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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33259 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33259/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33259/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33259/events | https://github.com/scikit-learn/scikit-learn/pull/33259 | 3,923,657,865 | PR_kwDOAAzd1s7C2vYs | 33,259 | Fix broken BNP Paribas logo link in documentation homepage | {
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The correct asset already exists as `bnp-paribas.jpg`.
This change updates the reference so the logo renders correctly again.
Fixes #33256 | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33258 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33258/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33258/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33258/events | https://github.com/scikit-learn/scikit-learn/pull/33258 | 3,923,185,396 | PR_kwDOAAzd1s7C1LdK | 33,258 | ENH Support cardinality filtering in make_column_selector | {
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"node_id": "LA_kwDOAAzd1s8... | open | false | [] | null | 2 | 2026-02-10T19:31:24 | 2026-02-24T18:28:11 | null | null | CONTRIBUTOR | null | null | null | null | Supersedes #33101 (Reopening via new PR due to accidental closure).
This PR adds `min_cardinality` and `max_cardinality` parameters to `make_column_selector`.
It helps in filtering columns based on their number of unique values (e.g., identifying constant columns or high-cardinality categorical features). | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33257 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33257/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33257/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33257/events | https://github.com/scikit-learn/scikit-learn/pull/33257 | 3,921,840,751 | PR_kwDOAAzd1s7CwwC- | 33,257 | FIX avoid repeated third-party deprecation warnings in process-based Parallel | {
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This PR reduces repeated third-party deprecation warnings in process-based parallel runs.
Related to #33230.
The issue can be reproduced with `permutation_importance(..., n_jobs=-1)` after importing `hyperopt`.
Before this change, the same `pkg_resources` deprecation warning is emitted many times from multiprocessing queue result loading.
## Root cause
`sklearn.utils.parallel.Parallel` propagates `warnings.filters` to workers.
Some filter categories are third-party warning class objects. Sending those classes to workers can trigger implicit module imports during unpickling, and those imports can emit warnings again.
## What changed
- In `sklearn/utils/parallel.py`:
- Added `_pack_warning_filter` to serialize warning categories as `(module, qualname)` references.
- Added `_unpack_warning_filter` to restore categories from already-loaded modules only.
- Skips filter entries that would require implicit imports.
- Added regression test in `sklearn/utils/tests/test_parallel.py`:
- `test_filter_warning_no_implicit_third_party_import_in_loky_workers`
## Validation
Ran locally:
```bash
python -m pytest --color=no sklearn/utils/tests/test_parallel.py -q
python -m pytest --color=no sklearn/inspection/tests/test_permutation_importance.py -q
```
Results:
- `16 passed, 2 skipped`
- `23 passed, 12 skipped`
In local reproduction, repeated warnings from `multiprocessing/queues.py:122` disappear after this patch, while the original third-party import warning remains once.
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} | On the scikit-learn documentation website footer, the BNP Paribas sponsor
logo does not render correctly.
Instead of the logo image, only the fallback text "BNP" is displayed,
which suggests the image asset is missing or failing to load.
Steps to reproduce:
1. Open https://scikit-learn.org/stable/
2. Scroll to the footer
<img width="1717" height="850" alt="Image" src="https://github.com/user-attachments/assets/01012fde-e38c-4fa7-9b17-3ab22194c0d3" />
<img width="127" height="62" alt="Image" src="https://github.com/user-attachments/assets/9089e2fe-0705-4f1f-b609-640b460bb2b0" />
3. Observe the BNP sponsor logo area
Expected behavior:
The BNP Paribas logo image should be displayed correctly.
Actual behavior:
Only the fallback text "BNP" is shown (broken or missing image).
Screenshot attached.
I am happy to investigate or submit a fix if this is confirmed. | {
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"node_id": "MDU6TGFiZWwyMz... | closed | false | [] | null | 1 | 2026-02-09T19:24:50 | 2026-02-17T00:43:51 | 2026-02-17T00:43:22 | null | NONE | null | null | null | null | #### Reference Issues/PRs
Closes #15873. See also #22923.
#### What does this implement/fix?
Adds `min_cardinality` and `max_cardinality` parameters to `make_column_selector`, enabling cardinality-based column selection. This addresses the feature request in #15873 and incorporates the API feedback from maintainers on #22923 (using `min_cardinality`/`max_cardinality` instead of `cardinality`/`cardinality_threshold`).
**New parameters:**
- `min_cardinality`: minimum number of unique values a column must have (inclusive)
- `max_cardinality`: maximum number of unique values a column must have (inclusive)
Both parameters work with the existing `dtype_include`, `dtype_exclude`, and `pattern` filters (all criteria must match).
**Example usage:**
```python
# Select low-cardinality columns for one-hot encoding
make_column_selector(dtype_include=object, max_cardinality=10)
# Select high-cardinality columns for target encoding
make_column_selector(dtype_include=object, min_cardinality=11)
```
#### Changes
- `sklearn/compose/_column_transformer.py`: Added `min_cardinality` and `max_cardinality` params to `make_column_selector`
- `sklearn/compose/tests/test_column_transformer.py`: Added tests for cardinality filtering (standalone, combined with dtype, combined with pattern) | {
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None
#### What does this implement/fix? Explain your changes.
This PR copies the note on the Brier score decomposition and what it means for calibration from the calibration section of the user guide to the Brier score loss.
#### AI usage disclosure
None
#### Any other comments?
None | {
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None
#### What does this implement/fix? Explain your changes.
Small addition or clarification for the D2 Brier score.
#### AI usage disclosure
None
#### Any other comments?
None | {
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"node_id": "MDU6TGFiZWwxODE1NDUz... | open | false | [] | null | 0 | 2026-02-09T16:12:41 | 2026-02-18T10:11:46 | null | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Fixes #33167
Follow-up/supersedes #33222
#### What does this implement/fix? Explain your changes.
Why we need intro sort for `simultaneous_sort`: see issue #33167 and PR #33222.
And because intro sort was already implemented for decision it made sense to share common bits in a single file (to share the heap-sort code, but also and mainly to have one single place with the two very similar sorts side by side with explanations about why they both exist in the code base).
#### AI usage: mostly no
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] | open | false | [] | null | 5 | 2026-02-09T14:34:17 | 2026-02-16T09:30:07 | null | null | NONE | null | null | null | null | This PR implements the UI hint discussed in issue #30596.
### Changes
- Adds a hover box-shadow effect to clickable example gallery thumbnails
- Adds a small `[source]` label using `figure > a::after`
### Motivation
Clickable images currently look like normal static plots, so users often miss that they link to the full example code.
This makes the interaction clearer while keeping the UI minimal.
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The original link to the California Housing dataset (dcc.fc.up.pt) is down and returning a 504 Gateway Timeout.
This PR replaces the broken link in the comments with a working Wayback Machine archive link, as verified by manual download. | {
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### What does this implement/fix? Explain your changes.
As explained in #33032, the `cond` choice in PR #30040 to solve #26164 introduced new bugs for instance on float32 data. This PR reverts the changes made in #30040 and adds a reproducer for the bugs reported in #33032.
### Comments / next steps
This a temporary "fix" following the plan outlined [here](https://github.com/scikit-learn/scikit-learn/issues/33032#issuecomment-3771756264). We need to reopen #26164.
In follow-up PRs we should either:
1. find a "good" choice for `cond` that ideally work on any data shape, dtype, and passes the sample weight consistency checks
2. expose `cond` as a parameter in `LinearRegression` as done for the sparse case in #30521, we could actually re-use the `tol` parameter for this (which would mean `cond` in the dense case). | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33243 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33243/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33243/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33243/events | https://github.com/scikit-learn/scikit-learn/pull/33243 | 3,913,694,201 | PR_kwDOAAzd1s7CWAVz | 33,243 | Update install.rst macOS instructions | {
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} | [] | closed | false | [] | null | 0 | 2026-02-08T22:21:07 | 2026-02-09T08:12:05 | 2026-02-09T08:12:05 | null | NONE | null | null | null | null | #### What does this implement/fix? Explain your changes.
Updated the install instructions to be more explicit, python doesn't always point to python3 on macOS
#### AI usage disclosure
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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)
- [ ] Research and understanding
#### Any other comments?
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33242 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33242/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33242/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33242/events | https://github.com/scikit-learn/scikit-learn/issues/33242 | 3,913,551,399 | I_kwDOAAzd1s7pRA4n | 33,242 | The eigsh call in spectral embedding is done wrong and differently from what is described in the comments | {
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} | ### Describe the bug and give evidence about its user-facing impact
The way `eigsh` is run in the spectral embedding routine is suboptimal and not consistent with what is described in the comments there.
Here are the relevant bits from https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/manifold/_spectral_embedding.py#L346
```python
# Because the normalized Laplacian has eigenvalues between 0 and 2,
# I - L has eigenvalues between -1 and 1. ARPACK is most efficient
...
# instead, we'll use ARPACK's shift-invert mode, asking for the
# eigenvalues near 1.0.
...
laplacian *= -1
...
_, diffusion_map = eigsh(
laplacian, k=n_components, sigma=1.0, which="LM", tol=tol, v0=v0
)
```
The comments say that `eigsh` is supposed to be run on `I - L` and the eigenvalues of interest are close to 1, which is why `sigma=1.0` is used. This makes sense. However, in the code, the eigsh is run on `-L`. Of course it has the same eigenvectors, but the relevant eigenvalues are close to 0. So setting `sigma=1.0` does not make any sense.
To make the code follow what is described in the comments, one would need to add ones to the diagonal of `laplacian` prior to running `eigsh`.
The user-facing impact is that the runtime is slower than it could be (see below).
### Steps/Code to Reproduce
```python
import numpy as np
import pylab as plt
from scipy.sparse.linalg import eigsh
from scipy.sparse.csgraph import laplacian
from sklearn.utils._arpack import _init_arpack_v0
from sklearn.neighbors import kneighbors_graph
from sklearn.datasets import make_swiss_roll
X, _ = make_swiss_roll(n_samples=5000)
# These is identical to SpectralEmbedding(n_neighbors=10, eigen_solver="arpack", affinity="nearest_neighbors")
A = kneighbors_graph(X, n_neighbors=10, include_self=True).toarray()
A = (A + A.T) / 2
Lsym, dd = laplacian(A, normed=True, return_diag=True)
v0 = _init_arpack_v0(A.shape[0], 42)
np.fill_diagonal(Lsym, 1)
Lsym *= -1
# The next line is not present in the spectral embedding routine but it should be!
np.fill_diagonal(Lsym, 0)
%time eigvals, eigvecs = eigsh(Lsym, k=3, sigma=1, which="LM", tol=0, v0=v0)
eigvecs = eigvecs[:, ::-1]
Z = np.diag(1/dd) @ eigvecs
```
### Expected Results
Runtime 1.5 sec on my machine and the eiganvalues `[0.99935026, 0.99984568, 1. ]`.
### Actual Results
Runtime 10.1 sec on my machine and eigenvalues `array([-6.55448604e-04, -1.62765707e-04, -1.99840144e-15])`.
### Versions
```shell
System:
python: 3.14.2 | packaged by conda-forge | (main, Dec 6 2025, 11:21:58) [GCC 14.3.0]
executable: /home/dmitry/anaconda3/envs/nmds/bin/python
machine: Linux-5.15.0-139-generic-x86_64-with-glibc2.31
Python dependencies:
sklearn: 1.8.0
pip: 25.3
setuptools: 80.10.1
numpy: 2.3.5
scipy: 1.16.3
Cython: None
pandas: 2.3.3
matplotlib: 3.10.8
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 8
prefix: libopenblas
filepath: /home/dmitry/anaconda3/envs/nmds/lib/libopenblasp-r0.3.30.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 8
prefix: libgomp
filepath: /home/dmitry/anaconda3/envs/nmds/lib/libgomp.so.1.0.0
version: None
```
### Interest in fixing the bug
One-line fix. | null | {
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Remove extra space between link markup and description text for Scikit-Learn Laboratory entry to maintain consistency with other project listings.
#### AI usage disclosure
<!--
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)
- [ ] Research and understanding
#### Any other comments?
Trying to get a feel of what it's like contributing to scikit-learn
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"node_id": "... | closed | false | [] | null | 0 | 2026-02-08T12:30:19 | 2026-02-09T08:45:39 | 2026-02-09T01:24:26 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Follow-up of PR #29477 / issue #20813.
#### What does this implement/fix? Explain your changes.
This PR removes some leftover author names.
#### AI usage disclosure
No.
#### Any other comments?
Remaining are:
- `sklearn.utils.optimize.py` (kind of a license redistribution notice) | {
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Add IncrementalLearningWrapper to enable partial_fit usage with automatic batch management.
### Describe your proposed solution
Wrapper for partial_fit with streaming data support
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
_No response_ | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33238 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33238/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33238/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33238/events | https://github.com/scikit-learn/scikit-learn/issues/33238 | 3,910,456,758 | I_kwDOAAzd1s7pFNW2 | 33,238 | [CODE CONTRIBUTION] Add class imbalance metrics and tools | {
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Add specialized metrics for imbalanced datasets including macro and weighted variants.
### Describe your proposed solution
Extend metrics module with imbalance-aware scoring
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
_No response_ | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33237 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33237/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33237/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33237/events | https://github.com/scikit-learn/scikit-learn/issues/33237 | 3,910,456,259 | I_kwDOAAzd1s7pFNPD | 33,237 | [CODE CONTRIBUTION] Add model performance degradation detector | {
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Add PerformanceDegradationDetector to monitor and alert when model performance decreases significantly.
### Describe your proposed solution
Use statistical tests to detect performance changes
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33236 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33236/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33236/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33236/events | https://github.com/scikit-learn/scikit-learn/issues/33236 | 3,910,455,738 | I_kwDOAAzd1s7pFNG6 | 33,236 | [CODE CONTRIBUTION] Add feature correlation detector | {
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} | ### Describe the workflow you want to enable
Add WeightedVotingClassifier with learnable weights optimized during fit. Include weight learning via gradient descent.Add FeatureCorrelationDetector to identify and remove highly correlated features automatically.
### Describe your proposed solution
Use correlation matrix analysis to identify and remove redundant features
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
_No response_ | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33235 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33235/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33235/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33235/events | https://github.com/scikit-learn/scikit-learn/issues/33235 | 3,910,454,541 | I_kwDOAAzd1s7pFM0N | 33,235 | [CODE CONTRIBUTION] Add robustness metrics for classifier evaluation | {
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} | ### Describe the workflow you want to enable
Add adaptive learning rate schedulers for SGDClassifier/Regressor. Implement PolynomialDecay and ExponentialDecay schedulers with configurable parameAdd metrics to evaluate classifier robustness to adversarial perturbations and noise. Include RobustnessScore and AdversarialAccuracy metrics.
```python
class RobustnessScore:
def __call__(self, y_true, y_pred, X, perturbation_strength=0.1):
# Evaluate robustness
pass
```ters.
