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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33158 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33158/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33158/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33158/events | https://github.com/scikit-learn/scikit-learn/issues/33158 | 3,874,821,024 | I_kwDOAAzd1s7m9ROg | 33,158 | RFC: `DecisionTree*`/`ExtraTree*`: why accepting a `splitter` parameter? (let's deprecate it) | {
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} | Classes `DecisionTree*` and `ExtraTree*` are exact copies, the only thing that differentiates them is the default value of the `splitter` parameter.
I think it's confusing to expose this parameter in the public API, I think `DecisionTree*`s should always use the "best-splitter" and `ExtraTree*` should always use the random-splitter.
**Why I think it's confusing:** because for instance a `ExtraTreeRegressor(splitter="best")` is absolutely not "An extremely randomized tree regressor" as the doc describes this model. It's just a normal `DecisionTreeRegressor()`. I was confused about that for a long time, I though that somehow this would still have some randomness in it / a different behavior from a decision tree. I understood it was not the case only by looking at the code.
I suspect this parameter exists for **historical reasons**: at one point in the past it seems that trees accepted custom splitters. But it's not the case anymore:
```Python
_parameter_constraints: dict = {
"splitter": [StrOptions({"best", "random"})],
...
}
```
Side node: we could also remove some code that handles the case of a custom splitter (see #33150), and the code that allows `Splitter` subclasses to be serializable (`__reduce__`, etc.).
What do you think? | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33155 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33155/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33155/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33155/events | https://github.com/scikit-learn/scikit-learn/pull/33155 | 3,870,126,563 | PR_kwDOAAzd1s7AGjQQ | 33,155 | CI Migrate pylatest_conda_forge_mkl build to GHA | {
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Towards #32434
#### What does this implement/fix? Explain your changes.
Move the `pylatest_conda_forge_mkl` unit tests to github actions.
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I believe the idea is to have manually triggered actions behave exactly as scheduled ones, so I conditioned the `SKLEARN_SKIP_NETWORK_TESTS` env var so that the network tests are performed for both scheduled and manually triggered runs.
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} | ### Describe the bug and give evidence about its user-facing impact
Describe the bug and give evidence about its user-facing impact In sklearn/utils/estimator_checks.py, the function _yield_classifier_checks yields check_class_weight_balanced_linear_classifier twice for linear classifiers that support the class_weight parameter.
This bug was discovered during a manual code review of estimator_checks.py. It affects any scikit-learn user or developer who uses check_estimator (or the underlying check generators) to validate estimators.
The impact is redundant test execution. For example, when running common tests for a LogisticRegression or a custom linear classifier using the mixin, this specific check (which involves fitting the estimator and verifying class weight handling) is executed twice. This is wasteful in terms of CI/CD time and can lead to duplicated error messages or logs, making debugging more tedious.
### Steps/Code to Reproduce
from sklearn.linear_model import LogisticRegression
from sklearn.utils.estimator_checks import _yield_classifier_checks
def get_name(check):
return getattr(check, "__name__",
getattr(check, "func", check).__name__)
clf = LogisticRegression()
checks = list(_yield_classifier_checks(clf))
check_names = [get_name(c) for c in checks]
duplicate_count = check_names.count(
'check_class_weight_balanced_linear_classifier'
)
print(f"Yield count: {duplicate_count}")
### Expected Results
The check should be yielded exactly once. Yield count: 1
### Actual Results
The check is currently yielded twice. Yield count: 2
### Versions
```shell
python: 3.14.0
numpy: 2.4.1
scipy: 1.17.0
joblib: 1.5.3
scikit-learn: 1.7.dev0 (from source)
```
### Interest in fixing the bug
I am interested in fixing this bug. I have already identified the root cause and implemented a fix.
Analysis of root cause: In _yield_classifier_checks, there are two consecutive if blocks that both check for isinstance(classifier, LinearClassifierMixin) and the presence of class_weight in parameters. Both blocks yield the same check. | {
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#### Reference Issues/PRs
Fixes #33112
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#### What does this implement/fix? Explain your changes.
This PR adds a .. note:: to the parallelism.rst documentation. It explains that a delay (startup overhead) is expected when calling parallel functions with n_jobs > 1 for the first time, due to the initialization of Python worker processes.
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I used AI assistance for:
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This clarification was requested to help users understand why the first call to cross_validate or other parallel estimators might be slower than subsequent calls.
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- test_multi_class_n_jobs[kernel3] | null | {
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Problems detected by ruff check, please fix them
### Running the ruff formatter ###
Problems detected by ruff format, please run ruff format and commit the result
### Running mypy ###
Problems detected by mypy, please fix them
### Running cython-lint ###
Problems detected by cython-lint, please fix them
### Checking for bad deprecation order ###
No problems detected related to deprecation order
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No problems detected related to doctest directives
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No problems detected related to joblib imports
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#### Reference Issues/PRs
RFC #33158
#### What does this implement/fix? Explain your changes.
The `splitter` parameter (and hence `self.splitter`) can never be a `Splitter` instance, because of the parameter constraint: `"splitter": [StrOptions({"best", "random"})]`. So I removed the branch checking for that.
#### AI usage: no
#### Any other comments?
I think this could help a bit in potential future changes I envision for trees (long term). | null | {
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This PR fixes a false negative in `check_estimator`, specifically in `check_requires_y_none`, for estimators that validate `y` using the `validate_params` decorator with an `array-like` constraint.
When `y=None`, `validate_params` raises an error of the form:
“The 'y' parameter of ... must be an array-like. Got None instead.”
This message was not matched by the set of accepted error patterns in `check_requires_y_none`, causing estimators that correctly reject `None` values to still fail the check.
Changes:
, Extend the accepted error patterns to include the `validate_params` wording for the `array-like` constraint (“must be an array-like. Got None instead”).
, Add a regression test to ensure estimators using `validate_params` for `y` validation pass `check_requires_y_none` while still rejecting `y=None`.
#### AI usage disclosure
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I used AI assistance for:
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
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} | 1 | 2026-01-28T18:16:03 | 2026-02-27T04:29:46 | 2026-02-27T04:29:46 | null | CONTRIBUTOR | null | null | null | null |
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#### Reference Issues/PRs
Fixes #33146
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#### What does this implement/fix? Explain your changes.
Fixed a bug in `BisectingKMeans` where the validation logic incorrectly checked the shape of custom `init` centroids against the global `n_clusters` (e.g., 3) instead of the local split count (2).
Changes:
- Updated `_validate_center_shape` in `_kmeans.py` to accept an optional `n_centers` argument.
- Updated `_init_centroids` to pass the local `n_clusters` (split count) to the validator.
- Added a regression test in `sklearn/cluster/tests/test_bisect_k_means.py`.
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Tested it on my fork https://github.com/lesteve/scikit-learn/actions/runs/21432189253 with `workflow_dispatch` and this works fine. | {
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} | When using `BisectingKMeans`, `n_clusters` greater than 2, using `init` to specify cluster seeds. A `ValueError` is raised if returning fewer than `n_clusters` seeds, but the value of `n_clusters` passed to `init` is (correctly, for bisecting k-means) always 2. The size of the returned object *must* match the "outer" `n_clusters` to avoid a `ValueError`, and changing the other cluster seeds gives a different clustering result.
In essence: is it intended behaviour for a *bisecting* method to require more than 2 initial cluster seeds per bisection, and if so, why does it change the result? Why do some seed orderings not change the result, and some do? (My basic testing is not exhaustive.)
------
In my case, I was running bisecting k-means clustering on image data. Hence, in my examples below I will use Scikit-Image data for similarity.
For example, this fails:
<details>
<summary>Calling `init` causes a panic</summary>
```python
import numpy as np
import skimage as ski
from sklearn.cluster import BisectingKMeans
def bk_init(X, n_clusters, random_state):
## note this is always 2:
print(n_clusters)
return np.array(
[[0], [len(X)]],
dtype=np.uint,
)
k = 6
img = ski.data.camera()
img_v = img.ravel()
img_d = img_v.reshape(-1, 1)
bk = BisectingKMeans(n_clusters=k, init=bk_init)
bk.fit(img_d)
```
</details>
with a `ValueError`:
<details>
<summary>`init` -> `ValueError` traceback</summary>
```
Traceback (most recent call last):
File "<python-input-5>", line 2, in <module>
bk.fit(img_d)
~~~~~~^^^^^^^
File "/<masked>/.venv/lib/python3.13/site-packages/sklearn/base.py", line 1336, in wrapper
return fit_method(estimator, *args, **kwargs)
File "/<masked>/.venv/lib/python3.13/site-packages/sklearn/cluster/_bisect_k_means.py", line 430, in fit
self._bisect(X, x_squared_norms, sample_weight, cluster_to_bisect)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/<masked>/.venv/lib/python3.13/site-packages/sklearn/cluster/_bisect_k_means.py", line 322, in _bisect
centers_init = self._init_centroids(
X,
...<4 lines>...
sample_weight=sample_weight,
)
File "/<masked>/.venv/lib/python3.13/site-packages/sklearn/cluster/_kmeans.py", line 1040, in _init_centroids
self._validate_center_shape(X, centers)
~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/<masked>/.venv/lib/python3.13/site-packages/sklearn/cluster/_kmeans.py", line 939, in _validate_center_shape
raise ValueError(
...<2 lines>...
)
ValueError: The shape of the initial centers (2, 1) does not match the number of clusters 6.
```
</details>
A work-around is to encapsulate the "outer" `n_clusters` in the `init` function:
<details>
<summary>Capturing outer `n_clusters`</summary>
```python
import numpy as np
import skimage as ski
from sklearn.cluster import BisectingKMeans
k = 6
def bk_init(X, n_clusters, random_state):
## this is still 2:
print(n_clusters)
i = np.zeros(
(k, 1), # NOTE not ``n_clusters``!
dtype=np.uint,
)
i[0], i[1] = (0, len(X))
return i
img = ski.data.camera()
img_v = img.ravel()
img_d = img_v.reshape(-1, 1)
bk = BisectingKMeans(n_clusters=k, init=bk_init)
bk.fit(img_d)
```
</details>
Also see [`_BisectingTree.split`][bts], which appears to show getting indices 0 and 1 only (but I'm probably misinterpreting it). But clearly something else is at play, because changing the ordering of the remaining indices changes the result **in some, but not all, cases** (though this doesn't cover every permutation):
<details>
<summary>Different results from different seeds</summary>
```python
print(bk.cluster_centers_) # previous result
```
```
[[156. ]
[159. ]
[174.86169245]
[ 12. ]
[ 10. ]
[ 0.5 ]]
```
```python
def bk_init(X, n_clusters, random_state):
return np.array(
## different results to the below and to each other:
# [[0], [64], [729], [5555], [1], [len(X)]],
# [[0], [1], [len(X)], [64], [729], [5555]],
## identical results (yes, even the first two):
# [[0], [729], [5555], [1], [len(X)], [64]],
# [[0], [5555], [1], [len(X)], [64], [729]],
# [[0], [len(X)], [64], [729], [5555], [1]],
# [[0], [len(X)], [729], [5555], [1], [64]],
# [[0], [len(X)], [5555], [1], [64], [729]],
# [[0], [len(X)], [1], [64], [729], [5555]],
[[0], [len(X)], [64], [729], [5555], [1]],
dtype=np.uint,
)
bk = BisectingKMeans(n_clusters=k, init=bk_init)
bk.fit(img_d)
print(bk.cluster_centers_)
```
```
[[127.49822064]
[126. ]
[116. ]
[112. ]
[ 11.96384212]
[ 0.5 ]]
```
</details>
(I'd like to run a test using `itertools.permutation`.)
