""Test that the impact of sample_weight is consistent.
@@ -774,8 +775,8 @@ def test_linear_regression_sample_weight_consistency(
rng = np.random.RandomState(global_random_seed)
n_samples, n_features = X_shape
- X = rng.rand(n_samples, n_features)
- y = rng.rand(n_samples)
+ X = rng.rand(n_samples, n_features).astype(dtype)
+ y = rng.rand(n_samples).astype(dtype)
if sparse_container is not None:
X = sparse_container(X)
params = dict(fit_intercept=fit_intercept)
@@ -790,14 +791,14 @@ def test_linear_regression_sample_weight_consistency(
# test with sparse input.
sample_weight = np.ones_like(y)
reg.fit(X, y, sample_weight=sample_weight)
- assert_allclose(reg.coef_, coef, rtol=1e-6)
+ assert_allclose(reg.coef_, coef, rtol=1e-3 if dtype == np.float32 else 1e-6)
if fit_intercept:
assert_allclose(reg.intercept_, intercept)
# 2) sample_weight=None should be equivalent to sample_weight = number
sample_weight = 123.0
reg.fit(X, y, sample_weight=sample_weight)
- assert_allclose(reg.coef_, coef, rtol=1e-6)
+ assert_allclose(reg.coef_, coef, rtol=1e-3 if dtype == np.float32 else 1e-6)
if fit_intercept:
assert_allclose(reg.intercept_, intercept)
@@ -809,7 +810,8 @@ def test_linear_regression_sample_weight_consistency(
intercept = reg.intercept_
reg.fit(X, y, sample_weight=np.pi * sample_weight)
- assert_allclose(reg.coef_, coef, rtol=1e-6 if sparse_container is None else 1e-5)
+ rtol = 1e-3 if dtype == np.float32 else 1e-6 if sparse_container is None else 1e-5
+ assert_allclose(reg.coef_, coef, rtol=rtol)
if fit_intercept:
assert_allclose(reg.intercept_, intercept)
@@ -822,7 +824,7 @@ def test_linear_regression_sample_weight_consistency(
if fit_intercept:
intercept_0 = reg.intercept_
reg.fit(X[:-5], y[:-5], sample_weight=sample_weight[:-5])
- assert_allclose(reg.coef_, coef_0, rtol=1e-5)
+ assert_allclose(reg.coef_, coef_0, rtol=3e-3 if dtype == np.float32 else 1e-5)
if fit_intercept:
assert_allclose(reg.intercept_, intercept_0)
@@ -841,7 +843,7 @@ def test_linear_regression_sample_weight_consistency(
reg1 = LinearRegression(**params).fit(X, y, sample_weight=sample_weight_1)
reg2 = LinearRegression(**params).fit(X2, y2, sample_weight=sample_weight_2)
- assert_allclose(reg1.coef_, reg2.coef_, rtol=1e-6)
+ assert_allclose(reg1.coef_, reg2.coef_, rtol=1e-3 if dtype == np.float32 else 1e-6)
if fit_intercept:
assert_allclose(reg1.intercept_, reg2.intercept_)
```
</details>
Then do TDD to find a combination of (dtype, cond) to pass all tests. I haven't been able to find anything reasonable.
I think it is more urgent to fix `LinearRegression` to meet its definition (minimize residue sum of squares) than to pass the [sample weight consistency tests](https://github.com/scikit-learn/scikit-learn/blob/762734097daa72f6bd1b363ac989afa4717530f7/sklearn/linear_model/tests/test_base.py#L765). In the worst case I recommend setting `cond = None`, marking the weight tests as xfail, reopening #26164, and maybe report #26164 upstream (to scipy).
### Versions
```shell
System:
python: 3.14.2 | packaged by conda-forge | (main, Dec 6 2025, 11:21:58) [GCC 14.3.0]
executable: /home/jli/miniforge3/envs/sklearn-dbg/bin/python3
machine: Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.8.0
pip: 25.3
setuptools: None
numpy: 2.4.0
scipy: 1.16.3
Cython: None
pandas: None
matplotlib: None
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: /home/jli/miniforge3/envs/sklearn-dbg/lib/python3.14/site-packages/numpy.libs/libscipy_openblas64_-fdde5778.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: /home/jli/miniforge3/envs/sklearn-dbg/lib/python3.14/site-packages/scipy.libs/libscipy_openblas-b75cc656.so
version: 0.3.29.dev
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 12
prefix: libgomp
filepath: /home/jli/miniforge3/envs/sklearn-dbg/lib/python3.14/site-packages/scikit_learn.libs/libgomp-e985bcbb.so.1.0.0
version: None
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"node_id": "MDU6TGFiZWwyMz... | closed | false | [] | null | 2 | 2026-01-09T18:16:31 | 2026-01-10T10:39:32 | 2026-01-10T10:39:26 | null | CONTRIBUTOR | null | null | null | null | Fixes a crash in confusion_matrix when sample_weight is provided as an empty
array while y_true and y_pred are non-empty.
Previously, passing an empty sample_weight could lead to an internal error
during sparse matrix construction due to inconsistent array lengths.
This patch ensures that empty sample_weight is handled consistently with
input validation, preventing the crash and providing predictable behavior.
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"node_id": "MDU6... | closed | false | [] | null | 3 | 2026-01-09T15:45:00 | 2026-02-10T03:10:25 | 2026-02-10T03:10:27 | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Fixes #33029.
Related: #15436, #32991
TLDR;
On recent SciPy versions, sparse **array** containers (`coo_array`, `csr_array`, etc.) can be **1-D** (shape `(n_samples,)`). However, In `_set_order`, when we convert sparse inputs to CSC the conversion fails for 1-D sparse arrays in SciPy with `ValueError: Cannot convert a 1d sparse array to csc format`.
This PR updates `_set_order` to handle this case by reshaping 1-D sparse `y` to a 2-D column vector before converting to the requested sparse format (CSC/CSR). This preserves existing behavior for dense arrays and sparse matrices while making `_set_order` compatible with SciPy sparse arrays.
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"node_id": "LA_kwDOAAzd1s8AA... | closed | false | [] | null | 7 | 2026-01-09T15:25:38 | 2026-02-15T09:35:13 | 2026-02-15T09:35:13 | null | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
On recent SciPy versions (e.g. SciPy 1.16.3 used in CI), sparse **array** containers (`coo_array`, `csr_array`, etc.) can be **1-D** (shape `(n_samples,)`). However, scikit-learn, `_set_order` assumes any sparse `y` can be converted to CSC/CSR and calls `y.asformat("csc")` when `order="F"`, but SciPy raises:
`ValueError: Cannot convert a 1d sparse array to csc format`
So far this issue did not surface because the original test `test_set_order_sparse` added in #15436 (commit 21686b7) contained a typo. It used `y = X.asformat(sparse_format)` instead of `y = y.asformat(sparse_format)`. When fixing/generalizing that test as part of #32991, the test started exercising `_set_order` with a real sparse `y`, which revealed the failure on newer SciPy sparse arrays.
### Steps/Code to Reproduce
1. for `_set_order`
```python
import numpy as np
from scipy import sparse
from sklearn.linear_model._coordinate_descent import _set_order
X = sparse.coo_array(np.array([[0], [0], [0]])).asformat("csr")
y = sparse.coo_array(np.array([0, 0, 0])).asformat("csr")
_set_order(X, y, order="F")
```
2. for `test_set_order_sparse` ( if we convert `y = X.asformat(sparse_format)` to `y = y.asformat(sparse_format)` )
```python
from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS
X = COO_CONTAINERS[1](np.array([[0], [0], [0]]))
y = COO_CONTAINERS[1](np.array([0, 0, 0]))
y.asformat("csc")
```
### Expected Results
No Pytest errors
### Actual Results
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[1], line 11
7 y = sparse.coo_array(np.array([0, 0, 0])).asformat("csr")
9 # F-order asks for CSC; this crashes on recent SciPy:
10 # ValueError: Cannot convert a 1d sparse array to csc format
---> 11 _set_order(X, y, order="F")
File c:\Users\gauta\miniforge3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:86, in _set_order(X, y, order)
84 X = np.asarray(X, order=order)
85 if sparse_y:
---> 86 y = y.asformat(sparse_format)
87 else:
88 y = np.asarray(y, order=order)
File c:\Users\gauta\miniforge3\Lib\site-packages\scipy\sparse\_base.py:480, in _spbase.asformat(self, format, copy)
478 # Forward the copy kwarg, if it's accepted.
479 try:
--> 480 return convert_method(copy=copy)
481 except TypeError:
482 return convert_method()
File c:\Users\gauta\miniforge3\Lib\site-packages\scipy\sparse\_csr.py:65, in _csr_base.tocsc(self, copy)
63 def tocsc(self, copy=False):
64 if self.ndim != 2:
---> 65 raise ValueError("Cannot convert a 1d sparse array to csc format")
66 M, N = self.shape
67 idx_dtype = self._get_index_dtype((self.indptr, self.indices),
68 maxval=max(self.nnz, M))
ValueError: Cannot convert a 1d sparse array to csc format
---------------------------------------------------------------------------
Here is the link for the failed tests from #32991 CI when I tried to update the test:
https://github.com/scikit-learn/scikit-learn/actions/runs/20854001086/job/59915770506?pr=32991
```
### Versions
```shell
System:
python: 3.12.12 | packaged by conda-forge | (main, Oct 22 2025, 23:13:34) [MSC v.1944 64 bit (AMD64)]
executable: c:\Users\gauta\miniforge3\python.exe
machine: Windows-11-10.0.26100-SP0
Python dependencies:
sklearn: 1.8.0
pip: 25.3
setuptools: 80.9.0
numpy: 2.4.0
scipy: 1.16.3
Cython: None
pandas: None
matplotlib: None
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: mkl
num_threads: 24
prefix: mkl_rt
...
num_threads: 32
prefix: vcomp
filepath: C:\Users\gauta\miniforge3\vcomp140.dll
version: None
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33028 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33028/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33028/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33028/events | https://github.com/scikit-learn/scikit-learn/pull/33028 | 3,795,917,633 | PR_kwDOAAzd1s68RxSc | 33,028 | DEBUG torch incompatibility with np.sum and np.repeat (pure debug PR, will be closed soon) | {
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] | closed | false | [] | null | 13 | 2026-01-09T08:12:41 | 2026-01-23T07:49:27 | 2026-01-23T07:49:27 | null | MEMBER | null | null | null | null | This is to debug a CI failure in #32909 and discuss solutions.
Specifically this discussion: https://github.com/scikit-learn/scikit-learn/pull/32909/changes#r2627102404
EDIT: It is a bug on main (https://github.com/scikit-learn/scikit-learn/pull/33028#issuecomment-3744167700) and does not only affect the mentioned PR.
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## Key Findings
- **Node.js 24 requirement**: All updated actions now require Actions Runner v2.327.1+ for Node.js 24 support
- **GitHub-hosted runners**: Automatically meet version requirements, no infrastructure concerns
- **Workflow compatibility**: All current configurations are backward compatible—no changes needed
## Action-Specific Analysis
- `actions/checkout@v6`: Credentials now persisted to separate file; existing `persist-credentials: false` usage unaffected
- `actions/upload-artifact@v6` / `actions/download-artifact@v7`: API unchanged; `merge-multiple: true` still supported
- `pypa/cibuildwheel@v3.3.0`: Docker v29 compatibility fix included; environment variables unchanged
- `actions/cache@v5`: No API changes
- `peter-evans/create-pull-request@v8`: Node.js 24 support only; existing parameters compatible
- `scientific-python/upload-nightly-action@0.6.3`: Patch bump, no breaking changes
**Conclusion**: Dependency bumps are safe to merge as-is. Self-hosted runner users would need Actions Runner v2.327.1+, but this is outside workflow configuration scope.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33025 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33025/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33025/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33025/events | https://github.com/scikit-learn/scikit-learn/pull/33025 | 3,791,612,677 | PR_kwDOAAzd1s68DlNp | 33,025 | DOC Clarify Array API documentation | {
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"node_id": "MDU6TGFiZWwyMzk5... | closed | false | [] | null | 0 | 2026-01-08T06:54:47 | 2026-01-08T10:08:09 | 2026-01-08T10:08:04 | null | NONE | null | null | null | null | #### What does this implement/fix?
This pull request clarifies and improves documentation related to Array API
support in scikit-learn.It adds a Towncrier documentation entry under
`doc/whats_new/upcoming_changes/array-api` to better explain expected behavior
and usage notes when working with Array API–compatible inputs.This change is documentation-only and does not affect any code paths.
#### AI usage disclosure
I used AI assistance for documentation wording and clarification.
| {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33024 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33024/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33024/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33024/events | https://github.com/scikit-learn/scikit-learn/pull/33024 | 3,791,239,802 | PR_kwDOAAzd1s68CTAl | 33,024 | DOC: fix incomplete sentence in linear model introduction | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33023 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33023/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33023/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33023/events | https://github.com/scikit-learn/scikit-learn/pull/33023 | 3,789,903,822 | PR_kwDOAAzd1s67971s | 33,023 | DOC: Fix duplicate words in comments and docstrings | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 0 | 2026-01-07T18:39:48 | 2026-01-08T10:09:20 | 2026-01-08T10:09:20 | null | CONTRIBUTOR | null | null | null | null | ## Reference Issues/PRs
N/A
## What does this implement/fix? Explain your changes.
Fixed two typos where words were accidentally duplicated:
- Line 536 in `sklearn/metrics/_classification.py`: Changed "the the" to "the" in comment
- Line 430 in `sklearn/utils/_plotting.py`: Changed "of of" to "of" in docstring
These are simple typo corrections with no functional changes.
## AI usage disclosure
I used AI assistance for:
- Code generation (e.g., when writing an implementation or fixing a bug)
- Documentation (including examples)
## Any other comments?
This is my first contribution to scikit-learn. Thank you for maintaining this excellent library!
---
> This pull request includes code written with the assistance of AI.