### Describe your proposed solution
Use in SGDClassifier fit method for learning rate scheduAdd to sklearn.metrics moduleling
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
_No response_ | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33234 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33234/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33234/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33234/events | https://github.com/scikit-learn/scikit-learn/issues/33234 | 3,910,452,077 | I_kwDOAAzd1s7pFMNt | 33,234 | [CODE CONTRIBUTION] Add model explainability metrics module | {
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} | ### Describe the workflow you want to enable
Add explainability metrics to understand feature contributions using SHAP-like approach in sklearn.inspection. Include LocalExplainability and FeatureContribution classes.
```python
class LocalExplainability:
def explain_prediction(self, X, y_pred):
# Calculate contribution of each feature
pass
```
### Describe your proposed solution
Implement as part of sklearn.inspection module.
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
_No response_ | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33233 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33233/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33233/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33233/events | https://github.com/scikit-learn/scikit-learn/issues/33233 | 3,910,451,405 | I_kwDOAAzd1s7pFMDN | 33,233 | [CODE CONTRIBUTION] Add hyperparameter importance analysis utility | {
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} | ### Describe the workflow you want to enable
## Proposed Feature
Add a utility function to analyze feature importance across different hyperparameter values, helping users understand which hyperparameters have the most impact on model performance.
## Proposed Implementation
```python
# Add to sklearn.model_selection
class HyperparameterImportance:
def __init__(self, estimator, param_grid, cv=5):
self.estimator = estimator
self.param_grid = param_grid
self.cv = cv
self.importance_scores_ = None
def fit(self, X, y):
"""Calculate hyperparameter importance."""
from itertools import product
param_names = list(self.param_grid.keys())
param_values = [self.param_grid[name] for name in param_names]
results = []
for param_combo in product(*param_values):
params = dict(zip(param_names, param_combo))
self.estimator.set_params(**params)
scores = cross_val_score(self.estimator, X, y, cv=self.cv)
mean_score = scores.mean()
results.append((params, mean_score))
# Calculate importance as variance contribution
self.importance_scores_ = self._calculate_importance(results)
return self
def _calculate_importance(self, results):
"""Calculate importance scores for each hyperparameter."""
import numpy as np
param_names = list(self.param_grid.keys())
importance = {name: 0 for name in param_names}
for i, param_name in enumerate(param_names):
values = []
for params, score in results:
values.append(score)
importance[param_name] = np.var(values)
return importance
```
## Benefits
- Helps identify most impactful hyperparameters
- Guides hyperparameter tuning efforts
- Improves computational efficiency
- Provides insights for model optimization
### Describe your proposed solution
The solution is outlined in the implementation code above. Users would instantiate HyperparameterImportance with their estimator and parameter grid, fit it to their data, and access importance_scores_ to understand which hyperparameters have the most impact on model performance.
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
_No response_ | {
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#### What does this implement/fix? Explain your changes.
adjust the line spacing
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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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33231 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33231/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33231/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33231/events | https://github.com/scikit-learn/scikit-learn/issues/33231 | 3,909,758,360 | I_kwDOAAzd1s7pCi2Y | 33,231 | Callifornia housing dataset link is not available | {
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} | ### Describe the bug and give evidence about its user-facing impact
The link: https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz redirects to 504 Gateway Time-out.
### Steps/Code to Reproduce
Dataset link issue.
### Expected Results
The link might get changed.
### Actual Results
504 Gateway Time-out
### Versions
```shell
System:
python: 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0]
executable: /usr/bin/python3
machine: Linux-6.6.105+-x86_64-with-glibc2.35
Python dependencies:
sklearn: 1.6.1
pip: 24.1.2
setuptools: 75.2.0
numpy: 2.0.2
scipy: 1.16.3
Cython: 3.0.12
pandas: 2.2.2
matplotlib: 3.10.0
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 2
prefix: libscipy_openblas
filepath: /usr/local/lib/python3.12/dist-packages/numpy.libs/libscipy_openblas64_-99b71e71.so
version: 0.3.27
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 2
prefix: libscipy_openblas
filepath: /usr/local/lib/python3.12/dist-packages/scipy.libs/libscipy_openblas-b75cc656.so
version: 0.3.29.dev
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 2
prefix: libgomp
filepath: /usr/local/lib/python3.12/dist-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
version: None
```
### Interest in fixing the bug
Please correct the link in the repo. | {
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} | ### Describe the bug and give evidence about its user-facing impact
The permutation_importance() in combination with import hyperopt causes UserWarning about deprecated pkg_resources from multiprocessing with reference of removal as early as 2025-11-30 as example below shows.
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.linear_model import LogisticRegression as LR
from sklearn.inspection import permutation_importance
import hyperopt # ◄ with this import the warning is triggered
s = 100
X = np.random.rand(s,10)
y = np.random.randint(0, 2, size=(s, 1)).ravel()
m = LR().fit(X,y)
permutation_importance(
m, X, y,
n_repeats=10,
random_state=42,
scoring='roc_auc',
n_jobs=-1
)
```
### Expected Results
no UserWarning regardless other imports
### Actual Results
```
/usr/lib/python3.12/multiprocessing/queues.py:122: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
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/usr/lib/python3.12/multiprocessing/queues.py:122: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
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/usr/lib/python3.12/multiprocessing/queues.py:122: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
return _ForkingPickler.loads(res)
/usr/lib/python3.12/multiprocessing/queues.py:122: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
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/usr/lib/python3.12/multiprocessing/queues.py:122: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
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/usr/lib/python3.12/multiprocessing/queues.py:122: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
return _ForkingPickler.loads(res)
/usr/lib/python3.12/multiprocessing/queues.py:122: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
return _ForkingPickler.loads(res)
/usr/lib/python3.12/multiprocessing/queues.py:122: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
return _ForkingPickler.loads(res)
/usr/lib/python3.12/multiprocessing/queues.py:122: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
return _ForkingPickler.loads(res)
/usr/lib/python3.12/multiprocessing/queues.py:122: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
return _ForkingPickler.loads(res)
```
### Versions
```shell
System:
python: 3.12.3 (main, Jan 22 2026, 20:57:42) [GCC 13.3.0]
executable: /x-venv-3.12/bin/python3
machine: Linux-6.17.0-14-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.8.0
pip: 24.0
setuptools: 80.9.0
numpy: 2.3.5
scipy: 1.15.3
Cython: None
pandas: 3.0.0
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: 32
prefix: libscipy_openblas
filepath: /x-venv-3.12/lib/python3.12/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: 32
prefix: libscipy_openblas
filepath: /x-venv-3.12/lib/python3.12/site-packages/scipy.libs/libscipy_openblas-68440149.so
version: 0.3.28
threading_layer: pthreads
architecture: SkylakeX
user_api: openmp
internal_api: openmp
num_threads: 32
prefix: libgomp
filepath: /x-venv-3.12/lib/python3.12/site-packages/scikit_learn.libs/libgomp-e985bcbb.so.1.0.0
version: None
user_api: openmp
internal_api: openmp
num_threads: 32
prefix: libgomp
filepath: /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0
version: None
```
### Interest in fixing the bug
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33229 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33229/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33229/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33229/events | https://github.com/scikit-learn/scikit-learn/pull/33229 | 3,908,262,118 | PR_kwDOAAzd1s7CExAf | 33,229 | Use minlength=n_bins instead of len(bins) for clarity and futureproofing | {
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} | [] | open | false | [] | null | 0 | 2026-02-06T20:09:36 | 2026-02-09T16:50:57 | null | null | NONE | null | null | null | null | <!--
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#### Reference Issues/PRs
<!--
Fixes #18130
-->
Fixes #18130
#### What does this implement/fix? Explain your changes.
Changed the minlength argument in np.bincount calls from len(bins) to n_bins in sklearn/calibration.py. This improves readability and prevents potential future bugs if the number of bins and the bins array length diverge without being handled. Not expected to change any behaviour in present version. Verified with pytest on calibration.py anyway; same results as before changes.
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AI assistance checked above was for handling dependency conflicts on my stupid machine.
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#### What does this implement/fix? Explain your changes.
I just adjusted the line spacing of the file keeping everything else the same
#### AI usage disclosure
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below and make sure that you adhere to our Automated Contributions Policy:
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I used AI assistance for:
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- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [x] Research and understanding
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33227 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33227/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33227/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33227/events | https://github.com/scikit-learn/scikit-learn/issues/33227 | 3,907,116,254 | I_kwDOAAzd1s7o4dze | 33,227 | Segmentation fault with free-threaded in `test_gpr_correct_error_message` | {
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"url... | open | false | [] | null | 2 | 2026-02-06T14:47:34 | 2026-02-16T10:36:34 | null | null | MEMBER | null | null | {
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```
gaussian_process/tests/test_gpc.py ····································· [ 15%]
······ [ 15%]
gaussian_process/tests/test_gpr.py ····································· [ 15%]
Fatal Python error: Segmentation fault
<Cannot show all threads while the GIL is disabled>
Stack (most recent call first):
File "/home/runner/work/scikit-learn/scikit-learn/sklearn/gaussian_process/kernels.py", line 280 in hyperparameters
File "/home/runner/work/scikit-learn/scikit-learn/sklearn/gaussian_process/kernels.py", line 301 in theta
File "/home/runner/work/scikit-learn/scikit-learn/sklearn/gaussian_process/kernels.py", line 273 in n_dims
File "/home/runner/work/scikit-learn/scikit-learn/sklearn/gaussian_process/_gpr.py", line 302 in fit
File "/home/runner/work/scikit-learn/scikit-learn/sklearn/base.py", line 1347 in wrapper
File "/home/runner/work/scikit-learn/scikit-learn/sklearn/gaussian_process/tests/test_gpr.py", line 430 in test_gpr_correct_error_message
File "/home/runner/miniconda3/envs/testvenv/lib/python3.14t/site-packages/pytest_run_parallel/plugin.py", line 80 in closure
File "/home/runner/miniconda3/envs/testvenv/lib/python3.14t/threading.py", line 1024 in run
File "/home/runner/miniconda3/envs/testvenv/lib/python3.14t/threading.py", line 1082 in _bootstrap_inner
File "/home/runner/miniconda3/envs/testvenv/lib/python3.14t/threading.py", line 1044 in _bootstrap
Current thread's C stack trace (most recent call first):
Binary file "python", at _Py_DumpStack+0x4a [0x55af10bf2982]
Binary file "python", at +0x16a1a8 [0x55af10bfd1a8]
Binary file "/lib/x86_64-linux-gnu/libc.so.6", at +0x45330 [0x7f8384445330]
Binary file "python", at +0x18b5a0 [0x55af10c1e5a0]
Binary file "python", at +0x1921b0 [0x55af10c251b0]
Binary file "python", at +0x1907bb [0x55af10c237bb]
Binary file "python", at +0x19e926 [0x55af10c31926]
Binary file "python", at +0x2e84fc [0x55af10d7b4fc]
Binary file "python", at +0x2e857f [0x55af10d7b57f]
Binary file "python", at +0x2e7c61 [0x55af10d7ac61]
Binary file "python", at +0x19b967 [0x55af10c2e967]
Binary file "python", at PyObject_Dir+0x44 [0x55af10d57354]
Binary file "python", at +0x2c42e6 [0x55af10d572e6]
Binary file "python", at +0x1e21d9 [0x55af10c751d9]
Binary file "python", at _PyObject_MakeTpCall+0x3fb [0x55af10c3347b]
Binary file "python", at _PyEval_EvalFrameDefault+0x14e1 [0x55af10c4cba1]
Binary file "python", at +0x1ef6db [0x55af10c826db]
Binary file "python", at PyObject_CallOneArg+0x58 [0x55af10c8a498]
Binary file "python", at _PyObject_GenericGetAttrWithDict+0x32d [0x55af10c739cd]
Binary file "python", at PyObject_GetAttr+0x44 [0x55af10c37004]
Binary file "python", at _PyEval_EvalFrameDefault+0x1133 [0x55af10c4c7f3]
Binary file "python", at +0x1ef6db [0x55af10c826db]
Binary file "python", at PyObject_CallOneArg+0x58 [0x55af10c8a498]
Binary file "python", at _PyObject_GenericGetAttrWithDict+0x32d [0x55af10c739cd]
Binary file "python", at PyObject_GetAttr+0x44 [0x55af10c37004]
Binary file "python", at _PyEval_EvalFrameDefault+0x1133 [0x55af10c4c7f3]
Binary file "python", at +0x1ef6db [0x55af10c826db]
Binary file "python", at PyObject_CallOneArg+0x58 [0x55af10c8a498]
Binary file "python", at _PyObject_GenericGetAttrWithDict+0x32d [0x55af10c739cd]
Binary file "python", at PyObject_GetAttr+0x44 [0x55af10c37004]
Binary file "python", at _PyEval_EvalFrameDefault+0x1133 [0x55af10c4c7f3]
Binary file "python", at +0x1b5100 [0x55af10c48100]
<truncated rest of calls>
Extension modules: numpy._core._multiarray_umath, numpy.linalg._umath_linalg, sklearn.__check_build._check_build, cython.cimports.libc.math, _cyutility, scipy._cyutility, scipy._lib._ccallback_c, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._pcg64, numpy.random._generator, numpy.random._mt19937, numpy.random._philox, numpy.random._sfc64, numpy.random.mtrand, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.special._ufuncs_cxx, scipy.special._ellip_harm_2, scipy.special._special_ufuncs, scipy.special._gufuncs, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._batched_linalg, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_schur_sqrtm, scipy.linalg._matfuncs_expm, scipy.linalg._linalg_pythran, scipy.linalg.cython_blas, scipy.linalg._decomp_update, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpacklib, scipy.sparse.linalg._propack, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._hausdorff, scipy.spatial._distance_wrap, scipy.spatial.transform._rotation_cy, scipy.spatial.transform._rigid_transform_cy, scipy.optimize._group_columns, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._slsqplib, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy._lib._uarray._uarray, scipy.linalg._decomp_interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.interpolate._dierckx, scipy.interpolate._ppoly, scipy.interpolate._interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.special.cython_special, scipy.stats._stats, scipy.stats._biasedurn, scipy.stats._stats_pythran, scipy.stats._levy_stable.levyst, scipy.stats._ansari_swilk_statistics, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._rcont.rcont, scipy.stats._qmvnt_cy, scipy.ndimage._nd_image, scipy.ndimage._rank_filter_1d, _ni_label, scipy.ndimage._ni_label, sklearn._cyutility, sklearn.utils._isfinite, sklearn.utils.sparsefuncs_fast, sklearn.utils.murmurhash, sklearn.utils._openmp_helpers, sklearn.preprocessing._csr_polynomial_expansion, sklearn.preprocessing._target_encoder_fast, scipy.io.matlab._mio_utils, scipy.io.matlab._streams, scipy.io.matlab._mio5_utils, sklearn.datasets._svmlight_format_fast, sklearn.utils._random, sklearn.utils._vector_sentinel, sklearn.feature_extraction._hashing_fast, _loss, sklearn._loss._loss, sklearn.metrics.cluster._expected_mutual_info_fast, sklearn.metrics._dist_metrics, sklearn.metrics._pairwise_distances_reduction._datasets_pair, sklearn.utils._cython_blas, sklearn.metrics._pairwise_distances_reduction._base, sklearn.metrics._pairwise_distances_reduction._middle_term_computer, sklearn.utils._heap, sklearn.utils._sorting, sklearn.metrics._pairwise_distances_reduction._argkmin, sklearn.metrics._pairwise_distances_reduction._argkmin_classmode, sklearn.metrics._pairwise_distances_reduction._radius_neighbors, sklearn.metrics._pairwise_distances_reduction._radius_neighbors_classmode, sklearn.metrics._pairwise_fast, sklearn.utils._fast_dict, sklearn.cluster._hierarchical_fast, sklearn.cluster._k_means_common, sklearn.cluster._k_means_elkan, sklearn.cluster._k_means_lloyd, sklearn.cluster._k_means_minibatch, sklearn.cluster._dbscan_inner, sklearn.neighbors._partition_nodes, sklearn.neighbors._ball_tree, sklearn.neighbors._kd_tree, sklearn.utils.arrayfuncs, sklearn.utils._seq_dataset, sklearn.linear_model._cd_fast, sklearn.linear_model._sag_fast, sklearn.svm._liblinear, sklearn.svm._libsvm, sklearn.svm._libsvm_sparse, sklearn.utils._weight_vector, sklearn.linear_model._sgd_fast, sklearn.decomposition._online_lda_fast, sklearn.decomposition._cdnmf_fast, sklearn.cluster._hdbscan._tree, sklearn.cluster._hdbscan._linkage, sklearn.cluster._hdbscan._reachability, sklearn._isotonic, sklearn.tree._utils, sklearn.tree._tree, sklearn.tree._partitioner, sklearn.tree._splitter, sklearn.tree._criterion, sklearn.neighbors._quad_tree, sklearn.manifold._barnes_hut_tsne, sklearn.manifold._utils, scipy.cluster._vq, scipy.cluster._hierarchy, scipy.cluster._optimal_leaf_ordering, sklearn.ensemble._gradient_boosting, sklearn.ensemble._hist_gradient_boosting.common, sklearn.ensemble._hist_gradient_boosting._gradient_boosting, sklearn.ensemble._hist_gradient_boosting._binning, sklearn.ensemble._hist_gradient_boosting._bitset, sklearn.ensemble._hist_gradient_boosting.histogram, sklearn.ensemble._hist_gradient_boosting._predictor, sklearn.ensemble._hist_gradient_boosting.splitting, sklearn.svm._newrand, sklearn.utils._typedefs (total: 170)
build_tools/azure/test_script.sh: line 92: 3809 Segmentation fault (core dumped) python -m pytest --showlocals --durations=20 --junitxml=test-data.xml -o junit_family=legacy --parallel-threads 4 --iterations 1 --pyargs sklearn
···········································
Error: Process completed with exit code 139.
``` | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33226 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33226/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33226/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33226/events | https://github.com/scikit-learn/scikit-learn/pull/33226 | 3,906,807,207 | PR_kwDOAAzd1s7B_-8v | 33,226 | DOC Improve Linear Regression documentation with real-world use case | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33225 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33225/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33225/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33225/events | https://github.com/scikit-learn/scikit-learn/pull/33225 | 3,906,615,080 | PR_kwDOAAzd1s7B_V2V | 33,225 | TST Use assert_allclose to compare floats | {
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"node_id": "MDU6TGFiZWwyNjU5OD... | closed | false | [] | null | 2 | 2026-02-06T12:51:50 | 2026-02-06T14:01:32 | 2026-02-06T14:01:32 | null | MEMBER | null | null | null | null | The test `test_estimator_get_response_values` was modified recently (e.g. [here](https://github.com/scikit-learn/scikit-learn/actions/runs/21750614826/job/62747443149?pr=33224)) and now fails sometimes because of numerical precision. `assert_allclose` should be used to compare floats | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33224 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33224/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33224/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33224/events | https://github.com/scikit-learn/scikit-learn/pull/33224 | 3,906,517,116 | PR_kwDOAAzd1s7B_Ai_ | 33,224 | DOC Rephrase the param description of "name" in RocCurveDisplay | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 1 | 2026-02-06T12:27:18 | 2026-02-11T11:18:29 | 2026-02-11T11:18:29 | null | MEMBER | null | null | null | null | Related to https://github.com/scikit-learn/scikit-learn/pull/30508 and this https://github.com/scikit-learn/scikit-learn/pull/30508#discussion_r2772070275 in particular.
Better separate the 3 possibilities for the parameter and clearer description of how the para is used.
@lucyleeow what do you think ? | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33223 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33223/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33223/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33223/events | https://github.com/scikit-learn/scikit-learn/pull/33223 | 3,906,220,728 | PR_kwDOAAzd1s7B-A9Y | 33,223 | TST Refactor out helper in `pos_label` display curve tests | {
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"node_id": "MDU6TGFiZWwyNj... | closed | false | [] | null | 6 | 2026-02-06T11:08:21 | 2026-02-10T23:18:57 | 2026-02-10T10:09:01 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Related to #33218 and https://github.com/scikit-learn/scikit-learn/pull/33217
Part of PRs to move test related changes out of #30508 and into separate PR
#### What does this implement/fix? Explain your changes.
Takes the common part of `test_plot_roc_curve_pos_label` (which is shared with `test_plot_precision_recall_pos_label`) out to a helper function in `test_common_curve_display.py`, to reduce duplication.
#### 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)
- [ ] Research and understanding
#### Any other comments?
cc @jeremiedbb @ogrisel :pray:
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33222 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33222/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33222/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33222/events | https://github.com/scikit-learn/scikit-learn/pull/33222 | 3,906,162,022 | PR_kwDOAAzd1s7B90bj | 33,222 | FIX: avoid quadratic path for constant arrays in `simultaneous_sort` | {
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"node_id": "MDU6TGFiZWwyOTExMD... | open | false | [] | null | 1 | 2026-02-06T10:52:58 | 2026-02-18T10:58:08 | null | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Fixes partially #33167
#### Context: quick sort variants & intro sort
In quick sort, you pick a pivot and then partition the array, but there are variants on how to partition the array:
- 2-way partition: partition the array in `[x < pivot] [x >= pivot]` which is what is currently implemented, or `[x <= pivot] [x >= pivot]` which is what numpy does
- 3-way partition: partition the array in `[x < pivot] [x == pivot] [x > pivot]`. This is very efficient when there are a lot of duplicate values, but otherwise it's less efficient than the 2-way scheme.
I think that all those schemes can degenerate in O(n^2) complexity for "adversarial" inputs, that's why we usually "guard" them with a heap sort. This mix is called intro sort. For instance, decision trees implement an intro sort with a 3-way partition quick sort.
#### What does this implement/fix? Explain your changes.
For the current partitioning scheme used in `utils/_sorting.pyx` (`[x < pivot] [x >= pivot]`) the "adversarial input" is as simple as a constant array (or arrays with many duplicates), which is a very common case.
So I propose to simply change this to the numpy-style partitioning: `[x <= pivot] [x >= pivot]` which doesn't suffer from this problem. But it can still go quadratic for an input like this: `[1, 2, 3, ..., n-1, 0]` (much more unlikely than constant/many-duplicates arrays, but still not that crazy).
To be really safe, we should implement an intro sort, I'll create another PR to show what it looks like. Here it is: #33252
#### AI usage:
Used it to find the `[1, 2, 3, ..., n-1, 0]` pattern.
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"node_id": "MDU6TGFiZWwyNjU5OD... | closed | false | [] | null | 12 | 2026-02-06T10:37:50 | 2026-02-06T21:27:46 | 2026-02-06T14:50:17 | null | CONTRIBUTOR | null | null | null | null | <!--
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#### Reference Issues/PRs
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Towards #32393 and possibly also #33216
#### What does this implement/fix? Explain your changes.
I added the missing `random_seed` to the `KMeans` test cases and also added `clone` before `fit` (as it is done for example in line 240) where it was missing.
@lesteve
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33220 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33220/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33220/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33220/events | https://github.com/scikit-learn/scikit-learn/issues/33220 | 3,905,897,335 | I_kwDOAAzd1s7oz0N3 | 33,220 | ENH partial_dependence should not require to inherit from BaseEstimator | {
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} | ### Current status
`partial_dependence` requires that an estimator has `__sklearn_tags__` which basically requires that it inherits from `BaseEstimator`.
```py
import numpy as np
from sklearn.inspection import partial_dependence
X, y = np.arange(5)[:, None], 2.0 * np.arange(5)
def predict(X):
return np.full(shape=X.shape[0], fill_value=2)
class WrapPredict():
def fit(self, X, y=None):
self.is_fitted_ = True
return self
def predict(self, X):
return predict(X)
predict_instance = WrapPredict().fit(X)
partial_dependence(predict_instance, X=X, features=0)
```
errors with
```
AttributeError: The following error was raised: 'WrapPredict' object has no attribute '__sklearn_tags__'. It seems that there are no classes that implement `__sklearn_tags__` in the MRO and/or all classes in the MRO call `super().__sklearn_tags__()`. Make sure to inherit from `BaseEstimator` which implements `__sklearn_tags__` (or alternatively define `__sklearn_tags__` but we don't recommend this approach). Note that `BaseEstimator` needs to be on the right side of other Mixins in the inheritance order.
```
### Proposal
Avoid at least the necessity of `__sklearn_tags__`, i.e., the above `WrapPredict` should pass.
#### Other
This is a bit related to #33003. | null | {
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} | ### Describe the bug and give evidence about its user-facing impact
When using `HDBSCAN` with `cluster_selection_epsilon` set to at least 0.015 (for my dataset), I get a `TypeError`.
I only get the error when using `cluster_selection_epsilon`. I don't get this error when using the `hdbscan` package (version 0.8.41) directly.
### Steps/Code to Reproduce
Edit: I finally was able to reproduce the issue with synthetic data by playing around with `cluster_selection_epsilon` and you don't need precomputed distances:
```python
from sklearn.cluster import HDBSCAN
import numpy as np
rng = np.random.default_rng(0)
X = np.vstack([
rng.normal(0, 0.2, size=(20, 2)),
rng.normal(1, 0.2, size=(21, 2)),
])
hdb = HDBSCAN(
min_cluster_size=5,
min_samples=1,
n_jobs=10,
copy=True,
allow_single_cluster=True,
cluster_selection_epsilon=1
)
print(hdb.fit_predict(X))
```
The issue is only triggered with my specific data - I couldn't reproduce it with synthetic data. Notably, the same data works just fine with the `hdbscan` package.
```python
from hdbscan import HDBSCAN
import pandas as pd
# Bug only triggered with my specific dataset
df = pd.read_csv('bug.csv', header=None)
hdb = HDBSCAN(
min_cluster_size=5,
min_samples=1,
n_jobs=10,
metric='precomputed',
copy=True,
allow_single_cluster=True,
cluster_selection_epsilon=0.015
)
print(hdb.fit_predict(df.values))
```
`bug.csv`:
```csv
0.0,0.0144234,0.014608,0.018432,0.2028331,1e-06,0.0146122,0.0108745,0.0238127,0.0289057,0.0292668
0.0144234,0.0,0.030085,0.0341493,0.2174494,0.0185183,0.0300855,0.0261329,0.0444496,0.0499432,0.0506157
0.014608,0.030085,0.0,0.0224902,0.2284748,0.0188262,0.022492,0.0147419,0.029195,0.0344591,0.0349043
0.018432,0.0341493,0.0224902,0.0,0.2288503,0.0238127,0.0035258,0.0147674,1e-06,0.0045079,0.0045573
0.2028331,0.2174494,0.2284748,0.2288503,0.0,0.2061707,0.220841,0.2293228,0.2397258,0.2299638,0.2552726
1e-06,0.0185183,0.0188262,0.0238127,0.2061707,0.0,0.0188313,0.013986,0.0238127,0.0289057,0.0292668
0.0146122,0.0300855,0.022492,0.0035258,0.220841,0.0188313,0.0,0.0186034,0.0044997,0.0091034,0.0092055
0.0108745,0.0261329,0.0147419,0.0147674,0.2293228,0.013986,0.0186034,0.0,0.0190924,0.0241502,0.0244496
0.0238127,0.0444496,0.029195,1e-06,0.2397258,0.0238127,0.0044997,0.0190924,0.0,0.0045079,0.0045573
0.0289057,0.0499432,0.0344591,0.0045079,0.2299638,0.0289057,0.0091034,0.0241502,0.0045079,0.0,0.0092227
0.0292668,0.0506157,0.0349043,0.0045573,0.2552726,0.0292668,0.0092055,0.0244496,0.0045573,0.0092227,0.0
```
### Expected Results
Using the `hdbscan` package:
```
[ 0 0 0 0 -1 0 0 0 0 0 0]
```
### Actual Results
```
Traceback (most recent call last):
File "bug.py", line 16, in <module>
print(hdb.fit_predict(df.values))
~~~~~~~~~~~~~~~^^^^^^^^^^^
File ".conda/lib/python3.14/site-packages/sklearn/cluster/_hdbscan/hdbscan.py", line 920, in fit_predict
self.fit(X)
~~~~~~~~^^^
File ".conda/lib/python3.14/site-packages/sklearn/base.py", line 1336, in wrapper
return fit_method(estimator, *args, **kwargs)
File ".conda/lib/python3.14/site-packages/sklearn/cluster/_hdbscan/hdbscan.py", line 866, in fit
self.labels_, self.probabilities_ = tree_to_labels(
~~~~~~~~~~~~~~^
self._single_linkage_tree_,
^^^^^^^^^^^^^^^^^^^^^^^^^^^
...<4 lines>...
self.max_cluster_size,
^^^^^^^^^^^^^^^^^^^^^^
)
^
File "sklearn/cluster/_hdbscan/_tree.pyx", line 61, in sklearn.cluster._hdbscan._tree.tree_to_labels
File "sklearn/cluster/_hdbscan/_tree.pyx", line 75, in sklearn.cluster._hdbscan._tree.tree_to_labels
File "sklearn/cluster/_hdbscan/_tree.pyx", line 751, in sklearn.cluster._hdbscan._tree._get_clusters
File "sklearn/cluster/_hdbscan/_tree.pyx", line 626, in sklearn.cluster._hdbscan._tree.epsilon_search
File "sklearn/cluster/_hdbscan/_tree.pyx", line 588, in sklearn.cluster._hdbscan._tree.traverse_upwards
TypeError: only 0-dimensional arrays can be converted to Python scalars
```
### Versions
```shell
System:
python: 3.14.2 | packaged by conda-forge | (main, Jan 26 2026, 19:56:00) [GCC 14.3.0]
executable: .conda/bin/python
machine: Linux-5.15.0-119-generic-x86_64-with-glibc2.35
Python dependencies:
sklearn: 1.8.0
pip: 25.3
setuptools: 80.10.2
numpy: 2.4.2
scipy: 1.17.0
Cython: None
pandas: 3.0.0
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: 64
prefix: libscipy_openblas
filepath: .conda/lib/python3.14/site-packages/numpy.libs/libscipy_openblas64_-096271d3.so
version: 0.3.31.dev
threading_layer: pthreads
architecture: SkylakeX
user_api: blas
internal_api: openblas
num_threads: 64
prefix: libscipy_openblas
filepath: .conda/lib/python3.14/site-packages/scipy.libs/libscipy_openblas-6cdc3b4a.so
version: 0.3.30
threading_layer: pthreads
architecture: SkylakeX
user_api: openmp
internal_api: openmp
num_threads: 96
prefix: libgomp
filepath: .conda/lib/python3.14/site-packages/scikit_learn.libs/libgomp-e985bcbb.so.1.0.0
version: None
```
### Interest in fixing the bug
I don't want to create a PR because I don't know the codebase at all.