[bts]: https://github.com/scikit-learn/scikit-learn/blob/c5497b7f7eacfaff061cf68e09bcd48aa93d4d6b/sklearn/cluster/_bisect_k_means.py#L46
------
<details>
<summary>Python and package versions</summary>
- `uv version`: `uv 0.9.26 (Homebrew 2026-01-15)`
- `uv run python --version`: `Python 3.13.11`
- `pyproject.toml`, dependencies:
+ `matplotlib>=3.10.8`
+ `multiprocess>=0.70.18`
+ `numpy>=2.3.5`
+ `scikit-image>=0.25.2`
+ `scikit-learn>=1.8.0`
+ `scipy>=1.16.3`
- `uv run python -c 'import sklearn; sklearn.show_versions()'`:
```
System:
python: 3.13.11 (main, Dec 5 2025, 16:06:33) [Clang 17.0.0 (clang-1700.6.3.2)]
executable: /<masked>/.venv/bin/python3
machine: macOS-15.7.3-arm64-arm-64bit-Mach-O
Python dependencies:
sklearn: 1.8.0
pip: None
setuptools: None
numpy: 2.3.5
scipy: 1.16.3
Cython: None
pandas: None
matplotlib: 3.10.8
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: openmp
internal_api: openmp
num_threads: 10
prefix: libomp
filepath: /<masked>/.venv/lib/python3.13/site-packages/sklearn/.dylibs/libomp.dylib
version: None
```
</details>
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"node_id": "MDU6TGFiZWwyMzk5Mz... | closed | false | [] | null | 1 | 2026-01-28T04:50:05 | 2026-01-28T10:43:58 | 2026-01-28T10:43:50 | null | NONE | null | null | null | null | Reference Issue: #33135
Fixes compatibility issue with check_requires_y_none when using @validate_params decorator.
What does this implement/fix? Explain your changes.
This PR fixes an issue where estimators using @validate_params for y parameter validation would fail the
check_requires_y_none test, while estimators using column_or_1d would pass.
Problem:
check_requires_y_none only accepted specific error message patterns. When @validate_params validates y and receives None
, it generates the error message: "The 'y' parameter of <ClassName>.fit must be an array-like. Got None instead." This message pattern was not in the allowed list.
Solution:
Add 'must be an array-like' to the allowed error messages in check_requires_y_none Fixes compatibility with @validate_params decorator for y parameter.
Add test case for EstimatorWithValidateParams Both column_or_1d and @validate_params are standard validation methods in scikit-learn, so both should be accepted by check_requires_y_none.
Changes:
Modified sklearn/utils/estimator_checks.py
- Added error message pattern to expected_err_msgs tuple Modified sklearn/utils/tests/test_estimator_checks.py
- Added test case verifying @validate_params compatibility AI usage disclosure
I used AI assistance for:
Commiting to github
Documentation (including examples) | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33144 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33144/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33144/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33144/events | https://github.com/scikit-learn/scikit-learn/pull/33144 | 3,863,214,346 | PR_kwDOAAzd1s6_vcs- | 33,144 | FEAT: Add new examples for various tree-based classifiers including Gradient Boosting, Decision Tree, Random Forest, AdaBoost, and Extra Trees. | {
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#### Reference Issues/PRs
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#### What does this implement/fix? Explain your changes.
This PR introduces a new directory `examples/tree_based_classifiers` to consolidate and demonstrate the usage of various tree-based classification algorithms in scikit-learn.
## Changes include:
1. **New Directory**: Created `examples/tree_based_classifiers` to house the new examples.
2. **Documentation**: Added a `README.txt` to provide an overview of the directory's contents, following the standard `rst` format.
3. **New Examples**: Implemented 6 unique example scripts, each highlighting a specific classifier and use case:
- `plot_decision_tree_classifier.py`: Visualizes the decision tree structure on the Wine dataset.
- `plot_random_forest_classifier.py`: Demonstrates feature importance evaluation using the Make Classification dataset.
- `plot_extra_trees_classifier.py`: Visualizes pixel importance on the Digits dataset.
- `plot_gradient_boosting_classifier.py`: Monitors Out-of-Bag (OOB) estimates vs. Test error (deviance).
- `plot_hist_gradient_boosting_classifier.py`: Compares training speed and accuracy between Histogram-based and standard Gradient Boosting on a large dataset.
- `plot_adaboost_classifier.py`: Visualizes decision boundaries on a non-linearly separable dataset (Gaussian Quantiles).
These examples serve to provide clear, identifying usage patterns for each of the major tree-based classifiers available in the library.
#### AI usage disclosure
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I used AI assistance for:
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- [ ] Test/benchmark generation
- [x] Documentation (including examples)
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#### Any other comments?
The examples are self-contained and execute quickly. Plotting is used where appropriate to provide visual intuition for the classifiers' behavior (e.g., decision boundaries, feature importances).
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33143 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33143/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33143/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33143/events | https://github.com/scikit-learn/scikit-learn/issues/33143 | 3,863,043,420 | I_kwDOAAzd1s7mQV1c | 33,143 | RFC Metric `pos_label` handling for multiclass (and multilabel) data | {
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> This is a request for comment (RFC), please do not open a pull request for this issue. If you wish to contribute to scikit-learn please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particular the section [New contributors](https://scikit-learn.org/dev/developers/contributing.html#new-contributors).
Noticed in https://github.com/scikit-learn/scikit-learn/pull/32755#discussion_r2734384418
Here are the metrics that have a `pos_label`:
https://github.com/scikit-learn/scikit-learn/blob/7fbe0c256679c635e55be0deeb6c833207149d41/sklearn/metrics/tests/test_common.py#L364-L380
Of these, some only support binary input (e.g., `roc_curve`, `confusion_matrix_at_thresholds`).
Of the metrics that also support multi-class input, most silently ignore the `pos_label` parameter if data is multiclass (or multilabel) but `pos_label` is also set. `average_precision_score` is the only one that raises an error:
https://github.com/scikit-learn/scikit-learn/blob/7fbe0c256679c635e55be0deeb6c833207149d41/sklearn/metrics/_ranking.py#L264-L274
I am not sure whether it is better to error or silently ignore (or third option, warn) - but some consistency is probably ideal.
How should `pos_label` be handled?
cc @StefanieSenger @AnneBeyer @ogrisel ?
Edit:
Side note, `brier_score_loss` and `d2_brier_score` do not explicitly state that `pos_label` is ignored, but probably should. Maybe you'd be interested in tackling this @AnneBeyer ?
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Fixes a few typos in `plot_confusion_matrix.py`.
#### AI usage disclosure
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I tried to explain the reasons we ask for all those requirements as @reshamas suggested. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33139 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33139/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33139/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33139/events | https://github.com/scikit-learn/scikit-learn/pull/33139 | 3,856,256,843 | PR_kwDOAAzd1s6_YXda | 33,139 | DOC Add statement on supported versions for array API | {
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"node_id": "LA_kwDOAAzd1s8AA... | closed | false | [] | null | 4 | 2026-01-26T14:40:06 | 2026-01-27T10:13:07 | 2026-01-27T09:11:15 | null | MEMBER | null | null | null | null | Clarify that we develop against the latest versions of dependencies for array API work and will not introduce code to backport fixes that are available in newer versions of our dependencies.
#### Reference Issues/PRs
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Example: Fixes #1234. See also #3456.
Please use keywords (e.g., Fixes) to create link to the issues or pull requests
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For example https://github.com/scikit-learn/scikit-learn/issues/32552#issuecomment-3782563204 and https://github.com/scikit-learn/scikit-learn/pull/33022, but we've discussed "what versions do we support?" a few times already in various issues and PRs.
#### What does this implement/fix? Explain your changes.
This adds a statement to the array API documentation stating that we only support the latest version of scikit-learn's dependencies. It also says that we will not introduce changes that backport things to older versions of dependencies - which is kind of redundant but given https://github.com/scikit-learn/scikit-learn/pull/33022 maybe worth stating explicitly (aka I shouldn't have made that PR).
#### 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?
I moved the environment variable paragraph as I was missing it in the "Enable array API support" section. In this case I went directly to that section without reading the page from the top. To me it makes sense to have it all in one place. Looking at it from a different angle: why is the env var thing mentioned at the very top, but how to enable it isn't? They both seem equally important.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33138 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33138/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33138/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33138/events | https://github.com/scikit-learn/scikit-learn/pull/33138 | 3,856,095,903 | PR_kwDOAAzd1s6_X1hF | 33,138 | Fix response_method='auto' for clusterers in DecisionBoundaryDisplay | {
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"node_id": "MDU6TGFi... | closed | false | [] | null | 2 | 2026-01-26T13:57:57 | 2026-01-28T13:56:24 | 2026-01-27T01:58:47 | null | NONE | null | null | null | null | Support clusterers in DecisionBoundaryDisplay when response_method="auto".
When the estimator is a clusterer, "auto" now selects "predict" when available
(e.g. KMeans).
If the clusterer does not implement predict (e.g. DBSCAN),
DecisionBoundaryDisplay.from_estimator raises a clear and explicit error
indicating that a decision boundary cannot be computed.
Added tests covering both cases.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33137 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33137/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33137/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33137/events | https://github.com/scikit-learn/scikit-learn/issues/33137 | 3,856,050,049 | I_kwDOAAzd1s7l1qeB | 33,137 | Analyze the practical relevance of GBT hyperparameters in the accuracy / training speed tradeoff | {
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} | The following paper ran many hyper-parameter tuning experiments.
- https://arxiv.org/abs/2207.08815
- supporting code: https://github.com/LeoGrin/tabular-benchmark
The raw CSV data is available for download here:
- https://github.com/LeoGrin/tabular-benchmark/tree/main/analyses/results
We could try to mine this data to find hyper-params configuration implemented in xgboost or lightgbm that:
- allow xgboost or lightgbm or catboost to be on the Pareto optimal frontier of the predictive/computational performance tradeoff;
- have no equivalent in scikit-learn.
This would help identify which missing entries of the table in #27873 have the most user-facing impact, and possibly also identify features of xgboost that are not implemented in scikit-learn at all while being very relevant to reach good predictive performance. | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33136 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33136/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33136/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33136/events | https://github.com/scikit-learn/scikit-learn/pull/33136 | 3,855,153,936 | PR_kwDOAAzd1s6_UxMz | 33,136 | MAINT Simpler array API lock file config | {
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The python.org no longer mentions conda as an installation method, so maybe the pytorch conda channel is no longer maintained.
I am not sure about the usefulness of adding the nvidia channel, so let's try without.
I don't know if we had any particular reason to set the MKL constraint.
The lock file update script works on my local machine (running macOS) but I am not sure if it would work on a linux x64 host.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33135 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33135/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33135/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33135/events | https://github.com/scikit-learn/scikit-learn/issues/33135 | 3,854,942,843 | I_kwDOAAzd1s7lxcJ7 | 33,135 | BUG: error message raised by validate_params cannot pass check_estimator | {
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"url": "ht... | closed | false | [] | null | 4 | 2026-01-26T08:18:28 | 2026-02-13T13:52:36 | 2026-02-02T16:31:06 | null | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
I maintain the scikit-learn compatible projects [fastcan](https://github.com/scikit-learn-contrib/fastcan) and use `check_estimator` to check its compatibility.
When I use `sklearn.utils.column_or_1d` to validate the argument `y` of `fit`, the error message of `None` input is accepted by `check_estimator`.
However, when I use `sklearn.utils._param_validation.validate_params`, the error message of `None` input is invalid for `check_estimator`.
Both `column_or_1d` and `validate_params` are standard ways to validate the function arguments. The error messages of both should be valid.
