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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33360 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33360/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33360/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33360/events | https://github.com/scikit-learn/scikit-learn/pull/33360 | 3,974,787,957 | PR_kwDOAAzd1s7FeipG | 33,360 | DOC Add explicit math formulas to dcg_score and ndcg_score | {
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"node_id": "MDU6TGFiZWwxODE1... | open | false | [] | null | 0 | 2026-02-22T15:18:39 | 2026-02-22T15:19:00 | null | null | NONE | null | null | null | null | ## What does this PR do?
Adds explicit LaTeX formulas to the docstrings of `dcg_score` and `ndcg_score` so users can immediately identify which definition of DCG this implementation uses.
### Problem
As reported in #32049, the prose description *"Sum the true scores ranked in the order induced by the predicted scores, after applying a logarithmic discount"* is technically correct but hard to parse without seeing the actual formula. Since there are two competing definitions of DCG in the literature (linear gains vs. exponential gains), users are forced to dig through the references to figure out which one scikit-learn implements.
### Changes
**`dcg_score`** — added after the opening description:
$$\text{DCG@k} = \sum_{i=1}^{k} \frac{\text{rel}_i}{\log_b(i + 1)}$$
with notation for $\text{rel}_i$ (true relevance at rank $i$), $b$ (logarithm base), and $k$ (truncation depth). A note clarifies that this uses **linear gains**, not the exponential variant ($2^{\text{rel}_i} - 1$).
**`ndcg_score`** — added after the opening description:
$$\text{NDCG@k} = \frac{\text{DCG@k}}{\text{IDCG@k}}$$
with a cross-reference to `dcg_score` for the full DCG definition.
### What this PR does NOT change
- No parameter descriptions modified
- No examples modified
- No code logic modified
- `ruff format` passes (verified locally)
Fixes #32049 | null | {
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Fixes #32848
- `nan_euclidean_distances(X)` could return an asymmetric distance matrix when `X` contains missing values, due to floating point rounding in intermediate dot products and subtractions.
- Fix enforces exact symmetry via `(D + D.T) / 2` when computing pairwise distances of a single array (`X is Y`).
## Test plan
- [x] Added non-regression test `test_nan_euclidean_distances_symmetric` that verifies exact (not approximate) symmetry
- [x] All 29 existing+new nan_euclidean tests pass
- [x] Pre-commit hooks pass (ruff, mypy, codespell)
## AI disclosure
AI tools (Claude Code) were used to assist with this contribution. All changes have been manually reviewed, tested, and are well understood by the contributor.
🤖 Generated with [Claude Code](https://claude.com/claude-code) | {
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Fixes #32585
- `QuantileTransformer(ignore_implicit_zeros=True)` subsampling was incorrectly computing the number of non-zero samples to keep as `subsample * nnz // n_samples`. For very sparse matrices (where `n_samples` vastly exceeds `nnz`), this evaluated to 0, producing empty arrays and all-zero quantiles.
- When `ignore_implicit_zeros=True`, implicit zeros are excluded from the quantile computation, so `n_samples` should be irrelevant to how many non-zero values are subsampled. The fix subsamples `subsample` elements directly from the non-zero data.
- The `ignore_implicit_zeros=False` path is unchanged but refactored to be scoped under its own branch for clarity.
## Test plan
- [x] Added non-regression test `test_quantile_transform_sparse_ignore_zeros_very_sparse` that constructs a very sparse matrix (nnz/n_samples ~ 1e-4) and verifies quantiles are non-degenerate
- [x] All 21 existing quantile-related tests pass
- [x] Pre-commit hooks pass (ruff, mypy, codespell)
## AI disclosure
AI tools (Claude Code) were used to assist with this contribution. All changes have been manually reviewed, tested, and are well understood by the contributor.
🤖 Generated with [Claude Code](https://claude.com/claude-code) | {
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} | 1 | 2026-02-22T14:17:22 | 2026-02-24T10:22:59 | 2026-02-24T10:22:59 | null | MEMBER | null | null | null | null | When we made `confusion_matrix_at_thresholds` public (previously named `_binary_clf_curve`) we forgot to make optional args kwarg only. Since it's already released in 1.8 we need to do that with deprecation.
ping @lucyleeow | {
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Hi I was running an automated test and ran into this issue.
Checked the changelog for Numpy as saw as of v2.4.0 that conversion of an array with ndim > 0 to a scalar is deprecated. The error below looks like this is linked to that deprecation.
```
/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/cluster/_hdbscan/hdbscan.py:866: in fit
self.labels_, self.probabilities_ = tree_to_labels(
sklearn/cluster/_hdbscan/_tree.pyx:61: in sklearn.cluster._hdbscan._tree.tree_to_labels
???
sklearn/cluster/_hdbscan/_tree.pyx:75: in sklearn.cluster._hdbscan._tree.tree_to_labels
???
sklearn/cluster/_hdbscan/_tree.pyx:751: in sklearn.cluster._hdbscan._tree._get_clusters
???
sklearn/cluster/_hdbscan/_tree.pyx:626: in sklearn.cluster._hdbscan._tree.epsilon_search
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
E TypeError: only 0-dimensional arrays can be converted to Python scalars
```
Not sure if this issue is applicable to other areas of the sklearn lib but hope it is not to big of an issue.
My env was using scikit-learn==1.8.0 and numpy==2.4.2
### Steps/Code to Reproduce
```
import numpy as np
from sklearn.cluster import HDBSCAN
X = np.random.normal(0, 1, 1000)
X = np.atleast_2d(X).T
clust = HDBSCAN(cluster_selection_epsilon=np.std(X) / np.sqrt(2))
clust.fit(X)
```
### Expected Results
No error is thrown
### Actual Results
```
/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/sklearn/cluster/_hdbscan/hdbscan.py:866: in fit
self.labels_, self.probabilities_ = tree_to_labels(
sklearn/cluster/_hdbscan/_tree.pyx:61: in sklearn.cluster._hdbscan._tree.tree_to_labels
???
sklearn/cluster/_hdbscan/_tree.pyx:75: in sklearn.cluster._hdbscan._tree.tree_to_labels
???
sklearn/cluster/_hdbscan/_tree.pyx:751: in sklearn.cluster._hdbscan._tree._get_clusters
???
sklearn/cluster/_hdbscan/_tree.pyx:626: in sklearn.cluster._hdbscan._tree.epsilon_search
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
E TypeError: only 0-dimensional arrays can be converted to Python scalars
```
### Versions
```shell
scikit-learn==1.8.0
numpy==2.4.2
```
### Interest in fixing the bug
Not sure how to fix | {
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"node_id": "MDU6TGFiZWwx... | open | false | [] | null | 9 | 2026-02-22T04:40:33 | 2026-03-04T21:29:07 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Supersedes #12866 #4899 and #7068 , https://github.com/scikit-learn/scikit-learn/pull/3346
Supersedes https://github.com/scikit-learn/scikit-learn/pull/29437 because the diff was too complicated relative to main.
Took some inspiration from: https://github.com/cakedev0/scikit-learn/pull/2, so I would say we should say this is co-authored with @cakedev0. Instead of only using a uint64_t to encode the bitset, I opt for `uint64_t*`, which future proofs our ability to handle much larger cardinality categories, which will be useful for the categorical support in ExtraTrees*.
#### What does this implement/fix? Explain your changes.
Implements splitting rules when categorical data is given in a decision tree classifier/regressor. For now, limits it to use the breiman shortcut to demonstrate the value and the main implementation changes. Downstream features (outlined in the next section) can be more simply added after this PR.
- Tree data structures are moved to maintain the cimport/import sequence (`ParentInfo`, `SplitRecord`, `Node`) -> `_utils.pxd`
- Adds partitioning of samples given a category (this complements nicely the partitioning of samples given a numerical threshold)
- Adds splitting logic given a category
- add deterministic unit-tests, performance tests, and basic error checks. The deterministic test for a tree I think is correct... Its very useful because it checks that we are tracking categorical splitting over multiple split nodes (at least 2 levels).
- implemented the breiman shortcut.
Also ran this benchmark, which compares one-hot-encoding (OHE), ordinal (ord), and native-categorical support (cat); I had to group-encode rare categories to make this all fit within the constraint of "256" max categories:
- [gist](https://gist.github.com/adam2392/2679337920b7839a15c4e821b1b17b11)
There are two results worth looking at. The first is using default parameters where `max_features = 'sqrt'`
<img width="1800" height="1500" alt="categorical_benchmark" src="https://github.com/user-attachments/assets/3af03673-7137-41dd-a0a7-a439b9f72fa3" />
Here, the only reason OHE is better is because it samples ~8x more features per split node. But it has ~10+x deeper trees, and almost 3-5x longer fittime.
The second is where we fix `max_features=None`, so it samples all features:
<img width="1800" height="1500" alt="categorical_benchmark" src="https://github.com/user-attachments/assets/7f567aa7-ff77-47d4-a6ea-a60b38470ba4" />
(Interestingly, if you order the categories by their occurrence, then the ordinal approach does quite well. This probably opens up a door of heuristics that we can later on add for categorical support. there is no 1 right-answer for everything, so I think it's totally reasonable).
Here, OHE is abysmally slow, and performance is essentially equivalent with native categorical support being on average the best. Surprisingly the ordinal strategy is quite good. It could be due to the fact that maybe I'm cutting categorical support down by group-encoding rare categories, which could be informative.
#### Open Questions / TODO In a downstream PR
1. [Feature] Add a bitset cache (i.e. a uint64_t memory view) that allows us to compute the category split as we traverse the tree for inference speedup
2. [Feature] Brute-force search; expands categorical support to multi-class and multi-output problems
3. [Refactor] Consolidate the `goes_left` operation to work at inference time too
4. [Refactor] Understand the bitset operations and merge these with the HistGradientBoosting bitset operations
5. [Feature] Implement categorical splitting for RandomSplitter for ExtraTrees. Pretty easy now that we have an arbitrary sized bitset, which we can expand to hold many categories in the case of a randomized tree
6. [Feature] Add support for sparse categorical splitting with best and random splitter
7. [Feature] Add support for categorical in exporting tree
#### Any other comments?
May be related to #24967
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"node_id": "MDU6TGFiZWwy... | closed | false | [] | null | 1 | 2026-02-22T00:34:41 | 2026-02-22T23:58:06 | 2026-02-22T23:58:02 | null | NONE | null | null | null | null | ## Summary
This PR fixes a performance issue in the spectral embedding function where was targeting the wrong eigenvalues, causing suboptimal convergence.
## Problem
The existing code computes (negative Laplacian) but the comments describe using (identity minus Laplacian) and targeting eigenvalues near 1.0 with . This mismatch causes to target eigenvalues near 0 instead of near 1.0, leading to poor convergence performance.
## Solution
Add the identity matrix after negating the Laplacian to properly compute :
```python
laplacian *= -1
# Add identity matrix to convert -L to I - L as described in comments
if sparse.issparse(laplacian):
laplacian.setdiag(laplacian.diagonal() + 1)
else:
np.fill_diagonal(laplacian, laplacian.diagonal() + 1)
```
## Performance Impact
Based on the issue reporter's testing, this fix provides **6.7x speedup** (1.5s vs 10.1s) for spectral embedding on graphs with 5000 samples.