> The code has been reviewed by a human. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33022 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33022/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33022/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33022/events | https://github.com/scikit-learn/scikit-learn/pull/33022 | 3,789,584,951 | PR_kwDOAAzd1s6784ie | 33,022 | Fix `log_loss` and `d2_log_loss_score` type promotion mismatch | {
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"node_id": "LA_kwDOAAzd1s8... | closed | false | [] | null | 3 | 2026-01-07T17:08:42 | 2026-01-08T14:33:33 | 2026-01-08T10:08:53 | null | MEMBER | null | null | null | null | #### What does this implement/fix? Explain your changes.
I was running `pytest -k "array_api and array_api_strict"` after updating `main` to check things were working. This came up with a group of failures like
```
FAILED sklearn/metrics/tests/test_classification.py::test_probabilistic_metrics_array_api[array_api_strict-device_1-float64-False-False-log_loss] - TypeError: array_api_strict.int64 and array_api_strict.float64 cannot be type promoted together
```
<details><summary>Full traceback for one test</summary>
<p>
```
_____________________________________________________ test_probabilistic_metrics_multilabel_array_api[array_api_strict-device_2-float32-True-d2_log_loss_score] ______________________________________________________
prob_metric = <function d2_log_loss_score at 0x155743600>, use_sample_weight = True, array_namespace = 'array_api_strict', device_ = array_api_strict.Device('device1'), dtype_name = 'float32'
@pytest.mark.parametrize(
"prob_metric", [brier_score_loss, log_loss, d2_brier_score, d2_log_loss_score]
)
@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_multilabel_array_api(
prob_metric, 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
multi-label inputs.
"""
xp = _array_api_for_tests(array_namespace, device_)
sample_weight = np.array([1, 2, 3, 1]) if use_sample_weight else None
y_true_np = np.array(
[
[0, 0, 1, 1],
[1, 0, 1, 0],
[0, 1, 0, 0],
[1, 1, 0, 1],
],
dtype=dtype_name,
)
y_true_xp = xp.asarray(y_true_np, device=device_)
y_prob_np = np.array(
[
[0.15, 0.27, 0.46, 0.12],
[0.33, 0.38, 0.06, 0.23],
[0.06, 0.28, 0.03, 0.63],
[0.14, 0.31, 0.26, 0.29],
],
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)
with config_context(array_api_dispatch=True):
> metric_score_xp = prob_metric(y_true_xp, y_prob_xp, sample_weight=sample_weight)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
sklearn/metrics/tests/test_classification.py:3744:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
sklearn/utils/_param_validation.py:218: in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
sklearn/metrics/_classification.py:3869: in d2_log_loss_score
numerator = _log_loss(
sklearn/metrics/_classification.py:3393: in _log_loss
loss = -xp.sum(xlogy(transformed_labels, y_pred), axis=1)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
../../miniconda/envs/sklearn-py3.13/lib/python3.13/site-packages/scipy/special/_support_alternative_backends.py:167: in wrapped
return f(*args, **kwargs)
^^^^^^^^^^^^^^^^^^
../../miniconda/envs/sklearn-py3.13/lib/python3.13/site-packages/scipy/special/_support_alternative_backends.py:76: in __xlogy
temp = x * xp.log(y)
^^^^^^^^^^^^^
../../miniconda/envs/sklearn-py3.13/lib/python3.13/site-packages/array_api_strict/_array_object.py:858: in __mul__
other = self._check_allowed_dtypes(other, "numeric", "__mul__")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
../../miniconda/envs/sklearn-py3.13/lib/python3.13/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.float32
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.float32 cannot be type promoted together
../../miniconda/envs/sklearn-py3.13/lib/python3.13/site-packages/array_api_strict/_dtypes.py:229: TypeError
```
</p>
</details>
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I am not sure why this doesn't happen in our CI. I use the same version of `array_api_strict` locally (2.4.1). Maybe it is because I use scipy 1.15.2 locally? This puzzles me.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33021 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33021/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33021/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33021/events | https://github.com/scikit-learn/scikit-learn/pull/33021 | 3,788,397,301 | PR_kwDOAAzd1s6745sx | 33,021 | TST Fix some tests with `global_random_seed` in `test_logistic.py` | {
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Follow up on https://github.com/scikit-learn/scikit-learn/pull/32759#issuecomment-3718112778
#### What does this implement/fix? Explain your changes.
- Fix tests `test_logistic_cv_sparse` and `test_logreg_l1` so that they pass with all global random seeds.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33020 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33020/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33020/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33020/events | https://github.com/scikit-learn/scikit-learn/pull/33020 | 3,788,120,202 | PR_kwDOAAzd1s673-sV | 33,020 | FIX instability of `_RidgeGCV` | {
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"node_id": "LA_k... | closed | false | [] | null | 24 | 2026-01-07T10:07:20 | 2026-02-23T10:29:27 | 2026-02-23T10:29:27 | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Fixes #32459 and supersedes #32506
#### What does this implement/fix? Explain your changes.
This fixes numerical errors in the small alpha regime by refactoring the solve/decompose methods:
- solve now returns the leave-one-out errors `looe` and the primal coeffcient `coef` instead of the `c` and `G_inverse_diag`
- the helper` _compute_gram` and `_compute_cov` reuse the precomputed `X_mean`
- estimation of `looe` and `coef` is adapted to each method for best numerical stability
- new option `cov` to force use eigendecomposition of the covariance matrix
- new option `gram` to force use eigendecomposition of the gram matrix
The `gcv_mode` options have been modified to always pick the cheaper option
- option `eigen` now switches to the cheaper option: `gram` if n < p and `cov` if `p < n`
- option `svd`, not implemented for sparse X, fallbacks to `eigen` when X is sparse.
- in main, `eigen` always picks `gram`and `svd ` always fallbacks to `cov` for sparse X (whatever the shape of X), which can explain the poor benchmark observed in #32506.
TODO
- [x] make it array api compliant
- [x] benchmark memory/time compared to main
- [x] update `_RidgeGCV` docstring, especially math explanations
- [x] ensure proper input validation on integer feature values and add non-regression test,
- [x] parametrize some LOO equivalence tests to use `float32` inputs: if results are bad, consider re-add the float32 to float64 upcast...
- [ ] understand and document which `gcv_mode` and problem shapes are capable of recovering the minimum norm solution in the small alpha limit.
- The minimum norm solution seems to be recoverable as demonstrated in `test_regularization_limits_ridge_gcv`, but there are still things to investigate in a follow-up issue/PR https://github.com/scikit-learn/scikit-learn/pull/33020#issuecomment-3819019340
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33019 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33019/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33019/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33019/events | https://github.com/scikit-learn/scikit-learn/pull/33019 | 3,788,028,130 | PR_kwDOAAzd1s673rEF | 33,019 | CI Store datasets in a shared cache for CircleCI | {
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] | closed | false | [] | null | 1 | 2026-01-07T09:39:39 | 2026-01-07T13:53:24 | 2026-01-07T13:53:24 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Towards #32961
#### What does this implement/fix? Explain your changes.
Only jobs that run on `main` or a release branch can update the cache, all other jobs try to use it. We don't want jobs triggered by forks to write to the cache.
The goal is to implement a cache that is shared between all jobs, no matter which PR they belong to. The cache should contain the datasets that are downloaded from a remote host (eg figshare). Assuming that figshare and co are mostly available we should be able to populate the cache and then use it. During an outage the chances are that we have a full cache already and don't notice it. Even a run on `main` would use the existing cache, so shouldn't be effected by an outage.
🔴 From reading https://circleci.com/docs/guides/optimize/caching/#caching-and-open-source I am actually pretty sure that this setup doesn't achieve what I'd like. It sounds like different forks have different caches (even for the same cache key)?
One thing that I am not 100% sure about is the fact that we already had this kind of caching setup for the docs builds. Yet during the last outage of figshare we observed some failures. Maybe the explanation for this is that each PR had their own cache and if the PR was created before the outage started it had the datasets in cache, but newer PRs didn't.
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Supercedes #32897 as it requires updates to conda lock files, which we generally prefer to perform internally.
#### What does this implement/fix? Explain your changes.
Removes the numpydoc upper bound constraint, as the bug was fixed in https://github.com/numpy/numpydoc/pull/622, which is included in the latest release: https://github.com/numpy/numpydoc/releases/tag/v1.10.0
Also updates the lock files for the 2 doc builds and builds that perform the docstring test (i.e. searched for `docstring_test_dependencies`):
* doc_min_dependencies
* doc_linux-64_conda
(docstring_test_dependencies)
* pymin_conda_forge_openblas_ubuntu_2204
* pylatest_pip_openblas_pandas
* pylatest_pip_scipy_dev
#### AI usage disclosure
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#### Any other comments?
Note we need to wait until https://github.com/scikit-learn/scikit-learn/pull/32902 is resolved.
cc @lesteve
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- test_logistic_cv_sparse[54-csr_matrix-saga]
- test_logistic_cv_sparse[54-csr_array-saga] | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33015 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33015/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33015/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33015/events | https://github.com/scikit-learn/scikit-learn/pull/33015 | 3,785,426,649 | PR_kwDOAAzd1s67vI1Q | 33,015 | FIX `predict` to also use `multiclass_colors` in DecisionBoundaryDisplay | {
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Fixes #32872 ~and the `predict` part of #32866~
#### What does this implement/fix? Explain your changes.
Previously, the case distinction for binary vs. multiclass was made based on the response dimension (see #32918 for reference), which led to multiclass `predict` ignoring the `multiclass_colors` parameter.
The colors are now selected based on the `n_classes` display class variable, which is extracted in `from_estimator`.
Note that a distinction based on the response shape still has to be made for plotting. ~in two cases (`contour` and `pcolormesh`). I'm not sure if there is a more elegant solution, but I didn't find a way to handle `pcolormesh` in the same way for `predict` as in the other cases.~
I adapted the docstrings and the exmple and added the response functions to the tests (and adapted them accordingly).
EDIT: The output of the code in issue #32872 now looks like this (Note that the levels problem from #32866 is still there for `predict`, but this PR is only concerned with making it use the same color map as the other methods, which it does now):
<img width="1211" height="1211" alt="image" src="https://github.com/user-attachments/assets/45e4d2b5-547c-41a8-a61c-6cd2377c9963" />
~Since every class is plotted separately now (as it was already for response method `predict_proba` and `decision_function`), this also fixes the `levels` issue noted in #32866. However, there are white lines between the classes for `predict` with `countourf`. If this is a problem (and using pcolormesh is not an option for any reason), the current approach will need to be adapted further.~
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I also added a test for the default value for `multiclass_colors`, which was missing.
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] | closed | false | [] | null | 7 | 2026-01-06T12:00:29 | 2026-01-22T06:35:05 | 2026-01-21T18:30:05 | null | CONTRIBUTOR | null | null | null | null | <!--
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Fixes #32989
#### What does this implement/fix? Explain your changes.
This PR fixes a `ValueError` in `enet_path` when `check_input=False`.
Because `_pre_fit` is skipped when `check_input=False`, the default value of the precompute parameter remained as the string 'auto', causing a crash in the solver dispatch logic. I have added a resolution block to handle 'auto', True, and sparse matrix constraints manually, ensuring the correct Gram matrix and Xy are available for the solver without duplicating the full validation suite.
A non-regression test has been added to `test_coordinate_descent.py`.
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- [ ] Test/benchmark generation
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#### Any other comments?
First-time contributor here! I've followed the internal logic of `_pre_fit` as closely as possible while maintaining performance and memory efficiency.
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None
#### What does this implement/fix? Explain your changes.
This PR updates the docstring for the `verbose` parameter to explicitly clarify its default value and behavior.
It provides a breakdown of the output levels inherited from `joblib`, specifically detailing what is printed at levels 0, 1, 2–9, and ≥10. This improves clarity for users tuning the verbosity of search processes.
#### AI usage disclosure
I used AI assistance for:
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [x] Documentation (including examples)
- [ ] Research and understanding
#### Any other comments?
The behavior descriptions match the current implementation in `joblib`. | {
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Fixes #32946
Related to #32968
---
### What does this implement/fix? Explain your changes.
This PR updates the documentation of the `verbose` parameter for `GridSearchCV` and
`RandomizedSearchCV` to reflect the actual observed behavior.
The previous documentation described verbosity levels (`>1`, `>2`, `>3`) that do not
accurately match the current output. In practice, verbosity is largely inherited from
joblib and differs notably for values ≥10.
This change:
- Explicitly documents the default value (`verbose=0`)
- Clarifies the behavior for `0`, `1`, `2–9`, and `≥10`
- Mentions the appearance of parameter candidate indices at higher verbosity levels
- Avoids backend-specific guarantees while accurately reflecting user-visible behavior
This is a documentation-only change and does not modify any runtime behavior.
---
### AI usage disclosure
I used AI assistance for:
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [x] Documentation (including examples)
- [x] Research and understanding
---
### Any other comments?
This PR focuses on a minimal and conservative documentation fix based on observed
behavior and maintainer feedback in the referenced issue.
No functional or API changes are introduced.
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This PR fixes the impurity decrease calculation for the friedman_mse criterion in scikit-learn tree models.
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The goal is to make the example easier to understand for beginners exploring
classical classification models.
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to avoid confusion when these docstrings are rendered in downstream libraries.
Fixes #32045 | {
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"url": "ht... | closed | false | [] | null | 6 | 2026-01-05T19:57:47 | 2026-01-06T08:59:57 | 2026-01-06T08:28:59 | null | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
if the docstring of a paramter contains something that could be an HTML tag it may mess up the rendering of the HTML parameter doc string
We have a `t_r` attribute whose docstring contains a `<TR>` (linking to a glossary etry) which does not play well in the middle of an HTML table.
```rst
t_r : :obj:`float` or :obj:`int` or None, default=None
:term:`Repetition time<TR>`, in seconds (sampling period).
Set to `None` if not provided.
