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} | 2 | 2025-11-20T07:22:42 | 2025-11-20T07:30:05 | 2025-11-20T07:30:05 | null | MEMBER | null | null | null | null | Addresses https://github.com/scikit-learn/scikit-learn/issues/32748 following suggestions by @lorentzenchr.
I guess I would still need to add a test checking that the coefficients are zero for the absent class. | {
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} | I may be missing something but it seems like the coefficient for a given class does not stay at zero when a class is missing.
I took the Iris dataset with the well known issue that `y` is ordered with 3 classes so that if you use `cv=KFold(3)` you will get the issue that each training fold will have 2 of the 3 classes, and the the test fold has the third class.
```py
import numpy as np
from sklearn.linear_model import LogisticRegressionCV
from sklearn.datasets import load_iris
from sklearn.model_selection import KFold
X, y = load_iris(return_X_y=True)
# Error occurs on `main` but on on this branch
lr = LogisticRegressionCV(cv=KFold(3), Cs=[1], fit_intercept=False).fit(X, y)
lr.coefs_paths_
```
As far as I can see there are no zero-coefficient anywhere?
```
{np.int64(0): array([[[ 0.36617189, 0.4181025 , -1.80054529, -1.59885258,
11.94538259]],
[[-0.27013869, 0.19659815, -1.0394569 , -0.4717853 ,
8.11303679]],
[[-0.2259761 , 0.55491805, -1.32935417, -0.56475712,
6.7920903 ]]]),
np.int64(1): array([[[-0.36863165, -0.41910848, 1.80385762, 1.60144714,
-4.03491417]],
[[ 0.26501596, -0.1994853 , 1.03704383, 0.47085702,
-1.69095992]],
[[ 0.22447638, -0.55428594, 1.33474803, 0.56733333,
-0.29154238]]]),
np.int64(2): array([[[ 2.45975917e-03, 1.00598025e-03, -3.31232754e-03,
-2.59456480e-03, -7.91046843e+00]],
[[ 5.12273225e-03, 2.88714428e-03, 2.41307063e-03,
9.28278209e-04, -6.42207687e+00]],
[[ 1.49971712e-03, -6.32115481e-04, -5.39386365e-03,
-2.57621573e-03, -6.50054792e+00]]])}
```
For completeness, the same snippet fails on `main` so I guess this could be turned into a test for the new behaviour if it becomes clearer what to test exactly (maybe a smoke test is enough?) ...
```
ValueError: cannot reshape array of size 15 into shape (3,1,3,newaxis)
```
> [!WARNING]
> @lesteve spotted a hickup (call it bug) that needs fixing.
#### Checking the assumptions
Smuggling in
```python
c, f = np.unique(y_train, return_counts=True)
cf = {int(ci):int(fi) for ci, fi in zip(c, f)}
print(f"y_train counts={cf}")
```
right before `coefs, Cs, n_iter = _logistic_regression_path(` around line 683 gives
```
y_train counts={1: 50, 2: 50}
y_train counts={0: 50, 2: 50}
y_train counts={0: 50, 1: 50}
```
This confirms: Class i is missing in fold i.
#### Why are the coefficients of all classes changed?
Because they enter the objective function via the penalty. Therefore, the above code comment should be modified.
#### Why there is a bug to fix?
Inside function `_logistic_regression_path`, around line 280
```py
le = LabelEncoder()
```
This should instead read
```py
le = LabelEncoder().fit(classes)
```
and the subsequent `fit_transform` should be changed to `transform` (this fixes a bug and makes it faster).
#### Remaining tasks
Even if this is fixed, there remains a bug if one passes another scorer, e.g.
```py
lr = LogisticRegressionCV(cv=KFold(3), Cs=[1], fit_intercept=False,scoring="neg_brier_score").fit(X, y)
```
```
ValueError: y_true contains only one label (0). Please provide the list of all expected class labels explicitly through the labels argument.
```
I would postpone fixing this to another future PR, maybe not part of 1.8. The situation is already better if the above is fixed.
_Originally posted by @lorentzenchr in https://github.com/scikit-learn/scikit-learn/pull/32073#discussion_r2539697181_
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"due_on": "2025-11-15T00:00:00",
"closed_at": "2025-12-23T17:07:32"
} | 7 | 2025-11-19T19:35:16 | 2026-02-27T21:20:00 | 2025-11-21T08:38:09 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Fix #32748 and #30832
Leftover from https://github.com/scikit-learn/scikit-learn/pull/32073#discussion_r2537432774.
#### What does this implement/fix? Explain your changes.
If a CV fold in `LogisticRegressionCV` has not all classes present, the result is more or less random (certain solvers emit convergence warnings). This PR adds a test (CI will first fail) and then fix the test.
#### Any other comments?
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32746 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32746/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32746/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32746/events | https://github.com/scikit-learn/scikit-learn/pull/32746 | 3,643,676,816 | PR_kwDOAAzd1s60Z6cT | 32,746 | DOC Add missing changelog entry for n_jobs deprecation in LogisticRegression | {
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] | closed | false | [] | null | 5 | 2025-11-19T15:37:54 | 2025-11-24T13:20:43 | 2025-11-24T07:55:24 | null | MEMBER | null | null | null | null | Main changes:
- more debug info to print opened PRs with autoclose label and then the number of PRs to close. This would allow to monitor the action before it can close its first PR https://github.com/scikit-learn/scikit-learn/pull/32743
- add `dry_run` variable to be able to set it to `False` and run the script locally without worrying about closing PRs
- use `timedelta` rather than difference of days
- more minor stuff
cc @StefanieSenger
Here is the output if I run it locally:
```
❯ python build_tools/github/autoclose_prs.py
Found 1 opened PRs with label autoclose
['Test autoclose in PR: '
'https://github.com/scikit-learn/scikit-learn/pull/32743']
Found 0 PRs to autoclose
[]
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} | 2 | 2025-11-19T15:05:49 | 2025-11-20T05:04:14 | 2025-11-19T17:49:44 | null | MEMBER | null | null | null | null | Fix a few things I noticed in https://scikit-learn.org/dev/whats_new/v1.8.html#version-1-8-0 | {
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Fixes #32739.
#### What does this implement/fix? Explain your changes.
This patch deprecates the `n_jobs` parameter in `LogisticRegression` and `LogisticRegressionCV` by adding a documentation directive and emitting a `FutureWarning` when the parameter is used. The runtime behavior is preserved for now.
#### Any other comments?
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Fix typo term: -> :term: in docstrings, which seem to have been copy pasta'd to several related functions (probably by me some time ago :grimacing: )
#### What does this implement/fix? Explain your changes.
#### Any other comments?
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> This issue is not open for contribution. If you are looking to contribute, please have a look at our [contributing doc](https://scikit-learn.org/dev/developers/contributing.html) and in particular the section [Issues for new contributors](https://scikit-learn.org/dev/developers/contributing.html#new-contributors).
Follow-up of https://github.com/scikit-learn/scikit-learn/pull/32073 once it is merged.
Simple part of https://github.com/scikit-learn/scikit-learn/issues/32162. | {
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Towards #32434
#### What does this implement/fix? Explain your changes.
Add a step in unit_tests GHA to run the `test_pytest_soft_dependency.sh` script.
#### Any other comments?
I tried to have a look at [#31192 (comment)](https://github.com/scikit-learn/scikit-learn/pull/31192#issuecomment-2809689421), and it seems to me that there was a test with both with both `array_api_strict` and `CHECK_PYTEST_SOFT_DEPENDENCY` [in this build](https://github.com/scikit-learn/scikit-learn/blob/dfef507ec7da79d32881f0bf6448c8412ea217f3/azure-pipelines.yml#L186) which has `CHECK_PYTEST_SOFT_DEPENDENCY: 'true'` and a lock file with `array_api_strict`. But I'm not sure which exact mechanism in the modifications of #31192 was supposed to break `test_estimator_checks.py`.
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Pyrefly is roughly 25% faster than mypy in pre-commit (saving roughly 3 seconds).
It finds 116 errors, that are silenced using `# pyrefly ignore` comments (it has a `--suppress-errors` option to insert them automatically, and `--remove-unused-ignores` to remove them when errors got fixed, making it easy to check/fix things step-by-step).
I haven't removed the 51 mypy `# type: ignore` comments yet. I'm not sure if pyrefly parses those as well (the docs don't suggest that), but when I tested removing them, that lead to (only) two new pyrefly errors (that can also be silenced with the respective pyrefly ignores).
But this suggest that they are checking different things, and it might be worth looking into the Pyrefly errors.
@adrinjalali
#### Any other comments?
There was one weird pyrefly error that only appeared when I ran pyrefly inside pre-commit (not when running pyrefly directly), and that could not be silenced (in `sklearn/tests/test_metaestimators_metadata_routing.py` where it (wrongly) complained that bool doesn't have .astype). I removed .astype(int) and checked that all the tests still pass with pytest, so apparently it was not necessary, but I'm still not sure why it only happened with pre-commit.
Related but unrelated:
I had to remove the `./` from the file paths in the codespell config section for it to actually ignore these files [UPDATE: probably also related to the codespell errors in this PR], and there are some unrelated ruff whitespace changes required for `pre-commit run --all-files` to run through (which could be fixed in a separate PR), and there is one weird ruff complaint remaining about an import in `benchmarks/bench_tsne_mnist.py` that I also didn't fix yet, because that doesn't seem to make sense and I'm not sure why it's suggesting that.
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#### What does this implement/fix? Explain your changes.
This PR fixes two redundant/incorrect assertions in scikit-learn test files:
1. `test_common.py`: replaced incorrect assertion `assert isinstance(frame_X, pd.DataFrame)` with `assert isinstance(frame_y, pd.DataFrame)` for multi-dimensional `y`.
2. `test_search.py`: fixed incorrect assertion `assert hasattr(grid_search, "cv_results_")` to `assert hasattr(random_search, "cv_results_")`.
All tests pass locally using the installed scikit-learn package on Windows.
#### Any other comments?
No other changes were made. Only test files were modified.
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#### Reference Issues/PRs
Motivated by reading our PR template while opening a PR and wondering if it was a bit long. I think there is truth in the idea that the longer we make something, the less likely people are to read it.
Related #32566
#### What does this implement/fix? Explain your changes.
* Shortens the section in the PR template asking for patience and suggesting ways to improve the PR. Opted for improving the FAQ that is linked to instead.
* I also had a look at the PR templates of [numpy](https://raw.githubusercontent.com/numpy/numpy/refs/heads/main/.github/PULL_REQUEST_TEMPLATE.md) and [scipy](https://raw.githubusercontent.com/scipy/scipy/refs/heads/main/.github/PULL_REQUEST_TEMPLATE.md) for inspiration - which are shorter
* added a section in the FAQ on ways to improve PR or issue - I noticed that there we say similar things in a few places now: [spam FAQ](https://scikit-learn.org/dev/faq.html#what-does-the-spam-label-for-issues-or-pull-requests-mean) and in the auto-close comment (#32504). I think having it in one place and linking it will shorten messages and make it easier for us to amend things in future.
* I am not sure where this best belongs. Potentially there could be a place for it in the contributing guide, but currently, I think the FAQ section is okay. As noted by @lesteve, our contributing guide is very long and covers many disparate topics (see: https://github.com/scikit-learn/scikit-learn/pull/32343#issuecomment-3371376718), and could do with a clean up to improve
#### Any other comments?
cc @StefanieSenger @adrinjalali @betatim
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Noticed while working on #32732
#### What does this implement/fix? Explain your changes.
* add links to the glossary for terms binary, multiclass, multilabel
* Minor update to `det_curve` to make it more consistent with `precision_recall_curve` and `roc_curve`
#### Any other comments?
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] | open | false | [] | null | 7 | 2025-11-18T01:29:35 | 2026-03-03T04:24:47 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Another approach for #31338 (which superseded #25639 - closes #25639)
closes #21391
#### What does this implement/fix? Explain your changes.
Uses the approach suggested by @jeremiedbb (https://github.com/scikit-learn/scikit-learn/pull/31338#issuecomment-3380468102) of using re-using `confusion_matrix_at_thresholds` (previously called `_binary_clf_curve`) to implement curve calculation - given a callable metric, calculates the metric for each threshold.