## Changes
- **sklearn/manifold/_spectral_embedding.py**: Add identity matrix after Laplacian negation
- **sklearn/manifold/tests/test_spectral_embedding_eigsh_fix.py**: Add comprehensive tests for eigenvalue correctness and performance regression prevention
## Testing
The fix includes tests that verify:
1. Eigenvalues are correctly near 1.0 (as expected for I-L)
2. Spectral embedding converges without errors
3. Results maintain expected variance (not degenerate)
Fixes #33242
## Checklist
- [x] I have read the [contribution guide](https://scikit-learn.org/dev/developers/contributing.html)
- [x] The change addresses a specific issue with clear performance benefits
- [x] Tests are included and cover the fix
- [x] The change aligns implementation with existing documentation/comments
- [x] No breaking changes - pure performance optimization | {
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] | open | false | [] | null | 0 | 2026-02-21T15:26:15 | 2026-02-21T15:26:26 | null | null | NONE | null | null | {
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} | Hi scikit-learn team,
thank you for keeping such an essential ML toolkit alive and healthy. Many modern LLM / RAG stacks still rely on scikit-learn components for preprocessing, feature pipelines, classical models, and evaluation baselines around an LLM core.
I maintain an MIT-licensed project called **WFGY Problem Map**, which is a 16-question diagnostic checklist for real-world RAG / LLM pipelines. It focuses on failure modes that happen in the data and retrieval layers (ingestion, chunking, indexing, vector search, evaluation gaps), not on fine-tuning the LLM itself.
Why I think this might be relevant for scikit-learn users:
- A lot of production RAG systems still combine scikit-learn pipelines with vector stores and LLMs.
- Several of the 16 failure modes talk about how features, labels, and evaluation metrics can drift once an LLM layer is added on top.
- The checklist is model- and framework-agnostic, so it does not depend on any deep learning library.
WFGY Problem Map has already been referenced by a few external ecosystems, for example:
- Harvard MIMS Lab’s ToolUniverse
- Multimodal RAG Survey by QCRI LLM Lab
- Rankify (University of Innsbruck)
Concrete suggestion:
If you feel it fits, would you consider adding a short “See also” style link in your docs or community resources, for users who are extending scikit-learn workflows into LLM / RAG pipelines?
> “RAG / LLM debugging checklist: WFGY Problem Map (16 failure modes)”
> https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md
Project home: https://github.com/onestardao/WFGY
Thank you for considering and for all your work on scikit-learn.
Best,
PSBigBig | null | {
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"node_id": "MDU6TGFiZW... | closed | false | [] | null | 0 | 2026-02-21T14:56:21 | 2026-02-22T07:20:23 | 2026-02-22T07:20:15 | null | NONE | null | null | null | null | Fixes #33167
## Summary
This PR fixes: Segfault in KDTree.query_radius
## Changes
```
sklearn/neighbors/tests/test_neighbors_tree.py | 25 ++++
sklearn/utils/_sorting.pyx | 165 +++++++++++++++++--------
2 files changed, 141 insertions(+), 49 deletions(-)
```
## Testing
Please review the changes carefully. The fix was verified against the existing test suite.
---
*This PR was created with the assistance of Claude Sonnet 4.6 by Anthropic | effort: high. Happy to make any adjustments!* | {
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"node_id": "MDU6TGFiZWwyMz... | closed | false | [] | null | 0 | 2026-02-21T14:12:47 | 2026-02-22T07:19:54 | 2026-02-22T07:19:46 | null | NONE | null | null | null | null | Fixes #33146
## Summary
This PR fixes: Using `BisectingKMeans` with `init` raises a possibly dubious `ValueError`
## Changes
```
.../upcoming_changes/sklearn.cluster/33146.fix.rst | 4 ++++
sklearn/cluster/_bisect_k_means.py | 4 +++-
sklearn/cluster/_kmeans.py | 10 +++++----
sklearn/cluster/tests/test_bisect_k_means.py | 24 ++++++++++++++++++++++
4 files changed, 37 insertions(+), 5 deletions(-)
```
## Testing
Please review the changes carefully. The fix was verified against the existing test suite.
---
*This PR was created with the assistance of Claude Sonnet 4.6 by Anthropic | effort: high. Happy to make any adjustments!* | {
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"node_id": "MDU6TGFiZWwy... | closed | false | [] | null | 0 | 2026-02-21T14:10:21 | 2026-02-22T07:19:16 | 2026-02-22T07:19:00 | null | NONE | null | null | null | null | Fixes #33242
## Summary
This PR fixes: The eigsh call in spectral embedding is done wrong and differently from what is described in the comments
## Changes
```
sklearn/manifold/_spectral_embedding.py | 28 ++++++++++------------------
1 file changed, 10 insertions(+), 18 deletions(-)
```
## Testing
Please review the changes carefully. The fix was verified against the existing test suite.
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"node_id": "LA_k... | open | false | [] | null | 1 | 2026-02-21T13:12:31 | 2026-03-06T11:08:24 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Similar to #32644.
#### What does this implement/fix? Explain your changes.
This adds array API support to `PoissonRegressor(solver="lbfgs")`.
#### AI usage disclosure
None
#### Any other comments?
~~Depends on #33345.~~(merged)
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None
#### What does this implement/fix? Explain your changes.
Small clean up to remove an unused attribute. `need_update_leaves_values` is redundant to attribute `differentiable` (if one is true, the other is false, and vice versa).
#### AI usage disclosure
None
#### Any other comments?
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Contributes to #26024.
#### What does this implement/fix? Explain your changes.
This makes the link functions in the private loss module array API compliant (for recent versions of scipy).
#### AI usage disclosure
None
#### Any other comments?
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"node_id": "MDU6TGFiZWwyMzk5Mz... | closed | false | [] | null | 0 | 2026-02-21T08:06:49 | 2026-02-22T07:18:43 | 2026-02-22T07:18:34 | null | NONE | null | null | null | null | Fixes #33294
## Summary
This PR fixes: `check_X_y` with list of dtypes converts polars 'X' incorrectly
## Changes
```
sklearn/utils/validation.py | 16 +++++++++++++++-
1 file changed, 15 insertions(+), 1 deletion(-)
```
## Testing
Please review the changes carefully. The fix was verified against the existing test suite.
---
*This PR was created with the assistance of Claude Sonnet 4.6 by Anthropic | effort: low. Happy to make any adjustments!* | {
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## Summary
This PR fixes: Segmentation fault with free-threaded in `test_gpr_correct_error_message`
## Changes
```
sklearn/gaussian_process/kernels.py | 15 ++++++++++-----
1 file changed, 10 insertions(+), 5 deletions(-)
```
## Testing
Please review the changes carefully. The fix was verified against the existing test suite.
---
*This PR was created with the assistance of Claude Sonnet 4.6 by Anthropic | effort: low. Happy to make any adjustments!* | {
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} | ### Describe the bug and give evidence about its user-facing impact
The chance_level line drawn by `PrecisionRecallDisplay.from_predictions` when `plot_chance_level=True` is not correct when `y_true` is an instance of `pytorch.Tensor`.
### Steps/Code to Reproduce
```python
# pytorch==2.8.0
from sklearn.metrics import PrecisionRecallDisplay
import torch
torch.manual_seed(42)
y_true = torch.cat([torch.ones(40), torch.zeros(60)]) # Chance level equal 0.4
y_score = torch.rand_like(y_true)
PrecisionRecallDisplay.from_predictions(y_true, y_score, plot_chance_level=True) # Wrong
PrecisionRecallDisplay.from_predictions(y_true.numpy(), y_score, plot_chance_level=True) # Correct
```
### Expected Results
Correct chance classifier line at y 0.4 with y_true as a numpy array.
<img width="448" height="434" alt="Image" src="https://github.com/user-attachments/assets/1c7b612d-bca1-4fdb-836e-f33bd0ef181c" />
### Actual Results
Wrong chance classifier line at y 0.0 with y_true as a pytorch tensor.
<img width="448" height="434" alt="Image" src="https://github.com/user-attachments/assets/4d68fe9e-c54d-4ba6-825f-62a117367679" />
### Versions
```shell
System:
python: 3.13.5 | packaged by Anaconda, Inc. | (main, Jun 12 2025, 16:37:03) [MSC v.1929 64 bit (AMD64)]
executable: C:\Users\lucas\miniconda\envs\pytorch28\python.exe
machine: Windows-11-10.0.26200-SP0
Python dependencies:
sklearn: 1.7.1
pip: 25.1
setuptools: 78.1.1
numpy: 2.1.2
scipy: 1.16.1
Cython: None
pandas: 2.3.3
matplotlib: 3.10.5
joblib: 1.5.1
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: openmp
internal_api: openmp
num_threads: 6
prefix: libiomp
filepath: C:\Users\lucas\miniconda\envs\pytorch28\Lib\site-packages\torch\lib\libiomp5md.dll
version: None
user_api: openmp
internal_api: openmp
num_threads: 1
prefix: libiomp
filepath: C:\Users\lucas\miniconda\envs\pytorch28\Lib\site-packages\torch\lib\libiompstubs5md.dll
version: None
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: C:\Users\lucas\miniconda\envs\pytorch28\Lib\site-packages\numpy.libs\libscipy_openblas64_-c16e4918366c6bc1f1cd71e28ca36fc0.dll
version: 0.3.27
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 12
prefix: vcomp
filepath: C:\Users\lucas\miniconda\envs\pytorch28\Lib\site-packages\sklearn\.libs\vcomp140.dll
version: None
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: C:\Users\lucas\miniconda\envs\pytorch28\Lib\site-packages\scipy.libs\libscipy_openblas-6b2103f2ae4d8547998b5d188e9801fb.dll
version: 0.3.28
threading_layer: pthreads
architecture: Haswell
```
### Interest in fixing the bug
Working on this, the root cause is due to these reasons:
- pytorch (and potentially other array libraries) defines scalars in such a way that their hash is different from their value and same values may have different hashes;
- `PrecisionRecallDisplay.from_predictions` relies on `Counter` class to compare `y_true` entries with `pos_label` values in order to generate `prevalence_pos_label` (chance level). https://github.com/scikit-learn/scikit-learn/blob/646da0f072a8afef6a980aa427a710311e67eb9d/sklearn/metrics/_plot/precision_recall_curve.py#L563-L565
- `Counter` is not able to group objects with same values since they have different hash values.
Comparing the values from `y_true` directly to `pos_label` to calculate `prevalence_pos_label` should be enough to fix this. | null | {
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None
#### What does this implement/fix? Explain your changes.
Minor code cleaning in private loss module.
#### AI usage disclosure
None.
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Towards #33323
#### What does this implement/fix? Explain your changes.
When used on an estimator holding callbacks, `clone` now discards the callbacks in the clone, raising a warning.
For callback-compatible meta estimator which need to clone sub-estimators, `propagate_callbacks` now offers a way to clone the sub-estimators while forwarding the callbacks to the clone and silencing the warning.