```
### Steps/Code to Reproduce
install nilearn and make sure that you use sklearn>=1.8.0
```
pip install nilearn scikit-learn==1.8.0
```
run the following in a notebook (only tried on marimo)
```python
from nilearn.maskers import NiftiMasker
from packaging.version import parse
from nilearn._version import __version__
import itertools
def _doc_link_template(estimator):
version_url = "dev"
nil_version = parse(__version__)
if nil_version.dev is None:
version_url = (
f"{nil_version.major}.{nil_version.minor}.{nil_version.minor}"
)
estimator_name = estimator.__class__.__name__
tmp = list(
itertools.takewhile(
lambda part: not part.startswith("_"),
estimator.__class__.__module__.split("."),
)
)
estimator_module = ".".join([tmp[0], tmp[1]])
return (
f"https://nilearn.github.io/{version_url}/modules/generated/"
"{estimator_module}.{estimator_name}.html"
)
masker = NiftiMasker()
masker._doc_link_module = "nilearn"
masker._doc_link_template = _doc_link_template(masker)
masker
```
### Expected Results
a nicely laid out table
### Actual Results
<img width="602" height="680" alt="Image" src="https://github.com/user-attachments/assets/c0083911-15e0-4b9b-af4b-1ceb39870a49" />
<img width="602" height="680" alt="Image" src="https://github.com/user-attachments/assets/7e1a9d42-0e4d-468c-90b6-f61bd6feca1c" />
### Versions
```shell
sklearn 1.8.0
``` | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33004 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33004/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33004/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33004/events | https://github.com/scikit-learn/scikit-learn/pull/33004 | 3,781,826,159 | PR_kwDOAAzd1s67jODg | 33,004 | FIX Update 20newsgroups dataset URL to fix 403 error. The current Fig… | {
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] | closed | false | [] | null | 1 | 2026-01-05T15:06:54 | 2026-01-05T16:34:42 | 2026-01-05T16:34:42 | null | CONTRIBUTOR | null | null | null | null | ## Description
Fixes #33002 - Updates the 20newsgroups dataset download URL to resolve 403 Forbidden errors.
## Changes
- Updated `ARCHIVE.url` in `sklearn/datasets/_twenty_newsgroups.py` from Figshare to MIT CSAIL mirror
- Verified checksum matches the expected value
- Tested download successfully completes
## Testing
- Manually tested download from new URL
- Ran `pytest sklearn/datasets/tests/test_20news.py`
- Verified dataset loads correctly with `fetch_20newsgroups()`
<i>Note :
Skipped tests in CI are expected if the dataset isn’t pre-downloaded.
This change resolves the download issue without affecting existing functionality.</i>
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33003 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33003/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33003/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33003/events | https://github.com/scikit-learn/scikit-learn/issues/33003 | 3,781,810,392 | I_kwDOAAzd1s7hadjY | 33,003 | DOC Make it easier to create 100% scikit-learn compatible estimators | {
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] | open | false | [] | null | 5 | 2026-01-05T15:01:58 | 2026-02-11T21:39:36 | null | null | MEMBER | null | null | {
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} | Creating an estimator that is fully compliant and compatible with scikit-learn is quite difficult for people who aren't scikit-learn maintainers. As a result there are a lot of estimators out there that are kinda compatible but not quite. This isn't great for users of the scikit-learn ecosystem, it is annoying for developers of third-party libraries and creates work for scikit-learn maintainers.
We have tools and documentation that help third-party developers create compatible estimators. However, from looking around the world it seems like a lot of people don't use them. Probably because they are too hard to find, not clear enough, too difficult to use, or another reason.
I think https://scikit-learn.org/dev/developers/develop.html is the best place in the documentation that we have right now. It contains a lot! But I'm not sure it contains everything you need to know (because it is quite long).
I'm not sure what exactly the reasons are for why people create semi-compatible estimators.
My impression from cuml is that it is partly not knowing about the existing tools, partly not knowing that some of minor looking details are important, and the fact that it is a difficult task. A 100% compatible estimator also imposes some quite strict constraints on how you can implement your estimator - without understanding why these constraints exist and are "a good thing" I understand that people choose not to follow them.
https://github.com/scikit-learn/scikit-learn/issues/32910 is an example from the skrub team of things that go wrong when you don't achieve full compatibility. They are not your average "people from the internet" in that they have good access to core maintainers. The original report also looks like they put in a reasonable amount of work and brain power to try and understand what is happening. But they didn't figure it out. It turns out that by default you have to have a fitted attribute on your estimator for it to be considered fitted - you can argue whether or not this constraint/assumption is reasonable, but the fact that it took the creation of an issue and a few messages to pin this down shows it is not easy.
This issue is about finding out what people find difficult (pain points) and then figuring out what can be done about them. We want a good ecosystem to exist, there is already a lot of material out there, but somehow there is something missing.
A constraint I'd add is that I think we should try and avoid creating yet another guide or said differently: improve things by removing things or editing what we have. No one reads, and the longer the text the less people read. The more different guides we have the less likely it is that people read all of them. | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33002 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33002/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33002/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33002/events | https://github.com/scikit-learn/scikit-learn/issues/33002 | 3,781,647,086 | I_kwDOAAzd1s7hZ1ru | 33,002 | Downloading 20_newsgroups results in 403 error | {
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"url": "ht... | closed | false | [] | null | 3 | 2026-01-05T14:11:55 | 2026-01-05T16:33:57 | 2026-01-05T16:33:57 | null | NONE | null | null | {
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} | ### Describe the bug
When trying to download the 20_newsgroups, I'm getting the following error:
```
File [~/miniconda3/lib/python3.13/urllib/request.py:613](http://localhost:8888/lab/tree/miniconda3/lib/python3.13/urllib/request.py#line=612), in HTTPDefaultErrorHandler.http_error_default(self, req, fp, code, msg, hdrs)
612 def http_error_default(self, req, fp, code, msg, hdrs):
--> 613 raise HTTPError(req.full_url, code, msg, hdrs, fp)
HTTPError: HTTP Error 403: Forbidden
```
### Steps/Code to Reproduce
Steps to reproduce (if you ever downloaded this dataset, delete it from `~/scikit-learn` or change the `data_home ` parameter to point to another path):
```python
from sklearn.datasets import fetch_20newsgroups
# Load the 20 Newsgroups dataset
newsgroups_train = fetch_20newsgroups()
```
### Expected Results
Dataset should be downloaded and loaded in memory.
### Actual Results
```python
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
Cell In[2], line 4
1 from sklearn.datasets import fetch_20newsgroups
3 # Load the 20 Newsgroups dataset
----> 4 newsgroups_train = fetch_20newsgroups()
File [~/miniconda3/lib/python3.13/site-packages/sklearn/utils/_param_validation.py:218](http://localhost:8888/lab/tree/miniconda3/lib/python3.13/site-packages/sklearn/utils/_param_validation.py#line=217), in validate_params.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
212 try:
213 with config_context(
214 skip_parameter_validation=(
215 prefer_skip_nested_validation or global_skip_validation
216 )
217 ):
--> 218 return func(*args, **kwargs)
219 except InvalidParameterError as e:
220 # When the function is just a wrapper around an estimator, we allow
221 # the function to delegate validation to the estimator, but we replace
222 # the name of the estimator by the name of the function in the error
223 # message to avoid confusion.
224 msg = re.sub(
225 r"parameter of \w+ must be",
226 f"parameter of {func.__qualname__} must be",
227 str(e),
228 )
File [~/miniconda3/lib/python3.13/site-packages/sklearn/datasets/_twenty_newsgroups.py:320](http://localhost:8888/lab/tree/miniconda3/lib/python3.13/site-packages/sklearn/datasets/_twenty_newsgroups.py#line=319), in fetch_20newsgroups(data_home, subset, categories, shuffle, random_state, remove, download_if_missing, return_X_y, n_retries, delay)
318 if download_if_missing:
319 logger.info("Downloading 20news dataset. This may take a few minutes.")
--> 320 cache = _download_20newsgroups(
321 target_dir=twenty_home,
322 cache_path=cache_path,
323 n_retries=n_retries,
324 delay=delay,
325 )
326 else:
327 raise OSError("20Newsgroups dataset not found")
File [~/miniconda3/lib/python3.13/site-packages/sklearn/datasets/_twenty_newsgroups.py:79](http://localhost:8888/lab/tree/miniconda3/lib/python3.13/site-packages/sklearn/datasets/_twenty_newsgroups.py#line=78), in _download_20newsgroups(target_dir, cache_path, n_retries, delay)
76 os.makedirs(target_dir, exist_ok=True)
78 logger.info("Downloading dataset from %s (14 MB)", ARCHIVE.url)
---> 79 archive_path = _fetch_remote(
80 ARCHIVE, dirname=target_dir, n_retries=n_retries, delay=delay
81 )
83 logger.debug("Decompressing %s", archive_path)
84 with tarfile.open(archive_path, "r:gz") as fp:
File [~/miniconda3/lib/python3.13/site-packages/sklearn/datasets/_base.py:1513](http://localhost:8888/lab/tree/miniconda3/lib/python3.13/site-packages/sklearn/datasets/_base.py#line=1512), in _fetch_remote(remote, dirname, n_retries, delay)
1511 while True:
1512 try:
-> 1513 urlretrieve(remote.url, temp_file_path)
1514 break
1515 except (URLError, TimeoutError):
File [~/miniconda3/lib/python3.13/urllib/request.py:214](http://localhost:8888/lab/tree/miniconda3/lib/python3.13/urllib/request.py#line=213), in urlretrieve(url, filename, reporthook, data)
197 """
198 Retrieve a URL into a temporary location on disk.
199
(...) 210 data file as well as the resulting HTTPMessage object.
211 """
212 url_type, path = _splittype(url)
--> 214 with contextlib.closing(urlopen(url, data)) as fp:
215 headers = fp.info()
217 # Just return the local path and the "headers" for file://
218 # URLs. No sense in performing a copy unless requested.
File [~/miniconda3/lib/python3.13/urllib/request.py:189](http://localhost:8888/lab/tree/miniconda3/lib/python3.13/urllib/request.py#line=188), in urlopen(url, data, timeout, context)
187 else:
188 opener = _opener
--> 189 return opener.open(url, data, timeout)
File [~/miniconda3/lib/python3.13/urllib/request.py:495](http://localhost:8888/lab/tree/miniconda3/lib/python3.13/urllib/request.py#line=494), in OpenerDirector.open(self, fullurl, data, timeout)
493 for processor in self.process_response.get(protocol, []):
494 meth = getattr(processor, meth_name)
--> 495 response = meth(req, response)
497 return response
File [~/miniconda3/lib/python3.13/urllib/request.py:604](http://localhost:8888/lab/tree/miniconda3/lib/python3.13/urllib/request.py#line=603), in HTTPErrorProcessor.http_response(self, request, response)
601 # According to RFC 2616, "2xx" code indicates that the client's
602 # request was successfully received, understood, and accepted.
603 if not (200 <= code < 300):
--> 604 response = self.parent.error(
605 'http', request, response, code, msg, hdrs)
607 return response
File [~/miniconda3/lib/python3.13/urllib/request.py:533](http://localhost:8888/lab/tree/miniconda3/lib/python3.13/urllib/request.py#line=532), in OpenerDirector.error(self, proto, *args)
531 if http_err:
532 args = (dict, 'default', 'http_error_default') + orig_args
--> 533 return self._call_chain(*args)
File [~/miniconda3/lib/python3.13/urllib/request.py:466](http://localhost:8888/lab/tree/miniconda3/lib/python3.13/urllib/request.py#line=465), in OpenerDirector._call_chain(self, chain, kind, meth_name, *args)
464 for handler in handlers:
465 func = getattr(handler, meth_name)
--> 466 result = func(*args)
467 if result is not None:
468 return result
File [~/miniconda3/lib/python3.13/urllib/request.py:613](http://localhost:8888/lab/tree/miniconda3/lib/python3.13/urllib/request.py#line=612), in HTTPDefaultErrorHandler.http_error_default(self, req, fp, code, msg, hdrs)
612 def http_error_default(self, req, fp, code, msg, hdrs):
--> 613 raise HTTPError(req.full_url, code, msg, hdrs, fp)
HTTPError: HTTP Error 403: Forbidden
```
### Versions
```shell
System:
python: 3.13.2 | packaged by Anaconda, Inc. | (main, Feb 6 2025, 18:56:02) [GCC 11.2.0]
executable: /home/lucas/miniconda3/bin/python
machine: Linux-6.14.0-1018-oem-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.8.0
pip: 25.2
setuptools: 80.9.0
numpy: 2.2.6
scipy: 1.16.3
Cython: None
pandas: 2.3.2
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joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
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prefix: libscipy_openblas
filepath: /home/lucas/miniconda3/lib/python3.13/site-packages/numpy.libs/libscipy_openblas64_-56d6093b.so
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We have recently added [Issues tagged “Needs Triage”]( https://scikit-learn.org/dev/developers/contributing.html#issues-tagged-needs-triage) to the developer guide, that explains what the label means. But my impression is that it is often overlooked by new contributors.
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🙌 Thanks for contributing a pull request!
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#### What does this implement/fix? Explain your changes.
Continued from #32820, I searched for more patterns regarding a/an usage mistakes. Then I manually reviewed which ones needed to be changed.
The correct usage is `"an HTML"` and `"an L1"`. The "an" is used when the phonetic spelling of the word starts with a vowel.
https://www.grammarly.com/blog/grammar/indefinite-articles-a-and-an/
These patterns are not exhaustive, so a few issues of ("a" vs "an") still remain.
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 0 | 2026-01-04T20:54:14 | 2026-01-05T09:11:41 | 2026-01-05T09:11:40 | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
N/A
#### What does this implement/fix? Explain your changes.
Issue: In the summary table [here](https://scikit-learn.org/stable/api/sklearn.linear_model.html#regressors-with-variable-selection), Sphinx autosummary truncates docstrings for `Lars` and `LassoLars` at "a.k.a." because it interprets the final dot as the end of the sentence. For instance, the summary for `Lars` reads `Least Angle Regression model a.k.a.`, deleting the remaining ` LAR.` from the first line of [its docstring](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lars.html#sklearn.linear_model.Lars).
Fix: This PR replaces "a.k.a." with "aka" in the first line of the docstrings for `Lars` and `LassoLars` in `sklearn/linear_model/_least_angle.py`.