### Steps/Code to Reproduce
```py
from sklearn.utils import column_or_1d
from sklearn.utils._param_validation import validate_params
from sklearn.utils.estimator_checks import check_requires_y_none
from sklearn.base import BaseEstimator
class ClassA(BaseEstimator):
@validate_params(
{
"X": ["array-like"],
"y": ["array-like"],
},
prefer_skip_nested_validation=True,
)
def fit(self, X, y):
return self
class ClassB(BaseEstimator):
@validate_params(
{
"X": ["array-like"],
},
prefer_skip_nested_validation=True,
)
def fit(self, X, y):
column_or_1d(y)
return self
A = ClassA()
B = ClassB()
```
### Expected Results
```py
check_requires_y_none("ClassA", A) # Pass
check_requires_y_none("ClassA", B) # Pass
```
### Actual Results
```py
check_requires_y_none("ClassA", A) # Fail
check_requires_y_none("ClassA", B) # Pass
```
### Versions
```shell
sklearn: 1.8.0
``` | {
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This PR adds a new Sphinx-Gallery example
(`examples/ensemble/plot_forest_warm_start_grid_search.py`)
demonstrating a manual grid search that leverages `warm_start=True`
for tree ensembles by progressively increasing `n_estimators`.
The example compares runtime and validation accuracy against a baseline
`GridSearchCV` evaluated on the same fixed train/validation split
(using `PredefinedSplit`), and visualizes the score surface with a heatmap.
#### Why is this needed?
`GridSearchCV` evaluates each parameter setting independently and therefore
does not reuse fitted state across configurations. For estimators supporting
`warm_start`, reusing the fitted forest when only increasing `n_estimators`
can reduce runtime in benchmarking-style workflows.
#### Key points / design choices
- Reuse is demonstrated only when increasing `n_estimators` while keeping
other hyperparameters fixed (changing `max_features` starts from a fresh model).
- The baseline uses `PredefinedSplit` to ensure an identical train/validation
split for a fair comparison.
- Dataset size and parameter grids are intentionally small to keep the example
fast and suitable for CI.
#### Example output (indicative)
On my machine, both approaches select the same best configuration, while the
warm-start manual search runs ~2x faster:
```text
Manual warm_start grid search
best params: max_features=sqrt, n_estimators=150
best valid accuracy: 0.9318
runtime: 3.482 s
GridSearchCV baseline (same split, fits from scratch)
best params: max_features=sqrt, n_estimators=150
best valid accuracy: 0.9318
runtime: 7.184 s
Test accuracy using manual best params: 0.9376
```
<img width="640" height="480" alt="Figure_1" src="https://github.com/user-attachments/assets/a3b1d176-a93e-4425-a6fa-d176fbcb754f" />
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"node_id": "LA_kwDOAAzd1s7eWo1... | closed | false | [] | null | 4 | 2026-01-23T21:47:42 | 2026-01-27T09:38:25 | 2026-01-27T08:56:57 | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Found this bug while working on #32193 (maybe the 3rd or 4th bug found while working on this test, really this is a good test :joy:)
Let me know if you want me to open an issue describing the bug, but for now it's described it in the next section.
#### The bug
```python
from sklearn.utils.stats import _weighted_percentile
_weighted_percentile([1, 3], [1, 1], average=True) # returns 2 as expected
_weighted_percentile([1, 2, 3], [1, 0, 1], average=True) # ! returns 1
_weighted_percentile([1, 2, 3, 4], [1, 0, 1, 0], average=True) # returns 2 as expected
```
#### What does this implement/fix? Explain your changes.
[Codex wrote:]
I traced it to the zero‑weight “plateau” handling in `_weighted_percentile`. In your second case, the CDF has a flat section (`[1, 1, 2]`). The code correctly finds the next index where the CDF increases (the last element), but then treats `next_index == max_idx` as “out of range” and snaps back to the current index, which forces the average to use the same value twice and returns `1.0`.
Changes:
- Allow `next_index == max_idx` so the “plus one” value can be the last element.
- Added regression test in `sklearn/utils/tests/test_stats.py` to cover the plateau case.
#### AI usage disclosure
I used AI assistance for:
- [ ] Initially finding the bug
- [x] Code generation (e.g., when writing an implementation or fixing a bug)
- [x] Test/benchmark generation
- [X] Documentation (including examples)
- [x] Research and understanding
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 1 | 2026-01-23T18:25:37 | 2026-02-05T12:37:06 | 2026-02-05T12:33:18 | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Fixes #33093. See also #33084.
#### What does this implement/fix? Explain your changes.
It implements suggestions made here https://github.com/scikit-learn/scikit-learn/issues/33093#issuecomment-3759684206
#### AI usage disclosure
I did **not** use AI assistance.
#### Any other comments?
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This PR addresses issue #33032 by improving the numerical stability of \LinearRegression\ specifically for \ loat32\ data, while maintaining strict consistency for \ loat64\.
### The Dual-Logic Strategy (Forest & Trees)
To ensure that this fix does not regress existing consistency tests (like #26164), we have implemented a targeted approach:
- **For \ loat32\**: We now set \cond=None\. This allows \scipy\ to use its internal, high-precision default, which is essential for large datasets (500k+ rows) where the previous formula made the threshold too coarse.
- **For \ loat64\**: We strictly preserve the original \max(X.shape) * eps\ formula. This ensures that all existing regression tests and sample weight consistency checks pass without any deviation.
### Impact & Evidence
- **Accuracy**: Restores correct rank identification (Rank 2) and reduces residues (~10% improvement) for large-scale \ loat32\ tasks.
- **Consistency**: Guaranteed 100% backward compatibility for \ loat64\ workflows.
### Summary of Changes
- Refactored \sklearn/linear_model/_base.py\ to use conditional \cond\ logic based on input dtype.
- Verified with large-scale correlated datasets and edge-case singular matrices. | {
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1.Improving clarity of the code and comments
2.Adding a fixed random_state for reproducibility
3.Making the plot behavior more consistent with the documentation
No change to algorithmic behavior or public APIs.
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] | closed | false | [] | null | 0 | 2026-01-21T17:55:48 | 2026-01-23T02:30:08 | 2026-01-23T02:30:08 | null | CONTRIBUTOR | null | null | null | null | Standardize error messages and string formatting to use f-strings for consistency:
- Line 80: Convert .format() to f-string in list comprehension
- Line 440: Convert .format() to f-string in error message
- Line 1468: Convert % formatting to f-string in error message
This improves code consistency and aligns with modern Python best practices.
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#### What does this implement/fix? Explain your changes.
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"... | closed | false | [] | null | 1 | 2026-01-21T14:14:45 | 2026-02-05T04:16:06 | 2026-02-05T04:16:06 | null | CONTRIBUTOR | null | null | null | null | <!--
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#### Reference Issues/PRs
Fixes #33079
#### What does this implement/fix? Explain your changes.
~The `bench_multilabel_metrics.py` script currently fails because it imports `jaccard_similarity_score`, which was removed in version 0.23.~
The `benchmarks/bench_multilabel_metrics.py` script is currently broken (imports removed function `jaccard_similarity_score`) and has been identified as not useful for current benchmarking needs.
Per [discussion in the issue](https://github.com/scikit-learn/scikit-learn/issues/33079#issuecomment-3790262496), this PR removes the file entirely to reduce technical debt rather than patching it.
#### AI usage disclosure
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#### Any other comments?
~In older versions, `jaccard_similarity_score` on multilabel data behaved identically to `accuracy_score` (Subset Accuracy). Since `accuracy` is already benchmarked in this file, I have switched the new entry to use `average='samples'`. This ensures we are benchmarking the actual Jaccard Index (Intersection over Union), which provides unique value compared to the existing metrics.~ | {
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} | ### Describe the workflow you want to enable
So far, `response_method="auto"` does not resolve correctly (list of options as for classifiers instead of `predict`).
The approach I implemented is: using PCA, followed by DBSCAN and Logistic Regression as a final step.
Properly showing the relevant datapoints, categorised/colored by their identified clusters, would be great without an extra call to `scatter`.
### Describe your proposed solution
As per discussion linked below, proper support for clusterers should be added to `DecisionBoundaryDisplay`.
### Describe alternatives you've considered, if relevant
Simply plotting a scatterplot on top does not necessarily yield the same class colors, because the clusters are not necessarily ordered in the same way as the `y` values.
### Additional context
https://github.com/scikit-learn/scikit-learn/pull/33015#issuecomment-3738401403 | null | {
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"node_id": "LA_kwDOAAzd1... | closed | false | [] | null | 6 | 2026-01-20T13:51:18 | 2026-02-05T03:20:25 | 2026-01-26T13:27:28 | null | CONTRIBUTOR | null | null | null | null | Fixes #33117
## Re-enable compressed cache for `fetch_kddcup99
### Why
The KDDCup99 cache is currently forced to be written uncompressed due to a legacy
decompression workaround. This significantly inflates disk usage for a very large
dataset. Re-enabling compression reduces storage requirements and disk I/O without
changing the public API or user-facing behavior.
### What changed
- Write cached `samples` and `targets` using `joblib.dump(..., compress=3)` in
`_kddcup99.py`.
- Add a regression test in `test_kddcup99.py` that builds a compressed cache and
verifies that `fetch_kddcup99` can load it correctly without network access.
### Review focus
- Confirm that the chosen compression level (`compress=3`) is a reasonable
trade-off between cache size reduction and CPU overhead.
- Validate that the new test reliably covers loading from a compressed cache and
does not require network access.
### AI usage disclosure
This pull request includes code checks and PR enhancement using AI.
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} | The `fetch_kddcup99` dataset loader currently forces `joblib.dump(..., compress=0)` when writing its local cache. This appears to be a historical workaround for an old decompression bug referenced in an inline comment, but the workaround is still present. As a result, the cache is stored uncompressed even though the dataset is very large, leading to unnecessary disk usage and extra IO.
<img width="689" height="323" alt="Image" src="https://github.com/user-attachments/assets/8da31fff-e2d9-45e4-beab-3bc88e5b088c" />
## Impact
- Significantly larger local cache footprint for the KDDCup99 dataset
- Increased disk usage and slower cache read/write operations
- Inconsistent with other dataset fetchers that use compressed joblib caches by default
## Steps/Code to Reproduce
```python
from sklearn.datasets import fetch_kddcup99
# Download and cache the dataset
fetch_kddcup99(download_if_missing=True)
# Inspect the cache directory (default):
# ~/scikit_learn_data/kddcup99_10-py3/
# Files: samples, targets
# Observe these files are uncompressed on disk.
```
## Expected Results
Cache files should be written with compression (e.g., compress=3) to reduce disk usage and IO.
## Actual Results
Cache files are always written uncompressed due to compress=0.
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"node_id": "MDU6TGFiZWwyNjU5OD... | closed | false | [] | null | 7 | 2026-01-20T10:36:00 | 2026-01-28T09:21:22 | 2026-01-27T13:55:02 | null | CONTRIBUTOR | null | null | null | null | Part of #32434
#### 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)
- [ ] Research and understanding
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} | This is to keep track and address several things we noticed in #33015
I will address them in follow-up PRs.
- [x] check number of (distinct) `colors == n_classes` (see: https://github.com/scikit-learn/scikit-learn/pull/33015/changes#r2703156456)
- [x] use same selection mechanism for all qualitative cmaps (that have `colors`) (see: https://github.com/scikit-learn/scikit-learn/pull/33015/changes#r2703134927)
- [ ] refactor and unify checks (some done in `from_estimator`, some in `plot`)
- [ ] use colors for binary problems as well (but keep `viridis` as default)
- [x] maybe even move color selection into a separate function?