#### AI usage disclosure
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 1 | 2026-02-20T15:41:45 | 2026-02-23T00:54:04 | 2026-02-23T00:54:00 | null | NONE | null | null | null | null | Fixes #32930
#### What does this implement/fix? Explain your changes.
->
This PR updates the docstrings for supervised clustering metrics in sklearn/metrics/cluster/_supervised.py. Previously, the parameters labels_true and labels_pred incorrectly specified dtype=integral (or int).
Since these metric functions support any array-like of discrete labels (including strings and discrete floats), I have removed the strict integer dtype requirement from the docstrings to prevent confusion and accurately reflect the underlying implementation.
#### AI usage disclosure
->
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- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [✅] Documentation (including examples)
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#### Any other comments?
-> NIL
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It might be worth adding a test in `test_progressbar.py` that verifies that the `RichTask` tree mirrors the `CallbackContext` tree. This would guard against regressions if `RichProgressMonitor` (which builds the tree) is modified in the future.
Both trees should have an identical structure except for the leaf nodes (max_subtasks=0) which are excluded in `RichTask.__iter__()`. | null | {
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This allows VarianceThreshold to handle the special case of all-NaN columns. | {
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"node_id": "LA_kwDOAAzd1s8AA... | closed | false | [] | null | 1 | 2026-02-20T08:19:26 | 2026-02-20T08:49:07 | 2026-02-20T08:48:55 | null | NONE | null | null | null | null | #### Reference Issues/PRs
Closes #31869 (partially - adds sigmoid and isotonic support)
Follows up on #32246 which added Array API support for temperature scaling.
## Summary
- Extends Array API support in `CalibratedClassifierCV` to `method="sigmoid"` (Platt scaling) and `method="isotonic"` calibration methods
- Since sigmoid calibration uses `scipy.optimize.minimize` (L-BFGS-B) and isotonic regression is implemented in Cython, both are inherently numpy-only. The approach converts predictions, labels, sample weights, and class arrays to numpy before fitting/predicting with these calibrators, then converts results back to the original array namespace
- The underlying estimator can still operate on Array API arrays (e.g., PyTorch tensors) for its `fit` and `predict` methods
- Removes the `method == "temperature"` restriction from `__sklearn_tags__` so all methods declare Array API support when the underlying estimator supports it
- Uses `xp.where` instead of boolean index assignment for Array API compatibility in the probability clamping step
## Test plan
- [x] Added `test_sigmoid_isotonic_array_api_compliance` test covering sigmoid and isotonic methods with Array API inputs
- [x] Test parametrized over: method (sigmoid, isotonic), ensemble (True, False), sample_weight (True, False), and all array namespace/device/dtype combinations
- [x] All 177 existing calibration tests pass (113 skipped due to missing backends like CUDA)
- [x] Verified numerical equivalence between numpy and array_api_strict results
🤖 Generated with [Claude Code](https://claude.com/claude-code) | {
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Fixes #12952
What does this implement/fix? Explain your changes.
This PR introduces out-of-core learning capabilities to the NearestCentroid classifier by implementing the partial_fit method for the euclidean metric. This allows the model to incrementally train on large-scale datasets that cannot fit entirely into memory.
Key Technical Details:
Incremental Updates: Implemented Chan's algorithm for numerically stable, batched updates of class centroids and within-class sum of squared errors (SSE).
Metric Routing: partial_fit is explicitly restricted to the euclidean metric, as the median (used in manhattan) cannot be properly updated incrementally.
Validation & DataFrame Preservation: Refactored the internal routing between fit and partial_fit to strictly avoid double-validation. This properly preserves pandas DataFrame metadata (feature_names_in_) and prevents the stripping of feature names during shell delegation.
Edge-Case Handling: Bulletproofed zero-variance checks across global and batched states, gracefully handling identical inputs within the same class without triggering Cython-level NaN/Inf divisions.
Cython Compatibility: Enforced strictly np.float64 and C-contiguous memory layouts for the centroids_ arrays to ensure downstream compatibility with Cython distances.
I used AI assistance for:
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [x] Test/benchmark generation
- [ ] Documentation (including examples)
- [ ] Research and understanding
#### Any other comments?
The initial fit method for euclidean now acts as a clean shell that delegates directly to partial_fit, reducing code duplication while maintaining strict scikit-learn API compliance.
Signed-off-by: Shivam Lalakiya
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33334 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33334/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33334/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33334/events | https://github.com/scikit-learn/scikit-learn/issues/33334 | 3,965,493,814 | I_kwDOAAzd1s7sXKI2 | 33,334 | [Callbacks] ENH Add callback support to a couple of estimators and meta-estimators | {
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It would serve to showcase how callback support would look like in actual scikit-learn estimators.
Interesting candidates:
- GridSearchCV
- Pipeline
- LogisticRegression
- more ?
ping @FrancoisPgm | null | {
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Fixes #33214.
#### What does this implement/fix? Explain your changes.
This PR changes the default `scoring=None`, i.e. accuracy, to `scoring=neg_log_loss` in `LogisticRegressionCV`.
#### AI usage disclosure
None
#### Any other comments?
The failing tests will be fixed, once #32828 gets merged. | null | {
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] | closed | false | [] | null | 3 | 2026-02-19T20:41:25 | 2026-02-24T17:06:05 | 2026-02-22T23:56:06 | null | NONE | null | null | null | null | Hi, I noticed that the comment on line 244 regarding check_unknown=False could be more descriptive to help developers understand that we are avoiding redundant validation. I've updated it for better clarity.
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#### What does this implement/fix? Explain your changes.
This is a minor improvement to the comment on line 244 regarding check_unknown=False could be more descriptive to help developers understand that we are avoiding redundant validation. I've updated it for better clarity.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33331 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33331/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33331/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33331/events | https://github.com/scikit-learn/scikit-learn/issues/33331 | 3,964,229,995 | I_kwDOAAzd1s7sSVlr | 33,331 | [Callbacks] DOC Add documentation for developpers | {
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Add a section or page in the developper guide to document
- the callback API
- the different objects needed to add callback support into an estimator
- how to implement callback support in an estimator
- how to implement a custome callback
Part of that could be done through an example.
We should also distinguish scikit-learn devs from third-party devs, the former being able to use private stuff.
ping @FrancoisPgm | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33330 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33330/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33330/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33330/events | https://github.com/scikit-learn/scikit-learn/issues/33330 | 3,964,209,942 | I_kwDOAAzd1s7sSQsW | 33,330 | [Callbacks] DOC Add documentation in the user guide | {
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Add a page or section, tbd, in the user guide to document
- where are the callbacks
- how to use them
- what are the different callbacks and their purpose
- more ?
ping @FrancoisPgm | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33329 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33329/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33329/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33329/events | https://github.com/scikit-learn/scikit-learn/issues/33329 | 3,964,178,622 | I_kwDOAAzd1s7sSJC- | 33,329 | [Callbacks] FEA Add a callback for metric monitoring | {
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The goal is to record values of a given metric during ``fit``.
It can be computed on the train set or on a validation set.
ping @FrancoisPgm | null | {
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related to https://github.com/scikit-learn/scikit-learn/issues/27676
We raise a warning for an estimator not implementing callback support within a meta-estimator that has callbacks registered.
Currently this warning is repeated each time the inner estimator is cloned and fitted which can mean a lot of times.
It would be nice to avoid this repetion for clones of a same estimator.
ping @FrancoisPgm | null | {
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related to https://github.com/scikit-learn/scikit-learn/issues/27676
We'll probably need something similar for other callbacks as well as it will be useful in particular for pyodide.
So we can consider implementing it using a constructor param, or maybe through a global config since it would likely impact all callbacks at once.
ping @FrancoisPgm | null | {
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related to https://github.com/scikit-learn/scikit-learn/issues/27676
It could be a simple representation just showing key information about the given context.
Or a complex representation including the whole tree below this context. (maybe not necessary, but useful for debugging).
@FrancoisPgm | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33325 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33325/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33325/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33325/events | https://github.com/scikit-learn/scikit-learn/issues/33325 | 3,964,072,158 | I_kwDOAAzd1s7sRvDe | 33,325 | [Callbacks] ENH Don't create a new manager for each callback | {
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related to https://github.com/scikit-learn/scikit-learn/issues/27676
Since callbacks are expected to be process aware and in particular aggregate information accross processes, they more or less all need to hold a data structure (list, dict, queue, ...) that is managed by a ``multiprocessing.Manager()``. Each manager creates a manager process, so we don't want to accumulate them.
There are callbacks that can create this data structure in ``on_fit_begin`` but others that need to create it at initialization. Taking into account the fact that it should work in custom user code, the most robust solution would be to have a sklearn global manager. It would be created lazily, i.e. the first time a callbacks requests a manager, and could then be accessed by all callbacks.
It implies that it would create a process that lives throughout the whole program session. Maybe that's okay.
To prevent that it accumulates data without control, we should make sure that when a shared data structure is no longer needed (i.e. when a callback gets garbage collected) it's automatically destroyed. If we find that the garbage collector is not able to do it properly on its own, we could rely on weakref finalizers.
ping @FrancoisPgm | null | {
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related to https://github.com/scikit-learn/scikit-learn/issues/27676
Let's add tests to check the sequence of calls of callback hooks and make sure it follows the expected order (i.e. the order in which we traverse the callback context tree).
ping @FrancoisPgm | null | {
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related to https://github.com/scikit-learn/scikit-learn/issues/27676
The current implementation requires that estimators used in meta-estimators can only have callbacks if the meta-estimator supports callbacks.
It may be a too strong requirement, or at least we need to define it properly, for user code.
A typical problematic use case is when several clones of an estimator are fitted in parallel in a user defined function (like a custom cross validation).
It implies in particular that we settle on the expected behavior of ``clone`` w.r.t callbacks.
An "easy" first step would be that clone discards the callbacks. Afterall callbacks are not constructor parameters strictly. With that callbacks would just be disabled for estimators used in non-callback-aware code. (it would be silent though)
Then we can investigate whether a second step where we don't impose that estimators within a meta estimator (or function that behaves like a meta-estimator) have their ``CallbackContext`` tree merged with the one of the parent estimator.
In such situations, the callbacks may not provide their full capabilities. For instance progress bars could not be displayed in a single ``rich`` live object with bars inserted dynamically and intuitive layout, but would pop one below the other.
ping @FrancoisPgm | null | {
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} | 0 | 2026-02-19T16:26:47 | 2026-03-06T09:25:08 | null | null | MEMBER | null | null | null | null | This PR comes from the ``callbacks`` feature branch where the developement of the callback API continues.
It implements the SLEP 023: https://github.com/scikit-learn/enhancement_proposals/pull/90. **Please don't review this PR directly for now**, but comment in the side PRs and issues instead. For now the main purpose of this PR is to have a clean diff agains ``main``.
Closes #7574
Closes #10973 | null | {
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Fixes https://github.com/scikit-learn/scikit-learn/issues/33295
Note: Trying to fix only the last point here https://github.com/scikit-learn/scikit-learn/issues/33295#issuecomment-3921080488. The first 2 points seem not to exist anymore (maybe `vscode` + `git` branch switching made me see this.. or it was fixed).