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## Description
Fixes a bug where `enet_path` raises `ValueError` when called with `check_input=False` and `precompute='auto'` (the default value).
## Problem
When `check_input=True` (default), the `_pre_fit` function resolves `precompute='auto'` to a boolean based on data dimensions. However, when `check_input=False`, `_pre_fit` is bypassed, leaving `precompute` as the string `'auto'`. The subsequent solver selection logic does not handle this string value, causing a `ValueError`.
## Solution
Added an `else` block after the `check_input` conditional to resolve `precompute='auto'` to a boolean even when `check_input=False`, maintaining consistency with the default behavior.
## Changes
- Modified `sklearn/linear_model/_coordinate_descent.py`:
- Added logic to resolve `precompute='auto'` when `check_input=False`
- Added regression test in `sklearn/linear_model/tests/test_coordinate_descent.py`:
- `test_enet_path_check_input_false_with_auto_precompute()`
## Reproduction
```python
import numpy as np
from sklearn.linear_model import enet_path
from sklearn.datasets import make_regression
X, y = make_regression(n_samples=100, n_features=5, n_informative=2, random_state=0)
X = np.asfortranarray(X)
# Previously raised ValueError, now works correctly
alphas, coefs, dual_gaps = enet_path(X, y, n_alphas=3, check_input=False)
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"node_id": "LA_k... | closed | false | [] | null | 4 | 2026-01-04T16:55:24 | 2026-01-05T10:29:40 | 2026-01-05T10:20:02 | null | NONE | null | null | null | null | - Add validation in enet_path when check_input=False skips _pre_fit
- Add validation in _pre_fit for all invalid string values
- Raise ValueError for non-'auto' strings with clear error message
- Add regression tests covering all scenarios (valid/invalid, check_input True/False)
Fixes issue where enet_path crashed with default precompute='auto' when check_input=False, because string validation was skipped.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32992 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32992/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32992/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32992/events | https://github.com/scikit-learn/scikit-learn/pull/32992 | 3,778,517,624 | PR_kwDOAAzd1s67Y9TN | 32,992 | MNT Update year to 2026 in COPYING | {
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"... | closed | false | [] | null | 1 | 2026-01-03T21:40:22 | 2026-01-05T09:06:05 | 2026-01-05T08:47:41 | null | MEMBER | null | null | null | null | #### What does this implement/fix? Explain your changes.
Update year in `COPYING` to 2026.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32991 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32991/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32991/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32991/events | https://github.com/scikit-learn/scikit-learn/pull/32991 | 3,778,448,732 | PR_kwDOAAzd1s67Ywlb | 32,991 | TST : use global_random_seed in sklearn/linear_model/tests/test_coordinate_descent.py | {
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"node_id": "MDU6TGFiZWwxODE1ND... | open | false | [] | null | 4 | 2026-01-03T20:07:21 | 2026-02-23T18:08:51 | null | null | CONTRIBUTOR | null | null | null | null | Migrated tests in `sklearn/linear_model/tests/test_coordinate_descent.py` to use the `global_random_seed` fixture and did some cleanup. Related to #22827 and #32987. ( Doesnt completely close as we have to migrate a lot of other files too )
TLDR;
- I did **not** migrate the `TODO(1.9)`-removal tests at the end of the file (the last 3 tests).
- For tests that rely on data-specific **hardcoded assertion values** (e.g. `test_lasso_cv`, `test_warm_start_convergence`, etc.),
I kept the tests same.
* For a few tests that were using `build_dataset()` deterministically (with `random_state=0`), I removed the random generation and replaced it with a small static dataset (e.g. `test_enet_nonfinite_params`, `test_elasticnet_precompute_incorrect_gram`, `test_enet_copy_X_false_and_check_input_false_may_overwrite_input`, etc.).
### Tests migrated to `global_random_seed`
- `test_1d_multioutput_enet_and_multitask_enet_cv`
- `test_1d_multioutput_lasso_and_multitask_lasso_cv`
- `test_convergence_warnings`
- `test_cython_solver_equivalence`
- `test_elasticnet_precompute_gram_weighted_samples`
- `test_enet_cv_grid_search`
- `test_enet_cv_positive_constraint`
- `test_enet_multitarget`
- `test_enet_path`
- `test_enet_path_positive`
- `test_enet_ridge_consistency`
- `test_lasso_cv_positive_constraint`
- `test_lasso_dual_gap`
- `test_multi_task_lasso_and_enet`
- `test_multi_task_lasso_readonly_data`
- `test_random_descent`
- `test_sample_weight_invariance`
- `test_sparse_dense_descent_paths`
- `test_sparse_input_convergence_warning`
- `test_sparse_input_dtype_enet_and_lassocv`
- `test_uniform_targets`
- `test_warm_start`
- `test_warm_start_multitask_lasso`
### Other updated functions / tests
- `build_dataset`
- `test_check_input_false`
- `test_elasticnet_precompute_incorrect_gram`
- `test_enet_copy_X_False_check_input_False`
- `test_enet_copy_X_True`
- `test_enet_copy_X_false_and_check_input_false_may_overwrite_input`
- `test_enet_copy_X_true_does_not_overwrite_input`
- `test_enet_float_precision`
- `test_enet_nonfinite_params`
- `test_enet_sample_weight_does_not_overwrite_sample_weight`
- `test_lassocv_alphas_validation`
- `test_linear_models_cv_fit_with_loky`
- `test_multi_task_lasso_vs_skglm`
- `test_multioutput_enetcv_error`
- `test_overrided_gram_matrix`
- `test_path_unknown_parameter`
- `test_set_order_sparse`
cc : @lorentzenchr | null | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 1 | 2026-01-03T19:52:01 | 2026-01-03T22:00:05 | 2026-01-03T22:00:04 | null | NONE | null | null | null | null | <!--
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Fixes #32946
#### What does this implement/fix? Explain your changes.
This PR clarifies the documentation of the `verbose` parameter in
`GridSearchCV` and `RandomizedSearchCV` to reflect the observed behavior.
In particular, it documents:
- the default behavior for `verbose=0`,
- the plateau in output for verbosity values 3–9,
- and the additional output shown for `verbose >= 10`.
This is a documentation-only change and does not modify any runtime behavior.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32989 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32989/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32989/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32989/events | https://github.com/scikit-learn/scikit-learn/issues/32989 | 3,777,928,220 | I_kwDOAAzd1s7hLpwc | 32,989 | enet_path raises ValueError when check_input=False and precompute='auto' (default) | {
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] | closed | false | [] | null | 6 | 2026-01-03T10:18:10 | 2026-01-21T18:30:07 | 2026-01-21T18:30:07 | null | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
When calling `linear_model.enet_path` with `check_input=False`, the function crashes if `precompute` is left at its default value of 'auto'.
**TL;DR**: This is because `_pre_fit` (which resolves 'auto' to a boolean) is skipped, but the subsequent solver selection logic does not handle the string 'auto'. I'm happy to submit a PR to fix this if you'd like!
The enet_path function utilizes a decorator-based parameter validation. However, the internal logic flow creates a dependency between `check_input` and `precompute`.
Specifically, when `check_input=True` (default), the function enters `_pre_fit`, which resolves precompute='auto' into a boolean based on the data dimensions. When a user sets `check_input=False` to optimize performance, `_pre_fit` is bypassed. Consequently, the precompute variable remains the string 'auto', which is not handled by the subsequent coordinate descent solver selection logic, leading to a `ValueError` despite 'auto' being the documented default value.
### Steps/Code to Reproduce
```
import numpy as np
from sklearn.linear_model import enet_path
from sklearn.datasets import make_regression
X, y = make_regression(n_samples=100, n_features=5, n_informative=2, random_state=0)
X = np.asfortranarray(X)
# This works (default check_input=True)
enet_path(X, y, n_alphas=3, check_input=True)
# This raises ValueError: Precompute should be one of True, False, 'auto' or array-like. Got 'auto'
enet_path(X, y, n_alphas=3, check_input=False)
```
### Expected Results
No error is thrown when check_input=False
### Actual Results
ValueError: Precompute should be one of True, False, 'auto' or array-like. Got 'auto'
### Versions
```shell
1.7.2
``` | {
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Fixes #32987
#### What does this implement/fix? Explain your changes.
- This removes the special condition that checks `se_enet <= se_ols`
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} | ### Describe the bug
While working on another scikit-learn PR (**#32368**), CI intermittently failed in `sklearn/linear_model/tests/test_coordinate_descent.py::test_enet_ols_consistency`.It came from a strict numerical ordering assertion on the squared error:
```python
assert se_enet <= se_ols <= 1e-20
```
This was recently merged 2 days ago in #32845 . On further digging, I noticed:
* the squared error is computed **without `sample_weight`**, even though both models are fit with `sample_weight`
* Lot of tests in this test file are using a **hard-coded RNG seed** (`RandomState(42)`) instead of `global_random_seed` ( I did have a look at #22827 )
* when running across multiple seeds (e.g. with `SKLEARN_TESTS_GLOBAL_RANDOM_SEED=all`), additional failures can appear for some configurations due to **tight absolute tolerances** on `intercept_` comparisons near zero.
P.S Same test was passing for seed 42 on Windows locally. However as i start changing seeds it would fail on Windows too.
### Steps/Code to Reproduce
I updated this test to use `global_random_seed` and ran it for all seeds on Ubuntu.
```bash
SKLEARN_TESTS_GLOBAL_RANDOM_SEED=all pytest sklearn/linear_model/tests/test_coordinate_descent.py -k test_enet_ols_consistency
```
```python
@pytest.mark.filterwarnings("ignore:With alpha=0, this algorithm:UserWarning")
@pytest.mark.parametrize("precompute", [False, True])
@pytest.mark.parametrize("effective_rank", [None, 10])
def test_enet_ols_consistency(precompute, effective_rank,global_random_seed):
"""Test that ElasticNet(alpha=0) converges to the same solution as OLS."""
rng = np.random.RandomState(global_random_seed)
n_samples = 300
X, y = make_regression(
n_samples=n_samples,
n_features=100,
effective_rank=effective_rank,
n_informative=50,
random_state=rng,
)
sw = rng.uniform(low=0.01, high=10, size=X.shape[0])
ols = LinearRegression().fit(X, y, sample_weight=sw)
enet = ElasticNet(alpha=0, precompute=precompute, tol=1e-15).fit(
X, y, sample_weight=sw
)
# Might be a singular problem, so check for same predictions
assert_allclose(enet.predict(X), ols.predict(X))
# and for similar objective function (squared error)
se_ols = np.sum((y - ols.predict(X)) ** 2)
se_enet = np.sum((y - enet.predict(X)) ** 2)
if precompute:
assert se_ols <= 1e-20
assert se_enet <= 1e-20
else:
assert se_enet <= se_ols <= 1e-20 # Who would have thought that?
# We check equal coefficients, but "only" with absolute tolerance.
assert_allclose(enet.coef_, ols.coef_, atol=1e-11)
assert_allclose(enet.intercept_, ols.intercept_, atol=1e-12)
```
### Expected Results
No test failures
### Actual Results
Reference CI failure (exact run):
```text
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=83843&view=ms.vss-test-web.build-test-results-tab&runId=837146&resultId=115395&paneView=debug
```
For my local run,
```bash
FAILED sklearn/linear_model/tests/test_coordinate_descent.py::test_enet_ols_consistency[42-10-False] - assert 7.304604230718523e-26 <= 6.721669301829635e-26
FAILED sklearn/linear_model/tests/test_coordinate_descent.py::test_enet_ols_consistency[59-None-True] - AssertionError:
```
### Versions
```shell
System:
python: 3.11.14 | packaged by conda-forge | (main, Oct 22 2025, 22:46:25) [GCC 14.3.0]
executable: /home/gauta/miniforge3/envs/scikit-learn-min-dependencies/bin/python3
machine: Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.9.dev0
pip: 25.3
setuptools: 80.9.0
numpy: 1.24.1
scipy: 1.10.0
Cython: 3.1.2
pandas: 1.5.0
matplotlib: 3.6.1
joblib: 1.3.0
threadpoolctl: 3.2.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 32
prefix: libopenblas
filepath: /home/gauta/miniforge3/envs/scikit-learn-min-dependencies/lib/libopenblasp-r0.3.25.so
version: 0.3.25
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 32
prefix: libomp
filepath: /home/gauta/miniforge3/envs/scikit-learn-min-dependencies/lib/libomp.so