I think I like this approach better:
* Brings `decision_threshold_curve` in line with the other curve functions we have (`det_curve`, `precision_recall_curve`, `roc_curve`), which all use `confusion_matrix_at_thresholds`.
* all of these curve functions would under go the same validation, errors/changes are more easily made for all curve functions
* As you can see much fewer code changes required for this vs using `_CurveScorer`
* we could undo the changes in #29216
* parts of `_CurveScorer` are probably unnecessary for this function, e.g., `sign` - as this is a stand alone function (vs being used inside an estimator (`TunedThresholdClassifierCV`), this is probably not needed the user can manipulate the output as required. Also `_CurveScorer` is particular in mapping thresholded labels to original classes (requiring call to `unique(y)`), which isn't really needed in this case
* having to instantiate a class for this function is probably over-complicating things
#### Any other comments?
Working on this also raised two questions:
* I noticed in `sklearn/metrics` most modules have a leading `_` (e.g., `_regression.py`). I thought this implied private module/functions?
* This function was originally named `metric_per_threshold` , then changed to `decision_threshold_curve`, since it is already in the `metric` module, the name metric was considered redundant (see https://github.com/scikit-learn/scikit-learn/pull/25639#discussion_r1606857931). Is 'decision' meant to imply that this curve is meant to help with determining the threshold to use? Is term commonly used (genuine question, as I don't have the background in this area)?
cc @jeremiedbb @glemaitre , and just in case you have time @StefanieSenger | null | {
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Towards #32434
#### What does this implement/fix? Explain your changes.
Add a step in unit_tests GHA to run the `test_docs.sh` script.
#### Any other comments?
The script checks for scipy_doctest to be installed to run, so until a build with this version of scipy is added to the matrix it will not execute the tests in the GHA workflow.
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"node_id": "MDU6TGFiZWwy... | closed | false | [] | null | 2 | 2025-11-17T15:22:01 | 2025-11-20T09:44:15 | 2025-11-17T17:06:48 | null | NONE | null | null | null | null | ### Summary
Add a simple benchmark script to compare vanilla scikit-learn `RandomForest` vs Intel-optimized `sklearnex`, and add a tiny smoke test. This is a draft PR to provide reproducible numbers and a safe starting point for discussion on histogram-based splitting and bagging ensembles.
### Files added
- `benchmarks/benchmark_histogram_vs_intel.py`
- `sklearn/ensemble/tests/test_smoke_hist_vs_exact.py`
### Local benchmark results (my machine)
=== n_samples = 50,000 ===
training_time(s) = 3.525, test_acc = 0.9643 (using_intel=True)
=== n_samples = 100,000 ===
training_time(s) = 8.011, test_acc = 0.9635 (using_intel=True)
=== n_samples = 200,000 ===
training_time(s) = 19.350, test_acc = 0.9654 (using_intel=True)
Intel-optimized sklearn used (`sklearnex.patch_sklearn()`).
### Related issues
Refs #32704 and #27873
### Next steps I can help with
- Add more benchmarks (real datasets, memory measurements).
- Prototype a pure-Python histogram splitter and add unit tests.
- Help design an opt-in API for histogram splitting in `RandomForest` / bagging ensembles.
— @vanshy-git
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} | 4 | 2025-11-17T13:01:56 | 2026-02-11T20:03:41 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Relates to #27439.
#### What does this implement/fix? Explain your changes.
If right now, it is officially not public, let's remove it from the public API list.
#### Any other comments?
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Follow up of #32246
#### What does this implement/fix? Explain your changes.
- Add the `xp` parameter within the docstring of `_fit_classifier_calibrator_pair` and `_fit_calibrator`
#### Any other comments?
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32726 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32726/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32726/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32726/events | https://github.com/scikit-learn/scikit-learn/pull/32726 | 3,633,234,613 | PR_kwDOAAzd1s6z2egQ | 32,726 | DEP slices in _safe_indexing | {
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] | open | false | [] | null | 5 | 2025-11-17T12:57:32 | 2025-11-18T12:47:22 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Solves #27439 and #31290
#### What does this implement/fix? Explain your changes.
Deprecate passing `slice` objects as `indices` parameter in `_safe_indexing`.
#### Any other comments?
| null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32725 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32725/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32725/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32725/events | https://github.com/scikit-learn/scikit-learn/issues/32725 | 3,632,108,215 | I_kwDOAAzd1s7YfZK3 | 32,725 | Random-seed-dependent test failures in `macOS pylatest_conda_forge_arm` job | {
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"url"... | closed | false | [] | null | 9 | 2025-11-17T07:37:52 | 2025-11-27T15:56:15 | 2025-11-27T15:56:15 | null | MEMBER | null | null | {
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} | > [!WARNING]
> This is not a good first issue to contribute. Great if you are interested 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 [Issues for new contributors](https://scikit-learn.org/dev/developers/contributing.html#new-contributors).
While using the `[all random seeds]` commit-message marker in #32582, I noticed that some tests can fail under certain seeds. The failures appear in the `macOS pylatest_conda_forge_arm` job.
The CI failure can be found here: https://github.com/scikit-learn/scikit-learn/actions/runs/19417674441/job/55549227614?pr=32582
In particular, the following tests are affected:
<details>
<summary>
<code>affected_test[random_seed]</code>
</summary>
```bash
covariance/tests/test_graphical_lasso.py::test_graphical_lassos[95]
covariance/tests/test_robust_covariance.py::test_mcd
covariance/tests/test_robust_covariance.py::test_mincovdet_bias_on_normal[42-2000-10]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[6]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[14]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[19]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[23]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[30]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[32]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[37]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[47]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[50]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[55]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[61]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[64]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[67]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[72]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[79]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[80]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[88]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[93]
tree/tests/test_tree.py::test_absolute_errors_precomputation_function[94]
```
</details>
This can be reproduced by:
1. Using the `[all random seeds]` commit-message marker (see [here](https://scikit-learn.org/stable/developers/contributing.html#commit-message-markers)), or
2. Setting the environment variable on macOS (see [here](https://scikit-learn.org/stable/computing/parallelism.html#sklearn-tests-global-random-seed)):
```bash
$ export SKLEARN_TESTS_GLOBAL_RANDOM_SEED="all"
```
At first glance, the failures come from `assert_allclose`, so tweaking the `rtol` or `atol` in those tests should address the issue. This will need to be verified by pushing a commit with the `[all random seeds]` marker and letting the CI run to completion, which usually takes about two hours.
Edit: add more failed tests | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32724 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32724/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32724/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32724/events | https://github.com/scikit-learn/scikit-learn/pull/32724 | 3,631,750,338 | PR_kwDOAAzd1s6zxlsY | 32,724 | :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: | {
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] | closed | false | [] | null | 2 | 2025-11-17T05:03:13 | 2025-11-17T10:21:44 | 2025-11-17T10:21:44 | null | CONTRIBUTOR | null | null | null | null | Update lock files.
### 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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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32723 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32723/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32723/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32723/events | https://github.com/scikit-learn/scikit-learn/issues/32723 | 3,631,523,528 | I_kwDOAAzd1s7YdKbI | 32,723 | Two possible logic errors potentially caused by typos | {
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} | While reviewing the test suite, I found two redundant type checks that seem like logic errors caused by typos. Please check whether they are unintended mistakes.
1) In the function `check_as_frame(...)` of `/sklearn/datasets/tests/test_common.py`, the second assertion `assert isinstance(frame_X, pd.DataFrame)` should be `assert isinstance(frame_y, pd.DataFrame)` considering the code context.
```
# Test for return_X_y and as_frame=True
frame_X, frame_y = dataset_func(as_frame=True, return_X_y=True)
assert isinstance(frame_X, pd.DataFrame)
if frame_y.ndim > 1:
assert isinstance(frame_X, pd.DataFrame) # -> assert isinstance(frame_y, pd.DataFrame)
else:
assert isinstance(frame_y, pd.Series)
```
2) In the file `/sklearn/model_selection/tests/test_search.py`, the second assertion `assert hasattr(grid_search, "cv_results_")` should be `assert hasattr(random_search, "cv_results_")`.
```
def test_trivial_cv_results_attr():
# Test search over a "grid" with only one point.
clf = MockClassifier()
grid_search = GridSearchCV(clf, {"foo_param": [1]}, cv=2)
grid_search.fit(X, y)
assert hasattr(grid_search, "cv_results_")
random_search = RandomizedSearchCV(clf, {"foo_param": [0]}, n_iter=1, cv=2)
random_search.fit(X, y)
assert hasattr(grid_search, "cv_results_") # -> assert hasattr(random_search, "cv_results_")
``` | {
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#### Reference Issues/PRs
This PR is targeting the Issue (#32675) regarding the addition of the attributes `explained_variance_ratio_x_` and `explained_variance_ratio_y_` for the `_PLS` models.
Fixes [#32675](https://github.com/scikit-learn/scikit-learn/issues/32675)
Fixes [#19896](https://github.com/scikit-learn/scikit-learn/issues/19896)
Fixes [#30470](https://github.com/scikit-learn/scikit-learn/issues/30470)
#### What does this implement/fix? Explain your changes.
This PR implements the `explained_variance_ratio_x_` and `explained_variance_ratio_y_` for `_PLS` models, analogous to how PCA exposes `explained_variance_ratio_`. Not having access to it in the `PLSRegression` makes it difficult to:
- Quantify how much variance each latent variable captures,
- Compare models with different numbers of components, and
- Produce diagnostic or variance-explained plots (as is standard in different disciplines where latent space interpretability is important).
In the current PR, I propose adding two new attributes, that are calculated during fitting:
- `explained_variance_ratio_x_` : `ndarray` of shape (`n_components`)
Fraction of variance explained in X-space for each component.
- `explained_variance_ratio_y_` : `ndarray` of shape (`n_components`)
Fraction of variance explained in Y-space for each component.
In the initial issue (#32675) I suggested a solution for the `PLSRegression` model, including a calculation of the explained variance ratio for both matrices after right after fitting the superclass.
However, as I was working on it, I realized that a more elegant and extensible solution is to calculate the variance ratio directly in the parent class during fit. This gives:
- **Extensibility** the suggested solution directly extends the functionality to other models inheriting `_PLS` (`PLSRegression`, `PLSCanonical` and `CCA`) without having to handle additional logic (i.e., `deflation_mode` `canonical` vs `regression` (symmetric vs asymmetric)) as this logic is already handled during `supper().fit()`.
- **Consistency** all subclasses expose the same attributes without custom logic.
- **Performance** the suggested approach is more performant because it does not require to redundantly deflate the matrices again after fitting. The explained variances are calculated in each iteration during the fitting process.
To ensure correctness, the implementation was compared with literature benchmark values; additional details are provided in the Testing section.
I think the superclass implementation is the better design choice, but I’m happy to adopt the earlier proposal if the maintainers prefer that direction.
#### Testing
The following test actions have been done:
**Added tests**
The following test was added to `sklearn/cross_decomposition/tests/test_pls.py`:
- `test_pls_variance_ratio_X_y()`
This test runs on all PLS models that inherit the `_PLS` (`PLSRegression`, `PLSCanonical` and `CCA`). A description of the test is provided below:
1. For `PLSRegression`, `PLSCanonical` and `CCA`:
- ✅ Assert that the number of items in the `explained_variance_ratio_x_` and `explained_variance_ratio_y_` is the same as the `nr_components` in the model.
- ✅ Assert that the cumulative explained variance in `X` approaches 1 when using the maximum number of components.
(This holds for the synthetic test data; symmetric-deflation models may vary depending on the ranks of `X` and `y`.)
2. For `PLSRegression`:
- ✅ Assert that the variance of each component matches the reported literature values in the literature for the `X` and the `y` matrices [1].
- ✅ Assert that the cumulative variance in the `y` matrix is not larger than 1 variance when the max number of components is used (due to asymmetric deflation).