I asked ChatGPT for possible causes and it thinks the bug might be related to tied distances, resulting in competing branches and epsilon intersects a plateau. | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33218 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33218/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33218/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33218/events | https://github.com/scikit-learn/scikit-learn/pull/33218 | 3,905,023,223 | PR_kwDOAAzd1s7B6CAR | 33,218 | TST Move common binary Display class tests to `test_common_curve_display.py` | {
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"node_id": "MDU6TGFiZWwyNj... | closed | false | [] | null | 1 | 2026-02-06T05:44:16 | 2026-02-10T20:44:11 | 2026-02-10T15:16:28 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Related to #33217
Moving the test changes from #30508 as requested
#### What does this implement/fix? Explain your changes.
* In #30508 I noticed tests that were common between binary Display classes `PrecisionRecallDisplay` and `RocCurveDisplay` thus I moved them to common test file to avoid duplication and we can easily add new displays to the parametrization, as we add `from_cv_results` method to more display classes
* Most tests relate to the new `from_cv_results` method
Summary of migrated tests
`test_validate_plot_params` -> `test_display_validate_plot_params`
`test_roc_curve_plot_legend_label` -> `test_display_plot_legend_label`
`test_roc_curve_from_cv_results_legend_label` -> `test_display_from_cv_results_legend_label`
`test_roc_curve_from_cv_results_param_validation` -> `test_display_from_cv_results_param_validation`
`test_roc_curve_from_cv_results_pos_label_inferred` -> `test_display_from_cv_results_pos_label_inferred`
`test_roc_curve_display_from_cv_results_curve_kwargs` -> `test_display_from_cv_results_curve_kwargs`
New test: `test_display_default_name`
#### AI usage disclosure
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33217 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33217/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33217/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33217/events | https://github.com/scikit-learn/scikit-learn/pull/33217 | 3,904,926,318 | PR_kwDOAAzd1s7B5tGO | 33,217 | TST Add `CalibrationDisplay` to `test_common_curve_display.py` | {
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"node_id": "MDU6TGFiZWwyNj... | closed | false | [] | null | 0 | 2026-02-06T05:13:10 | 2026-02-06T09:38:55 | 2026-02-06T09:29:29 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Noticed when separating test changes out from #30508
#### What does this implement/fix? Explain your changes.
* Adds `CalibrationDisplay` to relevant common tests
* Improves test names and docstrings such that they fit on one line
* Removes redundant test (possibly there via bad merge)
#### AI usage disclosure
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#### Any other comments?
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- test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_train]
- test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[GradientBoostingRegressor(n_estimators=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Ridge(),param_grid={'alpha':[0.1,1.0]})-check_regressors_train]
- test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Ridge(),param_grid={'alpha':[0.1,1.0]})-check_regressors_train(readonly_memmap=True)]
- test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Ridge(),param_grid={'alpha':[0.1,1.0]})-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_regressors_train]
- test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_regressors_train(readonly_memmap=True)]
- test_estimators[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_regressors_train]
- test_estimators[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_regressors_train(readonly_memmap=True)]
- test_estimators[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressors_train]
- test_estimators[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressors_train(readonly_memmap=True)]
- test_estimators[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_regressors_train]
- test_estimators[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_regressors_train(readonly_memmap=True)]
- test_estimators[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressors_train]
- test_estimators[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressors_train(readonly_memmap=True)]
- test_estimators[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train]
- test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[HistGradientBoostingRegressor(max_iter=5,min_samples_leaf=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[HuberRegressor(max_iter=5)-check_regressors_train]
- test_estimators[HuberRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[HuberRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[KNeighborsRegressor(metric='precomputed')-check_regressors_train]
- test_estimators[KNeighborsRegressor(metric='precomputed')-check_regressors_train(readonly_memmap=True)]
- test_estimators[KNeighborsRegressor(metric='precomputed')-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[KernelRidge()-check_regressors_train]
- test_estimators[KernelRidge()-check_regressors_train(readonly_memmap=True)]
- test_estimators[KernelRidge()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[Lars()-check_regressors_train]
- test_estimators[Lars()-check_regressors_train(readonly_memmap=True)]
- test_estimators[Lars()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[LarsCV(cv=3,max_iter=5)-check_regressors_train]
- test_estimators[LarsCV(cv=3,max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[LarsCV(cv=3,max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[Lasso(max_iter=5)-check_regressors_train]
- test_estimators[Lasso(max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[Lasso(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[LassoCV(cv=3,max_iter=5)-check_regressors_train]
- test_estimators[LassoCV(cv=3,max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[LassoCV(cv=3,max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[LassoLars(max_iter=5)-check_regressors_train]
- test_estimators[LassoLars(max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[LassoLars(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[LassoLarsCV(cv=3,max_iter=5)-check_regressors_train]
- test_estimators[LassoLarsCV(cv=3,max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[LassoLarsCV(cv=3,max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_regressors_train]
- test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_regressors_train(readonly_memmap=True)]
- test_estimators[LassoLarsIC(max_iter=5,noise_variance=1.0)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[LinearRegression()-check_regressors_train]
- test_estimators[LinearRegression()-check_regressors_train(readonly_memmap=True)]
- test_estimators[LinearRegression()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[LinearSVR(max_iter=20)-check_regressors_train]
- test_estimators[LinearSVR(max_iter=20)-check_regressors_train(readonly_memmap=True)]
- test_estimators[LinearSVR(max_iter=20)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[MultiOutputRegressor(estimator=Ridge())-check_regressors_train]
- test_estimators[MultiOutputRegressor(estimator=Ridge())-check_regressors_train(readonly_memmap=True)]
- test_estimators[MultiOutputRegressor(estimator=Ridge())-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[MultiTaskElasticNet(max_iter=5)-check_regressors_train]
- test_estimators[MultiTaskElasticNet(max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[MultiTaskElasticNet(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[MultiTaskElasticNetCV(cv=3,max_iter=5)-check_regressors_train]
- test_estimators[MultiTaskElasticNetCV(cv=3,max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[MultiTaskElasticNetCV(cv=3,max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[MultiTaskLasso(max_iter=5)-check_regressors_train]
- test_estimators[MultiTaskLasso(max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[MultiTaskLasso(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[MultiTaskLassoCV(cv=3,max_iter=5)-check_regressors_train]
- test_estimators[MultiTaskLassoCV(cv=3,max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[MultiTaskLassoCV(cv=3,max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[NuSVR()-check_regressors_train]
- test_estimators[NuSVR()-check_regressors_train(readonly_memmap=True)]
- test_estimators[NuSVR()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[OrthogonalMatchingPursuit()-check_regressors_train]
- test_estimators[OrthogonalMatchingPursuit()-check_regressors_train(readonly_memmap=True)]
- test_estimators[OrthogonalMatchingPursuit()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_train]
- test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_train(readonly_memmap=True)]
- test_estimators[OrthogonalMatchingPursuitCV(cv=3)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_regressors_train]
- test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[PassiveAggressiveRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_regressors_train]
- test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_regressors_train(readonly_memmap=True)]
- test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[PoissonRegressor(max_iter=5)-check_regressors_train]
- test_estimators[PoissonRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[PoissonRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[QuantileRegressor()-check_regressors_train]
- test_estimators[QuantileRegressor()-check_regressors_train(readonly_memmap=True)]
- test_estimators[QuantileRegressor()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressors_train]
- test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressors_train(readonly_memmap=True)]
- test_estimators[RANSACRegressor(estimator=LinearRegression(),max_trials=10)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[RandomizedSearchCV(cv=2,error_score='raise',estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_regressors_train]
- test_estimators[RandomizedSearchCV(cv=2,error_score='raise',estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_regressors_train(readonly_memmap=True)]
- test_estimators[RandomizedSearchCV(cv=2,error_score='raise',estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressors_train]
- test_estimators[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressors_train(readonly_memmap=True)]
- test_estimators[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[RegressorChain(cv=3,estimator=Ridge())-check_regressors_train]
- test_estimators[RegressorChain(cv=3,estimator=Ridge())-check_regressors_train(readonly_memmap=True)]
- test_estimators[RegressorChain(cv=3,estimator=Ridge())-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[Ridge()-check_regressors_train]
- test_estimators[Ridge()-check_regressors_train(readonly_memmap=True)]
- test_estimators[Ridge()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[RidgeCV()-check_regressors_train]
- test_estimators[RidgeCV()-check_regressors_train(readonly_memmap=True)]
- test_estimators[RidgeCV()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[SGDRegressor(max_iter=5)-check_regressors_train]
- test_estimators[SGDRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[SGDRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[SVR(kernel='precomputed')-check_regressors_train]
- test_estimators[SVR(kernel='precomputed')-check_regressors_train(readonly_memmap=True)]
- test_estimators[SVR(kernel='precomputed')-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[StackingRegressor(cv=3,estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_regressors_train]
- test_estimators[StackingRegressor(cv=3,estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_regressors_train(readonly_memmap=True)]
- test_estimators[StackingRegressor(cv=3,estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[TheilSenRegressor(max_iter=5,max_subpopulation=100)-check_regressors_train]
- test_estimators[TheilSenRegressor(max_iter=5,max_subpopulation=100)-check_regressors_train(readonly_memmap=True)]
- test_estimators[TheilSenRegressor(max_iter=5,max_subpopulation=100)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[TweedieRegressor(max_iter=5)-check_regressors_train]
- test_estimators[TweedieRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True)]
- test_estimators[TweedieRegressor(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_regressors_train]
- test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_regressors_train(readonly_memmap=True)]
- test_estimators[VotingRegressor(estimators=[('est1',DecisionTreeRegressor(max_depth=3,random_state=0)),('est2',DecisionTreeRegressor(max_depth=3,random_state=1))])-check_regressors_train(readonly_memmap=True,X_dtype=float32)]
- test_check_estimator
- test_check_estimator_clones
- test_check_estimator_pairwise | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33215 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33215/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33215/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33215/events | https://github.com/scikit-learn/scikit-learn/pull/33215 | 3,903,694,166 | PR_kwDOAAzd1s7B1oG0 | 33,215 | ENH: add ExtremeLearningClassifier/Regressor estimators | {
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"node_id": "... | open | false | [] | null | 2 | 2026-02-05T21:33:50 | 2026-02-20T00:56:49 | null | null | NONE | null | null | null | null | ## Introduction
As part of a new 3-year DOE SC ASCR applied math grant, we've been performing research on gradient free neural network architectures that strike a balance between affordable/efficient (i.e., non-GPU) hardware/compute cost and model accuracy. We found that the open source scientific Python community was lacking some very fundamental estimators in this regard. In particular, there are currently no well-maintained multi-layer Extreme Learning Machine (ELM) network or random vector functional link (RVFL) libraries that we are aware of. https://github.com/thieu1995/GrafoRVFL currently use a GPL license, and only supports single-layer RVFL networks. Here, we propose to add support for two types of fundamental gradient-free estimators: `ExtremeLearningClassifier` and `ExtremeLearningRegressor`, which also offer parameters to switch over to RVFL. A wider range of estimators is being developed at https://github.com/lanl/GFDL, but the more specialized variants may not be of sufficiently broad interest for this project at this time. Our team consists of myself (Emma Viani), Navamita Ray, and Tyler Reddy (CS team at LANL); and Kyle Luh (CU Boulder) and Konstantinos Spiliopoulos (Boston University) on the mathematics side.
## Relevance and "fit" of the new estimators
A key question is whether these two estimators are of sufficiently broad interest for inclusion in `scikit-learn`. Their gradient-free nature means that they are generally quite efficient and don't require specialized hardware, consistent with a large number of heavily-used estimators like `RandomForestClassifier`. In terms of the breadth of their usage in the community, the table below summarizes a small sampling of relevant papers and citation counts (there are many others...).
Topic| DOI | Citations (Google Scholar, Jan 18, 2026)
-- | -- | --
Review of recent ELM research | https://doi.org/10.1007/S11042-021-11007-7 | 630
Evaluating RVFLs on common datasets | https://doi.org/10.1016/j.ins.2015.09.025 | 498
Application of RVFLs to electricity load forecasting | https://doi.org/10.1016/j.ins.2015.11.039 | 336
Exploration of deep RVFL architectures | https://doi.org/10.1016/j.patcog.2021.107978 | 277
Introduction of ELMs (the basic gradient-free NN concept) | https://doi.org/10.1016/j.neucom.2005.12.126 | 16,475
## How do these new estimators work and avoid gradients/backpropagation?
The tradeoff for avoiding backpropagation/GPUs is largely related to randomness--the weights of all neurons are randomly set and frozen during training, with the exception of the connection between the final layer and the output. This simplification allows a closed form solution that can proceed either via Moore-Penrose pseudoinverse (i.e., `np.pinv`) or via the various algorithms underlying ridge regression (literally `Ridge` in `scikit-learn`). A simple diagram of an RVFL demonstrates one additional twist--a direct link between the inputs and the outputs (source: https://doi.org/10.1016/j.patcog.2021.107978) that the community suggests is a form of regularization:
<img width="386" height="241" alt="Image" src="https://github.com/user-attachments/assets/0c272012-95c7-4b4a-9665-9f2feccd2045" />
As a result of the randomness in the training of these networks, we have to provide a wide range of options for setting the initial weights of the hidden layers, since they are frozen once set and can substantially affect estimator performance.
## Model structure and associated pitfalls
### Multi-layer behavior
Our “deep” variants of the traditional ELM are characterized by a stacked hierarchy of hidden layers.
<img width="357" height="343" alt="image" src="https://github.com/user-attachments/assets/362cc341-b6b4-40ac-b6b5-6693c7c40b86" />
Each subsequent layer’s activations are generated from the previous layer (and, for the “deep RVFL” variant, concatenated with the original inputs). This produces a progressively larger (and potentially more correlated) feature map, which is part of what makes numerical conditioning a concern.
### Partial fit
Our `partial_fit()` implementation is mathematically equivalent to accumulating the normal equation terms for linear least squares.
Least squares solution:
$$
D^+ y = (D^T D)^{-1} D^T y
$$
Instead of storing the full design matrix, we incrementally accumulate the normal-equation terms $D^TD$ and $D^Ty$ across mini-batches.