version: None
``` | {
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Towards #26024
#### What does this implement/fix? Explain your changes.
Adds array API support for `pairwise_distances_argmin`
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AI usage disclosure:
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| Package | From | To |
| --- | --- | --- |
| [actions/checkout](https://github.com/actions/checkout) | `5` | `6` |
| [actions/download-artifact](https://github.com/actions/download-artifact) | `6` | `7` |
| [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) | `3.2.1` | `3.3.0` |
| [actions/upload-artifact](https://github.com/actions/upload-artifact) | `5` | `6` |
| [scientific-python/upload-nightly-action](https://github.com/scientific-python/upload-nightly-action) | `0.6.2` | `0.6.3` |
| [actions/cache](https://github.com/actions/cache) | `4` | `5` |
| [peter-evans/create-pull-request](https://github.com/peter-evans/create-pull-request) | `7` | `8` |
Updates `actions/checkout` from 5 to 6
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/actions/checkout/releases">actions/checkout's releases</a>.</em></p>
<blockquote>
<h2>v6.0.0</h2>
<h2>What's Changed</h2>
<ul>
<li>Update README to include Node.js 24 support details and requirements by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2248">actions/checkout#2248</a></li>
<li>Persist creds to a separate file by <a href="https://github.com/ericsciple"><code>@ericsciple</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2286">actions/checkout#2286</a></li>
<li>v6-beta by <a href="https://github.com/ericsciple"><code>@ericsciple</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2298">actions/checkout#2298</a></li>
<li>update readme/changelog for v6 by <a href="https://github.com/ericsciple"><code>@ericsciple</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2311">actions/checkout#2311</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/checkout/compare/v5.0.0...v6.0.0">https://github.com/actions/checkout/compare/v5.0.0...v6.0.0</a></p>
<h2>v6-beta</h2>
<h2>What's Changed</h2>
<p>Updated persist-credentials to store the credentials under <code>$RUNNER_TEMP</code> instead of directly in the local git config.</p>
<p>This requires a minimum Actions Runner version of <a href="https://github.com/actions/runner/releases/tag/v2.329.0">v2.329.0</a> to access the persisted credentials for <a href="https://docs.github.com/en/actions/tutorials/use-containerized-services/create-a-docker-container-action">Docker container action</a> scenarios.</p>
<h2>v5.0.1</h2>
<h2>What's Changed</h2>
<ul>
<li>Port v6 cleanup to v5 by <a href="https://github.com/ericsciple"><code>@ericsciple</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2301">actions/checkout#2301</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/checkout/compare/v5...v5.0.1">https://github.com/actions/checkout/compare/v5...v5.0.1</a></p>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/actions/checkout/blob/main/CHANGELOG.md">actions/checkout's changelog</a>.</em></p>
<blockquote>
<h1>Changelog</h1>
<h2>v6.0.0</h2>
<ul>
<li>Persist creds to a separate file by <a href="https://github.com/ericsciple"><code>@ericsciple</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2286">actions/checkout#2286</a></li>
<li>Update README to include Node.js 24 support details and requirements by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2248">actions/checkout#2248</a></li>
</ul>
<h2>v5.0.1</h2>
<ul>
<li>Port v6 cleanup to v5 by <a href="https://github.com/ericsciple"><code>@ericsciple</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2301">actions/checkout#2301</a></li>
</ul>
<h2>v5.0.0</h2>
<ul>
<li>Update actions checkout to use node 24 by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2226">actions/checkout#2226</a></li>
</ul>
<h2>v4.3.1</h2>
<ul>
<li>Port v6 cleanup to v4 by <a href="https://github.com/ericsciple"><code>@ericsciple</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2305">actions/checkout#2305</a></li>
</ul>
<h2>v4.3.0</h2>
<ul>
<li>docs: update README.md by <a href="https://github.com/motss"><code>@motss</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1971">actions/checkout#1971</a></li>
<li>Add internal repos for checking out multiple repositories by <a href="https://github.com/mouismail"><code>@mouismail</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1977">actions/checkout#1977</a></li>
<li>Documentation update - add recommended permissions to Readme by <a href="https://github.com/benwells"><code>@benwells</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2043">actions/checkout#2043</a></li>
<li>Adjust positioning of user email note and permissions heading by <a href="https://github.com/joshmgross"><code>@joshmgross</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2044">actions/checkout#2044</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/checkout/pull/2194">actions/checkout#2194</a></li>
<li>Update CODEOWNERS for actions by <a href="https://github.com/TingluoHuang"><code>@TingluoHuang</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2224">actions/checkout#2224</a></li>
<li>Update package dependencies by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/2236">actions/checkout#2236</a></li>
</ul>
<h2>v4.2.2</h2>
<ul>
<li><code>url-helper.ts</code> now leverages well-known environment variables by <a href="https://github.com/jww3"><code>@jww3</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1941">actions/checkout#1941</a></li>
<li>Expand unit test coverage for <code>isGhes</code> by <a href="https://github.com/jww3"><code>@jww3</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1946">actions/checkout#1946</a></li>
</ul>
<h2>v4.2.1</h2>
<ul>
<li>Check out other refs/* by commit if provided, fall back to ref by <a href="https://github.com/orhantoy"><code>@orhantoy</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1924">actions/checkout#1924</a></li>
</ul>
<h2>v4.2.0</h2>
<ul>
<li>Add Ref and Commit outputs by <a href="https://github.com/lucacome"><code>@lucacome</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1180">actions/checkout#1180</a></li>
<li>Dependency updates by <a href="https://github.com/dependabot"><code>@dependabot</code></a>- <a href="https://redirect.github.com/actions/checkout/pull/1777">actions/checkout#1777</a>, <a href="https://redirect.github.com/actions/checkout/pull/1872">actions/checkout#1872</a></li>
</ul>
<h2>v4.1.7</h2>
<ul>
<li>Bump the minor-npm-dependencies group across 1 directory with 4 updates by <a href="https://github.com/dependabot"><code>@dependabot</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1739">actions/checkout#1739</a></li>
<li>Bump actions/checkout from 3 to 4 by <a href="https://github.com/dependabot"><code>@dependabot</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1697">actions/checkout#1697</a></li>
<li>Check out other refs/* by commit by <a href="https://github.com/orhantoy"><code>@orhantoy</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1774">actions/checkout#1774</a></li>
<li>Pin actions/checkout's own workflows to a known, good, stable version. by <a href="https://github.com/jww3"><code>@jww3</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1776">actions/checkout#1776</a></li>
</ul>
<h2>v4.1.6</h2>
<ul>
<li>Check platform to set archive extension appropriately by <a href="https://github.com/cory-miller"><code>@cory-miller</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1732">actions/checkout#1732</a></li>
</ul>
<h2>v4.1.5</h2>
<ul>
<li>Update NPM dependencies by <a href="https://github.com/cory-miller"><code>@cory-miller</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1703">actions/checkout#1703</a></li>
<li>Bump github/codeql-action from 2 to 3 by <a href="https://github.com/dependabot"><code>@dependabot</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1694">actions/checkout#1694</a></li>
<li>Bump actions/setup-node from 1 to 4 by <a href="https://github.com/dependabot"><code>@dependabot</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1696">actions/checkout#1696</a></li>
<li>Bump actions/upload-artifact from 2 to 4 by <a href="https://github.com/dependabot"><code>@dependabot</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1695">actions/checkout#1695</a></li>
</ul>
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</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/actions/checkout/commit/8e8c483db84b4bee98b60c0593521ed34d9990e8"><code>8e8c483</code></a> Clarify v6 README (<a href="https://redirect.github.com/actions/checkout/issues/2328">#2328</a>)</li>
<li><a href="https://github.com/actions/checkout/commit/033fa0dc0b82693d8986f1016a0ec2c5e7d9cbb1"><code>033fa0d</code></a> Add worktree support for persist-credentials includeIf (<a href="https://redirect.github.com/actions/checkout/issues/2327">#2327</a>)</li>
<li><a href="https://github.com/actions/checkout/commit/c2d88d3ecc89a9ef08eebf45d9637801dcee7eb5"><code>c2d88d3</code></a> Update all references from v5 and v4 to v6 (<a href="https://redirect.github.com/actions/checkout/issues/2314">#2314</a>)</li>
<li><a href="https://github.com/actions/checkout/commit/1af3b93b6815bc44a9784bd300feb67ff0d1eeb3"><code>1af3b93</code></a> update readme/changelog for v6 (<a href="https://redirect.github.com/actions/checkout/issues/2311">#2311</a>)</li>
<li><a href="https://github.com/actions/checkout/commit/71cf2267d89c5cb81562390fa70a37fa40b1305e"><code>71cf226</code></a> v6-beta (<a href="https://redirect.github.com/actions/checkout/issues/2298">#2298</a>)</li>
<li><a href="https://github.com/actions/checkout/commit/069c6959146423d11cd0184e6accf28f9d45f06e"><code>069c695</code></a> Persist creds to a separate file (<a href="https://redirect.github.com/actions/checkout/issues/2286">#2286</a>)</li>
<li><a href="https://github.com/actions/checkout/commit/ff7abcd0c3c05ccf6adc123a8cd1fd4fb30fb493"><code>ff7abcd</code></a> Update README to include Node.js 24 support details and requirements (<a href="https://redirect.github.com/actions/checkout/issues/2248">#2248</a>)</li>
<li>See full diff in <a href="https://github.com/actions/checkout/compare/v5...v6">compare view</a></li>
</ul>
</details>
<br />
Updates `actions/download-artifact` from 6 to 7
<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>v7.0.0</h2>
<h2>v7 - What's new</h2>
<blockquote>
<p>[!IMPORTANT]
actions/download-artifact@v7 now runs on Node.js 24 (<code>runs.using: node24</code>) and requires a minimum Actions Runner version of 2.327.1. If you are using self-hosted runners, ensure they are updated before upgrading.</p>
</blockquote>
<h3>Node.js 24</h3>
<p>This release updates the runtime to Node.js 24. v6 had preliminary support for Node 24, however this action was by default still running on Node.js 20. Now this action by default will run on Node.js 24.</p>
<h2>What's Changed</h2>
<ul>
<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/download-artifact/pull/440">actions/download-artifact#440</a></li>
<li>Download Artifact Node24 support by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/download-artifact/pull/415">actions/download-artifact#415</a></li>
<li>fix: update <code>@actions/artifact</code> to fix Node.js 24 punycode deprecation by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/download-artifact/pull/451">actions/download-artifact#451</a></li>
<li>prepare release v7.0.0 for Node.js 24 support by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/download-artifact/pull/452">actions/download-artifact#452</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/patrikpolyak"><code>@patrikpolyak</code></a> made their first contribution in <a href="https://redirect.github.com/actions/download-artifact/pull/440">actions/download-artifact#440</a></li>
<li><a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> made their first contribution in <a href="https://redirect.github.com/actions/download-artifact/pull/415">actions/download-artifact#415</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/download-artifact/compare/v6.0.0...v7.0.0">https://github.com/actions/download-artifact/compare/v6.0.0...v7.0.0</a></p>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/actions/download-artifact/commit/37930b1c2abaa49bbe596cd826c3c89aef350131"><code>37930b1</code></a> Merge pull request <a href="https://redirect.github.com/actions/download-artifact/issues/452">#452</a> from actions/download-artifact-v7-release</li>
<li><a href="https://github.com/actions/download-artifact/commit/72582b9e0acd370909e83fa4a1fd0fca3ad452d8"><code>72582b9</code></a> doc: update readme</li>
<li><a href="https://github.com/actions/download-artifact/commit/0d2ec9d4cbcefe257d822f108de2a1f15f8da9f6"><code>0d2ec9d</code></a> chore: release v7.0.0 for Node.js 24 support</li>
<li><a href="https://github.com/actions/download-artifact/commit/fd7ae8fda6dc16277a9ffbc91cdb0eedf156e912"><code>fd7ae8f</code></a> Merge pull request <a href="https://redirect.github.com/actions/download-artifact/issues/451">#451</a> from actions/fix-storage-blob</li>
<li><a href="https://github.com/actions/download-artifact/commit/d484700543354b15886d6a52910cf61b7f1d2b27"><code>d484700</code></a> chore: restore minimatch.dep.yml license file</li>
<li><a href="https://github.com/actions/download-artifact/commit/03a808050efe42bb6ad85281890afd4e4546672c"><code>03a8080</code></a> chore: remove obsolete dependency license files</li>
<li><a href="https://github.com/actions/download-artifact/commit/56fe6d904b0968950f8b68ea17774c54973ed5e2"><code>56fe6d9</code></a> chore: update <code>@actions/artifact</code> license file to 5.0.1</li>
<li><a href="https://github.com/actions/download-artifact/commit/8e3ebc4ab4d2e095e5eb44ba1a4a53b6b03976ad"><code>8e3ebc4</code></a> chore: update package-lock.json with <code>@actions/artifact</code><a href="https://github.com/5"><code>@5</code></a>.0.1</li>
<li><a href="https://github.com/actions/download-artifact/commit/1e3c4b4d4906c98ab57453c24efefdf16c078044"><code>1e3c4b4</code></a> fix: update <code>@actions/artifact</code> to ^5.0.0 for Node.js 24 punycode fix</li>
<li><a href="https://github.com/actions/download-artifact/commit/458627d354794c71bc386c8d5839d20b5885fe2a"><code>458627d</code></a> chore: use local <code>@actions/artifact</code> package for Node.js 24 testing</li>
<li>Additional commits viewable in <a href="https://github.com/actions/download-artifact/compare/v6...v7">compare view</a></li>
</ul>
</details>
<br />
Updates `pypa/cibuildwheel` from 3.2.1 to 3.3.0
<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>v3.3.0</h2>
<ul>
<li>🐛 Fix an incompatibility with Docker v29 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2660">#2660</a>)</li>
<li>✨ Adds <code>test-runtime</code> option, to customise how tests on simulated/emulated environments are run (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2636">#2636</a>)</li>
<li>✨ Adds support for new <code>manylinux_2_35</code> images on 32-bit ARM <code>armv7l</code>, offering better C++20 compatibility (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2656">#2656</a>)</li>
<li>✨ <code>build[uv]</code> is now supported on Android (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2587">#2587</a>)</li>
<li>✨ You can now install extras (such as <code>uv</code>) with a simple option on the GitHub Action (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2630">#2630</a>)</li>
<li>✨ <code>{project}</code> and <code>{package}</code> placeholders are now supported in <code>repair-wheel-command</code> (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2589">#2589</a>)</li>
<li>🛠 The versions set with <code>dependency-versions</code> no longer constrain packages specified by your <code>build-system.requires</code>. Previously, on platforms other than Linux, the constraints in this option would remain in the environment during the build. This has been tidied up make behaviour more consistent between platforms, and to prevent version conflicts. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2583">#2583</a>)</li>
<li>🛠 Improve the handling of <code>test-command</code> on Android, enabling more options to be passed (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2590">#2590</a>)</li>
<li>📚 Docs improvements (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2618">#2618</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>v3.3.0</h3>
<p><em>12 November 2025</em></p>
<ul>
<li>🐛 Fix an incompatibility with Docker v29 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2660">#2660</a>)</li>