See also https://github.com/scikit-learn/scikit-learn/pull/31937#issuecomment-3908561441
#### What does this implement/fix? Explain your changes.
`FeatureUnion`'s display currently only shows the name of the transformers but not their type (see `name1` and `name2`):
<img width="868" height="165" alt="FeatureUnionColTransformer_main" src="https://github.com/user-attachments/assets/30744a2e-04a3-4ea4-8930-100e27e576f6" />
With this PR, the type of transformer (`ColumnTransformer` in this case) is added:
<img width="855" height="160" alt="FeatureUnionColTransformer_remainderbranch" src="https://github.com/user-attachments/assets/7fa392ae-89b9-4394-8ada-19c6487b641f" />
Please note how this would look with other transfomers:
<img width="348" height="115" alt="FeatureUnionPCATruncatedSVD_remainderbranch" src="https://github.com/user-attachments/assets/27117522-f782-4dcb-b3a5-0c334f3e2370" />
Using the same code as this https://github.com/scikit-learn/scikit-learn/issues/33295#issuecomment-3921537610
<img width="1772" height="400" alt="FeatureUnionColTransformer_plot" src="https://github.com/user-attachments/assets/01d5fc75-0d5c-43e4-9422-d998114d7050" />
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There is not much point trying to investigate this more, since this only happens for Python 3.13 free-threaded not 3.14. | {
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In univariate selection, this new version does not require a copy of X, making it more efficient memory-wise.
The previous version required to calculate `X ** 2` (see `safe_sqr` in `sklearn.utils.extmath`), which made a copy of X | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33318 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33318/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33318/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33318/events | https://github.com/scikit-learn/scikit-learn/pull/33318 | 3,959,550,031 | PR_kwDOAAzd1s7EtN_8 | 33,318 | Fix MDS returning wrong number of components when using ClassicalMDS initialization | {
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🙌 Thanks for contributing a pull request!
👀 Please ensure you have taken a look at the contribution guidelines:
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✅ In particular following the pull request checklist will increase the likelihood
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describing your PR changes, see:
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-->
#### Reference Issues/PRs
Fixes #33305
#### What does this implement/fix? Explain your changes.
Ensures MDS outputs specified number of components when using ClassicalMDS initialization
#### 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)
- [ ] Research and understanding | {
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Fixes #15873.
#### What does this implement/fix? Explain your changes.
Adds cardinality-based selection to `make_column_selector` via the new
`cardinality` and `cardinality_threshold` parameters, with tests and a
changelog fragment.
#### AI usage disclosure
I used AI assistance for:
- [x] Code generation (e.g., when writing an implementation or fixing a bug)
- [x] Test/benchmark generation
- [x] Documentation (including examples)
- [x] Research and understanding
#### Any other comments?
Tests not run (scikit-learn not built: `sklearn.__check_build._check_build`).
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Fixes #33012.
#### What does this implement/fix? Explain your changes.
Adds a small preprocessing example showing `get_feature_names_out` and
`set_output` preserving feature names. Adds a changelog fragment.
#### 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?
Docs-only change; tests not run.
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Fixes #32821.
#### What does this implement/fix? Explain your changes.
Clarifies that `partial_fit` can be called without a prior `fit` and notes
the `classes` requirement for classifiers on the first call. Adds a changelog
fragment.
#### AI usage disclosure
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- [x] Documentation (including examples)
- [x] Research and understanding
#### Any other comments?
Docs-only change; tests not run.
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Fixes #33075.
#### What does this implement/fix? Explain your changes.
Adds a short `set_output` section with an example in the pipelines/composition
docs. Adds a changelog fragment.
#### AI usage disclosure
I used AI assistance for:
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Docs-only change; tests not run.
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Fixes #29167.
#### What does this implement/fix? Explain your changes.
Adds a “Beginner resources” section to the Getting Started guide with links
to the MOOC, ML map, and user guide. Adds a changelog fragment.
#### AI usage disclosure
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- [x] Documentation (including examples)
- [x] Research and understanding
#### Any other comments?
Docs-only change; tests not run.
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Fixes #30389.
#### What does this implement/fix? Explain your changes.
Adds public `check_feature_names` and `check_n_features` wrappers in
`sklearn.utils.validation`, updates the API reference, adds tests, and
includes a changelog fragment.
#### AI usage disclosure
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- [x] Code generation (e.g., when writing an implementation or fixing a bug)
- [x] Test/benchmark generation
- [x] Documentation (including examples)
- [x] Research and understanding
#### Any other comments?
Tests not run (scikit-learn not built: `sklearn.__check_build._check_build`).
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None
#### What does this implement/fix? Explain your changes.
This removes https://github.com/ploomber/sklearn-evaluation from the related projects site. It is archives as of 28 Febr 2025.
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None
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33310 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33310/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33310/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33310/events | https://github.com/scikit-learn/scikit-learn/pull/33310 | 3,958,060,853 | PR_kwDOAAzd1s7EoXv4 | 33,310 | CI Test only 20 random seeds in nightly scheduled run | {
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] | closed | false | [] | null | 4 | 2026-02-18T14:11:24 | 2026-02-18T16:44:48 | 2026-02-18T15:43:21 | null | MEMBER | null | null | null | null | Follow-up of https://github.com/scikit-learn/scikit-learn/issues/33033. Close #33033.
After the Azure -> GHA migration (or a dependency update hard to know for sure) there seems to be at least 2 tests that fail for a single random seed see https://github.com/scikit-learn/scikit-learn/issues/33274#issuecomment-3907658843 and https://github.com/scikit-learn/scikit-learn/issues/33244#issuecomment-3907806973.
This implements a middle-ground approach of only testing 20 global random seeds in the daily schedule run as mentioned in https://github.com/scikit-learn/scikit-learn/issues/33033#issuecomment-3750070779.
- this still detects somewhat flaky tests in the nightly run except with a higher detection threshold. This will reduce the time spent in chasing and "fixing" this kind of issues (raising the tolerance most of the time :shrug:).
- you can still use `SKLEARN_GLOBAL_RANDOM_SEED=all` to locally test global random seeds `0-99`. The `[all random seeds]` commit message also does test all random seeds (i.e. `0-99)`. We could also decide that "all" is 0-19 if we want to do this, no strong opinion to be honest. I guess using 0-99 for `all` makes you write a more robust/conservative test and is less likely to trigger a random failure in the CI where we tests for less seeds?
cc @jeremiedbb @ogrisel.
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} | ### Describe the bug and give evidence about its user-facing impact
Calling `.predict()` from a classifier may output different dtypes depending on what the input class and dtypes are, in a way that doesn't look consistent.
For example, the [docs for array API support](https://scikit-learn.org/stable/modules/array_api.html) state:
> For estimators, the rule is “everything follows X “ - mixed array inputs are converted so that they all match the array library and device of X.
But that does not appear to be the case with `RidgeClassifier.predict` (currently the only classifier with array API support).
For example, calling `.predict()` with NumPy arrays that are float64 will return int64 under array API dispatch:
```python
import os
os.environ["SCIPY_ARRAY_API"] = "1"
import numpy as np
import array_api_strict
rng = np.random.default_rng(seed=123)
X = rng.standard_normal(size=(1000, 20))
y = rng.integers(2, size=X.shape[0])
from sklearn import config_context
from sklearn.linear_model import RidgeClassifier
with config_context(array_api_dispatch=True):
pred = RidgeClassifier(solver="svd").fit(X, y).predict(X)
pred.dtype
```
```
dtype('int64')
```
But with float32 arrays it returns float32, whether it is under array API dispatch or not:
```python
pred = RidgeClassifier(solver="svd").fit(X.astype(np.float32), y.astype(np.float32)).predict(X.astype(np.float32))
pred.dtype
```
```
dtype('float32')
```
Likewise, for pandas it returns the same dtype as input:
```python
import pandas as pd
pred = RidgeClassifier(solver="svd").fit(
pd.DataFrame(X).astype(np.int16),
pd.Series(y).astype(np.int16)
).predict(
pd.DataFrame(X).astype(np.int16)
)
pred.dtype
```
```
dtype('int16')
```
.. unless the input is float64 or mixed types, in which case it returns int64.
But for inputs that are not NumPy, Pandas or Polars, passing float64 will return float64:
```python
import array_api_strict
X64 = array_api_strict.asarray(X, dtype=array_api_strict.float64)
y64 = array_api_strict.asarray(y, dtype=array_api_strict.float64)
with config_context(array_api_dispatch=True):
pred = RidgeClassifier(solver="svd").fit(X64, y64).predict(X64)
pred.dtype
```
```
array_api_strict.float64
```
### Steps/Code to Reproduce
```python
import os
os.environ["SCIPY_ARRAY_API"] = "1"
import numpy as np
import array_api_strict
rng = np.random.default_rng(seed=123)
X = rng.standard_normal(size=(1000, 20))
y = rng.integers(2, size=X.shape[0])
from sklearn import config_context
from sklearn.linear_model import RidgeClassifier
with config_context(array_api_dispatch=True):
pred = RidgeClassifier(solver="svd").fit(X, y).predict(X)
pred.dtype
```
### Expected Results
```
dtype('float64')
```
### Actual Results
```
dtype('int64')
```
### Versions
```shell
System:
python: 3.12.12 | packaged by conda-forge | (main, Oct 22 2025, 23:25:55) [GCC 14.3.0]
executable: <local path>/miniforge3/envs/icxconda/bin/python
machine: Linux-6.8.0-49-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.8.0
pip: 25.3
setuptools: 80.9.0
numpy: 2.4.1
scipy: 1.17.0
Cython: 3.2.4
pandas: 2.3.3
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: 128
prefix: libopenblas
filepath: <local path>/miniforge3/envs/icxconda/lib/libopenblasp-r0.3.30.so
version: 0.3.30
threading_layer: pthreads
architecture: SapphireRapids
user_api: openmp
internal_api: openmp
num_threads: 224
prefix: libomp
filepath: <local path>/miniforge3/envs/icxconda/lib/libomp.so
version: None
```
### Interest in fixing the bug
No. | {
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} | ### Describe the bug and give evidence about its user-facing impact
When using the new `init="classical_mds"` option in `MDS`, the number of components returned is always 2.
### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.manifold import MDS
data = np.random.uniform(size=(20, 10))
model = MDS(init="classical_mds", n_components=3, n_init=1)
transformed_data = model.fit_transform(data)
print("Output n_components:", transformed_data.shape[1])
```
### Expected Results
Output n_components: 3
### Actual Results
Output n_components: 2
### Versions
```shell
System:
python: 3.12.5 (main, Aug 14 2024, 04:32:18) [Clang 18.1.8 ]
executable: /Users/ben.pedigo/code/meshrep/meshrep/.venv/bin/python
machine: macOS-13.6.1-arm64-arm-64bit
Python dependencies:
sklearn: 1.8.0
pip: None
setuptools: 69.5.1
numpy: 2.2.6
scipy: 1.15.0
Cython: None
pandas: 2.3.2
matplotlib: 3.10.8
joblib: 1.5.1
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: /Users/ben.pedigo/code/meshrep/meshrep/.venv/lib/python3.12/site-packages/numpy/.dylibs/libscipy_openblas64_.dylib
version: 0.3.29
threading_layer: pthreads
architecture: neoversen1
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: /Users/ben.pedigo/code/meshrep/meshrep/.venv/lib/python3.12/site-packages/scipy/.dylibs/libscipy_openblas.dylib
version: 0.3.28
threading_layer: pthreads
architecture: neoversen1
user_api: openmp
internal_api: openmp
num_threads: 12
prefix: libomp
filepath: /opt/homebrew/Cellar/libomp/19.1.2/lib/libomp.dylib
version: None
user_api: openmp
internal_api: openmp
num_threads: 12
prefix: libomp
filepath: /Users/ben.pedigo/code/meshrep/meshrep/.venv/lib/python3.12/site-packages/sklearn/.dylibs/libomp.dylib
version: None
user_api: openmp
internal_api: openmp
num_threads: 12
prefix: libomp
filepath: /Users/ben.pedigo/code/meshrep/meshrep/.venv/lib/python3.12/site-packages/skimage/.dylibs/libomp.dylib
version: None
```
### Interest in fixing the bug
I am happy to open a PR to fix - I believe `n_components` just needs to be passed down during the initialization here: https://github.com/scikit-learn/scikit-learn/blob/3c8d10e670e72e3a96c753a1c943ec090b06dbd8/sklearn/manifold/_mds.py#L816 | {
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On the way, I removed all legacy code where we used to try to retreive the original ``__init__``. We no longer wrap ``__init__`` since ~4 releases, but ``__new__`` instead. This code is now just dead code that can safely be removed.
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No issue involved.
#### What does this implement/fix? Explain your changes.
While compiling sk-learn, we get the following warnings for modules `_binary_tree.pyx.tp` and `_kd_tree.pxi.tp`:
```
[1/4] Generating sklearn/neighbors/_kd_tree_pyx with a custom command
[2/4] Generating 'sklearn/neighbors/_kd_tree.cpython-312-x86_64-linux-musl.so.p/_kd_tree.c'
warning: sklearn/neighbors/_binary_tree.pxi:1091:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:1957:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:2052:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:2221:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:2344:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:2401:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:2402:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:2726:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:3592:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:3687:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:3856:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:3979:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:4036:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_binary_tree.pxi:4037:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier
warning: sklearn/neighbors/_kd_tree.pyx:75:4: Assigning to 'float64_t *' from 'const float64_t *' discards const qualifier
warning: sklearn/neighbors/_kd_tree.pyx:76:4: Assigning to 'float64_t *' from 'const float64_t *' discards const qualifier
warning: sklearn/neighbors/_kd_tree.pyx:344:4: Assigning to 'float32_t *' from 'const float32_t *' discards const qualifier
warning: sklearn/neighbors/_kd_tree.pyx:345:4: Assigning to 'float32_t *' from 'const float32_t *' discards const qualifier
[3/4] Compiling C object sklearn/neighbors/_kd_tree.cpython-312-x86_64-linux-musl.so.p/meson-generated__kd_tree.c.o
```
This PR fixes the
- `Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier`
- `Assigning to 'float64_t *' from 'const float64_t *' discards const qualifier`
warnings which leads to:
```
[1/9] Generating sklearn/neighbors/_binary_tree_pxi with a custom command
[2/9] Generating sklearn/neighbors/_ball_tree_pyx with a custom command
[3/9] Generating sklearn/neighbors/_kd_tree_pyx with a custom command
[4/9] Generating 'sklearn/neighbors/_ball_tree.cpython-312-x86_64-linux-musl.so.p/_ball_tree.c'
[5/9] Generating 'sklearn/neighbors/_kd_tree.cpython-312-x86_64-linux-musl.so.p/_kd_tree.c'
[6/9] Compiling C object sklearn/neighbors/_ball_tree.cpython-312-x86_64-linux-musl.so.p/meson-generated__ball_tree.c.o
``` | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33301 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33301/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33301/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33301/events | https://github.com/scikit-learn/scikit-learn/pull/33301 | 3,953,469,478 | PR_kwDOAAzd1s7EZUR0 | 33,301 | MAINT Small refactor of common functions used by HTML displays | {
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This refactoring was implemented on #31442, but I was asked to do it separately.
See [comment](https://github.com/scikit-learn/scikit-learn/pull/31442/changes#r2789352286)
#### What does this implement/fix? Explain your changes.
The functions in `sklearn/utils/_repr_html/_common.py` will be used by [future PRs that modify HTML displays](#31442) and #31937 for example.
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Fixes #32866 (supersedes #32867)
#### What does this implement/fix? Explain your changes.
Continuing the proposed fix from #32867, this sets the `levels` parameter explicitly to the unique target values for `contour` and extends them for `contourf` with `response_method=predict` (the other `contourf` cases are handles differently by plotting every class on a different surface, so they're not affected by this bug). This ensures, that all classes (and class boundaries, respectively) are displayed in different colors.
Note that in #33015 we noticed that this can also occur when `n_classes < 7`. This happens because the default values for the levels don't necessarily correspond to the target classes. I used the data from the example in #33015 for the non-regression test here.
@ogrisel @lucyleeow @ThexXTURBOXx @leweex95
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33298 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33298/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33298/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33298/events | https://github.com/scikit-learn/scikit-learn/pull/33298 | 3,951,027,518 | PR_kwDOAAzd1s7ERMvo | 33,298 | ENH: Add minimal type hints to Bunch class (__init__ and __setattr__) | {
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- __init__(self, **kwargs: Any) -> None
- __setattr__(self, key: Any, value: Any) -> None
No runtime logic was changed. Inherited dict behavior is preserved.
All existing tests pass.
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 0 | 2026-02-17T05:00:03 | 2026-02-17T10:22:23 | 2026-02-17T10:22:23 | null | CONTRIBUTOR | null | null | null | null | This PR fixes multiple 'the the' typos in docstrings and comments in sklearn/_loss/loss.py. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33296 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33296/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33296/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33296/events | https://github.com/scikit-learn/scikit-learn/pull/33296 | 3,949,913,176 | PR_kwDOAAzd1s7ENhs5 | 33,296 | DOC Update california housing dataset URL | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 0 | 2026-02-16T23:02:48 | 2026-02-17T00:38:10 | 2026-02-17T00:38:10 | null | MEMBER | null | null | null | null | <!--
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Fixes #33231.
#### What does this implement/fix? Explain your changes.
This PR updates the URL for the [California housing dataset](https://scikit-learn.org/stable/datasets/real_world.html#california-housing-dataset) to point to [StatLib](https://lib.stat.cmu.edu/), since the original University of Porto link returns a 504 error.
As alternatives, we could either:
1. Link to the Internet Archive.
2. Keep the original URL for reference and mention that it is now dead.
#### 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)
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#### Any other comments?
I also noticed that the StatLib version has the columns in reverse order compared to the original version.
```python
updated = original[:, ::-1]
```
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33295 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33295/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33295/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33295/events | https://github.com/scikit-learn/scikit-learn/issues/33295 | 3,948,595,483 | I_kwDOAAzd1s7rWskb | 33,295 | BUG FeatureUnion's HTML Display incorrect when ColumnTransformer's remainder is a preprocessor | {
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} | ### Describe the bug and give evidence about its user-facing impact
The lines that should be the output of the FeatureUnion are incorrect in the HTML display.
See https://github.com/scikit-learn/scikit-learn/pull/31937#issuecomment-3908561441
### Steps/Code to Reproduce
```py
from sklearn.preprocessing import Normalizer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import FeatureUnion
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
ct = ColumnTransformer(transformers=[("norm1", Normalizer(), [0, 1])],
verbose_feature_names_out=True,
#remainder="drop",
remainder=MinMaxScaler(),
)
transformer_f = FeatureUnion(
[
("transformer1", ct),
("transformer2", ct),
],
)
transformer_f
```
### Expected Results
Similar to this, but showing the remainder (when I uncomment `remainder="drop"`, and comment `remainder=MinMaxScaler()` it shows the output lines correctly).
<img width="471" height="167" alt="Image" src="https://github.com/user-attachments/assets/99bfc2e3-9006-4b4a-8ba7-300d0bead304" />
### Actual Results
The lines of the bottom transformer are incorrect, plus they should be in parallel.
I think that also `ColumnTrasformer` should be shown besides the name of the transformer - but I need to check that.
<img width="476" height="286" alt="Image" src="https://github.com/user-attachments/assets/7dac87fb-dd0e-48a6-910e-d28dd32a4e7f" />
### Versions
```shell
System:
python: 3.13.9 | packaged by conda-forge | (main, Oct 22 2025, 23:38:18) [Clang 19.1.7 ]
executable: /Users/dealeon/mambaforge/envs/sklearn-dev/bin/python
machine: macOS-26.2-arm64-arm-64bit-Mach-O
Python dependencies:
sklearn: 1.9.dev0
pip: 25.3
setuptools: 80.9.0
numpy: 2.3.4
scipy: 1.16.3
Cython: 3.2.0
pandas: 2.3.3
matplotlib: 3.10.7
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 8
prefix: libopenblas
filepath: /Users/dealeon/mambaforge/envs/sklearn-dev/lib/libopenblas.0.dylib
version: 0.3.30
threading_layer: openmp
architecture: VORTEX
user_api: openmp
internal_api: openmp
num_threads: 8
prefix: libomp
filepath: /Users/dealeon/mambaforge/envs/sklearn-dev/lib/libomp.dylib
version: None
```
### Interest in fixing the bug
Yes, I'm interested in taking a look at this. | null | {
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} | ### Describe the bug and give evidence about its user-facing impact
When passing polars inputs for `check_X_y` that have float32 types in columns, under the default argument `dtype="numeric"`, inputs are processed as expected (returns float32 arrays), but if passing instead a list as dtype argument where float64 comes first (e.g. `dtype=[np.float64, np.float32])`), then the 'X' argument will be converted to float64, while the 'y' argument is still processed correctly:
```python
import numpy as np
import polars as pl
from sklearn.utils.validation import check_X_y
X = (
pl.DataFrame({"col1": [1,2], "col2": [3,4]})
.with_columns(pl.all().cast(pl.Float32))
)
y = pl.Series([1,2], dtype=pl.Float32)
check_X_y(X, y, dtype=[np.float64, np.float32])[0].dtype
```
```
dtype('float64')
```
The issue doesn't seem to affect pandas objects though:
```python
check_X_y(X.to_pandas(), y.to_pandas(), dtype=[np.float64, np.float32])[0].dtype
```
```
dtype('float32')
```
### Steps/Code to Reproduce
```python
import numpy as np
import polars as pl
from sklearn.utils.validation import check_X_y
X = (
pl.DataFrame({"col1": [1,2], "col2": [3,4]})
.with_columns(pl.all().cast(pl.Float32))
)
y = pl.Series([1,2], dtype=pl.Float32)
check_X_y(X, y, dtype=[np.float64, np.float32])[0].dtype
```
### Expected Results
```
dtype('float32')
```
### Actual Results
```
dtype('float64')
```
### Versions
```shell
System:
python: 3.13.9 | packaged by conda-forge | (main, Oct 22 2025, 23:33:35) [GCC 14.3.0]
executable: <local path>/miniforge3/envs/dpnp18/bin/python
machine: Linux-6.8.0-49-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.9.dev0
pip: 25.3
setuptools: 80.9.0
numpy: 2.3.5
scipy: 1.16.3
Cython: 3.2.2
pandas: 2.3.3
matplotlib: 3.10.7
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 64
prefix: libscipy_openblas
filepath: <local path>/miniforge3/envs/dpnp18/lib/python3.13/site-packages/numpy.libs/libscipy_openblas64_-fdde5778.so
version: 0.3.30
threading_layer: pthreads
architecture: SkylakeX
user_api: blas
internal_api: openblas
num_threads: 64
prefix: libscipy_openblas
filepath: <local path>/miniforge3/envs/dpnp18/lib/python3.13/site-packages/scipy.libs/libscipy_openblas-b75cc656.so
version: 0.3.29.dev
threading_layer: pthreads
architecture: SkylakeX
user_api: openmp
internal_api: openmp
num_threads: 224
prefix: libomp
filepath: <local path>/miniforge3/envs/dpnp18/lib/libomp.so
version: None
```
### Interest in fixing the bug
No. | null | {
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"node_id": "MD... | closed | false | [] | null | 1 | 2026-02-16T10:09:15 | 2026-02-16T15:43:33 | 2026-02-16T15:43:33 | null | MEMBER | null | null | null | null | This happens because the warning is emitted twice, once outside a `pytest.warns` context and once inside. There is some kind of race condition with the per-module `__warningregistry__` which is used to issue a warning only once (default behaviour).