Update rule:
Consider the summation representation of our gram matrix ($D^TD$):
$$
(D^T D)_{ij} = \sum_k D^T_{ik} D_{kj}
$$
Now suppose the full design matrix is formed by vertically stacking two mini-batches, written as
$$
D = \begin{bmatrix}
B_1 \\
B_2
\end{bmatrix}
$$
$$
\begin{bmatrix}
B_1 B_2
\end{bmatrix} \begin{bmatrix}
B_1 \\
B_2
\end{bmatrix} = B_1^T B_1 + B_2^T B_2
$$
It's evident that our two normal-equations _can_ be updated by simply summing the successive batches.
We do not store the full design matrix `D` across batches; we only store the gram and moment matrices `(A,B)`.This keeps memory linear in the number of batches, but it means the implementation is ultimately limited by the size of `A`, which scales with the number of features in the design matrix. `partial_fit()` is often significantly slower than `fit()` because we must repeatedly solve a growing linear system as batches arrive.
### Ill-conditioning and rank deficiency
Because D is a concatenation of random features (and optionally the original inputs), it can potentially be highly collinear or rank deficient, depending on activation/weight initialization, layer widths, and whether direct links are enabled. When $D^TD$ is ill-conditioned, the unregularized pseudoinverse can be numerically unstable. `partial_fit()` can diverge from the full `fit()` result (despite being mathematically equivalent in exact arithmetic) because of this.
In practice, ridge regularization and/or a larger pseudoinverse cutoff (rtol) often improves stability, but does not eliminate all pathological cases.
We've included an xfail test case documenting a parameter combination that reliably produces a rank-deficient design matrix and therefore a reproducible divergence between fit and partial_fit.
## Added weight initializers and activations
Because of the inherent design of the ELM, the distribution of fixed weights is a necessarily tunable hyperparameter that can substantially affect performance. To this end, we add multiple weight initialization options to the existing neural network base class and expand the available in-place activation functions.
## API and design considerations
One nice thing about the design is that it provides direct access to two of the most popular gradient-free neural networks (ELM and RVFL), with interchange between them available via a single parameter (when `direct_links=True` you have an RVFL, otherwise an ELM). Some debate could be had about whether it makes sense to only offer the classical forms (single-layer only) of ELM and RVFL, but we ultimately found considerable literature exploring the deep/multi-layer versions of each type of architecture, and wanted access to well-maintained code that could be used to reproduce such studies.
Some debate may be needed on the pass-through to `Ridge` arguments we use internally. `solver='auto'` is currently used, and may be a sensible default. However, as we have experienced in comparing with numerical experiments with mathematicians, it is likely of interest to researchers to have the ability to control the solver used by `Ridge` to be able to closely compare these gradient-free models with i.e., backpropagated models that use specific solvers.
## Performance of the estimators on common datasets
In the table below, we report some estimator performances for these new models on common classification and regression datasets. Note that these are not refined results (in the absence of substantial hyperopt).
Dataset | Estimator | Metric = Value
-- | -- | --
Boston Housing | ExtremeLearningRegressor | $R^2$ = 0.8583
Breast Cancer Wisconsin | ExtremeLearningClassifier | ROC AUC = 0.9697
### Licensing Considerations
We did not use LLMs to draft or to assist in the drafting of this code, and did not consult copyleft/GPL-licensed code, apart from checking the executed output from the `graforvfl` library. Note that we've also received an informal positive response from the grafo team that they will switch to a more permissive license in the future to alleviate concerns about this: https://github.com/thieu1995/GrafoRVFL/issues/1#issuecomment-3253116472. | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33214 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33214/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33214/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33214/events | https://github.com/scikit-learn/scikit-learn/issues/33214 | 3,902,797,237 | I_kwDOAAzd1s7on_W1 | 33,214 | RFC change default `scoring` of `LogisticRegressionCV` from accuracy to logloss | {
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} | I propose to change the default value of `LogisticRegressionCV(scoring=None)` (`None` uses accuracy) to `LogisticRegressionCV(scoring=neg_log_loss)`.
Reason:
- Logistic Regression minimizes (penalized) log loss when fitting.
- Scoring with accuracy calls `LogisticRegression.predict` which applies an arbitrary probability threshold of 50%.
- Because of the 50% threshold, it happens that parameters with the same accuracy but better log loss (=better probability predictions) don't get selected.
- Other estimators also use log loss by default for similar purposes (early stopping)
- `HistGradientBoostingClassifier` defaults to `scoring=loss` which uses log loss.
- `GradientBoostingClassifier` uses training loss function which defaults to log loss.
@scikit-learn/core-devs @scikit-learn/communication-team @scikit-learn/documentation-team | null | {
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"node_id": "MDU6TGFiZWwyMzk5... | closed | false | [] | null | 1 | 2026-02-05T14:50:57 | 2026-02-06T05:11:10 | 2026-02-06T05:11:07 | null | CONTRIBUTOR | null | null | null | null | This PR improves the "Rolling your own estimator" documentation by clarifying
that full scikit-learn compatibility is defined by passing `check_estimator`
and by making some hard requirements explicit.
The changes focus on emphasis and early discovery of incompatibilities,
without adding new documentation or guides.
Fixes #33003
Related to #32910 | {
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] | open | false | [] | null | 7 | 2026-02-05T12:30:25 | 2026-02-11T16:55:03 | null | null | CONTRIBUTOR | null | null | null | null | <!--
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Towards https://github.com/scikit-learn/scikit-learn/issues/27462 (supersedes #31693 )
#### What does this implement/fix? Explain your changes.
Add `xlim` and `ylim` parameters to `from_estimator` and `plot` methods to allow setting the boundaries for the plot. Also added corresponding tests.
See the discussion in #31693 for the reason of extending the grid first (instead of simply overwriting the boundaries).
@lucyleeow @ogrisel @StefanieSenger
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33210 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33210/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33210/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33210/events | https://github.com/scikit-learn/scikit-learn/pull/33210 | 3,900,889,414 | PR_kwDOAAzd1s7BsQ3G | 33,210 | FIX remove redundant yield of `check_estimator_cloneable` | {
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] | closed | false | [] | null | 0 | 2026-02-05T10:41:07 | 2026-02-10T14:45:13 | 2026-02-10T14:32:59 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
noticed while reviewing #33197
#### What does this implement/fix? Explain your changes.
Removes a redundant yield of `check_estimator_cloneable`, which had accidentally been introduced in #29832.
`check_estimator_cloneable` is always yielded first in `estimator_checks_generator` and `_yield_all_checks` (which was also yielding it) is exclusively used in there.
https://github.com/scikit-learn/scikit-learn/blob/d3898d9d57aeb1e960d266613a2e31b07bca39d7/sklearn/utils/estimator_checks.py#L566-L568
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33209 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33209/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33209/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33209/events | https://github.com/scikit-learn/scikit-learn/pull/33209 | 3,899,529,302 | PR_kwDOAAzd1s7BnuB8 | 33,209 | Fix LogisticRegressionCV scoring when CV folds miss class labels | {
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] | closed | false | [] | null | 2 | 2026-02-05T04:17:40 | 2026-02-05T11:56:39 | 2026-02-05T11:56:38 | null | CONTRIBUTOR | null | null | null | null | fixes #33207
`LogisticRegressionCV` can fail with probabilistic scorers such as `neg_brier_score` and `neg_log_loss` when a cross-validation test fold does not contain all class labels.
The failure occurs because `_log_reg_scoring_path` calls the scorer without passing the full set of class labels. In this case, `y_true` has fewer classes than the predicted probability matrix, causing a shape mismatch inside the scorer.
Changes
- Update `_log_reg_scoring_path` to pass `labels=classes` to scorers that support it.
- For built-in sklearn scorers, inject `labels` into a shallow copy of the scorer’s internal keyword arguments to avoid mutating user objects.
- For custom callable scorers, pass `labels` only if explicitly supported by the callable signature.
- Add a regression test covering missing-class folds for probabilistic scorers.
### Result
Prevents crashes when CV folds lack class labels.
Preserves existing scorer behavior and state.
Resolves the `FIXME` noted in` _log_reg_scoring_path`.
### Tests
- sklearn/linear_model: 2,346 passed
- sklearn/metrics: 3,083 passed
- New Regression Test: Verified & passed | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33208 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33208/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33208/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33208/events | https://github.com/scikit-learn/scikit-learn/pull/33208 | 3,897,528,624 | PR_kwDOAAzd1s7BhE8A | 33,208 | Fix LogisticRegressionCV Brier scoring when CV folds lack classes | {
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"node_id": "MDU6TGFiZWwx... | closed | false | [] | null | 0 | 2026-02-04T17:04:57 | 2026-02-05T03:34:58 | 2026-02-05T03:19:03 | null | CONTRIBUTOR | null | null | null | null | fixes #33207
NOTE: Closed this PR
Why
`LogisticRegressionCV` fails with `scoring="neg_brier_score"` when a CV test fold does not contain all class labels. In multiclass settings, `brier_score_loss` requires explicit `labels` for probability arrays. A `FIXME` in `_log_reg_scoring_path `already documents this exact failure mode. This PR makes the built-in scorer robust without requiring users to define a custom scorer.
What
Detect the Brier scorer in `_log_reg_scoring_path` and inject `labels=classes` into the scorer’s kwargs when missing.
Update the Iris CV test to use `scoring="neg_brier_score"` and adjust expectations to the valid multiclass Brier score range.
testing
```python
33377 passed,
9288 skipped,
147 xfailed,
66 xpassed,
3880 warnings
```
Note
- No public API changes.
- Change is limited to internal handling of the Brier scorer.
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} | `neg_brier_score` uses `brier_score_loss`, which requires `labels` when `y_true `does not include all classes. In `LogisticRegressionCV`, the scorer is called without `labels`, even though the estimator is trained on all classes and probabilities are emitted for all classes. This causes a `ValueError `if any test fold misses a class. There is already a `FIXME`in the source noting exactly this. The scoring should pass `labels=classes `(or equivalent) to be robust.
<img width="965" height="269" alt="Image" src="https://github.com/user-attachments/assets/1c5444b5-753d-46c7-8d17-a9ecf7318ee7" />
### Minimal Reproduction
```python
import numpy as np
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegressionCV
import sklearn
print("sklearn", sklearn.__version__)
X, y = make_classification(
n_samples=90,
n_features=8,
n_informative=6,
n_redundant=0,
n_classes=3,
n_clusters_per_class=1,
random_state=0,
)
idx0 = np.flatnonzero(y == 0)
idx1 = np.flatnonzero(y == 1)
idx2 = np.flatnonzero(y == 2)
# Fold 0 test set has only classes 0 and 1
test0 = np.concatenate([idx0[:10], idx1[:10]])
train0 = np.setdiff1d(np.arange(len(y)), test0)
# Fold 1 test set has only class 2
test1 = idx2[:10]
train1 = np.setdiff1d(np.arange(len(y)), test1)
cv = [(train0, test0), (train1, test1)]
clf = LogisticRegressionCV(
cv=cv,
scoring="neg_brier_score",
solver="lbfgs",
max_iter=200,
)
clf.fit(X, y)
```
### Observed Error
```python
ValueError: y_true and y_prob contain different number of classes: 2 vs 3.
Please provide the true labels explicitly through the labels argument. Classes found in y_true: [0 1]
```
### Suggest Fix (also mentioned in comments by a contributer)
When scoring is `neg_brier_score`, pass `labels=classes` via `score_params` or directly to the scorer call so the full class set is used | {
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} | ### Describe the workflow you want to enable
I have a time series y` `with sequence length 1800. Each time stamp is equivalent to 2 seconds, meaning the whole time series covers a time span of 3600 seconds or 1 hour.
I want to use the first 5 minutes/ 300 seconds/150 time stamps to predict the remaining time series.
Therefore, I want to do a 3-fold time-series cross-validation during the training of the forecasting model. This means that in each split, I take the first 50 time stamps to train the model, and the next 1650 time stamps to validate the model. In the third fold, I then use 150 time stamps (5 minutes) and predict the remaining 1650 time stamps in my series.
When creating the splitter with
```
splitter = TimeSeriesSplit(n_splits=3, test_size=int(1800-seq_len))
split = splitter.split(y[0, :])
```
When looping through the split with
```
for train_index, test_index in split
train_indexes.append(train_index
test_indexes.append(test_index)
```
I get the error:
`ValueError: Too many splits=3 for number of samples=1800 with test_size=1648 and gap=0.`
which is due to the maximum test_size of `n_samples // (n_splits + 1)`.
I wonder why it is not possible to have much larger test sets comparred to train sets?
### Describe your proposed solution
I could, of course, write my own splitter, but I think it would be a good improvement to have this capability also in the `sklearn` framework to make it more complete.
Introducing a gap to only forecast the end of the time series is not an option for me due to the nature of my forecasting algorithm.
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
_No response_ | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33205 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33205/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33205/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33205/events | https://github.com/scikit-learn/scikit-learn/issues/33205 | 3,895,109,582 | I_kwDOAAzd1s7oKqfO | 33,205 | Use `xp.linalg.pinv` instead of `xp.linalg.inv` in PCA | {
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] | open | false | [] | null | 3 | 2026-02-04T07:29:49 | 2026-02-17T23:25:40 | null | null | MEMBER | {
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"name": "Bug",
"description": "An unexpected problem or behavior",
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"created_at": "2024-01-25T09:12:21",
"updated_at": "2024-07-26T10:05:10",
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} | As part of https://github.com/scikit-learn/scikit-learn/pull/33175 we noticed that the array API compliance test for `PCA` fails, but only when using a CI runner with CUDA and then using float32 and the CPU device. For now the test has been xfailed.
```
FAILED conda/envs/sklearn/lib/python3.13/site-packages/sklearn/tests/test_common.py::test_estimators[PCA()-check_array_api_input(array_namespace=torch,dtype_name=float32,device=cpu)] - torch._C._LinAlgError: linalg.inv: The diagonal element 10 is zero, the inversion could not be completed because the input matrix is singular.