Please add, if I missed anything @lucyleeow @ogrisel | null | {
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"node_id": "MDU6TGFiZWwyOTExMDU3... | closed | false | [] | null | 2 | 2026-01-20T10:03:00 | 2026-01-23T17:03:09 | 2026-01-23T17:03:08 | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Fixes #33113
The bug was detected by the test from this PR: https://github.com/scikit-learn/scikit-learn/pull/32193/
#### What does this implement/fix? Explain your changes.
First, I considered removing the erroneous `break` (see the issue).
But then I benchmarked `_any_isnan_axis0`, and I noticed it was not much faster than just `np.isnan(...).any(axis=0)` which anyway is incomparably fast compared to fitting a tree. So I propose to just remove this function.
#### AI usage disclosure
I used AI assistance for finding where the bug detected by my test came from.
#### Other comments
I didn't add a regression test as it's already covered by https://github.com/scikit-learn/scikit-learn/pull/32193/, but I can add one if needed.
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} | ### Describe the bug
The `break` [here](https://github.com/scikit-learn/scikit-learn/blob/afc095a7b17d5528d62f55f07cab39f963299c78/sklearn/tree/_utils.pyx#L85) is wrong, and in some edge cases it will prevent `_any_isnan_axis0` detecting NaNs for some features. Then because of that, the splitter won't considers splits with NaNs on the left (because it's not aware this feature has NaNs).
### Steps/Code to Reproduce
```Python
import numpy as np
from numpy import nan
from sklearn.tree import DecisionTreeRegressor
X = np.array([
[nan, nan],
[1, 0],
[1, 1]
])
y = np.array([0, 0, 1])
tree = DecisionTreeRegressor(max_depth=1).fit(X, y)
assert (tree.tree_.impurity[1:] == 0).all()
```
### Expected Results
The best split is found => No assertion error
The best split: `X[:, 1] < 0.5` and missing values to the left node.
### Actual Results
Assertion error.
### Versions
```shell
System:
python: 3.12.11 (main, Aug 18 2025, 19:19:11) [Clang 20.1.4 ]
executable: /home/arthur/dev-perso/scikit-learn/sklearn-env/bin/python
machine: Linux-6.14.0-37-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.9.dev0
pip: None
setuptools: 80.9.0
numpy: 2.3.5
scipy: 1.16.3
Cython: 3.2.1
pandas: 2.3.3
matplotlib: 3.10.7
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libscipy_openblas
filepath: /home/arthur/dev-perso/scikit-learn/sklearn-env/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-fdde5778.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libscipy_openblas
filepath: /home/arthur/dev-perso/scikit-learn/sklearn-env/lib/python3.12/site-packages/scipy.libs/libscipy_openblas-b75cc656.so
version: 0.3.29.dev
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 16
prefix: libgomp
filepath: /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0
version: None
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"url": ... | closed | false | [] | null | 4 | 2026-01-20T09:10:34 | 2026-01-30T09:07:51 | 2026-01-30T09:07:51 | null | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
The time needed to`cross_validate` can have a big (x100) slow down when using parallel jobs (`n_jobs > 1`).
Note that the effect is difficult to reproduce as it happens only on the first few calls to `cross_validate`, perhaps some weird caching mechanism is at play ?
### Steps/Code to Reproduce
```python
from sklearn.datasets import make_regression
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_validate
import time
est = Ridge()
X, y = make_regression(100, 5, random_state=22)
for idx in range(5):
start_time = time.time()
scores = cross_validate(est, X, y, n_jobs=2)
elapsed_time = time.time() - start_time
print(f"{idx=} {elapsed_time=:.2f}")
```
### Expected Results
About the same time for each call.
### Actual Results
```
idx=0 elapsed_time=1.15 # about 100x slower
idx=1 elapsed_time=0.01
idx=2 elapsed_time=0.01
idx=3 elapsed_time=0.01
idx=4 elapsed_time=0.01
```
### Versions
```shell
System:
python: 3.13.5 | packaged by conda-forge | (main, Jun 16 2025, 08:24:05) [Clang 18.1.8 ]
executable: /Users/abaker/miniforge3/envs/sklearn-dev/bin/python
machine: macOS-14.5-arm64-arm-64bit-Mach-O
Python dependencies:
sklearn: 1.9.dev0
pip: 25.2
setuptools: 80.9.0
numpy: 2.3.2
scipy: 1.16.1
Cython: 3.1.3
pandas: 2.3.2
matplotlib: 3.10.5
joblib: 1.5.1
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 8
prefix: libopenblas
filepath: /Users/abaker/miniforge3/envs/sklearn-dev/lib/libopenblas.0.dylib
version: 0.3.30
threading_layer: openmp
architecture: VORTEX
user_api: openmp
internal_api: openmp
num_threads: 8
prefix: libomp
filepath: /Users/abaker/miniforge3/envs/sklearn-dev/lib/libomp.dylib
version: None
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#### Reference Issues/PRs
Ref: https://github.com/scikit-learn/scikit-learn/pull/33015#discussion_r2704225655
#### What does this implement/fix? Explain your changes.
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#### Reference Issues/PRs
Fixes #32614
#### What does this implement/fix? Explain your changes.
This PR fixes the incompatibility between `StackingRegressor`/`StackingClassifier` and `TimeSeriesSplit` (and other non-partitioning cross-validation strategies).
**Problem:**
Previously, `cross_val_predict` required that the CV splitter creates a perfect partition where every sample appears in exactly one test set. This check rejected valid time-series CV strategies like `TimeSeriesSplit`, which intentionally reserves early samples for training only to maintain temporal order and prevent data leakage.
**Solution:**
The strict partition check has been replaced with a more flexible validation that allows non-partitioning CV splitters while still ensuring:
1. No overlap between train and test sets in any split
2. Each sample appears in at most one test set
3. At least some samples generate predictions
**Changes:**
- Added `_check_cv_is_valid_for_predict()` function to validate CV splitters with relaxed requirements
- Updated `cross_val_predict()` to use the new validation function
- Added comprehensive tests for `TimeSeriesSplit` with `cross_val_predict`, `StackingRegressor`, and `StackingClassifier`
**Impact:**
This enables proper time-series stacking without data leakage, allowing users to:
- Use `StackingRegressor`/`StackingClassifier` with `TimeSeriesSplit`
- Leverage ensemble methods for time-series forecasting
- Maintain temporal validity in cross-validation
**Backward Compatibility:**
All existing CV strategies (KFold, StratifiedKFold, etc.) continue to work exactly as before. The change only relaxes the validation to accept additional valid strategies.
**Example Usage (now works):**
```python
from sklearn.ensemble import StackingRegressor
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.model_selection import TimeSeriesSplit
estimators = [
('lr', LinearRegression()),
('ridge', Ridge())
]
stacker = StackingRegressor(
estimators=estimators,
final_estimator=LinearRegression(),
cv=TimeSeriesSplit(n_splits=5) # Now supported!
)
stacker.fit(X, y)
```
#### AI usage disclosure
I used AI assistance for:
- [x] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [x] Documentation (including examples)
- [x] Research and understanding
#### Any other comments?
**Testing:**
- All existing tests pass without modification
- Manually tested with the reproducer from issue #32614
- Verified backward compatibility with traditional CV splitters
**Note on Partial Predictions:**
When using non-partitioning CV splitters like `TimeSeriesSplit`, `cross_val_predict` returns predictions only for samples that appear in test sets. This is the correct behavior for time series, as it prevents future data from leaking into past predictions while still using all available data to train base estimators.
I'm happy to address any feedback or make adjustments as needed. Thank you for reviewing!
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 1 | 2026-01-19T15:09:08 | 2026-01-20T02:07:22 | 2026-01-20T02:07:22 | null | NONE | null | null | null | null | DOC: clarify error message when estimator response method is invalid
Fixes #33093
The previous error message incorrectly implied that an estimator without
a `predict` method was always a regressor. This change removes that
assumption and clarifies the expected behavior.
This improves clarity for users encountering this error.
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] | open | false | [] | null | 8 | 2026-01-19T11:37:39 | 2026-03-06T09:22:59 | null | null | MEMBER | null | null | {
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> This is a request for comment (RFC), please do not open a pull request for this issue. If you wish to contribute to scikit-learn please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particular the section [New contributors](https://scikit-learn.org/dev/developers/contributing.html#new-contributors).
Noticed while reviewing #33015. Also related to #32866
#31553 reverted the plotting behaviour when `plot_method='contour'` such that only thresholded integers are used plotting, for all plot methods, not continuous proba/decision function values.
This means that the contour lines show where the predicted class changes from one to the other:
<img width="1600" height="896" alt="Image" src="https://github.com/user-attachments/assets/d583b915-d44a-4071-98fd-e68deb41bd7b" />
instead of contour lines showing how continuous predicted values change, over the region where that class dominates:
<img width="1600" height="896" alt="Image" src="https://github.com/user-attachments/assets/62babf2a-83f0-42c7-8f6a-e14971373d0a" />
(figures taken from #31546)
The problem is that without setting `levels` mpl infers number of levels, 9 seems to be the default, resulting in a lot of overlapping lines.
<details open>
<summary>Details</summary>
The [contourf](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contourf.html#matplotlib.pyplot.contourf) docs aren't very helpful - it says `levels` is optional but does not tell you what the default is. Digging into the code, it seems that [`_process_contour_level_args`](https://github.com/matplotlib/matplotlib/blob/ebf9b14f7f41029e2c61dd09cc37dae98a9bc10d/lib/matplotlib/contour.py#L1011C9-L1011C36) is responsible. It calls [`_ensure_locator_exists`](https://github.com/matplotlib/matplotlib/blob/ebf9b14f7f41029e2c61dd09cc37dae98a9bc10d/lib/matplotlib/contour.py#L957) which does:
```python
if self.locator is None:
if self.logscale:
self.locator = ticker.LogLocator(numticks=N)
else:
if N is None:
N = 7 # Hard coded default
self.locator = ticker.MaxNLocator(N + 1, min_n_ticks=1)
```
which seems to call [`MaxNLocator`](https://matplotlib.org/stable/api/ticker_api.html#matplotlib.ticker.MaxNLocator), with `n_bins=7+1`, which gives max 9 (n_bins + 1) ticks. This does seem to be what happens:
```python
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.linear_model import LogisticRegression
iris = load_iris()
X = iris.data[:, :2]
classifier = LogisticRegression().fit(X, iris.target)
# Create the contour plot
disp = DecisionBoundaryDisplay.from_estimator(
classifier,
X,
plot_method='contour',
xlabel=iris.feature_names[0],
ylabel=iris.feature_names[1],
)
# Access the matplotlib axes and contour object
ax = disp.ax_
contour = disp.surface_
print(f"Number of classes: {len(iris.target_names)}")
print(f"Number of contour levels: {len(contour.levels)}")
print(f"Contour levels (decision values): {contour.levels}")
```
```
Number of classes: 3
Number of contour levels: 9
Contour levels (decision values): [0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ]
```
</details>
The other problem is that we pass `n_classes` colors, but this does not match the number of levels (the passed colours are used to colour each level). In the case above, we pass 3 colours but there are 9 levels and from the docs:
> The sequence is cycled for the levels in ascending order. If the sequence is shorter than the number of levels, it's repeated.
Thus the colours don't make sense and don't really correspond to any individual class.