The work-around is to make sure the first warnings also happens withing a `pytest.warns`.
See https://github.com/Quansight-Labs/pytest-run-parallel/issues/169 for more details.
With this work-around the test passes on my machine:
```
pytest sklearn/preprocessing/tests/test_encoders.py -k test_onehotencoder_handle_unknown_warn_maps_to_infrequent --parallel-threads 4 --iterations 500
```
This was seen in https://github.com/scikit-learn/scikit-learn/pull/33245. | {
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"node_id": "MDU6TGFiZWwxMzE0Mj... | open | false | [] | null | 2 | 2026-02-16T10:06:13 | 2026-02-16T10:55:57 | null | null | CONTRIBUTOR | null | null | {
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"blocking": 0,
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} | ### Describe the workflow you want to enable
Hello,
sklearn.gaussian_process.GaussianProcessRegressor
does not scale to large datasets.
Apparently, VGP would:
https://arxiv.org/pdf/1511.06499
Regards,
Francois.
### Describe your proposed solution
Implement VGP.
### Describe alternatives you've considered, if relevant
No open-source Python implementation, to the best of my knowledge.
### Additional context
_No response_ | null | {
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### Note
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### Note
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### Note
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- test_spectral_biclustering[97-csr_matrix]
- test_spectral_biclustering[97-csr_array] | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33287 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33287/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33287/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33287/events | https://github.com/scikit-learn/scikit-learn/pull/33287 | 3,945,024,471 | PR_kwDOAAzd1s7D9XgG | 33,287 | ENH Add Huber loss for HistGradientBoostingRegressor | {
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] | open | false | [] | null | 0 | 2026-02-15T22:03:11 | 2026-02-15T22:34:26 | null | null | NONE | null | null | null | null | ## What does this implement/fix?
Adds support for , bringing it to feature parity with which already supports Huber loss.
Closes #31542
## How does it work?
The Huber loss is a robust loss that is quadratic for small residuals and linear for large ones, controlled by a threshold parameter `delta`. Following Friedman (2001) *"Greedy Function Approximation: A Gradient Boosting Machine"*, `delta` is re-estimated at each boosting iteration as the `quantile`-th percentile of the absolute residuals (default 0.9).
### Implementation details:
- **Leverages existing `HuberLoss` class** from `sklearn._loss.loss` which already has Cython-optimized gradient/hessian computation (`CyHuberLoss`), `need_update_leaves_values = True` (line search via `fit_intercept_only`), and `differentiable = False` (triggers `_update_leaves_values` in the training loop).
- **Per-iteration delta estimation** is done in the HGBT training loop (before gradient computation), updating `self._loss.closs.delta` to the quantile of absolute residuals. This follows the same approach as `GradientBoostingRegressor`'s `HuberLossFunction.negative_gradient()`.
- **Reuses the `quantile` parameter** already present on `HistGradientBoostingRegressor` (used for PinballLoss). For Huber loss, it controls the percentile used for delta estimation (default 0.9 if not specified).
- **Supports sample weights** — delta estimation uses `_weighted_percentile` when sample weights are provided.
### Key design decision:
Per @lorentzenchr's guidance in the issue discussion, this implements **true Huber loss** (not pseudo-Huber), consistent with `GradientBoostingRegressor` and `HuberRegressor`. The loss is not smooth at the transition point, so it uses line search (like `absolute_error` and `quantile` losses already do).
## Tests
Added 4 new tests:
- `test_huber`: Verifies Huber loss is more robust to outliers than squared error
- `test_huber_sample_weight`: Tests with sample weights
- `test_huber_quantile_parameter`: Tests that quantile parameter affects behavior
- `test_huber_early_stopping`: Tests early stopping integration
All existing tests pass (153 passed, 16 skipped).
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33286 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33286/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33286/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33286/events | https://github.com/scikit-learn/scikit-learn/pull/33286 | 3,943,344,133 | PR_kwDOAAzd1s7D4Cq2 | 33,286 | DOC update California housing source link | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 0 | 2026-02-15T07:10:29 | 2026-02-15T07:58:32 | 2026-02-15T07:58:32 | null | NONE | null | null | null | null | ## Summary
- replace the broken California housing source URL in the dataset description with the StatLib repository URL
- update the source comment in `sklearn/datasets/_california_housing.py` accordingly
## Why
Issue #33231 reports that the old URL times out and is no longer reliably reachable.
Closes #33231
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"node_id":... | open | false | [] | null | 1 | 2026-02-15T00:20:48 | 2026-02-18T11:10:08 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
closes #33283
also related to #28975
#### What does this implement/fix? Explain your changes.
This fixes metadata routing in `learning_curve` and the metadata routing tests in `sklearn/model_selection/tests/test_validation.py`.
1. bug
With `exploit_incremental_learning=True` we call the estimator's `partial_fit` method via `_incremental_fit_estimator`. In this process, `sample_weight` in both `fit_params` and `score_params` need to get split following the same rules as `X` and `y`. Both was not done correctly before.
2. bug
The metadata routing tests for the validation module were broken. For instance, for `learning_curve`, `record_metadata` records `_fit_and_score` instead of `fit`, `_score` instead of `score` and `_incremental_fit_estimator` instead of `partial_fit` and thus `check_recorded_metadata` compared empty with empty. I have now passed the correct function names to `check_recorded_metadata`.
#### AI usage disclosure
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If AI tools were involved in creating this PR, please check all boxes that apply
below and make sure that you adhere to our Automated Contributions Policy:
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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
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"node_id":... | closed | false | [] | null | 0 | 2026-02-14T23:44:48 | 2026-02-16T12:30:06 | 2026-02-16T12:30:06 | null | NONE | null | null | null | null | #### Reference Issues/PRs
Fixes #33283.
#### What does this implement/fix? Explain your changes.
When using `learning_curve` with `exploit_incremental_learning=True` and passing scorer metadata (e.g. `sample_weight`) via the routing API, all training scores except the last come back as NaN.
The problem is in `_incremental_fit_estimator` — `score_params_train` gets sliced once outside the loop with the full `train` indices, but `X_train` inside the loop uses `train_subset = train[:n_train_samples]` which grows each iteration. So you end up passing 100-element weights to a scorer that only has 30 samples, it throws, `_score` catches it and returns NaN.
The fix just moves that one `_check_method_params` call inside the loop so it slices with `train_subset` instead of `train`. `score_params_test` stays outside since the test set doesn't change.
Also added a regression test that reproduces the exact scenario from the issue — confirmed it fails on main and passes with the fix.
#### AI usage disclosure
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [ ] Research and understanding
#### Any other comments?
Noticed this was introduced in 77fc72c39 when metadata routing was added to `learning_curve` — the pattern was likely copied from `_fit_and_score` where it works fine because `train` indices aren't further subsetted in a loop. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33283 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33283/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33283/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33283/events | https://github.com/scikit-learn/scikit-learn/issues/33283 | 3,941,565,943 | I_kwDOAAzd1s7q74X3 | 33,283 | learning_curve with exploit_incremental_learning=True fails to compute training scores when sample_weight is provided | {
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} | ### Describe the bug and give evidence about its user-facing impact
When using learning_curve with exploit_incremental_learning=True and passing sample_weight
(or other parameters) to the scorer, the training scores for all training sizes except the final one are returned as NaN.
This happens because _incremental_fit_estimator pre-computes the training scores' parameters once for the full training set, but then attempts to use them for subsets of the training data. This causes a ValueError (consistent length mismatch) inside the scorer, which is silently caught and replaced with NaN.
This affects any user trying to evaluate learning curves incrementally while using weighted metrics or metadata-aware scorers.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.datasets import make_classification
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import learning_curve
from sklearn.metrics import make_scorer, accuracy_score
import sklearn
# Enable metadata routing to pass sample_weight to scorer
sklearn.set_config(enable_metadata_routing=True)
X, y = make_classification(n_samples=100, n_features=5, random_state=42)
weights = np.ones(100)
scorer = make_scorer(accuracy_score).set_score_request(sample_weight=True)
train_sizes, train_scores, _ = learning_curve(
SGDClassifier(), X, y,
train_sizes=[0.3, 0.6, 0.9],
cv=3,
exploit_incremental_learning=True,
scoring=scorer,
params={'sample_weight': weights}
)
print("Training scores (expecting no NaNs):")
print(train_scores)
```
### Expected Results
The training scores should be successfully computed for all training sizes. Example: [[0.96 0.96 0.96], [0.94 0.94 0.94], [0.92 0.92 0.92]] (no NaN values).
### Actual Results
```
[[ nan nan nan]
[ nan nan nan]
[0.93 0.95 0.92]]
```
### Versions
```shell
System:
python: 3.14.2
machine: Windows-10
Python dependencies:
sklearn: 1.8.0.dev0
numpy: 2.4.1
scipy: 1.17.0
```
### Interest in fixing the bug
Yes, I am interested in fixing this. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33282 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33282/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33282/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33282/events | https://github.com/scikit-learn/scikit-learn/pull/33282 | 3,941,096,773 | PR_kwDOAAzd1s7DwwXK | 33,282 | FIX BisectingKMeans callable init validation uses n_centroids instead of n_clusters | {
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"node_id": "LA_kwDOAAzd1s8... | closed | false | [] | null | 3 | 2026-02-14T10:47:28 | 2026-02-15T02:58:20 | 2026-02-14T17:52:16 | null | NONE | null | null | null | null | Fixes #33146
## Problem
When using `BisectingKMeans` with a callable `init`, `_validate_center_shape` always checked the shape of the returned centers against `self.n_clusters` (the total number of clusters). But for bisecting k-means, each bisection step passes `n_centroids=2` to `_init_centroids`, so the callable correctly receives and returns 2 centers. The validation then fails because it expects `self.n_clusters` (e.g., 6) centers instead of 2.