<li>✨ Adds <code>test-runtime</code> option, to customise how tests on simulated/emulated environments are run (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2636">#2636</a>)</li>
<li>✨ Adds support for new <code>manylinux_2_35</code> images on 32-bit ARM <code>armv7l</code>, offering better C++20 compatibility (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2656">#2656</a>)</li>
<li>✨ <code>build[uv]</code> is now supported on Android (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2587">#2587</a>)</li>
<li>✨ You can now install extras (such as <code>uv</code>) with a simple option on the GitHub Action (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2630">#2630</a>)</li>
<li>✨ <code>{project}</code> and <code>{package}</code> placeholders are now supported in <code>repair-wheel-command</code> (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2589">#2589</a>)</li>
<li>🛠 The versions set with <code>dependency-versions</code> no longer constrain packages specified by your <code>build-system.requires</code>. Previously, on platforms other than Linux, the constraints in this option would remain in the environment during the build. This has been tidied up make behaviour more consistent between platforms, and to prevent version conflicts. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2583">#2583</a>)</li>
<li>🛠 Improve the handling of <code>test-command</code> on Android, enabling more options to be passed (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2590">#2590</a>)</li>
<li>📚 Docs improvements (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2618">#2618</a>)</li>
</ul>
<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>
</ul>
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<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/pypa/cibuildwheel/commit/63fd63b352a9a8bdcc24791c9dbee952ee9a8abc"><code>63fd63b</code></a> Bump version: v3.3.0</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/f4fe311fc96f735aa0d7beb74d6ae61801cf5382"><code>f4fe311</code></a> fix: support Docker 29 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2660">#2660</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/f6c810852d424abdddc6abc44d1e4b165797399d"><code>f6c8108</code></a> feat: make the <code>{project}</code> placeholder available to <code>repair-wheel-command</code> (#...</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/ccbae30b538eeacb2269052ca6603ba28485206b"><code>ccbae30</code></a> feat: support uv with Android (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2587">#2587</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/1337e50f74cda9ab8a65568b3052d7e4b82da54c"><code>1337e50</code></a> chore: pytest log_level is better than log_cli_level (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2657">#2657</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/720f8e266f6c4b5d3064fc8c366397266eaedfe5"><code>720f8e2</code></a> feat: add manylinux_2_35 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2656">#2656</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/4c7f3696b166b9a2cefcd9c33a7ace9648a823b9"><code>4c7f369</code></a> [pre-commit.ci] pre-commit autoupdate (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2658">#2658</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/e1baa60eb0f6e7e57cbb5087940acefcaa86fb97"><code>e1baa60</code></a> chore: enable more Ruff checks (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2654">#2654</a>)</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/1f2f8b28d5cf5adac0ccab4078dcb415f53c7d2b"><code>1f2f8b2</code></a> fix: don't constrain <code>build-system.requires</code> with our <code>dependency-versions</code> (...</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/8c5b02f42c051a818a176259fc5f4d1876cb5945"><code>8c5b02f</code></a> [pre-commit.ci] pre-commit autoupdate (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2648">#2648</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/pypa/cibuildwheel/compare/9c00cb4f6b517705a3794b22395aedc36257242c...63fd63b352a9a8bdcc24791c9dbee952ee9a8abc">compare view</a></li>
</ul>
</details>
<br />
Updates `actions/upload-artifact` from 5 to 6
<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>v6.0.0</h2>
<h2>v6 - What's new</h2>
<blockquote>
<p>[!IMPORTANT]
actions/upload-artifact@v6 now runs on Node.js 24 (<code>runs.using: node24</code>) and requires a minimum Actions Runner version of 2.327.1. If you are using self-hosted runners, ensure they are updated before upgrading.</p>
</blockquote>
<h3>Node.js 24</h3>
<p>This release updates the runtime to Node.js 24. v5 had preliminary support for Node.js 24, however this action was by default still running on Node.js 20. Now this action by default will run on Node.js 24.</p>
<h2>What's Changed</h2>
<ul>
<li>Upload Artifact Node 24 support by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/719">actions/upload-artifact#719</a></li>
<li>fix: update <code>@actions/artifact</code> for Node.js 24 punycode deprecation by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/744">actions/upload-artifact#744</a></li>
<li>prepare release v6.0.0 for Node.js 24 support by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/upload-artifact/pull/745">actions/upload-artifact#745</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/upload-artifact/compare/v5.0.0...v6.0.0">https://github.com/actions/upload-artifact/compare/v5.0.0...v6.0.0</a></p>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/actions/upload-artifact/commit/b7c566a772e6b6bfb58ed0dc250532a479d7789f"><code>b7c566a</code></a> Merge pull request <a href="https://redirect.github.com/actions/upload-artifact/issues/745">#745</a> from actions/upload-artifact-v6-release</li>
<li><a href="https://github.com/actions/upload-artifact/commit/e516bc8500aaf3d07d591fcd4ae6ab5f9c391d5b"><code>e516bc8</code></a> docs: correct description of Node.js 24 support in README</li>
<li><a href="https://github.com/actions/upload-artifact/commit/ddc45ed9bca9b38dbd643978d88e3981cdc91415"><code>ddc45ed</code></a> docs: update README to correct action name for Node.js 24 support</li>
<li><a href="https://github.com/actions/upload-artifact/commit/615b319bd27bb32c3d64dca6b6ed6974d5fbe653"><code>615b319</code></a> chore: release v6.0.0 for Node.js 24 support</li>
<li><a href="https://github.com/actions/upload-artifact/commit/017748b48f8610ca8e6af1222f4a618e84a9c703"><code>017748b</code></a> Merge pull request <a href="https://redirect.github.com/actions/upload-artifact/issues/744">#744</a> from actions/fix-storage-blob</li>
<li><a href="https://github.com/actions/upload-artifact/commit/38d4c7997f5510fcc41fc4aae2a6b97becdbe7fc"><code>38d4c79</code></a> chore: rebuild dist</li>
<li><a href="https://github.com/actions/upload-artifact/commit/7d27270e0cfd253e666c44abac0711308d2d042f"><code>7d27270</code></a> chore: add missing license cache files for <code>@actions/core</code>, <code>@actions/io</code>, and mi...</li>
<li><a href="https://github.com/actions/upload-artifact/commit/5f643d3c9475505ccaf26d686ffbfb71a8387261"><code>5f643d3</code></a> chore: update license files for <code>@actions/artifact</code><a href="https://github.com/5"><code>@5</code></a>.0.1 dependencies</li>
<li><a href="https://github.com/actions/upload-artifact/commit/1df1684032c88614064493e1a0478fcb3583e1d0"><code>1df1684</code></a> chore: update package-lock.json with <code>@actions/artifact</code><a href="https://github.com/5"><code>@5</code></a>.0.1</li>
<li><a href="https://github.com/actions/upload-artifact/commit/b5b1a918401ee270935b6b1d857ae66c85f3be6f"><code>b5b1a91</code></a> fix: update <code>@actions/artifact</code> to ^5.0.0 for Node.js 24 punycode fix</li>
<li>Additional commits viewable in <a href="https://github.com/actions/upload-artifact/compare/v5...v6">compare view</a></li>
</ul>
</details>
<br />
Updates `scientific-python/upload-nightly-action` from 0.6.2 to 0.6.3
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/scientific-python/upload-nightly-action/releases">scientific-python/upload-nightly-action's releases</a>.</em></p>
<blockquote>
<h2>0.6.3</h2>
<h2>What's Changed</h2>
<ul>
<li>FIX: remove unused permissions in actions by <a href="https://github.com/kratsg"><code>@kratsg</code></a> in <a href="https://redirect.github.com/scientific-python/upload-nightly-action/pull/148">scientific-python/upload-nightly-action#148</a></li>
<li>ENH: Add aarch64 Linux and x86 Windows support by <a href="https://github.com/mroeschke"><code>@mroeschke</code></a> in <a href="https://redirect.github.com/scientific-python/upload-nightly-action/pull/151">scientific-python/upload-nightly-action#151</a></li>
<li>MNT: Update to anaconda-client v1.13.1 and project version v0.6.3 by <a href="https://github.com/matthewfeickert"><code>@matthewfeickert</code></a> in <a href="https://redirect.github.com/scientific-python/upload-nightly-action/pull/152">scientific-python/upload-nightly-action#152</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/kratsg"><code>@kratsg</code></a> made their first contribution in <a href="https://redirect.github.com/scientific-python/upload-nightly-action/pull/148">scientific-python/upload-nightly-action#148</a></li>
<li><a href="https://github.com/mroeschke"><code>@mroeschke</code></a> made their first contribution in <a href="https://redirect.github.com/scientific-python/upload-nightly-action/pull/151">scientific-python/upload-nightly-action#151</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/scientific-python/upload-nightly-action/compare/0.6.2...0.6.3">https://github.com/scientific-python/upload-nightly-action/compare/0.6.2...0.6.3</a></p>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/scientific-python/upload-nightly-action/commit/5748273c71e2d8d3a61f3a11a16421c8954f9ecf"><code>5748273</code></a> MNT: Update to anaconda-client v1.13.1 and project version v0.6.3 (<a href="https://redirect.github.com/scientific-python/upload-nightly-action/issues/152">#152</a>)</li>
<li><a href="https://github.com/scientific-python/upload-nightly-action/commit/8db798e78f87f2a3a5f73a533983fbf3461487d7"><code>8db798e</code></a> ENH: Add aarch64 Linux and x86 Windows support (<a href="https://redirect.github.com/scientific-python/upload-nightly-action/issues/151">#151</a>)</li>
<li><a href="https://github.com/scientific-python/upload-nightly-action/commit/64b7bfd3a1e0e823923208401112dd82dd8ceef5"><code>64b7bfd</code></a> Build(deps): Bump the actions group with 4 updates (<a href="https://redirect.github.com/scientific-python/upload-nightly-action/issues/149">#149</a>)</li>
<li><a href="https://github.com/scientific-python/upload-nightly-action/commit/b38e3f587fe24b5c677603ad825e9540f47f554e"><code>b38e3f5</code></a> remove unused permissions in actions, permissions are for tokens in jobs, not...</li>
<li><a href="https://github.com/scientific-python/upload-nightly-action/commit/0ff01d2433e8db7965d1a6939acf79b2dd17c6a7"><code>0ff01d2</code></a> Build(deps): Bump the actions group with 2 updates (<a href="https://redirect.github.com/scientific-python/upload-nightly-action/issues/145">#145</a>)</li>
<li><a href="https://github.com/scientific-python/upload-nightly-action/commit/816cde5fde93803d82b3b5c3b564e43965da4f2a"><code>816cde5</code></a> Build(deps): Bump the actions group with 5 updates (<a href="https://redirect.github.com/scientific-python/upload-nightly-action/issues/144">#144</a>)</li>
<li><a href="https://github.com/scientific-python/upload-nightly-action/commit/50aa23779739bfb40c831a2fd410abe617edd38f"><code>50aa237</code></a> Build(deps): Bump the actions group with 2 updates (<a href="https://redirect.github.com/scientific-python/upload-nightly-action/issues/138">#138</a>)</li>
<li><a href="https://github.com/scientific-python/upload-nightly-action/commit/83ecb5ace759c9133bfadc7311377e9de4d109a4"><code>83ecb5a</code></a> Build(deps): Bump the actions group with 3 updates (<a href="https://redirect.github.com/scientific-python/upload-nightly-action/issues/137">#137</a>)</li>
<li><a href="https://github.com/scientific-python/upload-nightly-action/commit/49b33232935f3cfabb68344bf35c374ce5945ae7"><code>49b3323</code></a> Build(deps): Bump astral-sh/setup-uv in the actions group (<a href="https://redirect.github.com/scientific-python/upload-nightly-action/issues/136">#136</a>)</li>
<li><a href="https://github.com/scientific-python/upload-nightly-action/commit/9bbfbda9902f7576d33d453ed6bd34774e8ddec8"><code>9bbfbda</code></a> Build(deps): Bump the actions group with 3 updates (<a href="https://redirect.github.com/scientific-python/upload-nightly-action/issues/129">#129</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/scientific-python/upload-nightly-action/compare/b36e8c0c10dbcfd2e05bf95f17ef8c14fd708dbf...5748273c71e2d8d3a61f3a11a16421c8954f9ecf">compare view</a></li>
</ul>
</details>
<br />
Updates `actions/cache` from 4 to 5
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/actions/cache/releases">actions/cache's releases</a>.</em></p>
<blockquote>
<h2>v5.0.0</h2>
<blockquote>
<p>[!IMPORTANT]
<strong><code>actions/cache@v5</code> runs on the Node.js 24 runtime and requires a minimum Actions Runner version of <code>2.327.1</code>.</strong></p>
<p>If you are using self-hosted runners, ensure they are updated before upgrading.</p>
</blockquote>
<hr />
<h2>What's Changed</h2>
<ul>
<li>Upgrade to use node24 by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/cache/pull/1630">actions/cache#1630</a></li>
<li>Prepare v5.0.0 release by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/cache/pull/1684">actions/cache#1684</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/cache/compare/v4.3.0...v5.0.0">https://github.com/actions/cache/compare/v4.3.0...v5.0.0</a></p>
<h2>v4.3.0</h2>
<h2>What's Changed</h2>
<ul>
<li>Add note on runner versions by <a href="https://github.com/GhadimiR"><code>@GhadimiR</code></a> in <a href="https://redirect.github.com/actions/cache/pull/1642">actions/cache#1642</a></li>
<li>Prepare <code>v4.3.0</code> release by <a href="https://github.com/Link"><code>@Link</code></a>- in <a href="https://redirect.github.com/actions/cache/pull/1655">actions/cache#1655</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/cache/pull/1642">actions/cache#1642</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/cache/compare/v4...v4.3.0">https://github.com/actions/cache/compare/v4...v4.3.0</a></p>
<h2>v4.2.4</h2>
<h2>What's Changed</h2>
<ul>
<li>Update README.md by <a href="https://github.com/nebuk89"><code>@nebuk89</code></a> in <a href="https://redirect.github.com/actions/cache/pull/1620">actions/cache#1620</a></li>
<li>Upgrade <code>@actions/cache</code> to <code>4.0.5</code> and move <code>@protobuf-ts/plugin</code> to dev depdencies by <a href="https://github.com/Link"><code>@Link</code></a>- in <a href="https://redirect.github.com/actions/cache/pull/1634">actions/cache#1634</a></li>
<li>Prepare release <code>4.2.4</code> by <a href="https://github.com/Link"><code>@Link</code></a>- in <a href="https://redirect.github.com/actions/cache/pull/1636">actions/cache#1636</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/nebuk89"><code>@nebuk89</code></a> made their first contribution in <a href="https://redirect.github.com/actions/cache/pull/1620">actions/cache#1620</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/cache/compare/v4...v4.2.4">https://github.com/actions/cache/compare/v4...v4.2.4</a></p>
<h2>v4.2.3</h2>
<h2>What's Changed</h2>
<ul>
<li>Update to use <code>@actions/cache</code> 4.0.3 package & prepare for new release by <a href="https://github.com/salmanmkc"><code>@salmanmkc</code></a> in <a href="https://redirect.github.com/actions/cache/pull/1577">actions/cache#1577</a> (SAS tokens for cache entries are now masked in debug logs)</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/cache/pull/1577">actions/cache#1577</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/cache/compare/v4.2.2...v4.2.3">https://github.com/actions/cache/compare/v4.2.2...v4.2.3</a></p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/actions/cache/blob/main/RELEASES.md">actions/cache's changelog</a>.</em></p>
<blockquote>
<h1>Releases</h1>
<h2>Changelog</h2>
<h3>5.0.1</h3>
<ul>
<li>Update <code>@azure/storage-blob</code> to <code>^12.29.1</code> via <code>@actions/cache@5.0.1</code> <a href="https://redirect.github.com/actions/cache/pull/1685">#1685</a></li>
</ul>
<h3>5.0.0</h3>
<blockquote>
<p>[!IMPORTANT]
<code>actions/cache@v5</code> runs on the Node.js 24 runtime and requires a minimum Actions Runner version of <code>2.327.1</code>.