3. For `PLSCanonical` and `CCA` it is expected that the cumulative variance explained in the `y` matrix adds to 1 when the max number of components are used.
[1].. Abdi, H. (2003) Partial Least Squares (PLS) Regression. In Lewis-Beck M., Bryman A., Futing T. (Eds.), Encyclopedia of Social Sciences Research Methods. Thousand Oaks (CA): Sage.
**Run the following test suite:**
- ✅ `pytest sklearn/cross_decomposition/tests`/test_pls.py [69/69 passing]
- ✅ `pytest sklearn/tests/test_common.py -k PLSRegression -v` [65/66 passing, 1 skipped]*
- ✅ `pytest sklearn/tests/test_common.py -k PLSCanonical -v` [65/66 passing, 1 skipped]*
- ✅ `pytest sklearn/tests/test_common.py -k CCA -v` [65/66 passing, 1 skipped]*
(Skipped tests are unrelated and also skipped on main.)
#### Documentation
**Added docstrings**
- ✅ The following docstrings were added to `PLSRegression`, `PLSCanonical` and `CCA`.
```python
"""[PLSRegression] / [PLSCanonical] / [CCA]
[...]
explained_variance_ratio_x_ : ndarray of shape (n_components,)
Percentage of variance explained by each of the selected components in `X`.
explained_variance_ratio_y_ : ndarray of shape (n_components,)
Percentage of variance explained by each of the selected components in `y`.
"""
```
NOTE: other attributes include the release version at which the attribute became available (e.g., `.. versionadded:: 1.0`) I have not added version tags yet; I will include them once maintainers confirm the target release.
- ✅ Docstrings were added for the helper function `_calculate_variance_xy()`
```python
"""Calculates the variance of the X and y matrices
The flags has_x_variance and has_y_variance are included
as guards to prevent crashes on constant data
"""
```
- ✅ Docstrings were added to the new unit test function `test_pls_variance_ratio_X_y()`
A reference to the literature values is included in this reference.
**Building documentation**
The documentation was build as indicated in the [contributor documentation page](https://scikit-learn.org/dev/developers/contributing.html#contribute-documentation). As suggested in the guide, the generated HTML files were inspected to verify the successful built of the documentation. This was done for all three models in scope and an example is shown in the image below.
- ✅ Documentation for `PLSRegression`.
- ✅ Documentation for `PLSCanonical`.
- ✅ Documentation for `CCA`.
<img width="1619" height="513" alt="PLSRegression Docs" src="https://github.com/user-attachments/assets/2517e121-e457-4c9a-9aa8-87f8bfce2e2e" />
#### Examples
Right now, this is only exemplified in the documentation Since it is a rather small addition, I am not sure what would be best:
- to be included in the examples page,
- to be included as a docstring examlpe,
- or leave it as it is as the attributes are documented in the method docstrings
Happy to add an example if desired 😄
#### Performance
The computation is integrated into the existing iterative deflation performed during fit, so the overhead is minimal. No additional matrix factorizations or extra passes over the data are introduced.
There is no impact on estimator instantiation time, or on `.transform()` or `.predict()`. | null | {
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- Raise clear TypeError suggesting to use ClassName() instead of ClassName
- Add test to verify the improved error message
#### Reference Issues/PRs
Fixes #32719
#### What does this implement/fix? Explain your changes.
This PR improves the error message when a user accidentally passes an uninstantiated class to a Pipeline step instead of an instance.
**Before (confusing error):**
AttributeError: 'numpy.ndarray' object has no attribute '_validate_params'
**After (clear error):**
TypeError: All steps should be estimator instances (objects), not classes.
Step 'pca' is a class: (PCA). Did you forget parentheses?
Use PCA() instead of PCA.
**Changes made:**
- Added validation in `_validate_steps()` to check if a step is a class using `inspect.isclass()`
- Raises a clear `TypeError` with a helpful message
- Added test `test_pipeline_step_class_instead_of_instance()` to verify the improved error message
#### Any other comments?
This is my first contribution to scikit-learn. I've manually reviewed all changes and run the full test suite. All pipeline tests pass (141 passed, 5 skipped). Please let me know if any changes are needed!
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N/A
#### What does this implement/fix? Explain your changes.
This PR adds a new test (`test_bincount`) to `sklearn/utils/tests/test_array_api.py`.
This test provides full coverage for the `_bincount` function, which was previously untested. It verifies both the native `xp.bincount` path and the NumPy-fallback path (for libraries like `array-api-strict`) across all supported devices and dtypes.
#### Any other comments?
This addresses a critical gap in test coverage identified during code analysis. | {
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} | ### Describe the bug
While building a pipeline I forgot the parenthesis during a step creation in the pipeline. I'm really not proud to admit that it took me a while to realize the mistake that I've made. I thought that maybe the error message in this instance could be improved if you guys agree.
I feel like it's a pretty easy non-breaking fix this one.
Cheers!
### Steps/Code to Reproduce
```python
# Made a silly error while building a pipeline and
# thought that the error message could be improved.
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
import numpy as np
# Dummy data to showcase issue
X = np.ndarray(
shape=(10, 5),
buffer=np.random.rand(50),
dtype=float,
)
pipeline = Pipeline(steps=[
# ('step1', step1),
# ('step2', step2),
# ...
('pca', PCA), # <-- Error: PCA is not instantiated, currently it's an instance of abc.ABCMeta
])
# This will raise an error but the error message will be a bit unclear
X_transformed = pipeline.fit_transform(X)
```
### Expected Results
I would expect an error that told me that a step was not instantiated correctly, or is of type `abc.ABCMeta`, should be instance of `sklearn.decomposition.PCA`.
### Actual Results
```
Traceback (most recent call last):
File "test.py", line 23, in <module>
X_trainsformed = pipeline.fit_transform(X)
File "/opt/homebrew/Caskroom/miniconda/base/envs/dsml25/lib/python3.8/site-packages/sklearn/base.py", line 1152, in wrapper
return fit_method(estimator, *args, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/dsml25/lib/python3.8/site-packages/sklearn/pipeline.py", line 479, in fit_transform
return last_step.fit_transform(Xt, y, **fit_params_last_step)
File "/opt/homebrew/Caskroom/miniconda/base/envs/dsml25/lib/python3.8/site-packages/sklearn/utils/_set_output.py", line 157, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/dsml25/lib/python3.8/site-packages/sklearn/base.py", line 1145, in wrapper
estimator._validate_params()
AttributeError: 'numpy.ndarray' object has no attribute '_validate_params'
```
### Versions
```shell
System:
python: 3.8.20 | packaged by conda-forge | (default, Sep 30 2024, 17:48:42) [Clang 17.0.6 ]
executable: /opt/homebrew/Caskroom/miniconda/base/envs/dsml25/bin/python3
machine: macOS-26.1-arm64-arm-64bit
Python dependencies:
sklearn: 1.3.2
pip: 24.3.1
setuptools: 75.3.0
numpy: 1.24.4
scipy: 1.10.1
Cython: None
pandas: 2.0.3
matplotlib: 3.7.3
joblib: 1.4.2
threadpoolctl: 3.5.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 10
prefix: libopenblas
filepath: /opt/homebrew/Caskroom/miniconda/base/envs/dsml25/lib/libopenblas.0.dylib
version: 0.3.25
threading_layer: openmp
architecture: VORTEX
user_api: openmp
internal_api: openmp
num_threads: 10
prefix: libomp
filepath: /opt/homebrew/Caskroom/miniconda/base/envs/dsml25/lib/libomp.dylib
version: None
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32718 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32718/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32718/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32718/events | https://github.com/scikit-learn/scikit-learn/issues/32718 | 3,628,669,500 | I_kwDOAAzd1s7YSRo8 | 32,718 | BUG: trees: `criterion="friedman_mse"` is buggy for multi-output | {
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} | ### Describe the bug
The calculation implemented in `FriedmanMSE.proxy_impurity_improvement` is plain wrong for the multi-output case.
When reading the code, it's fairly obvious that outputs are mixed in a way that doesn't mathematically make sense.
**Note:** I open this issue just for the records, as `"friedman_mse"` will be removed anyway (see #32700 )
### Steps/Code to Reproduce
```Python
import numpy as np
from sklearn.tree import DecisionTreeRegressor, plot_tree
X = [[1], [2], [3], [4]]
y = np.array([
[1, -1],
[1, -1],
[0, 0],
[0, 0]
])
tree = DecisionTreeRegressor(criterion="friedman_mse")
tree.fit(X, y)
plot_tree(tree);
```
### Expected Results
Split in the middle (x <= 2.5), reducing the error to 0 in one split.
### Actual Results
non-optimal split:
<img width="515" height="389" alt="Image" src="https://github.com/user-attachments/assets/7d42c1b5-0647-4b46-b5f5-9daa3b550247" />
### 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](https://file+.vscode-resource.vscode-cdn.net/home/arthur/dev-perso/scikit-learn/sklearn-env/bin/python)
machine: Linux-6.14.0-35-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.8.dev0
pip: None
setuptools: 80.9.0
numpy: 2.3.4
scipy: 1.16.2
Cython: 3.1.5
pandas: 2.3.3
matplotlib: 3.10.7
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libscipy_openblas
filepath: [/home/arthur/dev-perso/scikit-learn/sklearn-env/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-8fb3d286.so](https://file+.vscode-resource.vscode-cdn.net/home/arthur/dev-perso/scikit-learn/sklearn-env/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-8fb3d286.so)
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libscipy_openblas
filepath: [/home/arthur/dev-perso/scikit-learn/sklearn-env/lib/python3.12/site-packages/scipy.libs/libscipy_openblas-b75cc656.so](https://file+.vscode-resource.vscode-cdn.net/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](https://file+.vscode-resource.vscode-cdn.net/usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0)
version: None
``` | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32717 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32717/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32717/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32717/events | https://github.com/scikit-learn/scikit-learn/pull/32717 | 3,628,424,998 | PR_kwDOAAzd1s6zmphr | 32,717 | DOC Add reference to valid metrics for `KDTree` and `BallTree` | {
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Fixes #32686
#### What does this implement/fix? Explain your changes.
This PR adds a reference to the `metric` parameter in the [<code>KDTree</code>](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree) and [<code>BallTree</code>](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html) documentation, linking it to the `valid_metrics` listed in the [user guide](https://scikit-learn.org/stable/modules/neighbors.html#kdtree-and-balltree-classes).
#### Any other comments?
Although CI doesn't display the updated documentation from the Cython files, a [Google search](https://www.google.com/search?q=site:%22scikit-learn.org%22+%22A+list+of+valid+metrics+for%22&filter=0&dpr=1.25) suggests that this PR only affects the [<code>KDTree</code>](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree) and [<code>BallTree</code>](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html) documentation.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32716 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32716/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32716/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32716/events | https://github.com/scikit-learn/scikit-learn/pull/32716 | 3,628,305,948 | PR_kwDOAAzd1s6zmQ9U | 32,716 | Added Apriori Algorithm for Association rule mining | {
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Fixes https://github.com/scikit-learn/scikit-learn/issues/32712
#### What does this implement/fix? Explain your changes.
Added Apriori algorithm in sklearn as per the method mentioned in https://github.com/scikit-learn/scikit-learn/issues/32712. Added a new module name association which can later be extended to add more association rule mining algorithms like FP Tree, DIC, etc.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32715 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32715/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32715/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32715/events | https://github.com/scikit-learn/scikit-learn/pull/32715 | 3,628,120,534 | PR_kwDOAAzd1s6zlqGZ | 32,715 | DOC Amend "Issues for New Contributors" in contributing guide | {
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#### Reference Issues/PRs
closes #32680
#### What does this implement/fix? Explain your changes.
Thanks for your input on #32680 .
I've amended the [Issues for New Contributors](https://scikit-learn.org/dev/developers/contributing.html#issues-for-new-contributors) section to:
* recommend that people start somewhere that interests them or they are already familiar with
* advised that we rarely use the 'Good first issue' label
* advise them to start by reading the contributing guide. I've highlighted particular sections, as our contributing guide is very long, but not sure if I've included the right/best sections.