---
Not sure what the best solution would be. We could specify `levels` such that we get `n_classes` levels, which would be a great improvement already. The question of what colour(s) the lines should be is more difficult. e.g., for the case above with 3 classes and levels at [1,2,3], 2 lines are drawn:
<img width="567" height="432" alt="Image" src="https://github.com/user-attachments/assets/101f6e59-565f-4d41-85bd-9ac932e02085" />
<details open>
<summary>Code</summary>
```python
from sklearn.datasets import load_iris
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.linear_model import LogisticRegression
iris = load_iris()
X = iris.data[:, :2]
classifier = LogisticRegression().fit(X, iris.target)
# Create the contour plot
disp = DecisionBoundaryDisplay.from_estimator(
classifier,
X,
plot_method='contour',
xlabel=iris.feature_names[0],
ylabel=iris.feature_names[1],
levels=[0,1,2],
)
```
</details>
one for level 0: the transition from 0 -> 1 / 0 -> 2 and one for level 1: the transition from 1 -> 2 (the level 1 line also draws a line from 0 -> 2, but I think that is due to interpolation of the 0 -> 2 transition being 1 ? - this is the part where there are both blue and orange lines). The line colours don't really match to a single class, mostly because we have 2 lines for 3 classes.
Maybe it would be best to just draw `contour` lines in just one colour?
Related: https://github.com/scikit-learn/scikit-learn/pull/33015#discussion_r2703246240
cc @AnneBeyer @ogrisel | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33106 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33106/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33106/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33106/events | https://github.com/scikit-learn/scikit-learn/issues/33106 | 3,828,613,509 | I_kwDOAAzd1s7kNAGF | 33,106 | CI Automatic update of CUDA CI lock-file fails | {
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[January 12 build log](https://github.com/scikit-learn/scikit-learn/actions/runs/20908719794/job/60067290832)
The symptom is that it fails after a very long time (more than 45 minutes in both cases) and exits with code 143, which means that it got a `SIGTERM` signal (143=128 + 15, 15=`SIGTERM`).
```
Preparing transaction: - done
Verifying transaction: | / done
Executing transaction: \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done
# 1 selected builds
- pylatest_conda_forge_cuda_array-api_linux-64, type: conda, tag: cuda
# Writing conda environments
# Writing conda lock files
# Locking dependencies for pylatest_conda_forge_cuda_array-api_linux-64
Error: Process completed with exit code 143.
```
I guess one of the first step would be to try to reproduce locally. | {
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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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#### What does this implement/fix? Explain your changes.
#### Summary
This PR aims aims at introducing a new preprocessing utility ``robust_minmax_scale`` that scales features to [0,1] while adopting robust quantile statistics instead of min/max. This is very important for real-world datasets, especially ones with extreme outliers.
#### Motivation
- Scikit-learn normally provides ``MinMaxScaler`` and ``RobustScaler`` used for scaling features, but no hybrid versions.
- Numerous applied data science workflows need a stable scaler that is not sensitive to outliers.
- The implementation is consistent with existing API patterns, and requires no new dependencies.
### What this PR includes
- New function ``robust_minmax_scale``
- Full documentation and examples
- Unit tests (``pytest``)
- API exposure in ``__init__.py``
- Conforms to existing sklearn preprocessing patterns
```python
from sklearn.preprocessing import robust_minmax_scale
X_scaled = robust_minmax_scale(X)
```
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This PR removes a redundant `from sklearn.datasets import load_iris` statement in `examples/tree/plot_iris_dtc.py`.
The function `load_iris` is already imported at the top of the file (line 23), making the second import inside the plotting section (line 33) unnecessary. This cleanup removes the duplicate import while preserving functionality.
#### Any other comments?
Verified locally by running the script `python examples/tree/plot_iris_dtc.py`. The script executed successfully and generated the plots without errors. | {
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This PR adds `min_cardinality` and `max_cardinality` parameters to `make_column_selector`.
Currently, `make_column_selector` allows selecting columns based on dtype or regex pattern. However, selecting columns based on the number of unique values (cardinality) is a common requirement in data preprocessing, for example:
- Identifying constant columns (cardinality = 1) to drop them.
- Distinguishing between high-cardinality categorical features (like IDs) and low-cardinality features (like gender/status).
I have implemented the logic to filter columns based on cardinality thresholds.
#### Example Usage
```python
import pandas as pd
from sklearn.compose import make_column_selector
# Create a sample dataframe
df = pd.DataFrame({
'constant': [1, 1, 1, 1, 1], # Cardinality = 1
'binary': ['Yes', 'No', 'Yes', 'No', 'Yes'], # Cardinality = 2
'category': ['A', 'B', 'C', 'A', 'B'], # Cardinality = 3
'id': [101, 102, 103, 104, 105] # Cardinality = 5 (High)
})
# Example 1: Select columns with more than 1 unique value (Remove constants)
selector_min = make_column_selector(min_cardinality=2)
print(f"Columns with min_cardinality=2: {selector_min(df)}")
# Expected: ['binary', 'category', 'id']
# Example 2: Select columns with low cardinality (e.g., for OneHotEncoding)
selector_max = make_column_selector(max_cardinality=4)
print(f"Columns with max_cardinality=4: {selector_max(df)}")
# Expected: ['constant', 'binary', 'category'] | {
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#### What does this implement/fix? Explain your changes.
#### Summary
This PR aims aims at introducing a new preprocessing utility ``robust_minmax_scale`` that scales features to [0,1] while adopting robust quantile statistics instead of min/max. This is very important for real-world datasets, especially ones with extreme outliers.
#### Motivation
- Scikit-learn normally provides ``MinMaxScaler`` and ``RobustScaler`` used for scaling features, but no hybrid versions.
- Numerous applied data science workflows need a stable scaler that is not sensitive to outliers.
- The implementation is consistent with existing API patterns, and requires no new dependencies.
### What this PR includes
- New function ``robust_minmax_scale``
- Full documentation and examples
- Unit tests (``pytest``)
- API exposure in ``__init__.py``
- Conforms to existing sklearn preprocessing patterns
```python
from sklearn.preprocessing import robust_minmax_scale
X_scaled = robust_minmax_scale(X)
```
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#### What does this implement/fix? Explain your changes.
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## Todo
* [ ] review the tests to condense them, diff is very large!
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] | closed | false | [] | null | 0 | 2026-01-16T13:09:35 | 2026-01-16T20:46:08 | 2026-01-16T20:46:08 | null | NONE | null | null | null | null | Added a plain -English sentence to the Ordinary Least Squares section to help beginners to understand the goal of the model
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33094 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33094/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33094/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33094/events | https://github.com/scikit-learn/scikit-learn/issues/33094 | 3,821,103,720 | I_kwDOAAzd1s7jwWpo | 33,094 | Add `show_data=False` option to `DecisionBoundaryDisplay.from_estimator` | {
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] | open | false | [] | null | 4 | 2026-01-16T09:12:12 | 2026-03-02T10:26:23 | null | null | MEMBER | null | null | {
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} | ### Describe the workflow you want to enable
While reviewing @AnneBeyer's #33015 PR, I thought it would be nice to be able to display the decision boundaries of a classifier along with the data points with consistent color maps (at least for classifiers, outlier detectors and regressors) instead of having to a manually scatter plot on the same `ax` instance.
### Describe your proposed solution
Add a new `show_data` boolean kwarg to the `from_estimator` method that already accepts the `X` values as argument. The `from_estimator` method would then call `estimator.predict(X)` to find the values of the target to be used to set the colors of the dots of the scatter plot.
The `from_estimator` could also take an optional `y` argument with true class labels. When provided, the scatter plot enabled from the `show_data=True` param of `from_estimator` would use the provided `y` values instead of using `estimator.predict(X)`.
The `__init__` of the display would then also accept the `X` and `y` values.
Ideally, the legend would make it explicit whether the scatter dots represent predictions or true labels, and could be overridden by a `data_label` kwarg passed to `from_estimator`.
Maybe `show_data=True` could even be the default.
### Describe alternatives you've considered, if relevant
While it's possible to do scatter plot manually, it's quite verbose and not necessarily trivial to understand if the colors used for the two plots are expected to be consistent or not.
I think people often want to overlay the training set with the decision boundary when generating such plots for educational purposes.
### Additional context
@AnneBeyer if you think this is a good feature to add, would be interested in contributing it (as a follow-up to #33015 and related PRs)?
Also cc @lucyleeow @glemaitre in case you already discussed this in the past. | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33093 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33093/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33093/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33093/events | https://github.com/scikit-learn/scikit-learn/issues/33093 | 3,818,938,532 | I_kwDOAAzd1s7joGCk | 33,093 | DOC error message when checking response values of an estimator not clear | {
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] | closed | false | [] | null | 7 | 2026-01-15T19:53:57 | 2026-02-05T12:33:20 | 2026-02-05T12:33:20 | null | CONTRIBUTOR | null | null | {
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} | ### Describe the issue linked to the documentation
Current function that checks response values of an estimator
assumes that if the estimator is not a classifier or an outlier detector, then it is a regressor.
In the latter case, if it doesn't find the `predict` function,
it raises an error stating that the estimator is a regressor: `ValueError: ... Got a regressor...`.
However, the estimator might be something other than a regressor.
For example, it can be a classification `Pipeline` with a MRO issue, see https://github.com/NeuroTechX/moabb/issues/815
Many other GitHub repositories have reported this ambiguous error, see list in https://github.com/scikit-learn/scikit-learn/pull/33084#issue-3814820493
The goal of this issue is to discuss how to correct the message by making it clearer,
specifically by no longer stating that it is a regressor.
### Suggest a potential alternative/fix
Current function `_get_response_values.py` in `scikit-learn/sklearn/utils/_response.py`
```python
else: # estimator is a regressor
if response_method != "predict":
raise ValueError(
f"{estimator.__class__.__name__} should either be a classifier to be "
f"used with response_method={response_method} or the response_method "
"should be 'predict'. Got a regressor with response_method="
f"{response_method} instead."
)
prediction_method = estimator.predict
y_pred, pos_label = prediction_method(X), None
```
could be corrected into
```python
elif is_regressor(estimator):
prediction_method = estimator.predict
y_pred, pos_label = prediction_method(X), None
else:
raise ValueError(
f"{estimator.__class__.__name__} is not recognized as a classifier, "
"an outlier detector or a regressor."
)
```
and, if necessary, we could add before `else`
```python
elif is_clusterer(estimator):
prediction_method = ...
y_pred, pos_label = ...
``` | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33092 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33092/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33092/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33092/events | https://github.com/scikit-learn/scikit-learn/pull/33092 | 3,818,511,237 | PR_kwDOAAzd1s69b0ge | 33,092 | DOC: clarify that Lasso.path() defaults to Elastic Net path (#33091) | {
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#### What does this implement/fix? Explain your changes.
clarified in the docstring that Lasso.path() defaults to Elastic Net path.
This is a Documentation-only change. no code functionality is affected .
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#### What does this implement/fix? Explain your changes.
A few clean ups around the splitting docs and error message.
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https://github.com/scikit-learn/scikit-learn/issues/32279#issuecomment-3755215113
Fix https://github.com/scikit-learn/scikit-learn/issues/32279.
#### What does this implement/fix? Explain your changes.
Test if the wheels CI hang with scipy 1.16.2 is removed in the current version (1.17) and the workaround can be removed.
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closes #32076
supersedes #32239 and #32843
#### What does this implement/fix? Explain your changes.
- turns `TargetEncoder` into a metadata router that routes `groups` to the internal splitter
- adds more input options for `cv` init param (as discussed here https://github.com/scikit-learn/scikit-learn/issues/32076#issuecomment-3307697377)
- exposes cv_ attribute
- add tests
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Fix related to https://github.com/scikit-learn/scikit-learn/pull/32888, noticed while working on https://github.com/scikit-learn/scikit-learn/pull/32565
#### What does this implement/fix? Explain your changes.
The error message was not formatted as f-string, thus the estimator name was not actually resolved, and the tests did not catch that. This is fixed here.