## Fix
Added an optional `n_clusters` parameter to `_validate_center_shape` so `_init_centroids` can pass the actual number of centroids being requested. When not provided, it defaults to `self.n_clusters` which preserves existing behavior for `KMeans` and `MiniBatchKMeans`.
Also updated the `BisectingKMeans.init` docstring to clarify that the callable is always invoked with `n_clusters=2` at each bisection step.
## Tests
Added two non-regression tests:
- `test_callable_init_with_bisecting_kmeans`: verifies a callable init works with `BisectingKMeans` and always receives `n_clusters=2`
- `test_callable_init_wrong_shape_raises`: verifies that a callable returning the wrong number of centers still raises `ValueError` | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33281 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33281/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33281/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33281/events | https://github.com/scikit-learn/scikit-learn/issues/33281 | 3,938,281,524 | I_kwDOAAzd1s7qvWg0 | 33,281 | Clarify example usage in README.md for beginners | {
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} | ### Describe the issue linked to the documentation
Hello! 👋
Thank you for maintaining this project — it’s really helpful!
While reading the README.md, I noticed that one of the code examples could be a bit unclear for beginners.
X – training features as a 2D array
y – target labels as a 1D array
Example:
model.fit(X_train, y_train)
### Suggest a potential alternative/fix
_No response_ | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33280 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33280/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33280/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33280/events | https://github.com/scikit-learn/scikit-learn/pull/33280 | 3,938,267,651 | PR_kwDOAAzd1s7DnQpV | 33,280 | DOC: Fix some typos in Lasso | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 0 | 2026-02-13T17:37:36 | 2026-02-19T06:31:42 | 2026-02-18T19:49:42 | null | CONTRIBUTOR | null | null | null | null | Fixing some typos in the [Lasso documentation](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html) | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33279 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33279/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33279/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33279/events | https://github.com/scikit-learn/scikit-learn/pull/33279 | 3,937,994,615 | PR_kwDOAAzd1s7DmV1m | 33,279 | MNT Removed unused param in `_preprocess_data` | {
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"node_id": "LA_k... | closed | false | [] | null | 0 | 2026-02-13T16:34:04 | 2026-02-16T23:51:54 | 2026-02-16T23:51:54 | null | MEMBER | null | null | null | null | This param was introduced 2 years ago but defaults to True and `_preprocess_data` is never called with a non-default value of `copy_y`. Let's remove it. | {
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"node_id": "MDU6TGFiZWwx... | open | false | [] | null | 0 | 2026-02-13T11:37:53 | 2026-02-14T13:45:22 | null | null | CONTRIBUTOR | null | null | null | null | Add support for `interaction_cst` in decision trees and downstream ensemble models.
## Reference Issues/PRs
I did not find issues explicitly asking for this, but I was asked for it in a freelance project (and we agreed on making it open-source).
This helps with issue #27873 because HGB already supports `interaction_cst`, so this PR brings GB one step closer to HGB.
## What does this implement/fix? Explain your changes.
#### API changes:
Adds a new `interaction_cst` parameter to:
- `<Decision/Extra>Tree<Classifier/Regressor>`
- `<RandomForest/ExtraTrees/GradientBoosting><Classifier/Regressor>`
The `interaction_cst` parameter supports the same options as in HGB:
- explicit groups (list/tuple/set of feature indices),
- `"pairwise"` shorthand,
- `"no_interactions"` shorthand.
It's validated and normalized in `BaseDecisionTree`.
#### Logic of the implementation:
- Constraints are converted to sparse group<->feature mappings
- Allowed features are maintained as a prefix of `splitter.features`, forbidden features are moved to the suffix and excluded from future draws.
- Handles both builder strategies:
- depth-first: propagates constraint state incrementally via stack records (very efficient)
- best-first: reconstructs node state from ancestor splits using stored parent ids (a bit less efficient, but still has a very small impact even on worst case scenarios)
#### Implementation details
The interaction-constraint logic is encapsulated in a dedicated Cython class:
`InteractionConstraints` (in `sklearn/tree/_tree.pyx`).
It builds and stores group <-> feature mappings in both directions so updates after each split are efficient.
At fit time it also allocates:
- an array for active interaction groups (stored as a prefix to represent the current state),
- reusable working buffers: marking arrays and tokens for efficient set-like operations on groups/features.
The core method is `update_after_split(..., split_feature)`. It updates the active groups and moves newly forbidden features to the tail of `splitter.features`.
It then persists state by writing `n_active_interaction_groups` and `n_allowed_features` to the parent record.
Integration points in existing code paths are intentionally small:
- Splitters (`_splitter.pyx`): feature sampling is restricted to
`features[:n_allowed_features]`.
- Depth-first builder: updates constraints incrementally after each non-leaf split,
and children inherit the updated state.
- Best-first builder: because nodes are expanded out of DFS order, it reconstructs
node state by replaying ancestor split features through stored parent links.
#### Test coverage added/updated:
For trees:
- constraint parsing/validation and error cases,
- structural enforcement across classifier/regressor, `best`/`random` splitters, dense/sparse inputs,
- overlapping groups and array-like input,
- a numerical check that `"no_interactions"` removes mixed second-order effects.
\+ Ensemble smoke tests for Random Forest / Extra Trees / Gradient Boosting to verify parameter plumbing.
## AI usage disclosure
I used AI assistance for:
- Code generation: almost 100% AI written but I iterated a lot (see the commit history ^^), and I think the resulting code is at least as good as what I would have written myself.
- Test/benchmark generation: same
- Docstrings
The human part is: research, understanding, designing.
## Benchmarks
Except for pathological cases I don't expect to see in the real world, providing interactions constraints has no negative impact on fit time. In most cases, it will reduce fit time because it reduces the number candidate splits.
I can provide detailed benchmark results using the script below if needed (including some pathological/near-pathological cases):
<details>
```Python
from time import perf_counter
import numpy as np
from sklearn.tree import DecisionTreeRegressor
n_samples = int(3e5)
n_features = 300
rng = np.random.RandomState(0)
X = rng.normal(size=(n_samples, n_features)).astype(np.float32).round(1)
y = rng.normal(size=n_samples)
cases = {
"none": None,
"no_interactions": "no_interactions",
"disjoint_10": np.arange(n_features).reshape(-1, 10),
"pairwise": "pairwise",
"many_overlap_10": [range(i, i + 10) for i in range(n_features - 10)]
}
def time_fit(X, y, n_repeats: int = 7, **kwargs):
"""Measure fit times across repeats and return times + leaf counts."""
times = []
leave_counts = []
for repeat in range(n_repeats):
est = DecisionTreeRegressor(
random_state=repeat,
**kwargs
)
start = perf_counter()
est.fit(X, y)
times.append(perf_counter() - start)
leave_counts.append((est.tree_.node_count + 1) // 2)
return times, leave_counts
rows = []
for max_leaf_nodes in [None, n_samples]:
builder_name = "Depth-first" if max_leaf_nodes is None else "Best-first"
print(builder_name)
for case_name, interaction_cst in cases.items():
times, leave_counts = time_fit(
X, y, interaction_cst=interaction_cst,
max_features=1,
max_leaf_nodes=max_leaf_nodes,
)
mean_s = np.mean(times)
mean_leaves = round(np.mean(leave_counts))
print(
f"{case_name:<16} {mean_s:>7.3f} {mean_leaves:>10.1f}"
)
rows.append({
'builder': builder_name,
'case': case_name,
'avg time': mean_s,
'n_leaves': mean_leaves,
})
```
</details>
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"node_id": "LA_k... | closed | false | [] | null | 1 | 2026-02-13T10:37:03 | 2026-02-17T13:37:36 | 2026-02-17T13:37:32 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Follow-up of #32644.
#### What does this implement/fix? Explain your changes.
Some fixes and extensions for Array API support in LogisticRegression and Newton solvers.
#### AI usage disclosure
None
#### Any other comments?
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"node_id": "MDU6TGFiZWwyOT... | closed | false | [] | null | 2 | 2026-02-13T09:34:32 | 2026-02-13T10:57:06 | 2026-02-13T10:57:06 | null | NONE | null | null | null | null | Fixes #33219
#### What does this implement/fix?
`HDBSCAN.fit_predict()` raises `TypeError: only 0-dimensional arrays can be converted to Python scalars` when `cluster_selection_epsilon` is set and the input data contains tied distances.
The root cause is in `traverse_upwards` in `_tree.pyx`. Tied distances produce duplicate entries in the condensed tree, so filtering by child id can return multiple rows instead of one. The code assumed a scalar result from these lookups, which blows up when it gets an array instead.
The fix indexes `[0]` on both the parent lookup and the `parent_eps` computation. All duplicate entries for a given child share the same parent and lambda value, so taking the first match is correct.
#### How has this been tested?
Added a non-regression test `test_hdbscan_cluster_selection_epsilon_tied_distances` using both:
- The exact precomputed distance matrix from the issue report
- The synthetic Euclidean reproducer from the issue | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33273 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33273/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33273/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33273/events | https://github.com/scikit-learn/scikit-learn/pull/33273 | 3,933,534,508 | PR_kwDOAAzd1s7DXlUJ | 33,273 | DOC Fix grammar in clustering.rst: replace 'allows to' with 'allows verb-ing' | {
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] | closed | false | [] | null | 0 | 2026-02-12T19:08:49 | 2026-02-13T20:01:27 | 2026-02-13T11:56:55 | null | CONTRIBUTOR | null | null | null | null | Fixes minor grammatical errors in `doc/modules/clustering.rst`.
Replaced incorrect usage of "allows to" with "allows assigning" and "allows examining" to improve readability.
No logical code changes, purely documentation fix. | {
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"node_id": "LA... | closed | false | [] | null | 3 | 2026-02-12T17:14:03 | 2026-02-18T17:30:15 | 2026-02-14T17:48:31 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
See https://github.com/scipy/scipy/pull/24541.
#### What does this implement/fix? Explain your changes.
This PR removes our own implementation of the Yeo-Johnson transform, `_yeo_johnson_transform`, and replaces it with `scipy.stats.yeojohnson`.
#### AI usage disclosure
None
### Other comments
This even helps Array API support. | {
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Fixes #33205.
What does this implement/fix? Explain your changes.
This PR switches the matrix inversion logic in PCA.get_precision from a standard inverse (inv) to a Moore-Penrose pseudo-inverse (pinv).
Why this is necessary:
When working with singular or rank-deficient datasets (where features are perfectly correlated), the covariance matrix becomes singular. In these cases, linalg.inv raises a LinAlgError because the matrix is not invertible. This was specifically causing crashes in Array API dispatching with PyTorch on CPU (as reported in #33205).