Note - I agree with @lesteve's sentiment here: https://github.com/scikit-learn/scikit-learn/pull/32343#issuecomment-3371376718, in that our contributing page contains a lot of different topics and could do with a clean up. This PR works within the way the current doc is.
#### Any other comments?
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This PR adds a unit test that verifies that `check_random_state` raises a clear
and descriptive `ValueError` when passed a string seed.
### What does this implement?
- Added a dedicated test: `test_check_random_state_string_error_message`
- File: `sklearn/utils/tests/test_validation.py`
- The test checks that passing a string such as `"hello"` to
`check_random_state` raises a `ValueError` with a helpful message describing
the accepted seed types.
### Motivation
`check_random_state("some string")` already raises a `ValueError`, but the
associated message was not being explicitly validated. Adding this test improves
error-message coverage and helps ensure consistency for users and contributors.
### Tests
All tests pass locally:
```bash
pytest sklearn/utils/tests/test_validation.py::test_make_rng -q
pytest sklearn/utils/tests/test_validation.py::test_check_random_state_string_error_message -q
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32713 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32713/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32713/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32713/events | https://github.com/scikit-learn/scikit-learn/pull/32713 | 3,626,426,664 | PR_kwDOAAzd1s6zf-4p | 32,713 | FIX `remainder` parameter for column transformer visual block | {
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#### Reference Issues/PRs
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See https://github.com/scikit-learn/scikit-learn/issues/32146#issuecomment-3450807154
#### What does this implement/fix? Explain your changes.
This PR corrects the ColumnTransformer dotted-line. It was wrongly including the regressor, when remainder was "passthrough".
This was wrong:
<img width="302" height="145" alt="Screenshot 2025-11-25 at 18 00 13" src="https://github.com/user-attachments/assets/2802c52c-4dee-462c-9635-a0514bc72ab1" />
Corrected:
<img width="302" height="145" alt="Screenshot 2025-11-25 at 17 59 29" src="https://github.com/user-attachments/assets/918e1582-1b72-4eca-ab77-ea8b6b4792e6" />
The changes on `estimator.py` are meant to remove '[ ]' below the regressor.
@glemaitre
#### Any other comments?
I don't know if changelog file is needed.
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} | ### Describe the workflow you want to enable
I would like scikit-learn to support association rule mining by implementing the Apriori algorithm. This would allow users to efficiently find frequent itemsets and generate association rules directly within the scikit-learn framework, similar to other supervised and unsupervised learning algorithms.
Use cases include market basket analysis, recommendation systems, and pattern discovery in categorical datasets. The workflow would involve:
- Inputting transactional or categorical data.
- Generating frequent itemsets with a minimum support threshold.
- Producing association rules with metrics such as confidence, lift, and leverage.
### Describe your proposed solution
Implement an Apriori class or function in the sklearn.preprocessing or sklearn.association module that provides:
- fit() to generate frequent itemsets.
- generate_rules() to produce association rules.
- Support for configurable metrics: support, confidence, lift, leverage.
- Compatibility with pandas DataFrames and NumPy arrays.
### Describe alternatives you've considered, if relevant
Currently, Python libraries like mlxtend
provide Apriori and association rule mining. However, integrating it into scikit-learn would:
- Ensure consistent API with scikit-learn estimators.
- Enable seamless integration with pipelines and transformers.
- Benefit users who prefer a single framework for all ML tasks.
### Additional context
- Association rule mining is a common unsupervised learning task, widely used in industry.
- Many users are already using mlxtend alongside scikit-learn, but a native implementation would improve performance, maintainability, and ecosystem consistency. | {
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What it adds
File: examples/cluster/plot_kmeans_shapes_dtypes.py | {
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- test_logistic_cv_sparse[86-csr_matrix-sag]
- test_logistic_cv_sparse[86-csr_matrix-saga]
- test_logistic_cv_sparse[86-csr_array-sag]
- test_logistic_cv_sparse[86-csr_array-saga] | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32709 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32709/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32709/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32709/events | https://github.com/scikit-learn/scikit-learn/pull/32709 | 3,622,867,251 | PR_kwDOAAzd1s6zUH0e | 32,709 | DOC Fix swapped axes in linear model coefficient plots (Issue #32706) | {
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Fixes #32706.
#### What does this implement/fix? Explain your changes.
Corrected two instances where AGE and EXPERIENCE coefficients were plotted in the wrong order.
This aligns the scatter plots with the axis labels and fixes the inconsistency raised in Issue #32706.
This PR updates two `plt.scatter()` calls in the linear model coefficient interpretation example where
`AGE` and `EXPERIENCE` were plotted in the wrong order.
According to the axis labeling (Experience on the x-axis and Age on the y-axis), the arguments to
`plt.scatter()` needed to be swapped. Both plots have now been corrected for consistency.
#### Any other comments?
The modification affects only example documentation code and does not change any library behavior.
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"node_id": "MDU6TGFiZWwyOTExMDU3... | closed | false | [] | null | 8 | 2025-11-13T20:35:56 | 2026-01-10T15:50:07 | 2026-01-10T15:50:06 | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Towards #32700 (deprecation before complete removal).
Fixes:
- #32707
- #32718
#### What does this implement/fix? Explain your changes.
- Remove class `FriedmanMSE(MSE)` in `sklearn/tree/_criterion.pyx`
- Deprecate `"friedman_mse"` for trees & forests (`if criterion="friedman_mse": criterion="squared_error"` + deprecation warning)
- Deprecate `criterion` param for gradient boosting
- Adapt the doc/docstrings/tests/... accordingly
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32707 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32707/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32707/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32707/events | https://github.com/scikit-learn/scikit-learn/issues/32707 | 3,621,844,598 | I_kwDOAAzd1s7X4PZ2 | 32,707 | BUG: tree/forest regressor: impurity decrease calculation is wrong for criterion "friedman_mse" | {
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} | ### Describe the bug
Well, everything is in the title.
I noticed that while writing the issue #32700
I'm opening this issue just for the records, as we plan to remove `"friedman_mse"` criterion anyway.
### Steps/Code to Reproduce
```Python
import numpy as np
from sklearn.tree import DecisionTreeRegressor
X = np.arange(5).reshape(-1, 1)
y = [0, 1, 0, 1, 0]
reg = DecisionTreeRegressor(min_impurity_decrease=0.1, max_depth=1, criterion="friedman_mse")
reg.fit(X, y)
weighted_impurity = reg.tree_.impurity * reg.tree_.n_node_samples / X.shape[0]
actual_decrease = weighted_impurity[0] - weighted_impurity[1:3].sum()
assert actual_decrease >= 0.1
```
Or simply running the test `sklearn/tree/tests/test_tree.py::test_min_impurity_decrease` with criterion `"friedman_mse"`
### Expected Results
No error
### 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-6.14.0-35-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.8.dev0
pip: None
setuptools: 80.9.0
numpy: 2.3.4
scipy: 1.16.2
Cython: 3.1.5
pandas: 2.3.3
matplotlib: 3.10.7
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libscipy_openblas
filepath: /home/arthur/dev-perso/scikit-learn/sklearn-env/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-8fb3d286.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libscipy_openblas
filepath: /home/arthur/dev-perso/scikit-learn/sklearn-env/lib/python3.12/site-packages/scipy.libs/libscipy_openblas-b75cc656.so
version: 0.3.29.dev
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 16
prefix: libgomp
filepath: /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0
version: None
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"node_id": "LA_kwDOAAzd1s7Yb... | closed | false | [] | null | 0 | 2025-11-13T15:10:05 | 2025-11-18T16:00:52 | 2025-11-18T16:00:52 | null | NONE | null | null | {
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} | ### Describe the issue linked to the documentation
## Common pitfalls in the interpretation of coefficients of linear models
In line 396, `plt.scatter(coefs["AGE"], coefs["EXPERIENCE"])`, the Experience variable should be on x-axis if it were to follow the labelling in Line 392 `plt.xlabel("Experience coefficient")`.
Similar issue in line 632.
Here is the [codefile](https://github.com/scikit-learn/scikit-learn/blob/main/examples/inspection/plot_linear_model_coefficient_interpretation.py) on Github.
[Link](https://scikit-learn.org/stable/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#the-problem-of-correlated-variables) to documentation on main website
### Suggest a potential alternative/fix
Replace the code in line 396 & 632 with `plt.scatter(coefs["EXPERIENCE"], coefs["AGE"])`
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Context: https://github.com/scikit-learn/scikit-learn/pull/31829#issuecomment-3523758213 | {
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} | This issue is very related to #27873.
If/when the above is implemented, it should also be possible to refactor the tree code to leverage histogram splits for bagging-based tree ensembles.
Histogram-based split is the main reason, while scikit-learn-intelex's `RandomForestClassifier` is much faster that our `RandomForestClassifier` when trained on datasets with many data points. | null | {
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#### What does this implement/fix? Explain your changes.
Update the paper URL to Neighborhood Components Analysis:
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Original link does not work:
https://cs.nyu.edu/~roweis/papers/ncanips.pdf
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} | ### Describe the bug
In `sklearn/tree/_criterion.pyx`, criteria `"friedman_mse"` (class `FriedmanMSE(MSE)`) and `"squared_error"` (class `MSE`) are mathematically equivalent, they use different but equivalent formulas. This is confusing, and I think that:
- `"friedman_mse"` criterion should be deprecated then removed.
- the class should be removed
Because `"friedman_mse"` and `"squared_error"` are mathematically equivalent they produce the same trees/forests (up to float precision errors), as shown by the reproducer below.
Also, I tried removing all the code from `FriedmanMSE` class (leaving only the inheritance `FriedmanMSE(MSE)`) and all test passes from `sklearn/tree` and `sklearn/ensemble` except `test_huber_exact_backward_compat` which is sensitive to float rounding errors.
I also tried the other-way around (copy-pasting code from `FriedmanMSE(MSE)` in `MSE`) and except for one bug for which I'll open a separate issue, it worked as well.
Maths (from ChatGPT, but I carefully double-checked it):
<img width="1151" height="565" alt="image" src="https://github.com/user-attachments/assets/7523b114-9c7b-4a20-abeb-dcb2c6d158fc" />
Proof:
<details>
<img width="1271" height="857" alt="image" src="https://github.com/user-attachments/assets/83a92ec6-0f5e-4387-a9d3-fb1b397b0302" />
</details>
### Steps/Code to Reproduce
```Python
import numpy as np
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
params_list = [
{
'random_state': 0,
'max_leaf_nodes': max_leaf_nodes,
'max_depth': max_depth,
'ccp_alpha': ccp_alpha,
}
for max_depth in [None, 1, 2, 3, 5, 10]
for max_leaf_nodes in [None, 3, 6, 10, 20, 50, 100]
for ccp_alpha in [0, 0.005, 0.01]
]
for Reg in [DecisionTreeRegressor, RandomForestRegressor, GradientBoostingRegressor]:
print(Reg)
for params in params_list:
n = np.random.choice([10, 100, 1000, 10_000])
d = np.random.choice([1, 2, 3, 10])
if Reg in [GradientBoostingRegressor, RandomForestRegressor]:
params = {**params, 'n_estimators': 10}
X = np.random.rand(n, 1)
X_test = np.random.rand(n, 1)
y = np.random.rand(n) + X.sum(axis=1)
for w in [None, np.random.rand(n), np.random.pareto(1, size=n)]:
tree_mse = Reg(criterion="squared_error", **params)
tree_friedman = Reg(criterion="friedman_mse", **params)
assert np.allclose(
tree_mse.fit(X, y, sample_weight=w).predict(X_test),
tree_friedman.fit(X, y, sample_weight=w).predict(X_test)
)
if Reg is DecisionTreeRegressor:
assert np.allclose(tree_mse.tree_.impurity, tree_friedman.tree_.impurity)
```
### Expected Results
You would expect different criteria to give different results and hence this code to raise.
### Actual Results
Doesn't raise.