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"node_id": "LA_kwDOAAzd1s7eWo1... | closed | false | [] | null | 2 | 2026-01-15T11:10:31 | 2026-01-15T15:46:50 | 2026-01-15T15:29:27 | null | MEMBER | null | null | null | null | Fix https://github.com/scikit-learn/scikit-learn/issues/33047
### Cython issue
According to https://github.com/cython/cython/pull/7427#issuecomment-3753646484 the Cython issue is fixed in master.
I think it's actually slightly better to use the install from source rather than the Cython wheel. The Cython wheel is not updated daily so there is a small timing difference between `master`. It also does not provide us much since it is a pure Python wheel so the same as installing from source with `NO_CYTHON_COMPILE=true`.
### numpy VisbibleDeprecationWarning from joblib
This adds the warning to our ignored filters with a note linking to the joblib issue | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33086 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33086/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33086/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33086/events | https://github.com/scikit-learn/scikit-learn/pull/33086 | 3,816,890,933 | PR_kwDOAAzd1s69WWcL | 33,086 | Improve checking for string and multilabel inputs for classification metrics | {
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"node_id": "MDU6TGFiZWwxOD... | open | false | [] | null | 3 | 2026-01-15T10:14:33 | 2026-03-02T09:41:18 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Fixes #33045
#### What does this implement/fix? Explain your changes.
Mixed string and number inputs do not error for the following thresholded classification metrics:
* `accuracy_score`
* `hamming_loss`
* `zero_one_loss`
* `matthews_corrcoef`
* `confusion_matrix` - only when `labels` is set
when the mixed string and number inputs are:
* lists (e.g., ['a', 'b'], [1, 0])
* string is a numpy array that is NOT object dtype e.g., `np.array(['a','b'])`
<details open>
<summary>Details</summary>
Mixed string and number inputs are dealt with via `_union1d` (see #18192)
https://github.com/scikit-learn/scikit-learn/blob/6c084a8152d41419fb7baf2bc1692890385d6599/sklearn/metrics/_classification.py#L154-L165
However this only works for numpy arrays, where the string is specifically specified to be object type:
https://github.com/scikit-learn/scikit-learn/blob/0707e6287f128d824860d970896d414d9d96da7c/sklearn/metrics/tests/test_common.py#L1928-L1933
This test does not pass if the inputs are list or of dtype object is not forced. `np.union1d` first concatenates the arrays and then performs unique on the result. If the concatenated array is of dtype object, unique gives `TypeError`, however if object is not forced to be object, e.g.:
`np.union1d(np.array([1,2]), np.array(['a','b']))`
the resulting concatenation is of type 'U' and no error is raised when unique is run. (I am not sure why this case was not considered initially, but I think it would not be unusual/rare for the input to be a string numpy array that is NOT of object type)
</details>
The other classification metrics do raise an error, due to a call to [`unique_labels`](https://github.com/scikit-learn/scikit-learn/blob/66200f149ba6c64a3a93dd73e37bdeea87bb5db8/sklearn/utils/multiclass.py#L41) (see #33045 for details).
This PR enforces:
> thresholded metrics should raise an error if passed inconsistent types of inputs.
for all thresholded metrics for all input types by running [`unique_labels`](https://github.com/scikit-learn/scikit-learn/blob/66200f149ba6c64a3a93dd73e37bdeea87bb5db8/sklearn/utils/multiclass.py#L41) inside [`_check_targets`](https://github.com/scikit-learn/scikit-learn/blob/0707e6287f128d824860d970896d414d9d96da7c/sklearn/metrics/_classification.py#L75). This is an opinionated change so let me explain:
* `_check_targets` is ONLY used within thresholded metrics in `_classification.py` - thus a non backwards compatible change is less of an issue.
* for the metrics where `unique_labels` is also called, `unique_labels` is always called after `_check_targets`
* `_check_targets` and `unique_labels` raise errors for similar problematic input combinations, except:
* `unique_labels` checks label indicator inputs are of the same size (`_check_targets` does not)
* this means that the classification metrics (which previously did not run `unique_labels`) will have this extra check, which I think is a fix.
* (less importantly) `_check_targets` checks target length consistency and does not allow continuous input or any multioutput target types
* `unique_labels` is used elsewhere in the code (e.g., `LinearDiscriminantAnalysis`) - thus the changes made are backwards compatible and no changes in functionality was made
Cons of this approach:
* The error messages in `_check_targets` are more specific for classification metrics vs those in `unique_labels`. I added `unique_labels` right at the end of `_check_targets`, so all the current `_check_targets` will be raised in priority. The only difference is that for mixed str and int inputs, the error message from `unique_labels` is raised instead, but I have tried to improve this message.
Pros:
* Lets us remove `_union1d` from `_array_api.py` as this was the only place this function was used
* Passing `y` types to `unique_labels` reduces duplicate `type_of_target` calls (and thus duplicate `unique` calls - I know we cache unique but this is only relevant for numpy arrays, not necessarily other array API supported arrays)
I did also consider simply changing the logic in:
https://github.com/scikit-learn/scikit-learn/blob/6c084a8152d41419fb7baf2bc1692890385d6599/sklearn/metrics/_classification.py#L154-L165
such that list and non object numpy inputs are also handled, but went with the above approach. Happy to take other opinions though.
#### 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?
| null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33085 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33085/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33085/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33085/events | https://github.com/scikit-learn/scikit-learn/issues/33085 | 3,815,591,391 | I_kwDOAAzd1s7jbU3f | 33,085 | StratifiedGroupKFold produces empty / degenerate splits | {
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] | open | false | [] | null | 7 | 2026-01-15T01:52:22 | 2026-02-03T17:24:09 | null | null | NONE | null | null | {
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} | ### Describe the bug
StratifiedGroupKFold seems to be broken (Python 3.13, scikit-learn 1.8.0) for some fairly simple grouped classification datasets, quite frequently producing empty training or test sets.
While I am aware of the many issues covering poor stratification and the workarounds suggested (https://github.com/scikit-learn/scikit-learn/issues/24656, https://github.com/scikit-learn/scikit-learn/issues/12076), this is a much more annoying problem in automated pipelines, as we can't even assume that e.g. 5-fold actually produces 5 valid folds.
This seems not to be some rare example, but occurs very often with some pretty simple grouped classification datasets, even when the number of samples is quite large (see attached script `demo.py`).
I really hope I am not doing something dumb here, because this seems to be a pretty big issue otherwise. I think the issue is that, obviously, when the number of groups and/or labels is small, and when these are not both distributed uniformly and independently over the samples, then it can just be the case that you simply cannot satisfy all three goals of balanced folds, groups, and target / y labels simultaneously in a satisfactory way. However, in such cases, there should surely be a warning and some kind of natural fallback (perhaps not stratifying, or ignoring grouping, depending on an option), or perhaps an error.
But producing a degenerate fold definitely does not feel right.
[demo.py](https://github.com/user-attachments/files/24630346/demo.py)
### Steps/Code to Reproduce
```python
from sklearn.model_selection import StratifiedGroupKFold
y = [0, 2, 2, 2, 1, 2, 1, 1, 0, 0, 0, 0]
g = [0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1]
skf = StratifiedGroupKFold(n_splits=3, shuffle=False)
for fold, (ix_train, ix_test) in enumerate(skf.split(X=y, y=y, groups=g)):
if len(ix_train) == 0:
assert len(ix_test) == len(y)
degen = "training"
elif len(ix_test) == 0:
assert len(ix_train) == len(y)
degen = "test"
else:
continue
print(f"Degenerate (empty) {degen} set on fold {fold + 1} for data:")
print(f"y = {y}")
print(f"g = {g}")
```
### Expected Results
We should not be getting empty training or test sets with any k-fold method. This will almost always cause errors downstream.
### Actual Results
If you run `demo.py`, you will see a large number of combinations of grouping variables and classification targets cause degenerate splits.
### Versions
```shell
>>> import sklearn
>>> sklearn.show_versions()
System:
python: 3.13.9 (main, Oct 14 2025, 21:10:40) [Clang 20.1.4 ]
executable: /Users/me/Documents/groupkfold/.venv/bin/python
machine: macOS-15.5-arm64-arm-64bit-Mach-O
Python dependencies:
sklearn: 1.8.0
pip: None
setuptools: None
numpy: 2.4.1
scipy: 1.17.0
Cython: None
pandas: None
matplotlib: None
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: openmp
internal_api: openmp
num_threads: 8
prefix: libomp
filepath: /Users/me/Documents/groupkfold/.venv/lib/python3.13/site-packages/sklearn/.dylibs/libomp.dylib
version: None
``` | null | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 2 | 2026-01-14T20:49:42 | 2026-01-15T11:03:17 | 2026-01-15T11:03:16 | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
<!--
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#### What does this implement/fix? Explain your changes.
Current function that checks response values of an estimator
(`_get_response_values` in `scikit-learn/sklearn/utils/_response.py`)
assumes that if the estimator is not a classifier or an outlier detector, then it is a regressor.
In the latter case, if it doesn't find the `predict` function,
it raises an error stating that the estimator is a regressor: `ValueError: ... Got a regressor...`.
However, the estimator might be something other than a regressor.
For example, it can be a classification `Pipeline` with a MRO issue, see https://github.com/NeuroTechX/moabb/issues/815
Many other GitHub repositories have reported this ambiguous error:
https://github.com/dreamquark-ai/tabnet/issues/584
https://github.com/rasbt/mlxtend/issues/1129
https://github.com/dmlc/xgboost/issues/11284
https://github.com/mljar/mljar-supervised/issues/640
https://github.com/csinva/imodels/issues/221
[etc](https://github.com/search?q=should+either+be+a+classifier+to+be+used+with+response_method%3D&type=issues&p=1)
The goal of this pull-request is to correct the message by making it clearer,
specifically by no longer assuming that it is a regressor.
#### AI usage disclosure
I did **not** use AI assistance.
#### Any other comments?
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33083 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33083/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33083/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33083/events | https://github.com/scikit-learn/scikit-learn/pull/33083 | 3,813,923,297 | PR_kwDOAAzd1s69Mauo | 33,083 | MNT rename l1_reg to alpha in enet_coordinate_descent_multi_task | {
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#### What does this implement/fix? Explain your changes.
This PR aligns all the coordinate descent Cython implementations with regard to the naming of penalization strength, i.e. it reverts the renaming of 21706e36b977ee29eacfa45ada4410a67b41b86c:
- l1_reg -> alpha
- l2_reg -> beta
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33082 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33082/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33082/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33082/events | https://github.com/scikit-learn/scikit-learn/pull/33082 | 3,813,562,470 | PR_kwDOAAzd1s69LNVj | 33,082 | OC clarify array input dtype in supervised clustering metrics | {
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"node_id": "MDU6TGFiZWwyMz... | closed | false | [] | null | 0 | 2026-01-14T14:56:11 | 2026-01-14T22:06:23 | 2026-01-14T22:06:20 | null | NONE | null | null | null | null | This PR clarifies the input array dtype specification in supervised clustering metrics documentation.
The original text suggested that input arrays must be of integral dtype, but the implementation does not enforce this.
This update aligns documentation with actual behavior.
Fixes #32930
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33081 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33081/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33081/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33081/events | https://github.com/scikit-learn/scikit-learn/pull/33081 | 3,813,400,874 | PR_kwDOAAzd1s69KqbQ | 33,081 | CI Fix the unit tests GHA on windows | {
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] | closed | false | [] | null | 1 | 2026-01-14T14:14:54 | 2026-01-30T13:30:44 | 2026-01-16T12:17:43 | null | CONTRIBUTOR | null | null | null | null | <!--
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Fixes #33075
#### What does this implement/fix? Explain your changes.
Force the shell to be bash in the jobs to avoid windows using powershell and crashing the steps with bash commands.