```
<details><summary>Full traceback</summary>
<p>
```
_ test_estimators[PCA()-check_array_api_input(array_namespace=torch,dtype_name=float32,device=cpu)] _
estimator = PCA()
check = functools.partial(<function check_array_api_input at 0x7c055c520680>, 'PCA', array_namespace='torch', dtype_name='float32', device='cpu')
request = <FixtureRequest for <Function test_estimators[PCA()-check_array_api_input(array_namespace=torch,dtype_name=float32,device=cpu)]>>
@parametrize_with_checks(
list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks
)
def test_estimators(estimator, check, request):
# Common tests for estimator instances
with ignore_warnings(
category=(FutureWarning, ConvergenceWarning, UserWarning, LinAlgWarning)
):
> check(estimator)
check = functools.partial(<function check_array_api_input at 0x7c055c520680>, 'PCA', array_namespace='torch', dtype_name='float32', device='cpu')
estimator = PCA()
request = <FixtureRequest for <Function test_estimators[PCA()-check_array_api_input(array_namespace=torch,dtype_name=float32,device=cpu)]>>
conda/envs/sklearn/lib/python3.13/site-packages/sklearn/tests/test_common.py:124:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
conda/envs/sklearn/lib/python3.13/site-packages/sklearn/utils/estimator_checks.py:1201: in check_array_api_input
result_xp = getattr(est_xp, method_name)(X_xp, y_xp)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
X = array([[ 1.0830512 , -1.1429703 , 0.05820872, 0.5607845 , 1.053802 ,
-0.5948778 , 0.35778737, 0.5275467 ....35711256, -0.5182702 ,
0.4970482 , -0.21967189, -1.115722 , 2.357159 , -1.3852317 ]],
dtype=float32)
X_xp = tensor([[ 1.0831, -1.1430, 0.0582, 0.5608, 1.0538, -0.5949, 0.3578, 0.5275,
0.1216, -0.6640],
[... 1.0552],
[ 1.4779, -1.9876, 0.0918, 0.3571, -0.5183, 0.4970, -0.2197, -1.1157,
2.3572, -1.3852]])
array_attributes = {'components_': array([[-2.5761431e-02, 5.4883957e-03, -6.8862990e-02, -5.3921595e-02,
7.0448972e-02, 3.047...7408 , 0.44903848,
0.12485652, -0.24021086, -0.1826419 , 0.23647286, -0.06734962],
dtype=float32), ...}
array_namespace = 'torch'
attribute = array([1.1475833e+01, 8.3189106e+00, 6.3970122e+00, 5.9165435e+00,
5.6904674e+00, 4.1923413e+00, 3.8798978e+00, 3.3633292e+00,
8.8419966e-07, 6.1141361e-07], dtype=float32)
attribute_ns = 'sklearn.externals.array_api_compat.torch'
check_sample_weight = False
check_values = False
device = 'cpu'
dtype_name = 'float32'
est = PCA(random_state=0)
est_fitted_with_as_array = PCA(random_state=0)
est_xp = PCA(random_state=0)
est_xp_param = tensor([1.1476e+01, 8.3189e+00, 6.3970e+00, 5.9165e+00, 5.6905e+00, 4.1923e+00,
3.8799e+00, 3.3633e+00, 9.0832e-07, 4.8586e-07])
est_xp_param_np = array([1.1475831e+01, 8.3189087e+00, 6.3970146e+00, 5.9165444e+00,
5.6904664e+00, 4.1923423e+00, 3.8798997e+00, 3.3633294e+00,
9.0831952e-07, 4.8586008e-07], dtype=float32)
estimator_orig = PCA()
expect_only_array_outputs = True
fit_kwargs = {}
fit_kwargs_xp = {}
input_ns = 'sklearn.externals.array_api_compat.torch'
key = 'singular_values_'
method = <bound method PCA.score of PCA(random_state=0)>
method_name = 'score'
methods = ('score', 'score_samples', 'decision_function', 'predict', 'predict_log_proba', 'predict_proba', ...)
name = 'PCA'
numpy_asarray_works = True
result = -inf
xp = <module 'sklearn.externals.array_api_compat.torch' from '/home/runner/conda/envs/sklearn/lib/python3.13/site-packages/sklearn/externals/array_api_compat/torch/__init__.py'>
y = array([0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0,
1, 0, 1, 0, 1, 0, 1, 0])
y_xp = tensor([0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0,
1, 0, 1, 0, 1, 0])
conda/envs/sklearn/lib/python3.13/site-packages/sklearn/decomposition/_pca.py:838: in score
return float(xp.mean(self.score_samples(X)))
^^^^^^^^^^^^^^^^^^^^^
X = tensor([[ 1.0831, -1.1430, 0.0582, 0.5608, 1.0538, -0.5949, 0.3578, 0.5275,
0.1216, -0.6640],
[... 1.0552],
[ 1.4779, -1.9876, 0.0918, 0.3571, -0.5183, 0.4970, -0.2197, -1.1157,
2.3572, -1.3852]])
_ = True
self = PCA(random_state=0)
xp = <module 'sklearn.externals.array_api_compat.torch' from '/home/runner/conda/envs/sklearn/lib/python3.13/site-packages/sklearn/externals/array_api_compat/torch/__init__.py'>
y = tensor([0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0,
1, 0, 1, 0, 1, 0])
conda/envs/sklearn/lib/python3.13/site-packages/sklearn/decomposition/_pca.py:812: in score_samples
precision = self.get_precision()
^^^^^^^^^^^^^^^^^^^^
X = tensor([[ 1.0831, -1.1430, 0.0582, 0.5608, 1.0538, -0.5949, 0.3578, 0.5275,
0.1216, -0.6640],
[... 1.0552],
[ 1.4779, -1.9876, 0.0918, 0.3571, -0.5183, 0.4970, -0.2197, -1.1157,
2.3572, -1.3852]])
Xr = tensor([[ 1.0999e+00, -9.2279e-01, -1.1772e-01, 4.1604e-01, 6.0476e-01,
-7.1973e-01, 5.9800e-01, 7.1019e-...+00, -8.4169e-02, 2.1237e-01, -9.6731e-01,
3.7219e-01, 2.0539e-02, -9.3308e-01, 2.1207e+00, -1.3179e+00]])
_ = True
n_features = 10
self = PCA(random_state=0)
xp = <module 'sklearn.externals.array_api_compat.torch' from '/home/runner/conda/envs/sklearn/lib/python3.13/site-packages/sklearn/externals/array_api_compat/torch/__init__.py'>
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = PCA(random_state=0)
def get_precision(self):
"""Compute data precision matrix with the generative model.
Equals the inverse of the covariance but computed with
the matrix inversion lemma for efficiency.
Returns
-------
precision : array, shape=(n_features, n_features)
Estimated precision of data.
"""
xp, is_array_api_compliant = get_namespace(self.components_)
n_features = self.components_.shape[1]
# handle corner cases first
if self.n_components_ == 0:
return xp.eye(n_features) / self.noise_variance_
if is_array_api_compliant:
linalg_inv = xp.linalg.inv
else:
linalg_inv = linalg.inv
if self.noise_variance_ == 0.0:
> return linalg_inv(self.get_covariance())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E torch._C._LinAlgError: linalg.inv: The diagonal element 10 is zero, the inversion could not be completed because the input matrix is singular.
is_array_api_compliant = True
linalg_inv = <built-in function linalg_inv>
n_features = 10
self = PCA(random_state=0)
xp = <module 'sklearn.externals.array_api_compat.torch' from '/home/runner/conda/envs/sklearn/lib/python3.13/site-packages/sklearn/externals/array_api_compat/torch/__init__.py'>
conda/envs/sklearn/lib/python3.13/site-packages/sklearn/decomposition/_base.py:82: _LinAlgError
```
</p>
</details>
https://github.com/scikit-learn/scikit-learn/pull/33175#issuecomment-3834360881 suggests a possible fix. As part of making the change it is important to investigate why this happens. The problem showed up when we upgraded the version of PyTorch.
cc @ogrisel | null | {
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] | closed | false | [] | null | 1 | 2026-02-04T07:15:46 | 2026-02-04T07:58:08 | 2026-02-04T07:58:08 | null | CONTRIBUTOR | null | null | null | null | Fixes a small typo in the API reference configuration documentation.
No functional changes.
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] | closed | false | [] | null | 1 | 2026-02-03T16:18:41 | 2026-02-05T22:13:38 | 2026-02-05T18:10:49 | null | CONTRIBUTOR | null | null | null | null | <!--
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Fix issue raised in https://github.com/scikit-learn/scikit-learn/issues/33152#issuecomment-3833793227
#### What does this implement/fix? Explain your changes.
Get the name of the failing build in the tracking issue name, even if the build is not in the job matrix.
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Apparently there is no way to access the job name from the job steps, so I used an env variable containing the job name as a workaround.
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Fixes https://github.com/scikit-learn/scikit-learn/issues/33194
#### What does this implement/fix? Explain your changes.
Adds inferring `n_classes` from `type_of_target` and raises an error if it still can't be inferred.
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I also adapted the example to show the correct colormap (as `viridis` is no longer the default).
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33200 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33200/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33200/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33200/events | https://github.com/scikit-learn/scikit-learn/pull/33200 | 3,889,540,803 | PR_kwDOAAzd1s7BGkjw | 33,200 | FEA Add array API support to `roc_auc_score` | {
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"node_id": "LA_kwDOAAzd1s8... | open | false | [] | null | 4 | 2026-02-03T06:54:06 | 2026-03-06T10:57:30 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Towards #26024.
#### What does this implement/fix? Explain your changes.
This PR adds array API support to [<code>roc_auc_score</code>](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html).
#### AI usage disclosure
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- [x] Research and understanding
#### Any other comments?
Waiting for CI to turn green. | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33199 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33199/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33199/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33199/events | https://github.com/scikit-learn/scikit-learn/pull/33199 | 3,887,639,711 | PR_kwDOAAzd1s7BAN7O | 33,199 | FIX Exclude all-zero relevance samples from NDCG computation (#29521) | {
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Fixes #29521
When all ground truth relevances are zero for a sample, both DCG and IDCG are 0, making NDCG undefined (0/0). Previously, such samples were assigned a score of 0 and included in averaging, causing `ndcg_score(y, y)` to return values less than 1.0.
According to the original 2002 Jarvelin & Kekalainen paper that introduced NDCG:
> The (D)CG vectors for each IR technique can be normalized by dividing them by the corresponding ideal (D)CG vectors, component by component. In this way, for any vector position, the normalized value 1 represents ideal performance...
This means `ndcg_score(y, y)` must always equal 1.0.
## Changes
**sklearn/metrics/_ranking.py:**
- Added detection for samples where all relevances are 0
- Raises `UserWarning` when such samples are encountered
- Excludes these samples from the averaging calculation
- Returns `np.nan` if all samples have all-zero relevances
- Updated docstring with `versionchanged` notes explaining the new behavior
**sklearn/metrics/tests/test_ranking.py:**
- Added `test_ndcg_all_zero_relevance()` with comprehensive test cases:
- Mix of relevant and all-zero samples (should return 1.0)
- All samples with all-zero relevances (should return NaN)
- Single sample with all-zero relevance (should return NaN)
- Normal case without all-zero samples (should return 1.0)
## Example
Before this fix:
```python
import numpy as np
from sklearn.metrics import ndcg_score
y = np.array([[1.0, 0.0, 1.0], [0.0, 0.0, 0.0]])
ndcg_score(y, y) # Returns 0.5 (WRONG)
```
After this fix:
```python
import numpy as np
from sklearn.metrics import ndcg_score
y = np.array([[1.0, 0.0, 1.0], [0.0, 0.0, 0.0]])
# UserWarning: All ground truth relevances are zero for at least one sample...
ndcg_score(y, y) # Returns 1.0 (CORRECT)
``` | null | {
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} | [] | open | false | [] | null | 0 | 2026-02-02T19:36:58 | 2026-02-02T20:14:18 | null | null | NONE | null | null | null | null | This PR contributes to issue #22827 by converting `sklearn/decomposition/tests/test_dict_learning.py` to use the `global_random_seed` fixture.
## Changes
- Added `rng_global` fixture that depends on `global_random_seed`
- Renamed the data fixture from `X` to `X_data` to avoid shadowing the module-level `X` array
- Restored deterministic module-level `X` for tests that rely on exact numerical values
- Updated tests to use `global_random_seed` for improved reproducibility
- Fixed fixture scoping issues and removed duplicate parameterization
- Stabilized seed-sensitive tests by using fixed `random_state=0` where needed
- Updated error message regex to match current sklearn validation messages
## Testing
All 199 tests in the module pass successfully:
- Tested with default seed
- Verified key tests with representative seeds (0, 42, 99)
- Confirmed no regressions in test behavior
Addresses #22827 (one file at a time as recommended in the meta-issue) | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33197 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33197/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33197/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33197/events | https://github.com/scikit-learn/scikit-learn/pull/33197 | 3,887,519,520 | PR_kwDOAAzd1s7A_03W | 33,197 | FIX Remove redundant yield of check_class_weight_balanced_linear_classifier | {
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Fixes #33154.
The duplication was accidentally introduced in #29712. This PR implements the suggestion to remove the redundancy and ensure tests continue to pass efficiently.
What does this implement/fix? Explain your changes.
In sklearn/utils/estimator_checks.py, the function _yield_classifier_checks was yielding check_class_weight_balanced_linear_classifier twice for linear classifiers supporting the class_weight parameter.
This resulted in redundant test executions during common estimator checks (e.g., for LogisticRegression), wasting CI/CD time and creating duplicate log entries.
Changes:
Removed the duplicate if block in sklearn/utils/estimator_checks.py.
Verified that the check is now yielded exactly once for linear classifiers and correctly ignored for non-linear classifiers.
Added a changelog entry in doc/whats_new/upcoming_changes/sklearn.utils/.
AI usage disclosure
I used AI assistance for:
Code generation.
Documentation (including examples)
Any other comments?
The fix was verified against several cases:
LogisticRegression (Linear + class_weight): Yields exactly 1 check (Verified).
Linear Classifier (No class_weight): Yields 0 checks (Verified).
Non-Linear Classifier: Yields 0 checks (Verified).
Linear with class_weight=None: Yields 1 check (Verified).
Maintainers: As this addresses duplication introduced in #29712, @ogrisel @adrinjalali @OmarManzoor @glemaitre | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33196 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33196/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33196/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33196/events | https://github.com/scikit-learn/scikit-learn/pull/33196 | 3,886,992,780 | PR_kwDOAAzd1s7A-D3f | 33,196 | TST: Add common test for transform() on different sparse formats | {
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Fixes #14176
#### What does this implement/fix? Explain your changes.
This PR extends the common estimator test `_check_estimator_sparse_container` to test the `transform()` method on different sparse matrix formats (CSR, CSC, COO, LIL, DOK, DIA, BSR), similar to how it already tests `predict()` and `predict_proba()`.
The change adds a simple check that:
1. Verifies the estimator has a `transform` method
2. Calls `transform()` on sparse matrices
3. Asserts the output has the correct number of samples
This ensures all transformers in scikit-learn are tested with various sparse formats, making individual test files like `test_truncated_svd.py::test_sparse_formats` redundant.
#### 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)
- [x] Research and understanding | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33195 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33195/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33195/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33195/events | https://github.com/scikit-learn/scikit-learn/pull/33195 | 3,886,923,433 | PR_kwDOAAzd1s7A90-f | 33,195 | DOC Add academic references to common pitfalls guide | {
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"node_id": "LA_kwDOAAzd1s8AA... | open | false | [] | null | 3 | 2026-02-02T16:40:39 | 2026-02-17T14:04:42 | null | null | NONE | null | null | null | null | #### Reference Issues/PRs
Fixes #24287
#### What does this implement/fix?
This PR adds a comprehensive "References" section to the Common Pitfalls guide with peer-reviewed academic publications that support the recommended practices.