### 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-6.14.0-35-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.8.dev0
pip: None
setuptools: 80.9.0
numpy: 2.3.4
scipy: 1.16.2
Cython: 3.1.5
pandas: 2.3.3
matplotlib: 3.10.7
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libscipy_openblas
filepath: /home/arthur/dev-perso/scikit-learn/sklearn-env/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-8fb3d286.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libscipy_openblas
filepath: /home/arthur/dev-perso/scikit-learn/sklearn-env/lib/python3.12/site-packages/scipy.libs/libscipy_openblas-b75cc656.so
version: 0.3.29.dev
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 16
prefix: libgomp
filepath: /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0
version: None
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"node_id": "MDU6TGFiZWwyNjU5ODYz... | closed | false | [] | null | 1 | 2025-11-12T19:04:09 | 2026-01-27T09:24:14 | 2026-01-27T09:24:14 | null | CONTRIBUTOR | null | null | null | null | Since I started working on the tree module (in September 2025), I found 3 bugs related to impurity calculation, so I think it's worth trying to extend a bit the coverage around impurity.
#### Reference Issues/PRs
The three impurity related bugs:
- https://github.com/scikit-learn/scikit-learn/issues/32178
- https://github.com/scikit-learn/scikit-learn/issues/32707
- https://github.com/scikit-learn/scikit-learn/issues/32870
Note: I opened this PR a while ago, but I was waiting for #32708 to be merged before opening it.
#### What does this implement/fix? Explain your changes.
- Parameterize `test_min_impurity_decrease` to test all criteria.
- Greatly simplify the test.
=> better coverage with less loc.
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"node_id": "LA_kwDOAAzd1s7eWo1... | closed | false | [] | null | 2 | 2025-11-12T14:44:40 | 2025-11-14T16:30:51 | 2025-11-14T06:10:03 | null | MEMBER | null | null | null | null | Follow-up of #32688.
pytest 9.0.1 has been released 2 hours ago, which should fix the issue. Let's see if the wheels build is successful.
Edit: the wheels build was successful see [build log](https://github.com/scikit-learn/scikit-learn/actions/runs/19301332929/job/55196353527?pr=32698). | {
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The `.github/workflows/unit-tests.yml` script contains a working setup for collecting coverage and uploading it to codecov using GitHub Actions. This is probably a good starting point for the CUDA CI which is defined in `.github/workflows/cuda-ci.yml`. We might also have to adjust the options passed to `pytest` in the CUDA CI so that it generates coverage statistics.
One thing to double check/think about is how having a CI that only runs sometimes ends up being displayed on codecov when comparing commits. For example, if `main` has no coverage for some code because we don't run the CUDA CI on `main` will it always look like you have missing coverage on a PR? Or does it not matter because our coverage requirements are calculated on the diff only? | null | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 2 | 2025-11-11T21:38:40 | 2025-11-12T01:06:04 | 2025-11-12T01:06:00 | null | NONE | null | null | null | null | # Overview
This PR improves the documentation for the distance metric parameter in BinaryTree classes (KDTree and BallTree) to provide clearer guidance on valid metrics and address the limitation with callable functions.
# Checklist
- [x] Code changes made
- [x] Documentation updated
- [ ] Tests not applicable (documentation-only change)
# Proof
The documentation has been updated to clarify that:
- The valid metrics for each algorithm can be found using the `valid_metrics` attribute
- Different algorithms support different sets of metrics
- Callable functions are not supported due to performance issues
The changes make the documentation more precise and helpful for users trying to understand which metrics are available for each tree implementation.
Closes #32686 | {
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"node_id": "LA_kwDOAAzd1s8... | closed | false | [] | null | 1 | 2025-11-11T19:41:49 | 2025-12-12T05:23:41 | 2025-11-25T07:56:43 | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Towards #26024
#### What does this implement/fix? Explain your changes.
Add array API support to `davies_bouldin_score`
#### Any other comments? | {
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} | ### Describe the bug
Hello :)
I tried using the SimpleImputer and stumbled upon a probably undesired behaviour (at least a behaviour I wish I had the hand on).
I had a column containing only integers with an integer dtype (FYI, an integer column can't contains np.nan)) that I was trying to Impute by filling it with a constant float value. However, because of `same_kind` cast check here https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/impute/_base.py#L423-L425, this raises the following error.
```
ValueError: fill_value=0.0 (of type <class 'numpy.float32'>) cannot be cast to the input data that is dtype('int64'). If fill_value is a Python scalar, instead pass a numpy scalar (e.g. fill_value=np.uint8(0) if your data is of type np.uint8). Make sure that both dtypes are of the same kind.
```
Indeed, we have the following checks
```python
import numpy as np
np.can_cast(from_=np.float32, to=np.int64, casting="same_kind")
# False <= in this case, sklearn raises an error that's blocking my generic pipelines
np.can_cast(from_=np.int64, to=np.float32, casting="same_kind")
# True
np.can_cast(from_=np.float32, to=np.int64, casting="unsafe")
# True
```
which makes total sense.
Because I'm trying to write generic pipelines, I'd like to allow for unsafe type casting for the fill_value. This could be done by adding an option in the SimpleImputer `allow_fill_value_unsafe_casting` (quite verbose, but at least explicit). If True, the `casting` argument of `np.can_cast` would be `unsafe`.
### Steps/Code to Reproduce
```
import numpy as np
from sklearn.impute import SimpleImputer
X = np.array([1, 2, 3], dtype=np.int64)
imputer = SimpleImputer(strategy='constant', fill_value=np.float32(0))
imputer.fit(X.reshape(-1, 1))
```
### Expected Results
I expect this thing to work properly
### Actual Results
```
428 # print("fill_value_dtype", fill_value_dtype)
429 # print("X.dtype", X.dtype)
430 # Make sure we can safely cast fill_value dtype to the input data dtype
431 if not np.can_cast(fill_value_dtype, X.dtype, casting="same_kind"):
--> [432](https://file+.vscode-resource.vscode-cdn.net/Users/datategy/Documents/papai-ml/papai-ml-1/~/Documents/papai-ml/papai-ml-1/.venv/lib/python3.10/site-packages/sklearn/impute/_base.py:432) raise ValueError(err_msg)
434 return X
ValueError: fill_value=0.0 (of type <class 'numpy.float32'>) cannot be cast to the input data that is dtype('int64'). If fill_value is a Python scalar, instead pass a numpy scalar (e.g. fill_value=np.uint8(0) if your data is of type np.uint8). Make sure that both dtypes are of the same kind.
```
### Versions
```shell
System:
python: 3.10.11 (v3.10.11:7d4cc5aa85, Apr 4 2023, 19:05:19) [Clang 13.0.0 (clang-1300.0.29.30)]
executable: /Users/datategy/Documents/papai-ml/papai-ml-1/.venv/bin/python
machine: macOS-14.4.1-arm64-arm-64bit
Python dependencies:
sklearn: 1.7.2
pip: 25.3
setuptools: 65.5.0
numpy: 1.26.4
scipy: 1.13.1
Cython: None
pandas: 1.5.3
matplotlib: 3.8.4
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 8
prefix: libopenblas
filepath: /Users/datategy/Documents/papai-ml/papai-ml-1/.venv/lib/python3.10/site-packages/numpy/.dylibs/libopenblas64_.0.dylib
version: 0.3.23.dev
threading_layer: pthreads
architecture: armv8
user_api: blas
internal_api: openblas
num_threads: 8
prefix: libopenblas
filepath: /Users/datategy/Documents/papai-ml/papai-ml-1/.venv/lib/python3.10/site-packages/scipy/.dylibs/libopenblas.0.dylib
version: 0.3.27
threading_layer: pthreads
architecture: neoversen1
user_api: openmp
internal_api: openmp
num_threads: 8
prefix: libomp
filepath: /Users/datategy/Documents/papai-ml/papai-ml-1/.venv/lib/python3.10/site-packages/sklearn/.dylibs/libomp.dylib
version: None
``` | {
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} | ### Describe the bug
Extreme RAM spike and OOM after upgrade (0.24.2 → 1.1.1) for ultra‑wide sparse L1 Logistic Regression (liblinear & saga) – baseline 680 GB → >1.4 TB (ref #28993)
Summary
After upgrading scikit‑learn and Python, fitting an L1 Logistic Regression on a very large, sparse, high‑dimensional dataset now triggers a massive memory spike (baseline ~680 GB → allocates >1.4 TB → OOM). Previously (with 0.24.2) peak memory stabilized around 750–770 GB for the same data shape. Switching solvers (liblinear → saga) and using SGDClassifier did not resolve the issue.
Workload Scale
Samples: ~7,500,000
Features: ~550,000
Binary classification, L1 regularization
Input matrix: sparse (CSR)
Environments
Stable (old):
Python 3.9
scikit-learn 0.24.2
numpy 1.23.5
scipy 1.7.1
Peak during fit: ~750–770 GB
Problematic (new):
Python 3.10
scikit-learn 1.1.1
scipy 1.14.1
numpy 1.23.5
Baseline resident memory before calling .fit: ~680 GB (holding data & process)
During .fit: climbs rapidly past ~1.4 TB → OOM
Models Tried
Original:
Python
LogisticRegression(
solver='liblinear',
C=1.0,
penalty='l1',
intercept_scaling=3.0,
random_state=123,
)
Alternative:
Python
SGDClassifier(
loss='log_loss',
penalty='l1',
alpha=1.0,
random_state=123,
)
Attempt with saga (still spikes):
Python
LogisticRegression(
solver='saga',
C=1.0,
penalty='l1',
max_iter=1000,
n_jobs=-1,
random_state=123,
)
New Observation
Memory right before training (after data load / preprocessing) sits around 680 GB.
Training triggers a single large jump rather than gradual leakage across multiple fits.
This suggests a large dense working allocation or multiple parallel buffers rather than slow accumulation.
Questions
Are there known regressions or algorithmic changes between 0.24.2 and 1.1.1 that increase memory for sparse, high‑feature L1 logistic regression (liblinear or saga)?
Does liblinear (L1) or saga under certain conditions implicitly densify parts of X at this scale?
Could n_jobs=-1 for saga (or OpenMP threads) multiply large temporary buffers? Is single-thread execution recommended for memory control here?
Is intercept_scaling=3.0 relevant for any hidden copy or augmentation step that becomes costly at extreme dimensionality?
Are there scipy 1.14.1 sparse changes that alter memory layout or workspace usage compared to what 0.24.2 relied on?
Recommended solver / pipeline for this regime (7.5M × 550k, very sparse, L1): saga vs SGD vs feature hashing + SGD vs dimensionality reduction first?
Would upgrading to a newer scikit-learn (1.5.x / 1.6.dev) likely help (any memory PRs touching saga/liblinear)?
### Steps/Code to Reproduce
LogisticRegression(
solver='liblinear',
C=1.0,
penalty='l1',
intercept_scaling=3.0,
random_state=123,
)
### Expected Results
The peak memory usage should be below 780Gb.
### Actual Results
The memory is exceeding 1.5Tb
### Versions
```shell
old: 0.24.2
new: 1.1.1
``` | {
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**Fix rendering typo in docstring for roc_curve**
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See https://github.com/pytest-dev/pytest/issues/13895 for the pytest issue, `raise unittest.SkipTest` raises in pytest 9.0 released on November 8.
Could be a temporary work-around depending how fast pytest merges https://github.com/pytest-dev/pytest/pull/13912 and do a new release. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32687 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32687/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32687/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32687/events | https://github.com/scikit-learn/scikit-learn/pull/32687 | 3,607,634,175 | PR_kwDOAAzd1s6ygwBP | 32,687 | Make check_n_features and check_feature_names public | {
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] | closed | false | [] | null | 2 | 2025-11-10T11:43:02 | 2025-11-11T15:31:29 | 2025-11-11T15:31:28 | null | NONE | null | null | null | null | #### Reference Issues/PRs
Fixes #30389
#### What does this implement/fix? Explain your changes.
This PR makes the previously private functions `_check_n_features` and `_check_feature_names` part of the public API by renaming them to `check_n_features` and `check_feature_names` respectively.