It worked on my fork : https://github.com/FrancoisPgm/scikit-learn/actions/runs/20996152861/job/60353454478
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33080 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33080/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33080/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33080/events | https://github.com/scikit-learn/scikit-learn/pull/33080 | 3,813,396,804 | PR_kwDOAAzd1s69Kpje | 33,080 | TST Mark test_logreg_l1 as thread-unsafe | {
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```
pytest --parallel-threads 4 --iterations 10 sklearn/linear_model/tests/test_logistic.py -k 'test_logreg_l1' -vs
```
My best guess is that liblinear has some thread-safe issue with random number generation, similarly to https://github.com/scikit-learn/scikit-learn/issues/31883. There are precedents in this test file of marking some tests as thread-unsafe for similar reasons with a link to the same issue, for example https://github.com/scikit-learn/scikit-learn/blob/0e729831aeb5574c806e87a74bcefd4e76ac6bdc/sklearn/linear_model/tests/test_logistic.py#L382-L385
For completeness, this only happens when using `pytest-run-parallel` i.e. `--parallel-threads`.
cc @ogrisel. | {
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} | ### Describe the bug
I noticed that `benchmarks/bench_multilabel_metrics.py` is currently using `metrics.jaccard_similarity_score`. This function was deprecated and subsequently removed in **scikit-learn 0.23**. Consequently, this benchmark script fails to run on current versions of scikit-learn.
### Steps/Code to Reproduce
Run the benchmark script:
```bash
python benchmarks/bench_multilabel_metrics.py
```
### Expected Results
The script should run successfully, calculating performance metrics for the specified multilabel formats and printing the results table (and plots if requested).
### Actual Results
The script crashes immediately upon import with an `ImportError` because the function no longer exists in `sklearn.metrics`.
```
Traceback (most recent call last):
File "/workspaces/scikit-learn/benchmarks/bench_multilabel_metrics.py", line 17, in <module>
from sklearn.metrics import (
ImportError: cannot import name 'jaccard_similarity_score' from 'sklearn.metrics' (/workspaces/scikit-learn/.venv/lib/python3.12/site-packages/sklearn/metrics/__init__.py)
```
### Versions
```shell
System:
python: 3.12.1 (main, Nov 27 2025, 10:47:52) [GCC 13.3.0]
executable: /usr/local/python/3.12.1/bin/python3
machine: Linux-6.8.0-1030-azure-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.8.0
pip: 25.3
setuptools: 80.9.0
numpy: 2.4.1
scipy: 1.17.0
Cython: None
pandas: None
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: 2
prefix: libscipy_openblas
filepath: /workspaces/scikit-learn/.venv/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-fdde5778.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 2
prefix: libscipy_openblas
filepath: /workspaces/scikit-learn/.venv/lib/python3.12/site-packages/scipy.libs/libscipy_openblas-6cdc3b4a.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 2
prefix: libgomp
filepath: /workspaces/scikit-learn/.venv/lib/python3.12/site-packages/scikit_learn.libs/libgomp-e985bcbb.so.1.0.0
version: None
``` | {
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"node_id": "MDU6... | open | false | [] | null | 1 | 2026-01-14T09:43:15 | 2026-02-25T17:29:20 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Continuation of #29313
#### What does this implement/fix? Explain your changes.
This PR adds `_array_api.check_same_namespace` to raise an error if the array namespace used during `fit` and `predict`/`transform` do not match, and `_array_api.convert_estimator` to move the array attributes of an estimator to the namespace and device of a reference array. Eventually we should put `convert_estimator` in a public part of the API, because users should use it. For now I think it is Ok to have it in `sklearn.utils._array_api` as things are still experimental.
A change I've made compared to #29313 is that none of our library code turns on `sklearn.set_config(array_api_dispatch=True)` for its run ([original version in #29313](https://github.com/scikit-learn/scikit-learn/blob/f0fafc3f6cb12243b3db455ed6207679497beb0a/sklearn/utils/_array_api.py#L977-L986)). I think this makes sense because users should opt-in themselves. But it does open up the question of what exactly should happen if you use `convert_estimator` on an estimator without turning on array API support in scikit-learn. My thinking is there should be an exception to let people know they are doing something weird.
```python
>>> from sklearn.linear_model import Ridge
>>> import sklearn
>>> import torch
>>> from sklearn import datasets
>>> sklearn.set_config(array_api_dispatch=True)
>>> X, y = datasets.make_regression()
>>> X_torch = torch.asarray(X, device='cpu')
>>> y_torch = torch.asarray(y, device='cpu')
>>> ridge = Ridge().fit(X_torch, y_torch)
>>> type(ridge.predict(X_torch))
<class 'torch.Tensor'>
>>> ridge.predict(X)
Traceback (most recent call last):
...
ValueError: Inputs passed to Ridge.predict() must use the same array library and the same device as those passed to fit(). Array namespaces used during fit (sklearn.externals.array_api_compat.torch) and predict (sklearn.externals.array_api_compat.numpy) differ. You can convert the estimator to the same library and device as X with: 'from sklearn.utils._array_api import convert_estimator; estimator = convert_estimator(estimator, X)'
>>> from sklearn.utils._array_api import convert_estimator
>>> ridge = convert_estimator(ridge, X)
>>> type(ridge.predict(X))
<class 'numpy.ndarray'>
>>> ridge = convert_estimator(ridge, X_torch)
>>> type(ridge.predict(X_torch))
<class 'torch.Tensor'>
```
#### AI usage disclosure
There were quite a few merge conflicts which Cursor managed to resolve and it managed to figure out how to change from the old "what's new" system to the new one once I explained that there is a README to read.
I used AI assistance for:
- [x] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [ ] Research and understanding
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"url": ... | closed | false | [] | null | 2 | 2026-01-14T07:10:39 | 2026-02-18T19:31:25 | 2026-01-16T12:17:44 | null | MEMBER | null | null | {
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} | Now that our Windows build has moved to GHA in https://github.com/scikit-learn/scikit-learn/pull/33062, apparently there are some Windows shenanigans to figure out, see [build log](https://github.com/scikit-learn/scikit-learn/actions/runs/20980980046/job/60305758154). Maybe it's only a matter of using `shell: bash` in both steps below to make sure to use bash? This may well be the case that the default shell is powershell on Windows.
Two issues it seems (at least):
- `echo "SKLEARN_TESTS_GLOBAL_RANDOM_SEED=$((RANDOM % 100))" >> $GITHUB_ENV`
- `"$GITHUB_WORKFLOW Windows x64 pymin_conda_forge_openblas" \`
cc @FrancoisPgm
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33072 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33072/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33072/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33072/events | https://github.com/scikit-learn/scikit-learn/pull/33072 | 3,809,586,456 | PR_kwDOAAzd1s68-B-9 | 33,072 | DOC: warn about setting attributes in __init__ for estimators | {
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"node_id": "MDU6TGFiZWwyMzk5... | closed | false | [] | null | 0 | 2026-01-13T16:19:02 | 2026-01-15T00:58:56 | 2026-01-15T00:58:56 | null | CONTRIBUTOR | null | null | null | null | While experimenting with `check_estimator` as a third-party developer,
I encountered a failure caused by setting a non-parameter attribute in
`__init__`. This rule is easy to violate unintentionally and not very
visible in the current developer documentation.
This PR adds a short warning near the top of the estimator developer
guide to make this requirement easier to discover.
Related to #33003.
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] | closed | false | [] | null | 0 | 2026-01-13T16:03:25 | 2026-01-13T16:05:39 | 2026-01-13T16:05:03 | null | CONTRIBUTOR | null | null | null | null | Estimator should not set any attribute apart from parameters during init.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33070 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33070/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33070/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33070/events | https://github.com/scikit-learn/scikit-learn/pull/33070 | 3,809,512,160 | PR_kwDOAAzd1s689ybh | 33,070 | TST Avoid segmentation fault with free-threaded and pytest-run-parallel | {
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"node_id": "MDU6TGFiZWwyNjU5OD... | closed | false | [] | null | 3 | 2026-01-13T16:00:33 | 2026-03-03T10:46:31 | 2026-01-14T07:43:43 | null | MEMBER | null | null | null | null | (As kind of expected) I can reliably reproduce the segfault with (every single time I tried actually):
```
pytest --parallel-threads 4 --iterations 10 sklearn/utils/tests/test_response.py -k test_response_values_output_shape_ -v
```
Using a `clone` fixes it. As we have seen before, the issue is that the parametrized estimators is shared between the pytest-run-parallel threads and you call `.fit` on it.