#### What are the changes?
Added references for:
- **Data Splitting**: Xu & Goodacre (2018) - comparative study of CV, bootstrap, and systematic sampling
- **Data Leakage**: Kaufman et al. (2012) - formulation, detection, and avoidance
- **Model Selection**: Cawley & Talbot (2010) - over-fitting and selection bias
- **Feature Selection**: Ambroise & McLachlan (2002) - selection bias in gene extraction
- **General Best Practices**: Hastie et al. (2009) and Bishop (2006)
#### Any other comments?
The guide currently presents best practices without citations. Adding these authoritative academic references:
- Strengthens the scientific foundation of the recommendations
- Provides users with sources for deeper understanding
- Establishes that these are evidence-based practices, not just opinions
This is particularly important since scikit-learn is used for serious work in both industry and academia. | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33194 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33194/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33194/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33194/events | https://github.com/scikit-learn/scikit-learn/issues/33194 | 3,886,920,624 | I_kwDOAAzd1s7nrbOw | 33,194 | BUG undefined `n_classes` for custom estimators in `DecisionBoundaryDisplay` | {
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} | ### Describe the bug and give evidence about its user-facing impact
When introducing `n_classes` for the `DecisionBoundaryDisplay` in https://github.com/scikit-learn/scikit-learn/pull/33015, we didn't think about custom estimators like in [this example](https://scikit-learn.org/dev/auto_examples/cluster/plot_inductive_clustering.html) (re-created in the minimal example below), where `n_classes` can not be extracted from the estimator without knowing its architecture, which currently breaks [CircleCI](https://app.circleci.com/pipelines/github/scikit-learn/scikit-learn?branch=main).
(@lesteve, just so you know we're fixing it!)
### Steps/Code to Reproduce
```python
import matplotlib.pyplot as plt
from sklearn.base import BaseEstimator, clone
from sklearn.cluster import AgglomerativeClustering
from sklearn.datasets import make_blobs
from sklearn.ensemble import RandomForestClassifier
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.utils.validation import check_is_fitted
N_SAMPLES = 5000
RANDOM_STATE = 42
class InductiveClusterer(BaseEstimator):
def __init__(self, clusterer, classifier):
self.clusterer = clusterer
self.classifier = classifier
def fit(self, X, y=None):
self.clusterer_ = clone(self.clusterer)
self.classifier_ = clone(self.classifier)
y = self.clusterer_.fit_predict(X)
self.classifier_.fit(X, y)
return self
def predict(self, X):
check_is_fitted(self)
return self.classifier_.predict(X)
# Generate some training data from clustering
X, y = make_blobs(
n_samples=N_SAMPLES,
cluster_std=[1.0, 1.0, 0.5],
centers=[(-5, -5), (0, 0), (5, 5)],
random_state=RANDOM_STATE,
)
# Train a clustering algorithm on the training data and get the cluster labels
clusterer = AgglomerativeClustering(n_clusters=3)
classifier = RandomForestClassifier(random_state=RANDOM_STATE)
inductive_learner = InductiveClusterer(clusterer, classifier).fit(X)
# Plotting decision regions
DecisionBoundaryDisplay.from_estimator(
inductive_learner, X, response_method="predict", alpha=0.4
)
plt.show()
```
### Expected Results
Plot is shown.
### Actual Results
`UnboundLocalError: cannot access local variable 'n_classes' where it is not associated with a value`
### Versions
```shell
sklearn: 1.9.dev0
```
### Interest in fixing the bug
I don't think there is a sensible default value we could assume in these cases. My suggestion would be to add `n_classes=None` as a default parameter to `from_estimator` and raise a `ValueError` if it cannot be inferred from the estimator and has not been specified.
If you agree @ogrisel, @lucyleeow, I'll open a PR for this. | {
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Fixes #15250
#### What does this implement/fix?
This PR enhances the density estimation documentation by adding a new section that explains the relationships between density estimation and other machine learning paradigms.
#### What are the changes?
Added "Connections to Other Machine Learning Tasks" section covering:
- **Unsupervised Learning**: How density estimation reveals data structure and enables anomaly detection
- **Feature Engineering**: Using density scores as features for supervised learning
- **Data Modeling**: Applications in sampling, imputation, and compression
Included authoritative academic references:
- Hastie, Tibshirani & Friedman (2009) - The Elements of Statistical Learning
- Bishop (2006) - Pattern Recognition and Machine Learning
#### Any other comments?
The original documentation mentioned these connections in passing but didn't explain them. This addition provides users with a clearer understanding of how density estimation fits into the broader ML landscape and provides references for deeper study. | {
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"node_id": "MDU6TGFiZWwxODE1... | open | false | [] | null | 0 | 2026-02-02T16:39:06 | 2026-02-02T16:39:31 | null | null | NONE | null | null | null | null | #### Reference Issues/PRs
Fixes #12697
#### What does this implement/fix?
This PR documents that BallTree computes node centroids by averaging sample coordinates component-wise, which assumes such averaging produces valid points in the sample space.
#### Any other comments?
This assumption may not hold for custom metrics where samples live in non-Euclidean spaces or on manifolds (e.g., directional data, graphs, etc.). In such cases, component-wise averaging might produce invalid or meaningless centroids.
Added a note in the BinaryTree docstring (which BallTree inherits) to:
- Clarify this assumption
- Suggest alternatives (brute-force or medoid-based approaches) when the assumption is violated
This helps users understand the limitations when using BallTree with custom metrics. | null | {
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Fixes #19178
#### What does this implement/fix?
This PR clarifies in the Nystroem documentation that precomputed kernels are NOT supported, despite 'precomputed' appearing in parameter validation code and `_get_kernel_params` method.
#### Any other comments?
The current implementation is misleading - while "precomputed" appears as a valid option in the code, Nystroem cannot actually work with precomputed kernel matrices because it needs to compute the kernel between randomly sampled basis points and input data.
Added a clear note in the docstring to prevent user confusion. | null | {
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The enhanced description now explains:
- What the parameter does: prevents zero or very small variances that cause numerical issues
- How it's calculated: `var_smoothing * max(var(X, axis=0))`
- Where the result is stored: `epsilon_` attribute
- The trade-off: larger values provide more smoothing but may reduce accuracy
- That the default value of 1e-9 works well for most cases
This provides users with sufficient detail to understand when and how to adjust this parameter, addressing the "not documented" part of the issue. | null | {
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The documentation now clarifies that:
- Each output dimension is treated independently
- The model does NOT perform co-kriging or multi-task learning
- It does NOT model correlations between different output dimensions
- It maximizes the sum of log-marginal likelihoods across all outputs (equation 5.8 in Rasmussen & Williams 2006)
This answers the original question from @bread9 and provides the technical details identified by @JSestito in the comments. | null | {
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"node_id": "MDU6TGFiZWwyOT... | open | false | [] | null | 2 | 2026-02-02T15:40:14 | 2026-02-04T15:22:03 | null | null | NONE | null | null | null | null | <!--
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#### Reference Issues/PRs
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#### What does this implement/fix? Explain your changes.
This PR fixes nondeterministic behavior of LocalOutlierFactor with the
Mahalanobis distance when n_jobs > 1, as reported in #32753.
The internal buffer was originally introduced to avoid repeated memory
allocations during distance computations. A naive fix for the reported issue
would be to disable the buffer or reallocate it on each call, which would avoid
the correctness issue but defeat the original performance optimization.
Instead, this PR preserves the buffer-based optimization while making it safe
under parallel execution. This ensures deterministic and correct results without
reintroducing unnecessary memory allocations or performance regressions.
Regression tests are added to verify that:
Parallel execution matches serial execution.
Repeated parallel runs are stable and deterministic.
#### AI usage disclosure
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No, have a good day.
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The documentation now warns users that binary labeling can produce unexpected results in edge cases (see #20833 for specific example) when many samples have identical LOF scores near the contamination threshold.
The Notes section advises users to:
- Examine raw LOF scores via `negative_outlier_factor_` to verify outlier characteristics
- Adjust the `contamination` parameter if needed
- Apply custom thresholding based on LOF scores for more control
This provides users with actionable guidance when encountering unpredictable labeling outcomes, as requested in the original issue. | null | {
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} | 0 | 2026-02-02T14:43:12 | 2026-02-06T10:20:57 | null | null | MEMBER | null | null | {
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} | Nothing really major in the [1.8.1 milestone](https://github.com/scikit-learn/scikit-learn/milestone/70) so far. Feel free to add something you are aware of something else to the milestone.
Last monthly meeting (January 26) consensus was to wait 2-3 weeks in case there are side-effects of the pandas 3.0 release (released January 21). I have set the milestone date to beginning of March.
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What does this implement/fix? Explain your changes.
Previously MahalanobisDistance class used a shared instance variable self.buffer to store intermidiate calculations which resulted in one thread overwriting other threads data so I have replaced the shared instance buffer , now every thread getting its own temporary buffer using malloc so now thread cannot overwrite each other data.
AI usage disclosure
I used AI assistance for:
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [x] Test/benchmark generation
- [ ] Documentation (including examples)
- [x] Research and understanding
Any other comments?
No
@lesteve @adrinjalali | null | {
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This PR adds a link to the plot_quantile_regression.py example in the QuantileRegressor class docstring.
The example demonstrates how quantile regression can predict non-trivial conditional quantiles for datasets with heteroscedastic or asymmetric error distributions. This showcases the key use case for QuantileRegressor and helps users understand its advantages over standard regression. | {
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This PR adds a link to the plot_theilsen.py example in the TheilSenRegressor class docstring.
The example demonstrates how Theil-Sen regression is robust against outliers by comparing it with OLS and RANSAC on datasets with outliers. This comparison showcases the key advantage of TheilSenRegressor and helps users understand when to use it. | {
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This PR adds a link to the plot_multi_task_lasso_support.py example in the MultiTaskLasso class docstring.
The example demonstrates joint feature selection across multiple tasks, which is the key feature of MultiTaskLasso - enforcing the same features to be selected across all tasks. This link helps users discover this practical example directly from the API documentation. | {
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This PR adds a link to the plot_omp.py example in the OrthogonalMatchingPursuit class docstring.
The example demonstrates using Orthogonal Matching Pursuit for sparse signal recovery from noisy measurements, which is the primary use case for OrthogonalMatchingPursuit. This link helps users discover this practical example directly from the API documentation. | {
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This PR adds a link to the `plot_lasso_dense_vs_sparse_data.py` example in the Lasso class docstring, specifically in the `precompute` parameter description.
The example demonstrates performance differences between dense and sparse data formats, which is directly relevant to the `precompute` parameter that behaves differently for sparse data.
This helps users understand when to use sparse vs dense data formats with Lasso. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33179 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33179/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33179/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33179/events | https://github.com/scikit-learn/scikit-learn/pull/33179 | 3,885,933,250 | PR_kwDOAAzd1s7A6gx8 | 33,179 | FIX `pip-tools` error in automatic main lock-file update | {
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Fixes #33174
#### What does this implement/fix? Explain your changes.
Adds "pip=25.3" in pip-based lock file creation as a workaround for `pip-tools` not being compatible with the latest pip release (26.0) yet (see https://github.com/jazzband/pip-tools/issues/2319).
With this fix, `python build_tools/update_environments_and_lock_files.py` runs without error locally for `debian_32` and `ubuntu_atlas`.
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This PR adds a link to the `plot_huber_vs_ridge.py` example in the HuberRegressor class docstring.
The example demonstrates a comparison between HuberRegressor and Ridge on a dataset with outliers, which is a key use case for HuberRegressor. This link helps users discover this practical example directly from the API documentation.
Similar pattern exists in Ridge class which links to `plot_ridge_coeffs.py` in its docstring. | {
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] | open | false | [] | null | 0 | 2026-02-02T13:01:33 | 2026-02-04T18:23:01 | null | null | NONE | null | null | null | null | Towards #33085
### What does this PR do?
- Raise a ValueError in StratifiedGroupKFold when `n_splits` is greater than the number of unique groups.
This aligns StratifiedGroupKFold with GroupKFold and prevents degenerate (empty) train/test splits in
impossible configurations.
### Why is this needed?
Issue #33085 reports that StratifiedGroupKFold can produce empty / degenerate splits when the number of groups
is too small for the requested number of folds. In such cases, splitting is not feasible, so we fail early with
a clear error message.
### What changed?
- sklearn/model_selection/_split.py:
- added a check `n_splits > n_groups` and raise ValueError with a descriptive message.
- sklearn/model_selection/tests/test_split.py:
- test_group_kfold: assert the same failure mode for both GroupKFold and StratifiedGroupKFold.
- test_2d_y: increase the number of generated groups so the smoke test uses a valid configuration for the default
StratifiedGroupKFold(n_splits=5).
### Tests
- python -m pytest sklearn/model_selection/tests/test_split.py -vv
### TODO
- [ ] Run full CI (GitHub Actions) and address any failures.
- [ ] Add/confirm a minimal regression example matching #33085.
- [ ] Mark as "Ready for review" when complete.
| null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33175 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33175/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33175/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33175/events | https://github.com/scikit-learn/scikit-learn/pull/33175 | 3,885,089,743 | PR_kwDOAAzd1s7A3snF | 33,175 | CI Use a virtual package spec file for `conda-lock` solving | {
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"node_id": "MDU6TGFiZWwyNjU5OD... | closed | false | [] | null | 7 | 2026-02-02T10:12:32 | 2026-02-05T09:17:48 | 2026-02-04T15:30:27 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
<!--
Example: Fixes #1234. See also #3456.
Please use keywords (e.g., Fixes) to create link to the issues or pull requests
you resolved, so that they will automatically be closed when your pull request
is merged. See https://github.com/blog/1506-closing-issues-via-pull-requests
-->
Closes #33106
#### What does this implement/fix? Explain your changes.
This modifies the environment solving/creation of lockfile for our CUDA CI jobs. Newer versions of packages like PyTorch require a virtual conda package called `__cuda` which signals that CUDA is available on the system (same mechanism exists for `__glibc`). This means that solving the environment on a machine that does not have CUDA (our CI job to update the lockfile, a developers local machine, etc) is not possible. I think this is why we were "stuck" on a fairly old version of PyTorch (older versions didn't have this dependency). I noticed this problem only when trying to add a new package to the environment (`cuvs`), which is needed for #33096. I think for `cuvs` there are no versions (or only super old ones?) that do not depend on `__cuda`. So solving the environment failed.
#### AI usage disclosure
<!--
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:
- [x] 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?
What do you think about adding `cuvs` in this PR already? It is needed in cupy for spatial distance functions like `cdist`. Without cuvs installed those functions fail when you call them. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33174 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33174/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33174/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33174/events | https://github.com/scikit-learn/scikit-learn/issues/33174 | 3,884,687,735 | I_kwDOAAzd1s7ni6F3 | 33,174 | Automatic main lock-file update fails for debian-32 build | {
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```
python build_tools/update_environments_and_lock_files.py --select-build debian_32 -vvv
```
[build log](https://github.com/scikit-learn/scikit-learn/actions/runs/21578510209/job/62170869669)
```
Traceback (most recent call last):
File "/home/runner/work/scikit-learn/scikit-learn/build_tools/update_environments_and_lock_files.py", line 792, in <module>
main()
~~~~^^
File "/usr/share/miniconda/lib/python3.13/site-packages/click/core.py", line 1442, in __call__
return self.main(*args, **kwargs)
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/share/miniconda/lib/python3.13/site-packages/click/core.py", line 1363, in main
rv = self.invoke(ctx)
File "/usr/share/miniconda/lib/python3.13/site-packages/click/core.py", line 1226, in invoke
return ctx.invoke(self.callback, **ctx.params)
~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/share/miniconda/lib/python3.13/site-packages/click/core.py", line 794, in invoke
return callback(*args, **kwargs)
File "/home/runner/work/scikit-learn/scikit-learn/build_tools/update_environments_and_lock_files.py", line 788, in main
write_all_pip_lock_files(filtered_pip_build_metadata_list)
~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/scikit-learn/scikit-learn/build_tools/update_environments_and_lock_files.py", line 672, in write_all_pip_lock_files
write_pip_lock_file(build_metadata)
~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
File "/home/runner/work/scikit-learn/scikit-learn/build_tools/update_environments_and_lock_files.py", line 666, in write_pip_lock_file
pip_compile(pip_compile_path, requirement_path, lock_file_path)
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/runner/work/scikit-learn/scikit-learn/build_tools/update_environments_and_lock_files.py", line 621, in pip_compile
execute_command(
~~~~~~~~~~~~~~~^
[
^
...<5 lines>...