**Motivation:**
These utility functions are useful for custom estimators and third-party libraries that need to validate feature counts and feature names consistently with scikit-learn's standards. Making them public provides better support for the broader ecosystem and allows users to build estimators that follow scikit-learn conventions.
**Changes:**
- Renamed `_check_n_features` → `check_n_features` in `sklearn/utils/validation.py`
- Renamed `_check_feature_names` → `check_feature_names` in `sklearn/utils/validation.py`
- Updated docstrings to reflect the functions are now public (added "made public" to versionchanged notes)
- Added deprecated aliases for backward compatibility with `@deprecated` decorator
- The old private names (`_check_n_features`, `_check_feature_names`) emit `FutureWarning` and will be removed in version 1.9
- Updated `sklearn/utils/__init__.py` to export the new public functions in `__all__`
- Added comprehensive test coverage:
- `test_check_feature_names_public` - tests public function behavior
- `test_check_n_features_public` - tests public function behavior
- `test_check_feature_names_deprecated` - verifies deprecation warning
- `test_check_n_features_deprecated` - verifies deprecation warning
- `test_public_functions_exported` - confirms proper exports
- Updated API documentation in `doc/api_reference.py`
**Release Notes Entry:**
### `sklearn.utils`
- **Enhancement**: The functions `check_feature_names` and `check_n_features` are now public APIs (previously `_check_feature_names` and `_check_n_features`). These functions are useful for custom estimators to validate feature counts and feature names consistently with scikit-learn's conventions. The old private names are deprecated and will be removed in version 1.9.
:pr:`XXXXX` by :user:`Fazeel Usmani <FazeelUsmani>`. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32686 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32686/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32686/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32686/events | https://github.com/scikit-learn/scikit-learn/issues/32686 | 3,607,460,094 | I_kwDOAAzd1s7XBXj- | 32,686 | Clarify docs for allowable KDTree metrics | {
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} | ### Describe the issue linked to the documentation
https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html currently states, for the metrics kwarg: "Metric to use for distance computation. Default is “minkowski”, which results in the standard Euclidean distance when p = 2. A list of valid metrics for KDTree is given by the attribute valid_metrics. See the documentation of [scipy.spatial.distance](https://docs.scipy.org/doc/scipy/reference/spatial.distance.html) and the metrics listed in [distance_metrics](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.distance_metrics.html#sklearn.metrics.pairwise.distance_metrics) for more information on any distance metric."
Yet clicking on `distance_metrics` yields the following list of metrics: cityblock, cosine, euclidean, haversine, l1, l2, manhattan, nan_euclidean, which is different from the actual list of supported metrics (euclidean, l2, minkowski, p, manhattan, cityblock, l1, chebyshev, infinity). I could not find this list other than by actually printing out the value of `KDTree.valid_metrics`.
### Suggest a potential alternative/fix
Explicitly list the actually supported metrics in the docs (or at least, not link to a list that is different).
A few minor related possible improvements:
- There's a formatting issue immediately after the `metric` kwarg in https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html; likely the "Note:" should be moved to a real rst note (or indented as part of the doc for the metric kwarg).
- The list in https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.distance_metrics.html could group together equivalent metrics ("'l1', 'cityblock', 'manhattan': metrics.pairwise.manhattan_distance; 'l2', 'euclidean': ..., ) | {
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Improve formatting of `sklearn.mixture` API documentation
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Should we remove the 'good first issue' label?
Note that we mention this label in the "[Issues for new contributors](https://github.com/numpy/numpy/pull/21355)" section of the docs. It is also mentioned briefly at the end of the "[A typical workflow for triaging issues](https://scikit-learn.org/dev/developers/bug_triaging.html#a-typical-workflow-for-triaging-issues)" section.
Reasons for:
* currently there are no open issues with this label (https://github.com/scikit-learn/scikit-learn/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22)
* it is rare for good first issues to occur naturally. Often it requires maintainers specifically trying to think of one and spending time writing thorough details and guidance in an issue, probably what happens when searching for issues for people to work on in sprints, and even then, during the sprint, guidance is often required.
* it often attracts *many* low quality PRs - this is possibly because it's aggregated on sites such as https://goodfirstissue.dev/. It's difficult to find examples as we often remove the label but maybe #31318 and #30352 are examples of how much interest the label often attracts.
* it may be better for a user to start work on something that they use themselves and are familiar with, or something they already have background knowledge of. This would of course vary between people, making it a difficult label to use.
* related, as described in @betatim's [post](https://discuss.scientific-python.org/t/what-does-it-mean-to-be-a-project-that-is-welcoming-to-newcomers/2127/3) on scientific-python, the type of users attracted to this label may be simply interested in getting a PR merged in a 'big' project. From Ralf: 'Promising new contributors are almost always motivated by a problem/topic, not by “getting on the scoreboard”'
* for reference NumPy does not have a good first issue label (https://github.com/numpy/numpy/pull/21355) they do have a 'sprintable' label
This is just a request for comment, I understand if we decide to keep this label in the end.
cc @scikit-learn/core-devs | {
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#### What does this implement/fix? Explain your changes.
This changes a typo in the documentation in plot_calibration_multiclass.py
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"node_id": "LA_kwDOAAzd1s8AA... | closed | false | [] | null | 16 | 2025-11-08T09:47:34 | 2026-03-02T09:32:11 | 2025-11-26T13:26:29 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Related #30454
Found out in https://github.com/scikit-learn/scikit-learn/pull/32600#discussion_r2505943104
#### What does this implement/fix? Explain your changes.
Adds info on what happens when non-NumPy array input occurs with `array_api_dispatch=False`
I only realised that we do this and I think it would be nice if it was mentioned in the docs.
I thought I'd add a section on the `array_api_dispatch` as it no longer seemed to fit under the 'Example usage' section
#### Any other comments?
cc @OmarManzoor @lesteve
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32675 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32675/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32675/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32675/events | https://github.com/scikit-learn/scikit-learn/issues/32675 | 3,603,269,688 | I_kwDOAAzd1s7WxYg4 | 32,675 | Feature Request: Add explained variance ratios for X and Y to `PLSRegression` | {
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} | ### Describe the workflow you want to enable
Currently, `sklearn.cross_decomposition.PLSRegression` does not expose any measure of explained variance for either the predictor (`X`) or response (`Y`) spaces.
In different disciplines (such as multivartiate statistical process control, -omic sciences or chemometrics), the explained variance in `X` and `Y` spaces is a core interpretability metric and is routinely used to interpret PLS models — analogous to how PCA exposes `explained_variance_ratio_`. Not having access to it in the `PLSRegression` makes it difficult to:
- Quantify how much variance each latent variable captures,
- Compare models with different numbers of components, and
- Produce diagnostic or variance-explained plots (as is standard in different disciplines where latent space interpretability is important).
### Describe your proposed solution
#### Proposed solution
I propose adding two new attributes, automatically computed after fitting:
- `explained_x_variance_ratio_ `: `ndarray` of shape (`n_components`)
Fraction of variance explained in X-space for each component.
- `explained_y_variance_ratio_ `: `ndarray` of shape (`n_components`)
Fraction of variance explained in Y-space for each component.
These values are derived using standard PLS sequential deflation methodology (see Wegelin, 2000; Abdi, 2003), where variance explained = reduction in total variance after deflation per component, where each matrix is asymmetrically deflated per each component as follows:
$$X←X - \hat{X}_a = X - t_ap_a^T$$
$$Y←Y - \hat{Y}_a = Y - t_aq_a^T$$
This mirrors the `PCA` API (`explained_variance_ratio_`) and significantly improves usability for diagnostics and visualization.
#### Implementation sketch
```python
class PLSRegression(_PLS):
""" Documentation updated to include the follwoing attributes:
Attributes
----------
explained_x_variance_ratio_ : ndarray of shape (n_components,)
Explained variance ratio in X-space (predictors) for each component.
This measures how much variance in the predictor variables each latent
variable captures. Calculated in fit().
explained_y_variance_ratio_ : ndarray of shape (n_components,)
Explained variance ratio in Y-space (response) for each component.
This measures how much variance in the response
each latent variable explains. Calculated in fit().
"""
def __init__(self, ...):
[this section is not modified]
def fit(self, X, y):
super().fit(X, y)
# Calculate explained variance ratios
(
self.explained_x_variance_ratio_,
self.explained_y_variance_ratio_,
) = self._calculate_explained_variance_ratio(X, y)
return self
def _calculate_explained_variance_ratio(
self, X, y
) -> tuple[np.ndarray, np.ndarray]:
"""Calculate explained variance ratios using sequential deflation.
This implements the variance decomposition for PLS regression following
the deflation methodology described in Wegelin (2000).
This method calculates how much variance each component explains by
sequentially deflating the X and Y matrices. This is the standard
approach in PLS and provides accurate component-wise variance.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vectors. Accepts numpy arrays, pandas DataFrames.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target vectors. Accepts 1D (univariate) or 2D (multivariate) targets.
Returns
-------
tuple[ndarray, ndarray]
- X variance ratios of shape (n_components,)
- Y variance ratios of shape (n_components,)
"""
# Convert to arrays and ensure y is 2D (handles pandas DataFrame/Series)
X = np.asarray(X, dtype=float)
y_array = np.asarray(y, dtype=float)
y = np.atleast_2d(y_array).T if y_array.ndim == 1 else y_array
# Center X and Y (PLS already centers data, but we need the original
# centered versions)
X_centered = X - X.mean(axis=0)
y_centered = y - y.mean(axis=0)
# Check for scaling
if self.scale:
X_std = X.std(axis=0, ddof=1)
X_std[X_std == 0.0] = 1.0
X_centered /= X_std
y_std = y.std(axis=0, ddof=1)
y_std[y_std == 0.0] = 1.0
y_centered /= y_std
# Total variance in centered data
X_total_var = np.var(X_centered, axis=0, ddof=1).sum()
y_total_var = np.var(y_centered, axis=0, ddof=1).sum()
has_x_variance = not np.isclose(X_total_var, 0.0)
has_y_variance = not np.isclose(y_total_var, 0.0)
# Initialize matrices for deflation
X_current = X_centered.copy()
y_current = y_centered.copy()
X_var_ratios = []
y_var_ratios = []
# For each component, calculate variance explained then deflate
for a in range(self.n_components):
# Get scores and loadings for component a (using slicing to keep 2D)
t_a = self.x_scores_[:, a : a + 1] # (n_samples, 1)
p_a = self.x_loadings_[:, a : a + 1] # (n_features_X, 1)
q_a = self.y_loadings_[:, a : a + 1] # (n_features_y, 1)
# Reconstruct X and y using current component
X_hat = t_a @ p_a.T
y_hat = t_a @ q_a.T
# Variance of current residual before deflation
X_var_before = np.var(X_current, axis=0, ddof=1.0).sum()
y_var_before = np.var(y_current, axis=0, ddof=1.0).sum()
# Deflate X and y
X_current -= X_hat
y_current -= y_hat
# Variance of residual after deflation
X_var_after = np.var(X_current, axis=0, ddof=1.0).sum()
y_var_after = np.var(y_current, axis=0, ddof=1.0).sum()
# Store variance explained as ratio of total variance
if has_x_variance:
X_var_ratios.append((X_var_before - X_var_after) / X_total_var)
else:
X_var_ratios.append(0.0)
if has_y_variance:
y_var_ratios.append((y_var_before - y_var_after) / y_total_var)
else:
y_var_ratios.append(0.0)
return np.array(X_var_ratios), np.array(y_var_ratios)
```
#### Usage Example
```python
pls = PLSRegression().fit(X, y)
explained_x_variance = pls.explained_x_variance_ratio_
explained_y_variance = pls.explained_y_variance_ratio_
```
#### Testing
I have tested that this implementation provides the same results as the ones described by:
Abdi, H. (2003). Partial Least Squares (PLS) Regression. In Lewis-Beck M., Bryman A., Futing T. (Eds.), Encyclopedia of Social Sciences Research Methods. Thousand Oaks (CA): Sage.