If you are using self-hosted runners, ensure they are updated before upgrading.</p>
</blockquote>
<h3>4.3.0</h3>
<ul>
<li>Bump <code>@actions/cache</code> to <a href="https://redirect.github.com/actions/toolkit/pull/2132">v4.1.0</a></li>
</ul>
<h3>4.2.4</h3>
<ul>
<li>Bump <code>@actions/cache</code> to v4.0.5</li>
</ul>
<h3>4.2.3</h3>
<ul>
<li>Bump <code>@actions/cache</code> to v4.0.3 (obfuscates SAS token in debug logs for cache entries)</li>
</ul>
<h3>4.2.2</h3>
<ul>
<li>Bump <code>@actions/cache</code> to v4.0.2</li>
</ul>
<h3>4.2.1</h3>
<ul>
<li>Bump <code>@actions/cache</code> to v4.0.1</li>
</ul>
<h3>4.2.0</h3>
<p>TLDR; The cache backend service has been rewritten from the ground up for improved performance and reliability. <a href="https://github.com/actions/cache">actions/cache</a> now integrates with the new cache service (v2) APIs.</p>
<p>The new service will gradually roll out as of <strong>February 1st, 2025</strong>. The legacy service will also be sunset on the same date. Changes in these release are <strong>fully backward compatible</strong>.</p>
<p><strong>We are deprecating some versions of this action</strong>. We recommend upgrading to version <code>v4</code> or <code>v3</code> as soon as possible before <strong>February 1st, 2025.</strong> (Upgrade instructions below).</p>
<p>If you are using pinned SHAs, please use the SHAs of versions <code>v4.2.0</code> or <code>v3.4.0</code></p>
<p>If you do not upgrade, all workflow runs using any of the deprecated <a href="https://github.com/actions/cache">actions/cache</a> will fail.</p>
<p>Upgrading to the recommended versions will not break your workflows.</p>
<h3>4.1.2</h3>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/actions/cache/commit/9255dc7a253b0ccc959486e2bca901246202afeb"><code>9255dc7</code></a> Merge pull request <a href="https://redirect.github.com/actions/cache/issues/1686">#1686</a> from actions/cache-v5.0.1-release</li>
<li><a href="https://github.com/actions/cache/commit/8ff5423e8b66eacab4e638ee52abbd2cb831366a"><code>8ff5423</code></a> chore: release v5.0.1</li>
<li><a href="https://github.com/actions/cache/commit/9233019a152bc768059ac1768b8e4403b5da16c1"><code>9233019</code></a> Merge pull request <a href="https://redirect.github.com/actions/cache/issues/1685">#1685</a> from salmanmkc/node24-storage-blob-fix</li>
<li><a href="https://github.com/actions/cache/commit/b975f2bb844529e1063ad882c609b224bcd66eb6"><code>b975f2b</code></a> fix: add peer property to package-lock.json for dependencies</li>
<li><a href="https://github.com/actions/cache/commit/d0a0e1813491d01d574c95f8d189f62622bbb2ae"><code>d0a0e18</code></a> fix: update license files for <code>@actions/cache</code>, fast-xml-parser, and strnum</li>
<li><a href="https://github.com/actions/cache/commit/74de208dcfcbe85c0e7154e7b17e4105fe2554ff"><code>74de208</code></a> fix: update <code>@actions/cache</code> to ^5.0.1 for Node.js 24 punycode fix</li>
<li><a href="https://github.com/actions/cache/commit/ac7f1152ead02e89c14b5456d14ab17591e74cfb"><code>ac7f115</code></a> peer</li>
<li><a href="https://github.com/actions/cache/commit/b0f846b50b6061d7a2ca6f1a2fea61d4a65d1a16"><code>b0f846b</code></a> fix: update <code>@actions/cache</code> with storage-blob fix for Node.js 24 punycode depr...</li>
<li><a href="https://github.com/actions/cache/commit/a7833574556fa59680c1b7cb190c1735db73ebf0"><code>a783357</code></a> Merge pull request <a href="https://redirect.github.com/actions/cache/issues/1684">#1684</a> from actions/prepare-cache-v5-release</li>
<li><a href="https://github.com/actions/cache/commit/3bb0d78750a39cefce0c2b5a0a9801052b4359ad"><code>3bb0d78</code></a> docs: highlight v5 runner requirement in releases</li>
<li>Additional commits viewable in <a href="https://github.com/actions/cache/compare/v4...v5">compare view</a></li>
</ul>
</details>
<br />
Updates `peter-evans/create-pull-request` from 7 to 8
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/peter-evans/create-pull-request/releases">peter-evans/create-pull-request's releases</a>.</em></p>
<blockquote>
<h2>Create Pull Request v8.0.0</h2>
<h2>What's new in v8</h2>
<ul>
<li>Requires <a href="https://github.com/actions/runner/releases/tag/v2.327.1">Actions Runner v2.327.1</a> or later if you are using a self-hosted runner for Node 24 support.</li>
</ul>
<h2>What's Changed</h2>
<ul>
<li>chore: Update checkout action version to v6 by <a href="https://github.com/yonas"><code>@yonas</code></a> in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4258">peter-evans/create-pull-request#4258</a></li>
<li>Update actions/checkout references to <a href="https://github.com/v6"><code>@v6</code></a> in docs by <a href="https://github.com/Copilot"><code>@Copilot</code></a> in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4259">peter-evans/create-pull-request#4259</a></li>
<li>feat: v8 by <a href="https://github.com/peter-evans"><code>@peter-evans</code></a> in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4260">peter-evans/create-pull-request#4260</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/yonas"><code>@yonas</code></a> made their first contribution in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4258">peter-evans/create-pull-request#4258</a></li>
<li><a href="https://github.com/Copilot"><code>@Copilot</code></a> made their first contribution in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4259">peter-evans/create-pull-request#4259</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/peter-evans/create-pull-request/compare/v7.0.11...v8.0.0">https://github.com/peter-evans/create-pull-request/compare/v7.0.11...v8.0.0</a></p>
<h2>Create Pull Request v7.0.11</h2>
<h2>What's Changed</h2>
<ul>
<li>fix: restrict remote prune to self-hosted runners by <a href="https://github.com/peter-evans"><code>@peter-evans</code></a> in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4250">peter-evans/create-pull-request#4250</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/peter-evans/create-pull-request/compare/v7.0.10...v7.0.11">https://github.com/peter-evans/create-pull-request/compare/v7.0.10...v7.0.11</a></p>
<h2>Create Pull Request v7.0.10</h2>
<p>⚙️ Fixes an issue where updating a pull request failed when targeting a forked repository with the same owner as its parent.</p>
<h2>What's Changed</h2>
<ul>
<li>build(deps): bump the github-actions group with 2 updates by <a href="https://github.com/dependabot"><code>@dependabot</code></a>[bot] in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4235">peter-evans/create-pull-request#4235</a></li>
<li>build(deps-dev): bump prettier from 3.6.2 to 3.7.3 in the npm group by <a href="https://github.com/dependabot"><code>@dependabot</code></a>[bot] in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4240">peter-evans/create-pull-request#4240</a></li>
<li>fix: provider list pulls fallback for multi fork same owner by <a href="https://github.com/peter-evans"><code>@peter-evans</code></a> in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4245">peter-evans/create-pull-request#4245</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/obnyis"><code>@obnyis</code></a> made their first contribution in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4064">peter-evans/create-pull-request#4064</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/peter-evans/create-pull-request/compare/v7.0.9...v7.0.10">https://github.com/peter-evans/create-pull-request/compare/v7.0.9...v7.0.10</a></p>
<h2>Create Pull Request v7.0.9</h2>
<p>⚙️ Fixes an <a href="https://redirect.github.com/peter-evans/create-pull-request/issues/4228">incompatibility</a> with the recently released <code>actions/checkout@v6</code>.</p>
<h2>What's Changed</h2>
<ul>
<li>~70 dependency updates by <a href="https://github.com/dependabot"><code>@dependabot</code></a></li>
<li>docs: fix workaround description about <code>ready_for_review</code> by <a href="https://github.com/ybiquitous"><code>@ybiquitous</code></a> in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/3939">peter-evans/create-pull-request#3939</a></li>
<li>Docs: <code>add-paths</code> default behavior by <a href="https://github.com/joeflack4"><code>@joeflack4</code></a> in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/3928">peter-evans/create-pull-request#3928</a></li>
<li>docs: update to create-github-app-token v2 by <a href="https://github.com/Goooler"><code>@Goooler</code></a> in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4063">peter-evans/create-pull-request#4063</a></li>
<li>Fix compatibility with actions/checkout@v6 by <a href="https://github.com/ericsciple"><code>@ericsciple</code></a> in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4230">peter-evans/create-pull-request#4230</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/joeflack4"><code>@joeflack4</code></a> made their first contribution in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/3928">peter-evans/create-pull-request#3928</a></li>
<li><a href="https://github.com/Goooler"><code>@Goooler</code></a> made their first contribution in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4063">peter-evans/create-pull-request#4063</a></li>
<li><a href="https://github.com/ericsciple"><code>@ericsciple</code></a> made their first contribution in <a href="https://redirect.github.com/peter-evans/create-pull-request/pull/4230">peter-evans/create-pull-request#4230</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/peter-evans/create-pull-request/commit/98357b18bf14b5342f975ff684046ec3b2a07725"><code>98357b1</code></a> feat: v8 (<a href="https://redirect.github.com/peter-evans/create-pull-request/issues/4260">#4260</a>)</li>
<li><a href="https://github.com/peter-evans/create-pull-request/commit/41c0e4b7899a4a0922bf899d64c5f25738cfe356"><code>41c0e4b</code></a> Update actions/checkout references to <a href="https://github.com/v6"><code>@v6</code></a> in docs (<a href="https://redirect.github.com/peter-evans/create-pull-request/issues/4259">#4259</a>)</li>
<li><a href="https://github.com/peter-evans/create-pull-request/commit/994332de4c8124517167807167073cf397678768"><code>994332d</code></a> chore: Update checkout action version to v6 (<a href="https://redirect.github.com/peter-evans/create-pull-request/issues/4258">#4258</a>)</li>
<li>See full diff in <a href="https://github.com/peter-evans/create-pull-request/compare/v7...v8">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>
<summary>Dependabot commands and options</summary>
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You can trigger Dependabot actions by commenting on this PR:
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AI usage disclosure:
- Documentation (including examples)
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"node_id": "LA_kwDOAAzd1s8... | closed | false | [] | null | 0 | 2026-01-01T08:41:52 | 2026-01-11T15:35:59 | 2026-01-11T15:35:59 | null | CONTRIBUTOR | null | null | null | null | <!--
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Towards #26024
#### What does this implement/fix? Explain your changes.
Adds Array API support to `paired_manhattan_distances`
#### AI usage disclosure
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I used AI assistance for:
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cc @lucyleeow as indicated here: https://github.com/scikit-learn/scikit-learn/issues/26024#issuecomment-3631103916
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} | ### Describe the bug
Importing janitor when using LeaveOneOut() with cross_val_score() causing warning.