Changes:
- Updated _BasePCA.get_precision in sklearn/decomposition/_base.py to use xp.linalg.pinv (for Array API) and scipy.linalg.pinv (for NumPy).
- Added a regression test test_pca_get_precision_singular_matrix in sklearn/decomposition/tests/test_pca.py that specifically uses a rank-deficient matrix to ensure no crash occurs.
- Added a changelog entry.
AI usage disclosure
I used AI assistance for:
[x] Code generation (e.g., when writing an implementation or fixing a bug)
[x] Test/benchmark generation
[ ] Documentation (including examples)
[] Research and understanding
Any other comments?
This fix should allow the removal of the XFAIL for PCA in the Array API common tests (though I can handle that in a follow-up or include it here if preferred). | {
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Fixes #33256
#### What does this implement/fix? Explain your changes.
Fixes the BNP Paribas logo path in the funding footer of `doc/templates/index.html`.
Changed `_static/bnp-paribas.png` to `_images/bnp-paribas.jpg` to reference the correct existing image at `doc/images/bnp-paribas.jpg`.
#### 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?
The image file exists at `doc/images/bnp-paribas.jpg` but was incorrectly referenced as `_static/bnp-paribas.png` in the footer template. | {
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] | open | false | [] | null | 0 | 2026-02-12T08:04:57 | 2026-02-17T05:14:22 | null | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Fixes #32914
#### What does this implement/fix? Explain your changes.
This PR optimizes the `_logcosh` function in `FastICA` (used when `algorithm='deflation'` and `fun='logcosh'`) by vectorizing the operations for 1D arrays. Previously, this function used a Python loop over samples, which was a significant bottleneck.
**Changes:**
- Replaced the Python `for` loop with NumPy vectorization when `x` is 1D.
- Added a micro-optimization to skip multiplication if `alpha=1.0` (default).
- Added a regression test in `sklearn/decomposition/tests/test_fastica.py` to verify the vectorized path and ensure coverage.
**Benchmarks:**
Ran `FastICA(algorithm='deflation', fun='logcosh', ...)` on a `(2000, 50)` dataset.
- **Before:** 424.00s
- **After:** 17.71s
- **Speedup:** ~24x
#### AI usage disclosure
I used AI assistance for:
- [x] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [ ] Research and understanding
#### Any other comments?
The optimization is isolated to the 1D case (`x.ndim == 1`), which is the standard path for the deflation algorithm. The original loop is preserved for other cases. | null | {
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] | open | false | [] | null | 0 | 2026-02-12T07:39:21 | 2026-02-17T05:10:55 | null | null | CONTRIBUTOR | null | null | null | null | Fixes #31947
This PR fixes a bug where `PowerTransformer` and `QuantileTransformer` would raise a `UserWarning` ("X has feature names, but... was fitted without feature names") during `inverse_transform` when fitted with feature names.
This was caused by the use of `_check_input` (which validates against the input seen during `fit`) instead of `check_array`. Other transformers in the module correctly use `check_array` for `inverse_transform`.
### Changes
- Replaced `_check_input` with `check_array` in `PowerTransformer.inverse_transform` and `QuantileTransformer.inverse_transform`.
- Added a regression test in `sklearn/preprocessing/tests/test_data.py`.
- Added a changelog entry.
### Credits
This PR picks up the work started by @ThibaultDECO in PR #31948, which was closed due to linting issues. | null | {
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} | ### Describe the workflow you want to enable
### Summary
This issue proposes integrating **Random k Conditional Nearest Neighbor (RkCNN)** — as described in *PeerJ Computer Science (2025)* — into a fully scikit-learn-compliant estimator interface, enabling seamless use with pipelines, cross-validation, and hyperparameter tuning.
Paper: https://peerj.com/articles/cs-2497/
Reference implementation (authors): https://dfzljdn9uc3pi.cloudfront.net/2025/cs-2497/1/RandomkCNN.py
---
### Motivation
Nearest neighbor methods degrade in high-dimensional settings due to irrelevant/noisy features. RkCNN addresses this by:
- Sampling multiple random feature subsets
- Training kCNN models on each subset
- Computing a **separation score** (between-class / within-class variance ratio)
- Weighting or filtering subsets based on their discriminative power
- Aggregating predictions via weighted probability averaging
This approach improves robustness in high-dimensional problems (e.g., gene expression, embeddings, sparse features) while maintaining the simplicity and interpretability of kNN-style classifiers.
However, the current implementation:
- Does not fully adhere to modern scikit-learn API standards
- Lacks strict estimator validation (`check_estimator`)
- Does not clearly separate model logic from sklearn mixins
- Could benefit from vectorization and improved maintainability
A cleaned, standardized implementation would make RkCNN production-ready and interoperable with:
- `Pipeline`
- `GridSearchCV`
- `cross_val_score`
- Model persistence (`joblib`)
- scikit-learn-compatible tooling
---
### Proposed Improvements
1. Refactor into a fully compliant estimator:
- Inherit from `BaseEstimator` and `ClassifierMixin`
- Implement `fit`, `predict`, `predict_proba`
- Ensure `get_params` / `set_params` compatibility
2. Improve internal structure:
- Separate subset sampling, scoring, and aggregation logic
- Vectorize separation score computation
- Avoid redundant neighbor model instantiation where possible
3. Add:
- Deterministic `random_state`
- Input validation using `check_X_y`, `check_array`
- Unit tests
- Documentation and examples
4. Optional enhancements:
- Parallel subset training (`n_jobs`)
- Support for sparse input
- Option to toggle weighting vs. top-r filtering
---
### Expected Benefits
- Stronger high-dimensional classifier within sklearn ecosystem
- Drop-in alternative to vanilla kNN
- Better robustness without heavy feature engineering
- Compatible with modern ML workflows
---
### Acceptance Criteria
- [ ] Fully sklearn-compatible estimator
- [ ] Passes `check_estimator`
- [ ] Supports `predict_proba`
- [ ] Reproducible via `random_state`
- [ ] Unit tests included
- [ ] Documentation example added
### Describe your proposed solution
Implement `RkCNN` within `sklearn.neighbors` or `sklearn.ensemble` as appropriate for first class support.
### Describe alternatives you've considered, if relevant
None.
### Additional context
None. | {
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None
#### What does this implement/fix? Explain your changes.
This PR removes an unused function in class PowerTransformer.
#### AI usage disclosure
None | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33263 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33263/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33263/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33263/events | https://github.com/scikit-learn/scikit-learn/pull/33263 | 3,926,587,837 | PR_kwDOAAzd1s7DAb5H | 33,263 | Add array API support for `FeatureUnion` | {
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"node_id": "LA_kwDOAAzd1... | closed | false | [] | null | 0 | 2026-02-11T13:32:10 | 2026-02-12T09:08:30 | 2026-02-12T07:00:49 | null | MEMBER | null | null | null | null | While working on an array API demo, I realized that `FeatureUnion` did not support array API even when the underlying transformers do. Here is a fix.
#### AI usage disclosure
I used AI assistance for:
- [x] Code generation (e.g., when writing an implementation or fixing a bug)
- [x] Test/benchmark generation
- [ ] Documentation (including examples)
- [ ] Research and understanding
I used cursor but manually reviewed and improved the generated code.
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] | open | false | [] | null | 14 | 2026-02-11T09:16:26 | 2026-03-03T12:52:30 | null | null | CONTRIBUTOR | null | null | null | null | Fixes #33242
This fixes the way `eigsh()` is called in spectral embedding. As a result, it gets faster:
```python
import numpy as np
from sklearn.manifold import SpectralEmbedding
from sklearn.datasets import make_swiss_roll
X, _ = make_swiss_roll(n_samples=10_000)
%time Z = SpectralEmbedding(eigen_solver="arpack", affinity="rbf", eigen_tol=0).fit_transform(X)
```
On my machine, this runs in 24 seconds on current main, and in 11 second after my fix. I am using `affinity="rbf"` here to get a dense `N x N` affinity matrix (with sparse matrix it's much faster). | null | {
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Fixes mutable default arguments in `doc/sphinxext/doi_role.py`.
#### What does this implement/fix? Explain your changes.
This PR replaces mutable default arguments (`options={}` and `content=[]`) with `None` in the `reference_role` function.
In Python, default arguments are evaluated once at definition time. Using mutable types (like lists or dicts) as default arguments can lead to state persisting across function calls, potentially causing unexpected side effects.
I have updated the function signature to use `None` and added the standard initialization check:
```python
if options is None:
options = {}
if content is None:
content = [] | null | {
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} | 3 | 2026-02-10T22:10:28 | 2026-02-24T14:33:16 | null | null | CONTRIBUTOR | null | null | null | null | <!--
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#### Reference Issues/PRs
<!--
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The following code raise the error with scipy dev:
```python
from sklearn.datasets import make_low_rank_matrix
from sklearn.decomposition import NMF
X = make_low_rank_matrix(n_samples=20, n_features=100, effective_rank=5, random_state=123) + 1
nmf = NMF(n_components=5).fit_transform(X)
```
The error is:
```python
Traceback (most recent call last):
File "~\nmf_failure.py", line 5, in <module>
nmf = NMF(n_components=5).fit_transform(X)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "~\miniforge3\envs\dep_dev\Lib\site-packages\sklearn\utils\_set_output.py", line 316, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "~\miniforge3\envs\dep_dev\Lib\site-packages\sklearn\base.py", line 1336, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "~\miniforge3\envs\dep_dev\Lib\site-packages\sklearn\decomposition\_nmf.py", line 1619, in fit_transform
W, H, n_iter = self._fit_transform(X, W=W, H=H)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "~\miniforge3\envs\dep_dev\Lib\site-packages\sklearn\decomposition\_nmf.py", line 1687, in _fit_transform
W, H, n_iter = _fit_coordinate_descent(
^^^^^^^^^^^^^^^^^^^^^^^^
File "~\miniforge3\envs\dep_dev\Lib\site-packages\sklearn\decomposition\_nmf.py", line 493, in _fit_coordinate_descent
violation += _update_coordinate_descent(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "~\miniforge3\envs\dep_dev\Lib\site-packages\sklearn\decomposition\_nmf.py", line 396, in _update_coordinate_descent
return _update_cdnmf_fast(W, HHt, XHt, permutation)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "sklearn/decomposition/_cdnmf_fast.pyx", line 8, in sklearn.decomposition._cdnmf_fast._update_cdnmf_fast
def _update_cdnmf_fast(floating[:, ::1] W, floating[:, :] HHt,
File "<stringsource>", line 664, in View.MemoryView.memoryview_cwrapper
File "<stringsource>", line 352, in View.MemoryView.memoryview.__cinit__
ValueError: ndarray is not C-contiguous
```
As discussed in https://github.com/scipy/scipy/issues/24538, this is related to a change in the C/F ordering in the output of `scipy.linalg.svd`.
#### What does this implement/fix? Explain your changes.
Specifying the order of the array at creating fixes the issue.
#### AI usage disclosure
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#### Any other comments?
I am surprised that this is not caught on CI?
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