I have also tested that this implementation does not break the `scikit-learn` estimator API:
```python
# Test compliance with scikit-learn
def test_compliance_pls_regression():
# Arrange
transformer = PLSRegression()
# Act & Assert
check_estimator(transformer)
```
### Next steps
If maintainers agree this feature fits within scope, I would be happy to:
- Open a PR adding the attributes to PLSRegression,
- Include updated documentation and examples, and
- Add unit tests verifying correctness against synthetic datasets and published examples.
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
- No changes to existing API or behavior.
- Attributes are only added post-fit (similar to PCA’s `explained_variance_ratio_`).
- Could be extended to other PLS methods *(e.g., `PLSCanonical`) but it requires that other methods expose the `x_scores_` and the `y_scores_` (currently are not exposed) and to symmetrically deflate the `Y` matrix.
### Previous issues
This request is not new, there were some previous. issues:
- https://github.com/scikit-learn/scikit-learn/issues/19896 (open but no complete implementation suggested)
- https://github.com/scikit-learn/scikit-learn/issues/30470 (closed)
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#### What does this implement/fix? Explain your changes.
Add reference link to LocalOutlierFactor (LOF)
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Continued fixing typos from #32666.
#### What does this implement/fix? Explain your changes.
Correct the typos in a Benchmark file comments. I searched the codebase for ``"lets"`` usages, and most of them are appropriate.
#### Any other comments?
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} | ### Describe the issue linked to the documentation
In https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html the example is introduced as "Let’s say you are interested in the samples 10, 25, and 50, and want to know their class name."
However, the data indices are not used to index data.target_names in the code, so the code does not match the introduction.
Suggest changing the code to use the indices with output which is obviously what is expected.
### Suggest a potential alternative/fix
_No response_ | {
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Clarifies that `train_test_split` preserves input data types, which helps beginners understand why mixed input types (DataFrame + numpy array) result in mixed output types. Many beginners find it confusing when they get different types for X_train (DataFrame) and y_train (numpy array) when using mixed inputs.
#### Changes:
- Added `Notes` section explaining type preservation
- Added example showing mixed DataFrame and numpy array inputs
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32666 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32666/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32666/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32666/events | https://github.com/scikit-learn/scikit-learn/pull/32666 | 3,598,502,750 | PR_kwDOAAzd1s6yCgad | 32,666 | DOC: Correct many typos in code comments | {
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"node_id": "MDU6TGFiZWwyNjU5... | closed | false | [] | null | 2 | 2025-11-07T03:23:39 | 2025-11-07T20:46:47 | 2025-11-07T20:45:28 | null | CONTRIBUTOR | null | null | null | null | <!--
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#### What does this implement/fix? Explain your changes.
Correct many typos in code comments, especially on a/an usage.
#### Any other comments?
I searched for patterns such as ``" a e"`` and ``" an c"`` in the GitHub codebase, because they are most likely to contain grammatical mistakes. Then I manually reviewed which ones needed to be changed. For instance, ``"a error"`` is incorrect, but ``"a Euclidean"`` is correct. These patterns are not exhaustive, so a few issues of ("a" vs "an") still remain.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32665 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32665/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32665/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32665/events | https://github.com/scikit-learn/scikit-learn/issues/32665 | 3,595,356,150 | I_kwDOAAzd1s7WTMf2 | 32,665 | StandardScaler raises unclear error with empty feature names | {
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} | ### Describe the bug
When using StandardScaler with a pandas DataFrame that has empty strings as feature names (which can happen with certain data processing pipelines), the scaler raises a confusing error message that doesn't clearly indicate the root cause of the problem.
The error message should be more descriptive and guide users to fix the issue with their feature names.
### Steps/Code to Reproduce
```py
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
# Create DataFrame with empty string as column name
df = pd.DataFrame(np.random.randn(10, 3), columns=['feature1', '', 'feature3'])
# Try to fit StandardScaler
scaler = StandardScaler()
scaler.fit(df) # This raises an unclear error
```
### Expected Results
Either:
1. StandardScaler should raise a clear, informative error message like: "Feature names must be non-empty strings. Found empty string in column 1."
2. Or it should work by automatically handling empty feature names
3. Or raise a ValueError with a helpful message during validation
### Actual Results
KeyError: ''
The error message is not informative about what went wrong. It just shows that an empty string key was not found, without explaining that empty feature names are problematic.
### Versions
```shell
scikit-learn: 1.5.2
pandas: 2.2.0
numpy: 1.26.4
Python: 3.11.5
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#### Reference Issues/PRs
Follow up from #32509, specifically [this comment](https://github.com/scikit-learn/scikit-learn/pull/32509#discussion_r2454649769)
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#### What does this implement/fix? Explain your changes.
Move the installation and set up instructions for `pre-commit` to the development setup and remove the (**Optional**) marker. I also made it a sub-heading, so the linting message can point directly to it.
#### Any other comments?
I moved it to the section just before installing scikit-learn, because `pre-commit` is also already installed in Codespaces, so the instructions on using that (#32474) can then jump over everything up to this point.
@StefanieSenger @lesteve
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] | closed | false | [] | null | 3 | 2025-11-06T09:25:31 | 2025-11-08T01:07:07 | 2025-11-07T12:13:22 | null | MEMBER | null | null | null | null | Like for wikipedia (["Editing your own page"](https://en.wikipedia.org/wiki/Wikipedia:Editing_Your_Own_Page)) you should not propose your own work for inclusion in scikit-learn.
This adds to the inclusion criteria FAQ entry a sentence that states that you should not propose your own work for inclusion in scikit-learn. The idea is to make it clearer that scikit-learn does not want to be a platform for advertising new methods. scikit-learn wants to provide tried and tested methods in a format that practitioners from other fields can easily use ("machine-learning without the learning curve").
I wrote "Do not propose your own work" to leave the door open to experts/authors to contribute to the discussion and implementation. We want them to participate in this phase, but during the decision to (not) include their work they should probably not participate.
ping @glemaitre | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32662 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32662/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32662/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32662/events | https://github.com/scikit-learn/scikit-learn/pull/32662 | 3,594,889,095 | PR_kwDOAAzd1s6x2eil | 32,662 | DOC Clarify weighted percentile behavior when average=True | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 2 | 2025-11-06T09:23:08 | 2025-11-07T08:47:34 | 2025-11-07T08:47:27 | null | NONE | null | null | null | null | <!--
🙌 Thanks for contributing a pull request!
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#### Reference Issues/PRs
Fixes #32289
#### What does this implement/fix? Explain your changes.
This PR adds a `Notes` section to the `_weighted_percentile` to specify the behavior of `_weighted_percentile` when `average=True`. This clarification is based on the discussion from #32289.
#### Any other comments?
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32661 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32661/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32661/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32661/events | https://github.com/scikit-learn/scikit-learn/issues/32661 | 3,594,821,795 | I_kwDOAAzd1s7WRKCj | 32,661 | DOC Create triage guide in maintainer documentation | {
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] | open | false | [] | null | 5 | 2025-11-06T09:06:00 | 2025-11-27T06:05:55 | null | null | MEMBER | null | null | {
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} | As part of talking about triaging at the Scientific Python Summit I realised that we have quite some cumulative experience with triaging. There are also different approaches to things.
As a result I think it would be useful if we create a triaging guide as part of our developer/maintainer documentation. The goal of the guide would be to serve as a reference for what to do as a triager, what tools we have available to help with the process and so on.
Some initial things I can think of including:
* list of labels we have for triaging and what to use them for
* "please don't open a PR before the discussion has converged" as a policy (triagers should close these "early PRs" to help enforce this)
* bookmarked search queries/lists of issues
* [needs triage](https://github.com/scikit-learn/scikit-learn/issues?q=sort%3Aupdated-desc%20is%3Aissue%20is%3Aopen%20label%3A%22Needs%20Triage%22)
* [new PR without reviews](https://github.com/scikit-learn/scikit-learn/pulls?q=is%3Apr+is%3Aopen+review%3Anone+sort%3Acreated-desc)
* [new PR without reviews by a maintainer](https://github.com/search?q=repo%3Ascikit-learn%2Fscikit-learn+is%3Apr+is%3Aopen+author%3Abetatim+author%3Aogrisel+author%3Aglemaitre+author%3Alesteve+review%3Anone&type=pullrequests&s=created&o=desc)
* needs more maintainer usernames in the query
* we could have a similar query for recurring contributors, but how?
* [PRs waiting for second reviewer](https://github.com/scikit-learn/scikit-learn/pulls?q=sort%3Aupdated-desc+is%3Apr+is%3Aopen+review%3Anone+label%3A%22Waiting+for+Second+Reviewer%22)
* [Open PRs that you have reviewed in the past](https://github.com/scikit-learn/scikit-learn/pulls?q=sort%3Aupdated-desc+is%3Apr+is%3Aopen+reviewed-by%3A%40me+)
* look at PRs marked with "autoclose" and decide to potentially remove the label or re-open if the PR has significantly improved [scikit-learn/scikit-learn/issues (sort:updated-desc label:autoclose)](https://github.com/scikit-learn/scikit-learn/issues?q=sort%3Aupdated-desc%20label%3Aautoclose)
* integrate with https://scikit-learn.org/dev/developers/bug_triaging.html#triaging-operations-for-members-of-the-core-and-contributor-experience-teams
Can you think of more search queries that would be useful @lesteve?
xref https://github.com/scientific-python/summit-2025-nov/issues/6 | null | {
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] | closed | false | [] | null | 16 | 2025-11-06T08:15:22 | 2025-11-18T10:47:33 | 2025-11-17T13:59:27 | null | MEMBER | null | null | null | null | A maintainer who has the time and interest is an important ingredient in moving work forward. The goal of this PR is to make it easier to make people aware of this.
The idea is to provide a label ("Needs Decision") that can be applied to an issue that will trigger a comment to be added that explains that, based on past experience, work that has no maintainer to champion it is unlikely to be merged. The goal is to raise awareness about this fact to help set expectations. The goal is not to use this to discourage people or suggest that an issue is not interesting/important/useful. The goal is to explain why sometimes even very good ideas don't happen for a long time.
Why is this a label + bot? We could each have a message similar to this in our "saved messages" and post it. The downside to that is that everyone would have slightly different messages and that by posting in an issue you are marked as participating in it. This means you will receive notifications with a higher priority, it might also create the impression that you personally want to/don't want to participate in the discussion (instead of just making people aware of the fact).
A risk with this message is that contributors will start @-mentioning individual maintainers to attract their attention. If this starts happening we can address it then.
We discussed this topic at the Scientific Python Summit (@lesteve @StefanieSenger @glemaitre). What do others think?
xref https://github.com/scientific-python/summit-2025-nov/issues/6 | {
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} | 17 | 2025-11-06T06:06:22 | 2025-11-28T12:54:52 | 2025-11-26T04:01:40 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Partially solves #28711.
This is the the no-brainer of https://github.com/scikit-learn/scikit-learn/pull/32042#issuecomment-3249621324.
#### What does this implement/fix? Explain your changes.
This PR
- for class LogisticRegression
deprecates the parameters penalty
changes the default of l1_ratio from None to 0
l1_ratio=None is deprecated and forbidden as of 1.10
- for class LogisticRegressionCV
deprecates the parameters penalty
changes the default of l1_ratios from None to "warn" (=deprecation of None)
default of l1_ratios will change to (0,) in 1.10
l1_ratios=None is deprecated and forbidden as of 1.10
#### Any other comments? | {
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Unable to find junit file. Please see link for details. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32657 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32657/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32657/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32657/events | https://github.com/scikit-learn/scikit-learn/pull/32657 | 3,593,274,326 | PR_kwDOAAzd1s6xxM0j | 32,657 | MNT: Update test names to use `confusion_matrix_at_thresholds` | {
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"node_id": "MDU6TGFiZWwyNj... | closed | false | [] | null | 1 | 2025-11-06T00:02:02 | 2025-11-06T09:33:38 | 2025-11-06T09:30:41 | null | MEMBER | null | null | null | null |
#### Reference Issues/PRs
Follow up to #30134 where we renamed private `_binary_clf_curve` to `confusion_matrix_at_thresholds`
#### What does this implement/fix? Explain your changes.
Renames tests to use the new name `confusion_matrix_at_thresholds`, AFAICT there are now no references to `_binary_clf_curve` in the codebase.