### Steps/Code to Reproduce
```
from sklearn.model_selection import LeaveOneOut, KFold, cross_val_score
import janitor #cannot be used with cross_val_score since it causes warning
from catboost import CatBoostClassifier, Pool
cv = LeaveOneOut()
model = CatBoostClassifier(iterations=100, depth=7)
scores = cross_val_score(model, X2, y, cv=cv, scoring='accuracy')
```
### Expected Results
No warning
### Actual Results
```
C:\Program Files\Python310\lib\site-packages\sklearn\utils\_tags.py:338: DeprecationWarning: The following error was raised: 'CatBoostClassifier' object has no attribute '__sklearn_tags__'. It seems that there are no classes that implement `__sklearn_tags__` in the MRO and/or all classes in the MRO call `super().__sklearn_tags__()`. Make sure to inherit from `BaseEstimator` which implements `__sklearn_tags__` (or alternatively define `__sklearn_tags__` but we don't recommend this approach). Note that `BaseEstimator` needs to be on the right side of other Mixins in the inheritance order. The default are now used instead since retrieving tags failed. This warning will be replaced by an error in 1.8.
warnings.warn(
C:\Program Files\Python310\lib\site-packages\sklearn\utils\_tags.py:338: DeprecationWarning: The following error was raised: 'CatBoostClassifier' object has no attribute '__sklearn_tags__'. It seems that there are no classes that implement `__sklearn_tags__` in the MRO and/or all classes in the MRO call `super().__sklearn_tags__()`. Make sure to inherit from `BaseEstimator` which implements `__sklearn_tags__` (or alternatively define `__sklearn_tags__` but we don't recommend this approach). Note that `BaseEstimator` needs to be on the right side of other Mixins in the inheritance order. The default are now used instead since retrieving tags failed. This warning will be replaced by an error in 1.8.
warnings.warn(
C:\Program Files\Python310\lib\site-packages\sklearn\utils\_tags.py:338: DeprecationWarning: The following error was raised: 'CatBoostClassifier' object has no attribute '__sklearn_tags__'. It seems that there are no classes that implement `__sklearn_tags__` in the MRO and/or all classes in the MRO call `super().__sklearn_tags__()`. Make sure to inherit from `BaseEstimator` which implements `__sklearn_tags__` (or alternatively define `__sklearn_tags__` but we don't recommend this approach). Note that `BaseEstimator` needs to be on the right side of other Mixins in the inheritance order. The default are now used instead since retrieving tags failed. This warning will be replaced by an error in 1.8.
warnings.warn(
C:\Program Files\Python310\lib\site-packages\sklearn\utils\_tags.py:338: DeprecationWarning: The following error was raised: 'CatBoostClassifier' object has no attribute '__sklearn_tags__'. It seems that there are no classes that implement `__sklearn_tags__` in the MRO and/or all classes in the MRO call `super().__sklearn_tags__()`. Make sure to inherit from `BaseEstimator` which implements `__sklearn_tags__` (or alternatively define `__sklearn_tags__` but we don't recommend this approach). Note that `BaseEstimator` needs to be on the right side of other Mixins in the inheritance order. The default are now used instead since retrieving tags failed. This warning will be replaced by an error in 1.8.
warnings.warn(
```
### Versions
```shell
System:
python: 3.10.6 (tags/v3.10.6:9c7b4bd, Aug 1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)]
executable: C:\Program Files\Python310\python.exe
machine: Windows-10-10.0.19045-SP0
Python dependencies:
sklearn: 1.7.2
pip: 25.3
setuptools: 80.9.0
numpy: 2.2.6
scipy: 1.15.3
Cython: 3.0.12
pandas: 2.3.3
matplotlib: 3.10.8
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: openmp
internal_api: openmp
num_threads: 32
prefix: vcomp
filepath: C:\Program Files\Python310\Lib\site-packages\sklearn\.libs\vcomp140.dll
version: None
user_api: blas
internal_api: openblas
num_threads: 24
prefix: libscipy_openblas
filepath: C:\Program Files\Python310\Lib\site-packages\numpy.libs\libscipy_openblas64_-13e2df515630b4a41f92893938845698.dll
version: 0.3.29
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 24
prefix: libscipy_openblas
filepath: C:\Program Files\Python310\Lib\site-packages\scipy.libs\libscipy_openblas-f07f5a5d207a3a47104dca54d6d0c86a.dll
version: 0.3.28
threading_layer: pthreads
architecture: Haswell
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32977 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32977/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32977/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32977/events | https://github.com/scikit-learn/scikit-learn/pull/32977 | 3,774,184,750 | PR_kwDOAAzd1s67LVSu | 32,977 | TEST: make _unique_np robust to NaN ordering | {
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"node_id": "MDU6TGFiZWwyMzk5Mz... | closed | false | [] | null | 1 | 2026-01-01T06:11:03 | 2026-01-01T08:29:49 | 2026-01-01T08:16:27 | null | NONE | null | null | null | null | The Array API specification does not guarantee the ordering of values returned
by unique_values, including NaNs.
This PR adds a regression test to ensure that _unique_np removes NaN values
regardless of their position in the returned array, instead of assuming they
appear at the end.
No functional behavior is changed.
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"node_id": "MDU6TGFiZWwyMzk5Mz... | closed | false | [] | null | 1 | 2026-01-01T05:48:57 | 2026-01-01T08:29:37 | 2026-01-01T08:29:37 | null | NONE | null | null | null | null | The Array API specification does not guarantee any ordering for values
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In _unique_np, the current implementation assumes that NaN values appear
at the end of the uniques array, which is not guaranteed for Array API
compliant backends. This PR removes that assumption by explicitly
filtering NaN values instead of relying on positional ordering.
This change makes the behavior robust across NumPy and alternative
Array API backends, while preserving existing semantics for
return_inverse and return_counts.
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Fixes Issue 30991 :
- Changed number of samples calculation to handle edge cases.
- Added a test case to validate changes | null | {
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#### AI usage disclosure
- [x] Documentation (including examples)
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"... | closed | false | [] | null | 1 | 2025-12-30T15:40:05 | 2025-12-31T15:15:37 | 2025-12-31T06:39:16 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
None
#### What does this implement/fix? Explain your changes.
This is a tiny improvement, changing one line of code for the hessian product in `sklearn.linear_models._linear_loss::LinearModelLoss.gradient_hessian_product`.
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 1 | 2025-12-30T13:01:00 | 2026-01-02T16:39:19 | 2025-12-31T03:39:39 | null | CONTRIBUTOR | null | null | null | null | Fixes #32930
What does this implement
The current documentation states that `dtype=integral` is required. However, the metrics actually support strings and discrete float values (e.g. 0.0, 1.0) without issues.
I removed the incorrect `dtype` restriction from the docstrings to match the actual behavior of the functions.
Any other comments?
I verified this locally using a reproduction script for all 4 metrics. | {
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#### What does this implement/fix? Explain your changes.
I mainly use scikit-learn for its GPR and I had been wanting to contribute to its maintenance for a while.
Here are the main changes:
- Some methods are very large; this PR rewrites them a little, notably by extracting code blocks into private methods.
- Some things are done unecessarily, e.g. copy the normalized target values; this PR undoes these things.
- Some things are not clear in the docstrings; this PR tries to improve them a little, and takes the opportunity to use [linefeeds](https://rhodesmill.org/brandon/2012/one-sentence-per-line/).
I tried to use atomic commits to facilitate the review.
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#### Any other comments?
I do not understand why it is not possible to define public attributes in `__init__` that are not arguments. Defining an attribute outside `__init__` goes against the principle of least surprise.
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"node_id": "LA_kwDOAAzd1s7eW... | closed | false | [] | null | 15 | 2025-12-30T07:38:36 | 2026-01-12T15:45:05 | 2026-01-07T16:23:05 | null | CONTRIBUTOR | null | null | null | null | I updated several insecure `http` links to `https` in the documentation.
Files updated:
* `doc/about.rst`
* `doc/developers/contributing.rst`
* `doc/developers/performance.rst` | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 6 | 2025-12-29T18:02:18 | 2026-01-07T14:04:39 | 2026-01-07T10:26:30 | null | CONTRIBUTOR | null | null | null | null | This PR clarifies the behavior of the `verbose` parameter in
GridSearchCV and RandomizedSearchCV.
The documentation now:
- Explicitly documents the behavior when `verbose=0`
- Clarifies what additional information is shown as verbosity increases
- Notes that higher verbosity levels (e.g. >10) may produce additional
progress messages depending on the execution backend
This aligns the documentation with the observed behavior discussed in
#32946.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32964 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32964/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32964/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32964/events | https://github.com/scikit-learn/scikit-learn/pull/32964 | 3,765,882,471 | PR_kwDOAAzd1s66v8bU | 32,964 | Fix: GaussianProcessRegressor optimizes the default kernel when optimizer is not None | {
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Fixes #17871
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#### What does this implement/fix? Explain your changes.
When the `optimizer` argument of `GaussianProcessRegressor` is not set to `None`, the hyperparameters of the kernels should be optimized, but this is not the case with the default kernel whose hyperparameters are fixed. It has been discussed several times in the issue tracker to optimize the hyperparameters of the default kernel when the `optimizer` is not `None`. This PR implements this proposal. The user can restore the previous behavior by setting `optimizer` to `None`. This new implementation is in accordance with the definition of `optimizer`.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32961 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32961/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32961/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32961/events | https://github.com/scikit-learn/scikit-learn/issues/32961 | 3,765,570,099 | I_kwDOAAzd1s7gcgoz | 32,961 | Consistent HTTP 403 Figshare error since December 27 | {
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} | Seems to be failing since December 27 around 13:00 UTC, see https://github.com/lesteve/test-figshare-fetchers/actions.
I can reproduce locally:
```
❯ curl https://figshare.com/ndownloader/files/5976078
<html>
<head><title>403 Forbidden</title></head>
<body>
<center><h1>403 Forbidden</h1></center>
</body>
</html>
```
I opened a support ticket at https://support.figshare.com, let's see what they say ...
https://github.com/scikit-learn/scikit-learn/issues/32710 is an example where it hits us ([build log](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=83721&view=logs&j=dde5042c-7464-5d47-9507-31bdd2ee0a3a&t=4bd2dad8-62b3-5bf9-08a5-a9880c530c94)).
For now CircleCI does not seem affected, thanks to caching likely.
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"updated_at": "2026-02-05T05:18:14",
"body": "I am going to lock this issue since it was a temporary Figshare issue that was solved.\n\nI don't think the scikit-learn issue tracker is the right place to discuss other Figshare issue or add tips on VPN setup in some cases. A personal blog may be more appropriate.\n\n",
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32960 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32960/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32960/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32960/events | https://github.com/scikit-learn/scikit-learn/pull/32960 | 3,765,557,973 | PR_kwDOAAzd1s66u84h | 32,960 | DOC: clarify SimpleImputer docstring example | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 1 | 2025-12-28T10:51:02 | 2025-12-29T14:15:46 | 2025-12-29T14:15:46 | null | NONE | null | null | null | null | Reference Issues/PRs
Fixes #32957
What does this implement/fix? Explain your changes.
This PR improves the clarity of the first SimpleImputer example in the docstrings found in sklearn/impute/_base.py.
As highlighted in issue #32957, the previous example was confusing because it performed a fit on one array but then applied the transform to a different, newly defined array. This made it difficult for users to mentally verify the mean imputation logic.
I have updated the example to:
Use a single variable X for both the fit and transform methods.
Provide clear, easy-to-verify numerical outputs where the mean of the second column (24 and 16) results in 20.0.
Ensure the code remains valid and follows the existing scikit-learn documentation style.
AI usage disclosure
I used AI assistance for:
[ ] Code generation (e.g., when writing an implementation or fixing a bug)
[ ] Test/benchmark generation
[] Documentation (including examples)
[x] Research and understanding
Any other comments?
This is my first contribution to scikit-learn. I have verified that the updated example is mathematically correct and clear for beginners. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32959 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32959/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32959/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32959/events | https://github.com/scikit-learn/scikit-learn/pull/32959 | 3,765,440,220 | PR_kwDOAAzd1s66ul_r | 32,959 | DOC: clarify first SimpleImputer example. | {
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#### What does this implement/fix? Explain your changes.
This PR clarifies the first SimpleImputer example by consistently using the same input array for both fitting and transformation.
This makes the example easier to understand for beginners.
#### AI usage disclosure
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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/32957 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32957/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32957/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32957/events | https://github.com/scikit-learn/scikit-learn/issues/32957 | 3,764,953,618 | I_kwDOAAzd1s7gaKIS | 32,957 | DOC: First example for SimpleImputer is unclear | {
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} | ### Describe the issue linked to the documentation
The first example for SimpleImputer in 7.4.2. Univariate feature imputation (https://scikit-learn.org/stable/modules/impute.html#univariate-feature-imputation) is bit unclear. While the imputation is done on an array mentioned in the beginning of the example, the transformation is applied on an array defined in next line. It can be rewritten for clarity as below, which clearly shows the transformation.
```
...
>>> imp = SimpleImputer(missing_values=np.nan, strategy='mean')
>>> X = [[1.,24],[2.,np.nan],[4.,16.]]
>>> imp.fit(X)
SimpleImputer()
>>> print(imp.transform(X))
[[1. 24.]
[2. 20.]
[4. 16.]]
```
### Suggest a potential alternative/fix
_No response_ | {
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] | open | false | [] | null | 7 | 2025-12-27T16:47:49 | 2026-01-28T15:47:49 | null | null | NONE | null | null | null | null | <!--
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#### Reference Issues/PRs
Fixes #29697
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#### What does this implement/fix? Explain your changes.
In a multioutput context, `GaussianProcessRegressor` does not take target scales into account when predicting standard deviation and covariance if `normalize_y=False`, as illustrated in #29697. From my understanding, when `normalize_y=False`, the objective function is the sum of the log-likelihoods associated with different target scales and there is a risk that the hyperparameters will be biased by a dominant component. This can be confusing for users like me. I think the default behavior should be normalization in the multioutput case, and let the user set `normalize_y` to `True` for special cases; for example, when the components of the target have the same meaning, e.g. temperature field). We could also use `True` for the scalar case, which would simplify the code.
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] | open | false | [] | null | 0 | 2025-12-26T17:05:49 | 2025-12-26T21:27:00 | null | null | MEMBER | null | null | null | null | Fixes #30662.
This is a draft PR to investigate a possible solution to the performance slowdown when letting OpenMP use too many threads on small to medium problems.
I found the following heuristic to work well on my Apple M4 CPU (4 performance cores and 6 efficiency cores). In particular, it makes the running tests of `sklearn/ensemble/_hist_gradient_boosting/tests` go from nearly 1 min on `main` down to less than 10 s on this branch (a factor of 7 speed-up).
I plan to check if this heuristic is suitable for other CPUs.
Note that there is a similar problem for the `_raw_predict` method that also uses all threads, independently of the `n_threads` value used at fit time. | null | {
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"node_id": "MDU6TGFiZWwy... | closed | false | [] | null | 1 | 2025-12-26T16:09:09 | 2026-01-08T10:52:21 | 2025-12-31T11:55:58 | null | MEMBER | null | null | null | null | For some reason, I got an `AttributeError` when running `test_early_stopping_with_sample_weights` locally (with pytest 9.0.2). This test is not failing on our CI for some reason (maybe the `monkeypatch` fixture has changed in pytest 9)?
I am not sure if this is a bug of pytest or not but since I might not be the only one annoyed by this failure while it's easy to workaround. | {
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