cc @ogrisel
<details><summary>Output of pytest command</summary>
<p>
```
================================================= test session starts =================================================
platform linux -- Python 3.14.0, pytest-9.0.1, pluggy-1.6.0 -- /home/lesteve/micromamba/envs/scikit-learn-dev/bin/python
cachedir: .pytest_cache
rootdir: /home/lesteve/dev/scikit-learn
configfile: pyproject.toml
plugins: xdist-3.8.0, cov-7.0.0, run-parallel-0.8.0
collected 51 items / 38 deselected / 13 selected
Collected 51 items to run in parallel
sklearn/utils/tests/test_response.py::test_response_values_output_shape_[estimator0-predict-binary-expected_shape0] PARALLEL PASSED [ 7%]
sklearn/utils/tests/test_response.py::test_response_values_output_shape_[estimator1-predict_proba-binary-expected_shape1] PARALLEL PASSED [ 15%]
sklearn/utils/tests/test_response.py::test_response_values_output_shape_[estimator2-decision_function-binary-expected_shape2] PARALLEL PASSED [ 23%]
sklearn/utils/tests/test_response.py::test_response_values_output_shape_[estimator3-predict-multiclass-expected_shape3] PARALLEL PASSED [ 30%]
sklearn/utils/tests/test_response.py::test_response_values_output_shape_[estimator4-predict_proba-multiclass-expected_shape4] PARALLEL PASSED [ 38%]
sklearn/utils/tests/test_response.py::test_response_values_output_shape_[estimator5-decision_function-multiclass-expected_shape5] PARALLEL PASSED [ 46%]
sklearn/utils/tests/test_response.py::test_response_values_output_shape_[estimator6-predict-multilabel-expected_shape6] PARALLEL FAILED [ 53%]
sklearn/utils/tests/test_response.py::test_response_values_output_shape_[estimator7-predict_proba-multilabel-expected_shape7] PARALLEL FAILED [ 61%]
sklearn/utils/tests/test_response.py::test_response_values_output_shape_[estimator8-decision_function-multilabel-expected_shape8] PARALLEL FAILED [ 69%]
sklearn/utils/tests/test_response.py::test_response_values_output_shape_[estimator9-predict-binary-expected_shape9] PARALLEL FAILED [ 76%]
sklearn/utils/tests/test_response.py::test_response_values_output_shape_[estimator10-predict-multiclass-expected_shape10] PARALLEL FAILED [ 84%]
sklearn/utils/tests/test_response.py::test_response_values_output_shape_[estimator11-predict-binary-expected_shape11] Fatal Python error: Segmentation fault
<Cannot show all threads while the GIL is disabled>
Stack (most recent call first):
File "/home/lesteve/dev/scikit-learn/sklearn/tree/_classes.py", line 534 in predict
File "/home/lesteve/dev/scikit-learn/sklearn/utils/_response.py", line 244 in _get_response_values
File "/home/lesteve/dev/scikit-learn/sklearn/utils/tests/test_response.py", line 446 in test_response_values_output_shape_
File "/home/lesteve/micromamba/envs/scikit-learn-dev/lib/python3.14t/site-packages/pytest_run_parallel/plugin.py", line 80 in closure
File "/home/lesteve/micromamba/envs/scikit-learn-dev/lib/python3.14t/threading.py", line 1023 in run
File "/home/lesteve/micromamba/envs/scikit-learn-dev/lib/python3.14t/threading.py", line 1081 in _bootstrap_inner
File "/home/lesteve/micromamba/envs/scikit-learn-dev/lib/python3.14t/threading.py", line 1043 in _bootstrap
Current thread's C stack trace (most recent call first):
Binary file "python", at _Py_DumpStack+0x4a [0x5567efa97f6b]
Binary file "python", at +0x16b5c3 [0x5567efaa25c3]
Binary file "/usr/lib/libc.so.6", at +0x3e4d0 [0x7f203963e4d0]
Binary file "/home/lesteve/dev/scikit-learn/build/cp314t/sklearn/tree/_tree.cpython-314t-x86_64-linux-gnu.so", at +0x2987b [0x7f1fc355387b]
Binary file "/home/lesteve/dev/scikit-learn/build/cp314t/sklearn/tree/_tree.cpython-314t-x86_64-linux-gnu.so", at +0x3f095 [0x7f1fc3569095]
Binary file "/home/lesteve/dev/scikit-learn/build/cp314t/sklearn/tree/_tree.cpython-314t-x86_64-linux-gnu.so", at +0x3f89c [0x7f1fc356989c]
Binary file "/home/lesteve/dev/scikit-learn/build/cp314t/sklearn/tree/_tree.cpython-314t-x86_64-linux-gnu.so", at +0x400ff [0x7f1fc356a0ff]
Binary file "python", at PyObject_Vectorcall+0x37 [0x5567efadc5d7]
Binary file "python", at _PyEval_EvalFrameDefault+0x11dc [0x5567efaf1fec]
Binary file "python", at +0x1b6e60 [0x5567efaede60]
Binary file "python", at +0x2267d6 [0x5567efb5d7d6]
Binary file "python", at +0x22eaca [0x5567efb65aca]
Binary file "python", at +0x2fcd81 [0x5567efc33d81]
Binary file "python", at _PyEval_EvalFrameDefault+0x3841 [0x5567efaf4651]
Binary file "python", at +0x1b6e60 [0x5567efaede60]
Binary file "python", at +0x2267d6 [0x5567efb5d7d6]
Binary file "python", at +0x2fc4d9 [0x5567efc334d9]
Binary file "python", at +0x2fc465 [0x5567efc33465]
Binary file "/usr/lib/libc.so.6", at +0x9698b [0x7f203969698b]
Binary file "/usr/lib/libc.so.6", at +0x11a9cc [0x7f203971a9cc]
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, psutil._psutil_linux, 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._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._arpack, scipy.sparse.linalg._propack._spropack, scipy.sparse.linalg._propack._dpropack, scipy.sparse.linalg._propack._cpropack, scipy.sparse.linalg._propack._zpropack, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._hausdorff, scipy.spatial._distance_wrap, scipy.spatial.transform._rotation, scipy.spatial.transform._rigid_transform, 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.integrate._lsoda, 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pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.properties, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, 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, PIL._imaging, kiwisolver._cext, 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.neighbors._partition_nodes, sklearn.neighbors._ball_tree, sklearn.neighbors._kd_tree, sklearn.utils.arrayfuncs, sklearn.utils._seq_dataset, sklearn.linear_model._cd_fast, _loss, sklearn._loss._loss, 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.tree._utils, sklearn.neighbors._quad_tree, sklearn.tree._tree, sklearn.tree._partitioner, sklearn.tree._splitter, sklearn.tree._criterion, 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 (total: 198)
```
</p>
</details>
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"node_id": "MDU6TGFiZWwxODE1NDQ... | open | false | [] | null | 10 | 2026-01-13T13:32:44 | 2026-02-17T16:53:06 | null | null | NONE | null | null | null | null | #### Reference Issues/PRs
None
#### What does this implement/fix? Explain your changes.
`inspect.signature` is known to be very slow, it is not the best function for a hot path. It was identified as a performance bottleneck in our real workload. Since the method signature cannot change while maintaining method's identity, it becomes a perfect target for caching. Cache size of 32 should be sufficient to cover all currently existing kernels at once (and the memory footprint is about zero).
#### AI usage disclosure
I used AI assistance for: **never**
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [ ] Research and understanding
#### Any other comments?
Initially I identified this with cProfile in our real workload, and then confirmed with a tiny benchmark:
```python
from functools import lru_cache
import sys
import time
import numpy as np
from sklearn.gaussian_process import GaussianProcessRegressor, kernels
cached_signature = lru_cache(maxsize=32)(kernels.signature)
patched = False
match sys.argv:
case [_, 'patched']:
# just monkeypatch it in sklearn
kernels.signature = cached_signature
patched = True
# sample code from GPR docs:
# https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html
x = np.linspace(start=0, stop=10, num=1000).reshape(-1, 1)
y = np.squeeze(x * np.sin(x))
rng = np.random.RandomState(1)
training_indices = rng.choice(np.arange(y.size), size=6, replace=False)
x, y = x[training_indices], y[training_indices]
kernel = 1 * kernels.RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e2))
timings = []
for _ in range(50):
start = time.time()
gpr = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=10, random_state=42)
gpr.fit(x, y)
timings.append(time.time() - start)
mean = np.mean(timings) * 1000
std = np.std(timings) * 1000
print(f"Pathed: {patched}, time: {mean:.0f} ms +- {std:.0f} ms per iteration")
```
Output:
```bash
$ python b.py
Pathed: False, time: 131 ms +- 13 ms per iteration
$ python b.py patched
Pathed: True, time: 102 ms +- 10 ms per iteration
``` | null | {
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} | [] | open | false | [] | null | 12 | 2026-01-13T12:53:04 | 2026-02-03T10:40:53 | null | null | CONTRIBUTOR | null | null | null | null | <!--
🙌 Thanks for contributing a pull request!
👀 Please ensure you have taken a look at the contribution guidelines:
https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md
✅ In particular following the pull request checklist will increase the likelihood
of having maintainers review your PR:
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describing your PR changes, see:
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#### Reference Issues/PRs
<!--
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https://github.com/scikit-learn/scikit-learn/issues/31679#issuecomment-3713976683
#### What does this implement/fix? Explain your changes.
First I added a section on how (not) to select issues. Then I asked `Claude Haiku 4.5` to reformulate the file so that AI agents will follow it. (It did generate the requested AI disclosure line at the end :partying_face: )
I playes around with it by asking all available Copilot models to suggest issues to work on or to create a solution for an issue that is currently labelled "Needs Triage". `GPT-5 mini` did not follow the instructions, and when asked why responded that the text was optimized for humans and that it should include model parsable instructions in the beginning, so I added its suggestions as well.
I don't think there is a single way to address all agents, but it might help at least with some. If anyone want's to play around with it further, feel free. I exceeded my free quota for now. :sweat_smile:
@betatim @StefanieSenger
#### 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:
- [x] Text generation and refinement
#### Any other comments?
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33059 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33059/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33059/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33059/events | https://github.com/scikit-learn/scikit-learn/issues/33059 | 3,805,377,004 | I_kwDOAAzd1s7i0XHs | 33,059 | Add built-in utilities for high-dimensional biological data preprocessing (gene expression, omics data) | {
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} | ### Describe the workflow you want to enable
Many users apply scikit-learn to biological datasets such as gene expression matrices, RNA-seq counts, proteomics, and metabolomics data. These datasets usually have:
- Very high dimensionality (thousands of genes/features)
- Strong sparsity or zero inflation
- Batch effects and technical noise
- Requirement for specific normalization and filtering steps
Currently, users must rely on external libraries (scanpy, bioconductor, custom scripts) before they can use scikit-learn models. I want to enable a workflow where:
- Raw biological feature matrices (e.g., genes × samples) can be directly processed.
- Common bioinformatics preprocessing steps are available as sklearn-style transformers.
- These transformers can be used inside Pipeline and GridSearchCV.
This would make scikit-learn more accessible for bioinformatics and omics researchers.
### Describe your proposed solution
Add a small submodule or set of transformers focused on high-dimensional biological
- data, for example:
**GeneFilter**
- Remove genes with low variance, low counts, or low presence across samples.
**LogNormalize**
- Apply log(1 + x) or similar transformations common in RNA-seq and microarray analysis.
**BatchEffectScaler**
- Simple batch-aware scaling using provided batch labels.
**ZeroInflationFilter**
- Remove features dominated by zeros.
All transformers should:
- Follow sklearn API: fit, transform, fit_transform
- Work with NumPy arrays and pandas DataFrames
- Integrate with Pipeline
### Example usage:
```python
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.bio import GeneFilter, LogNormalize
pipe = Pipeline([
("filter", GeneFilter(min_presence=0.1)),
("log", LogNormalize()),
("scale", StandardScaler()),
("model", LogisticRegression())
])
```
### Describe alternatives you've considered, if relevant
Currently, users rely on:
- Scanpy / AnnData
- Bioconductor (R ecosystem)
- Custom preprocessing scripts
However, these tools are not tightly integrated with scikit-learn Pipelines and cross-validation tools. Bridging this gap would reduce friction between bioinformatics preprocessing and machine learning modeling.
### Additional context
Bioinformatics, genomics, and omics-based machine learning is a fast-growing field. Adding even basic biological-data-aware preprocessing tools would:
- Attract more researchers from life sciences
- Reduce dependency on external preprocessing stacks
- Make scikit-learn more friendly for interdisciplinary research
This proposal does not aim to replace specialized bioinformatics libraries, but to provide minimal, general-purpose utilities that cover common preprocessing needs before machine learning. | {
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when estimator.score returns a non-finite value.
This PR ensures such iterations are skipped without emitting warnings.
Includes a regression test for GH-32908. | {
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**Fixes #12952**
Added `partial_fit` to NearestCentroid so you can train it on data in batches.
**What it does:**
- You can now feed data to NearestCentroid bit by bit
- Updates the class centers as new data comes in
- Works like GaussianNB's partial_fit
**What I changed:**
- Added the partial_fit method
- Keeps track of how many samples per class we've seen
- Lets you add new classes later if they show up
- Still works with sparse data | {
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Fixes #32930
What does this implement/fix? Explain your changes.
This pull request standardizes the documentation for supervised clustering metrics in sklearn/metrics/cluster/_supervised.py.
Changes made:
Added dtype=integral to the labels_true and labels_pred parameters in the docstrings of all supervised clustering functions.
This ensures that users are aware of the expected discrete/integer input types, maintaining consistency across the scikit-learn API.
Functions updated:check_clusterings, contingency_matrix, pair_confusion_matrix, rand_score, adjusted_rand_score, homogeneity_completeness_v_measure, homogeneity_score, completeness_score, v_measure_score, mutual_info_score, adjusted_mutual_info_score, and normalized_mutual_info_score.
AI usage disclosure
I used AI assistance for:
[ ] Code generation (e.g., when writing an implementation or fixing a bug)
[ ] Test/benchmark generation
[x] Documentation (Refining the language of the PR description)
[x] Research and understanding (Analyzing the repository structure)
Any other comments?
This is my first contribution to scikit-learn. I have verified the changes and ensured only docstrings were modified. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/33055 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33055/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33055/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/33055/events | https://github.com/scikit-learn/scikit-learn/pull/33055 | 3,804,133,717 | PR_kwDOAAzd1s68sMs3 | 33,055 | BLD Add Android support | {
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* Requires https://github.com/pypa/cibuildwheel/pull/2695
* Requires https://github.com/mesonbuild/meson-python/pull/824
* Requires https://github.com/numpy/numpy/pull/30412
* Requires https://github.com/scipy/scipy/pull/24341
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