#### Any other comments?
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cc @adrinjalali | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 8 | 2025-11-05T22:24:18 | 2025-11-13T17:18:18 | 2025-11-13T11:34:16 | null | NONE | null | null | null | null | Maybe I misunderstood the documentation but from what I have seen the usual formula for idf is log(n/df(t)) and not log(n/(df(t)+1)). This is at least the standard definition in the Wikipedia [page](https://en.wikipedia.org/wiki/Tf%E2%80%93idf#Inverse_document_frequency) about tf-idf. | {
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## Summary
New contributors frequently comment “Can I take this?” on issues without the *help wanted* label. This creates notification noise and does not trigger CI assignment. The current contributing docs lead with “apply the ‘help wanted’ label to find issues… then comment `/take`”, which is easy to mislead as “`/take` is only for ‘help wanted’ issues”.
<img width="825" height="334" alt="Image" src="https://github.com/user-attachments/assets/2632b31e-51a1-45ee-997d-b9edb1646c9e" />
## evidence
- **Maintainers have posted warnings like “comments such as ‘can I work on this’ or ‘assign this to me’ may get you banned” on issues already being worked on.**
<img width="818" height="113" alt="Image" src="https://github.com/user-attachments/assets/96f4018f-1baa-498f-bc17-c9ce320de00d" />
- **Recent examples of “Can I take this?” / “assign this to me” appear across issues.** (*The following images are only used to demonstrate the existence of this situation and have no other meaning.*)
<img width="416" height="71" alt="Image" src="https://github.com/user-attachments/assets/3ca75454-669d-4845-b352-ff40ab5a80e6" />
<img width="787" height="90" alt="Image" src="https://github.com/user-attachments/assets/13abf620-edbe-4bea-b9c6-3bb143685a91" />
<img width="769" height="75" alt="Image" src="https://github.com/user-attachments/assets/9dee7c8c-ce6d-4cbc-8c92-a9a2f6f0e529" />
<img width="573" height="76" alt="Image" src="https://github.com/user-attachments/assets/88da0126-568f-46db-b650-9b98c00b61e3" />
- **This noise hides actionable updates and shifts cognitive load to maintainers.**
## cause
- The contribution guidelines are lengthy and detailed, which may make it difficult for beginners to quickly grasp the key points. If the opening example emphasizes a certain label, it can easily be misunderstood as "this is the only situation that can be done".In Contributing Doc,it emphasizes “help wanted” as the easy way to find issues and then says to comment `/take`. For some new contributer and skimmers, the mental model becomes:
***help wanted → allowed to /take; everything else → ask first***.
- Many newcomers, out of politeness or lack of confidence, will first ask, "Can I do this?" to avoid doing something pointless or infringing on others' territory. This reflects their uncertainty about community norms.
- The doc later explains time windows, stale/claimed PRs, etc., but many newcomers never read that.
- Placing “filter by *help wanted* … then comment `/take`” early in the doc can plausibly mislead newcomers due to well-documented effects: [**Serial-position effect**](https://en.wikipedia.org/wiki/Serial-position_effect?) makes early information disproportionately influential, so readers treat it as the operative rule;and the [**Web User Behaviour Directed by Information Scent**](https://www.interaction-design.org/literature/article/web-user-behaviour-directed-by-information-scent?) and the [**Default Effect**](https://en.wikipedia.org/wiki/Default_effect?) shows people often follow an implied “recommended” option when uncertain. Taken together, these explain why newcomers may ask “Can I take this?” on non-labelled issues.
## Proposal
1. **Front-load the universal rule right in the note box:**
- “`/take` works for any unclaimed issue; the ‘help wanted’ label is only a discovery aid.”
2. **Add a one-sentence callout to discourage noise:**
- “Please avoid comments like ‘Can I take this?’—use '`/take` instead. If you need design feedback, briefly outline your plan and then `/take`.”
3. **Link “how to check if an issue is already taken / time windows” from that same box.**
## Why do this?
- This does not change policy; it clarifies and surfaces the existing policy.
- No restructuring of the entire page—just a small copy edit and one cross-link.
- Reduce the noise.
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~~edit: I shouldn't have added a ping, this PR is not ready.~~ | {
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Suggested here https://github.com/scikit-learn/scikit-learn/pull/32646#discussion_r2493608193
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Fixes `permutation_importance` to work with Polars DataFrames when used in pipelines with `ColumnTransformer`.
## Problem
When using `permutation_importance` with Polars DataFrames in a pipeline containing a `ColumnTransformer` with string column names, it would fail with:
```
ValueError: Specifying the columns using strings is only supported for dataframes.
```
This occurred because:
1. Polars DataFrames don't have an `iloc` attribute, so they were converted to NumPy arrays by `check_array()`
2. Once converted to NumPy, the string column names couldn't be used for indexing
3. Polars DataFrames use different methods (`clone()` vs `copy()`) and require different shuffling logic
## Solution
- Added check to preserve Polars DataFrames instead of converting them to NumPy arrays
- Added Polars-specific handling for copying (using `clone()` instead of `copy()`)
- Added Polars-specific column shuffling using `with_columns()` since Polars DataFrames are immutable
- Added test case to prevent regression
## Changes
- Modified `sklearn/inspection/_permutation_importance.py`:
- Import `_is_polars_df_or_series` validation helper
- Skip `check_array` conversion for Polars DataFrames
- Use `clone()` for Polars DataFrames instead of `copy()`
- Add Polars-specific column shuffling logic
- Added `sklearn/inspection/tests/test_permutation_importance_polars.py` with test case
## Checklist
- [x] Added test for Polars DataFrame support
- [x] Test passes locally
- [ ] Added what's new entry (will add if requested)
## Example Usage
```python
import polars as pl
from sklearn.inspection import permutation_importance
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn import set_config
set_config(transform_output="polars")
# Create Polars DataFrame
X_pl = pl.DataFrame({'feature_0': [1,2,3,4,5],
'feature_1': [2,3,4,5,6]})
y = [0, 1, 0, 1, 0]
# Create pipeline with string column names
pipeline = Pipeline([
('preprocessor', ColumnTransformer([
('scaler', StandardScaler(), ['feature_0', 'feature_1'])
])),
('classifier', LogisticRegression())
])
pipeline.fit(X_pl, y)
result = permutation_importance(pipeline, X_pl, y, n_repeats=10) # Now works!
print(result.importances_mean)
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> This issue is already being worked on, a comment like "can I work on this" or "assign this issue to me" may get you banned from the repo
From git grep below
- [x] 3.10 -> 3.11 README.rst, pyproject.toml, meson.build, doc/developers/development_setup.rst
- [ ] `update_environment_and_lock_files.py` the version is misleading, it should be 3.12.3 in Ubuntu Atlas. For completeness, this is the Python version to create the pip requirements lock-file.
- [x] Some possible clean-up in `sklearn/preprocessing/_encoders.py`
- [x] `.circleci/config.yml` probably not used much since we are using the conda env, either bump or remove
- [ ] maybe other things I have missed
```
❯ git grep -P '3\.10\b'
.circleci/config.yml: - image: cimg/python:3.10.16
.github/workflows/check-sdist.yml: python-version: '3.10'
README.rst:.. |PythonMinVersion| replace:: 3.10
build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock:https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.10-hb711507_0.conda#0e0cbe0564d03a99afd5fd7b362feecd
build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock:https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.7-py313h683a580_0.conda#5858a4032f99c89b175f7f5161c7b0cd
build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock:https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.10.7-py313h78bf25f_0.conda#a9e249d3fa6fc485e307e62eb2d33c5a
build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock:https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.10.7-py314hd47142c_0.conda#28a65ed1cad5a165cba7e0b6c119de67
build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock:https://conda.anaconda.org/conda-forge/osx-64/matplotlib-3.10.7-py314hee6578b_0.conda#6e5ce49aa7e5bf46c32f1c166391789e
build_tools/azure/pylatest_conda_forge_osx-arm64_conda.lock:https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.10.7-py313h58042b9_0.conda#17046bd72a5be23b666bc6ee68d85b75
build_tools/azure/pylatest_conda_forge_osx-arm64_conda.lock:https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-3.10.7-py313h39782a4_0.conda#25f9bbc3a3000394a11aa72b30454ada
build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock:# pip matplotlib @ https://files.pythonhosted.org/packages/22/ff/6425bf5c20d79aa5b959d1ce9e65f599632345391381c9a104133fe0b171/matplotlib-3.10.7-cp313-cp313-manylinux2014_x8>
build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock:https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.10-hb711507_0.conda#0e0cbe0564d03a99afd5fd7b362feecd
build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock:https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.10.7-py311h1675fdf_0.conda#5d5926fd19717e4c86f06752bfe0870d
build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock:https://conda.anaconda.org/conda-forge/win-64/matplotlib-3.10.7-py311h1ea47a8_0.conda#1770853fbc9aa46906cd61df67d70818
build_tools/azure/ubuntu_atlas_lock.txt:# This file is autogenerated by pip-compile with Python 3.10
build_tools/circle/doc_linux-64_conda.lock:https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.10-hb711507_0.conda#0e0cbe0564d03a99afd5fd7b362feecd
build_tools/circle/doc_linux-64_conda.lock:https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.7-py311h0f3be63_0.conda#b4ec935aa9298e5498613ea66b3c3a98
build_tools/circle/doc_linux-64_conda.lock:https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.10.7-py311h38be061_0.conda#979c4fd79b6edb07fa602a02edcb2c43
build_tools/circle/doc_min_dependencies_linux-64_conda.lock:https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.10-hb711507_0.conda#0e0cbe0564d03a99afd5fd7b362feecd
build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock:https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.10-hb711507_0.conda#0e0cbe0564d03a99afd5fd7b362feecd
build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock:https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.7-py313h683a580_0.conda#5858a4032f99c89b175f7f5161c7b0cd
build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock:https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.10.7-py313h78bf25f_0.conda#a9e249d3fa6fc485e307e62eb2d33c5a
build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock:https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-renderutil-0.3.10-h5c728e9_0.conda#7beeda4223c5484ef72d89fb66b7e8c1
build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock:https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.10.7-py311hb9c6b48_0.conda#7c41eef230a6f2035a95005008e7e456
build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock:https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.10.7-py311hfecb2dc_0.conda#0f4bc7bb0509530cea460da7f20ac7a6
build_tools/update_environments_and_lock_files.py: "python_version": "3.10.4",
doc/developers/development_setup.rst:.. |PythonMinVersion| replace:: 3.10
doc/install.rst: Scikit-learn 1.0 supported Python 3.7—3.10.
doc/install.rst: Scikit-learn 1.7 requires Python 3.10 or newer.
doc/whats_new/v1.7.rst: only affects Python versions `>=3.10.0,<=3.10.11` and `>=3.11.0,<=3.11.3`.
pyproject.toml:requires-python = ">=3.10"
pyproject.toml: "Programming Language :: Python :: 3.10",
sklearn/externals/array_api_compat/cupy/_typing.py: # NumPy 1.x on Python 3.10 fails to parse np.dtype[]
sklearn/externals/array_api_compat/numpy/_typing.py: # NumPy 1.x on Python 3.10 fails to parse np.dtype[]
sklearn/meson.build: if not py.version().version_compare('>=3.10')
sklearn/meson.build: error('scikit-learn requires Python>=3.10, got ' + py.version() + ' instead')
sklearn/preprocessing/_encoders.py: Python versions before 3.10. The handling of `nan` values was improved
sklearn/preprocessing/_encoders.py: from Python 3.10 onwards, (c.f.
``` | {
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```
##[error]This is a scheduled macos-13 brownout. The macOS-13 based runner images are being deprecated. For more details, see https://github.com/actions/runner-images/issues/13046.
```
For more details: https://github.com/actions/runner-images/issues/13046 | {
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