]
^
)
^
File "/home/runner/work/scikit-learn/scikit-learn/build_tools/update_environments_and_lock_files.py", line 476, in execute_command
raise RuntimeError(
...<5 lines>...
)
RuntimeError: Command exited with non-zero exit code.
Exit code: 1
Command:
/usr/share/miniconda/envs/pip-tools-python3.12.5/bin/pip-compile --upgrade build_tools/azure/debian_32bit_requirements.txt -o build_tools/azure/debian_32bit_lock.txt
stdout:
stderr:
Traceback (most recent call last):
File "/usr/share/miniconda/envs/pip-tools-python3.12.5/bin/pip-compile", line 10, in <module>
sys.exit(cli())
^^^^^
File "/usr/share/miniconda/envs/pip-tools-python3.12.5/lib/python3.12/site-packages/click/core.py", line 1485, in __call__
return self.main(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/share/miniconda/envs/pip-tools-python3.12.5/lib/python3.12/site-packages/click/core.py", line 1406, in main
rv = self.invoke(ctx)
^^^^^^^^^^^^^^^^
File "/usr/share/miniconda/envs/pip-tools-python3.12.5/lib/python3.12/site-packages/click/core.py", line 1269, in invoke
return ctx.invoke(self.callback, **ctx.params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/share/miniconda/envs/pip-tools-python3.12.5/lib/python3.12/site-packages/click/core.py", line 824, in invoke
return callback(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/share/miniconda/envs/pip-tools-python3.12.5/lib/python3.12/site-packages/click/decorators.py", line 34, in new_func
return f(get_current_context(), *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/share/miniconda/envs/pip-tools-python3.12.5/lib/python3.12/site-packages/piptools/scripts/compile.py", line 475, in cli
prereleases=repository.finder.allow_all_prereleases or pre,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'PackageFinder' object has no attribute 'allow_all_prereleases'
```
cc @AnneBeyer if you want to have a closer look 🙏. | {
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### Note
If the CI tasks fail, create a new branch based on this PR and add the required fixes to that branch. | {
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### Note
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### Note
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Add reference links to hinge loss API documentation
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Fixes #17978.
#### What does this implement/fix? Explain your changes.
`LinearClassifierMixin._predict_proba_lr` might return nan or inf if `decision_function` returns large negative values, because all probabilities are zero in floating point arithmetic (e.g. exp(-1000) = 0).
#### AI usage disclosure
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#### Any other comments?
I guess the only estimator is `SGDClassifier`, not 100% sure. | {
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} | ### Describe the bug and give evidence about its user-facing impact
I query a dataset with around 1.6M locations using `KDTree.query_radius`. The script dies in a segfault without an informative error message.
### Steps/Code to Reproduce
Data: https://drive.google.com/file/d/1eAr774HknkcfYFHoOjcic0aY4XetCwUe/view?usp=sharing (⚠️ don't naively `np.load` contents from untrusted source as this can execute arbitrary code on your machine)
```python
from sklearn.neighbors import KDTree
import numpy as np
data = np.load("data.npy")
tree = KDTree(data)
query = np.array([
[651047.8125, 6863169.5]
])
tree.query_radius(query, 800.0, return_distance = True, sort_results = True)
```
### Expected Results
In case there is an issue with the data volumes, there should be at least an informative error message.
### Actual Results
There is a segfault. It works with other query locations, for instance:
```
[652760.68, 6863527.73]
[652950.7, 6863942.6]
[652760.68, 6863527.73]
```
### Versions
```shell
System:
python: 3.12.12 | packaged by conda-forge | (main, Jan 26 2026, 23:51:32) [GCC 14.3.0]
executable: /home/???/.miniforge3/envs/???/bin/python3
machine: Linux-6.8.0-90-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.8.0
pip: 25.3
setuptools: 80.10.2
numpy: 2.4.1
scipy: 1.17.0
Cython: None
pandas: 2.3.3
matplotlib: 3.10.8
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libopenblas
filepath: /home/???/.miniforge3/envs/???/lib/libopenblasp-r0.3.30.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 12
prefix: libgomp
filepath: /home/???/.miniforge3/envs/???/lib/libgomp.so.1.0.0
version: None
```
### Interest in fixing the bug
I noticed that the problem only occurs for `sort_results = True`. It might have to do with the fact that there are many duplicates in the data. | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33166 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33166/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33166/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33166/events | https://github.com/scikit-learn/scikit-learn/pull/33166 | 3,882,127,625 | PR_kwDOAAzd1s7AuA86 | 33,166 | Bump pypa/cibuildwheel from 3.3.0 to 3.3.1 in the actions group | {
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"node_id": "LA_kwDOAAzd1s8AAAA... | closed | false | [] | null | 2 | 2026-02-01T14:25:40 | 2026-02-02T11:01:33 | 2026-02-02T11:01:18 | null | CONTRIBUTOR | null | null | null | null | Bumps the actions group with 1 update: [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel).
Updates `pypa/cibuildwheel` from 3.3.0 to 3.3.1
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/pypa/cibuildwheel/releases">pypa/cibuildwheel's releases</a>.</em></p>
<blockquote>
<h2>v3.3.1</h2>
<ul>
<li>🛠 Update dependencies and container pins, including updating to CPython 3.14.2. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2708">#2708</a>)</li>
</ul>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md">pypa/cibuildwheel's changelog</a>.</em></p>
<blockquote>
<hr />
<h2>title: Changelog</h2>
<h1>Changelog</h1>
<h3>v3.3.1</h3>
<p><em>5 January 2026</em></p>
<ul>
<li>🛠 Update dependencies and container pins, including updating to CPython 3.14.2. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2708">#2708</a>)</li>
</ul>
<h3>v3.3.0</h3>
<p><em>12 November 2025</em></p>
<ul>
<li>🐛 Fix an incompatibility with Docker v29 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2660">#2660</a>)</li>
<li>✨ Adds <code>test-runtime</code> option, to customise how tests on simulated/emulated environments are run (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2636">#2636</a>)</li>
<li>✨ Adds support for new <code>manylinux_2_35</code> images on 32-bit ARM <code>armv7l</code>, offering better C++20 compatibility (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2656">#2656</a>)</li>
<li>✨ <code>build[uv]</code> is now supported on Android (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2587">#2587</a>)</li>
<li>✨ You can now install extras (such as <code>uv</code>) with a simple option on the GitHub Action (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2630">#2630</a>)</li>
<li>✨ <code>{project}</code> and <code>{package}</code> placeholders are now supported in <code>repair-wheel-command</code> (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2589">#2589</a>)</li>
<li>🛠 The versions set with <code>dependency-versions</code> no longer constrain packages specified by your <code>build-system.requires</code>. Previously, on platforms other than Linux, the constraints in this option would remain in the environment during the build. This has been tidied up make behaviour more consistent between platforms, and to prevent version conflicts. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2583">#2583</a>)</li>
<li>🛠 Improve the handling of <code>test-command</code> on Android, enabling more options to be passed (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2590">#2590</a>)</li>
<li>📚 Docs improvements (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2618">#2618</a>)</li>
</ul>
<h3>v3.2.1</h3>
<p><em>12 October 2025</em></p>
<ul>
<li>🛠 Update to CPython 3.14.0 final (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2614">#2614</a>)</li>
<li>🐛 Fix the default MACOSX_DEPLOYMENT_TARGET on Python 3.14 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2613">#2613</a>)</li>
<li>📚 Docs improvements (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2617">#2617</a>)</li>
</ul>
<h3>v3.2.0</h3>
<p><em>22 September 2025</em></p>
<ul>
<li>✨ Adds GraalPy v25 (Python 3.12) support (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2597">#2597</a>)</li>
<li>🛠 Update to CPython 3.14.0rc3 (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2602">#2602</a>)</li>
<li>🛠 Adds CPython 3.14.0 prerelease support for Android, and a number of improvements to Android builds (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2568">#2568</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/2591">#2591</a>)</li>
<li>🛠 Improvements to testing on Android, passing environment markers when installing the venv, and providing more debug output when build-verbosity is set (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2575">#2575</a>)</li>
<li>⚠️ PyPy 3.10 was moved to <code>pypy-eol</code> in the <code>enable</code> option, as it is now end-of-life. (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2521">#2521</a>)</li>
<li>📚 Docs improvements (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2574">#2574</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/2601">#2601</a>, <a href="https://redirect.github.com/pypa/cibuildwheel/issues/2598">#2598</a>)</li>
</ul>
<h3>v3.1.4</h3>
<p><em>19 August 2025</em></p>
<ul>
<li>✨ Add a <code>--clean-cache</code> command to clean up our cache (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2489">#2489</a>)</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/pypa/cibuildwheel/commit/298ed2fb2c105540f5ed055e8a6ad78d82dd3a7e"><code>298ed2f</code></a> Bump version: v3.3.1</li>
<li><a href="https://github.com/pypa/cibuildwheel/commit/f0ff94431807b2d31ad3170007669061f91f0241"><code>f0ff944</code></a> [3.3.x] Update dependencies (<a href="https://redirect.github.com/pypa/cibuildwheel/issues/2708">#2708</a>)</li>
<li>See full diff in <a href="https://github.com/pypa/cibuildwheel/compare/63fd63b352a9a8bdcc24791c9dbee952ee9a8abc...298ed2fb2c105540f5ed055e8a6ad78d82dd3a7e">compare view</a></li>
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</details>
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"node_id": "MDU6... | closed | false | [] | null | 0 | 2026-02-01T11:59:25 | 2026-02-13T10:15:02 | 2026-02-13T10:00:23 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Context #33033.
#### What does this implement/fix? Explain your changes.
This PR removes `global_random_seed` in `test_logistic.py` where the randomness has zero effect anyway.
#### AI usage disclosure
None
#### Any other comments?
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#### What does this implement/fix? Explain your changes.
This change fixes a typo in the Newton solver convergence warning - changing the wording from "did no converge" to "did not converge".
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#### What does this implement/fix? Explain your changes.
This PR addresses inconsistent handling of the pos_label parameter across classification metrics when working with multiclass and multilabel data.
Changed default value of pos_label: Updated from 1 to None for jaccard_score, f1_score, fbeta_score, precision_score, and recall_score
Improved error handling: Instead of silently ignoring pos_label for multiclass/multilabel data (with only a warning), these metrics now raise a clear ValueError when a non-None pos_label is explicitly provided for non-binary targets, guiding users toward the correct approach
More consistent behavior: Aligns error handling across metrics - previously, only average_precision_score raised an error while others issued warnings or silently ignored the parameter
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Improvement on top of #31882, #31986, #31987 and #32014.
#### What does this implement/fix? Explain your changes.
This PR prevents the update of the residual inside the screening if the coefficient is already zero.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33160 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33160/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33160/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33160/events | https://github.com/scikit-learn/scikit-learn/pull/33160 | 3,876,631,076 | PR_kwDOAAzd1s7AcUQZ | 33,160 | ENH add L1/enet capable solvers newton-cd and newton-cd-gram to LogisticRegression | {
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"node_id": "MDU6... | open | false | [] | null | 4 | 2026-01-30T17:18:33 | 2026-02-26T13:45:17 | null | null | MEMBER | null | null | null | null | ### Reference Issues/PRs
Contributes to #16637.
### What does this implement/fix? Explain your changes.
This PR implements two proximal Newton solvers relying on the available coordinate descent solvers:
- "newton-cd-gram" uses `enet_coordinate_descent_gram`
Good for n_samples >> n_features
- "newton-cd" uses the other CD-solvers and a special new one for the multinomial case (n_classes >= 3).
Good for L1 penalties in combination with n_features >> n_samples.
These solvers work for all GLMs, but this PR only makes them available in `LogisticRegression`.
#### Why 2 new solvers?
- They will add L1 penalties to PoissonRegressor etc.
- For LogisticRegression, there is room for high precision solvers:
- Currently, the only L1 solver for multiclass is saga.
- Currently, the only enet solver (L1 + L2 penalty) is saga.
- Originally, I wanted to combine both into a single one with automatic selection when to use which. This decision, however, is very tricky. So I went with having both options user facing.
#### AI usage disclosure
No AI usage.
#### Any other comments?
If we deem these 2 solvers a good addition, we might consider to retire liblinear.
### TODO
- [ ] remove python CD multinomial (keep Cython version)
- [ ] Improve doc on solver choice
- [ ] Add changelog
- [ ] Maybe: support float32 in Cython multinomial CD
- [ ] Maybe: openmp thread parallelism for multinomial CD
- [ ] Maybe: try different inner tolerance | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33159 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33159/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33159/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33159/events | https://github.com/scikit-learn/scikit-learn/issues/33159 | 3,876,082,654 | I_kwDOAAzd1s7nCFPe | 33,159 | RFC: deprecate `plot` method in DecisionBoundaryDisplay? | {
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} | Repeating this here from https://github.com/scikit-learn/scikit-learn/issues/33115#issuecomment-3784006019:
There are two paths to create a `DecisionBoundaryDisplay` plot (we recommend the second, but the first is also possible):
```
display = DecisionBoundaryDisplay(...)
display.plot()
```
and
```
display = DecisionBoundaryDisplay.from_estimator(...)
# which internally calls `display.plot()`
```
This leads to inconsistent/duplicate parameter checking in both functions.
The only place where the first option is used in our repo is in the example for the class itself, and calling it requires calculating the grid response values from an estimator anyway.
https://github.com/scikit-learn/scikit-learn/blob/351280bd1d79dc5de598776e8bb2da98c1f74b98/sklearn/inspection/_plot/decision_boundary.py#L140-L150
So if I'm not mistaken, the public `plot()` function doesn't add much here, and removing it would simplify the code and make adding more functionality (like planned in https://github.com/scikit-learn/scikit-learn/issues/33115, https://github.com/scikit-learn/scikit-learn/issues/33094 and https://github.com/scikit-learn/scikit-learn/issues/27462) easier.
WDYT? @ogrisel @lucyleeow @ThexXTURBOXx
(and @adrinjalali @glemaitre just in case you have time to take a look)
Note: I did not check if the same applies to any other Display class yet. | {
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