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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32953 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32953/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32953/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32953/events | https://github.com/scikit-learn/scikit-learn/pull/32953 | 3,763,530,990 | PR_kwDOAAzd1s66o0pN | 32,953 | feat: add preserve_output_dtypes config | {
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] | closed | false | [] | null | 0 | 2025-12-26T14:06:10 | 2025-12-26T14:06:25 | 2025-12-26T14:06:25 | null | NONE | null | null | null | null | ## Summary
- add preserve_output_dtypes to global config and wrappers
- ensure pandas outputs can retain input dtypes when untouched
- cover dtype preservation, skip scenarios, and duplicate column handling
## Testing
- pytest sklearn/utils/tests/test_set_output.py -q
## Issues
- #52 | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32952 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32952/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32952/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32952/events | https://github.com/scikit-learn/scikit-learn/issues/32952 | 3,763,308,316 | I_kwDOAAzd1s7gT4cc | 32,952 | Add Net Benefit for classifications metrics | {
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"node_id": "LA_kwDOAAzd1s7Ybv1... | closed | false | [] | null | 1 | 2025-12-26T11:25:56 | 2025-12-28T10:08:12 | 2025-12-28T10:08:12 | null | NONE | null | null | {
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} | ### Describe the workflow you want to enable
Net benefit is defined as a decision-analytic measure that quantifies the clinical value of a prediction model, test, or marker by putting its benefits and harms on the same scale. This is done by specifying an exchange rate—a clinical judgment about how much a harm (e.g., an unnecessary biopsy) can be tolerated in order to gain a benefit (e.g., detecting a cancer). The net benefit calculation then subtracts weighted harms from benefits to determine whether using the model or test would do more good than harm in practice, unlike traditional statistics such as sensitivity or AUC that do not directly reflect clinical value.
https://www.bmj.com/content/352/bmj.i6
### Describe your proposed solution
I would like to implement this into `_classification.py` , but want to make sure there's no major objection for adding new metrics (and later add a decision analysis curve as well: https://en.wikipedia.org/wiki/Decision_curve_analysis)
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
_No response_ | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32951 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32951/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32951/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32951/events | https://github.com/scikit-learn/scikit-learn/pull/32951 | 3,763,253,579 | PR_kwDOAAzd1s66n6BQ | 32,951 | DOC: clarify GridSearchCV verbose behaviour for higher values | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 1 | 2025-12-26T10:48:24 | 2025-12-26T15:13:57 | 2025-12-26T14:55:58 | null | CONTRIBUTOR | null | null | null | null | This PR clarifies the documentation of the `verbose` parameter in `GridSearchCV`.
The current documentation does not reflect the actual behavior for larger
values (e.g. >3 or >10). This update explains that higher values increase
verbosity and that the exact output depends on the execution backend.
Closes #32946
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32950 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32950/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32950/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32950/events | https://github.com/scikit-learn/scikit-learn/issues/32950 | 3,762,599,696 | I_kwDOAAzd1s7gRLcQ | 32,950 | AttributeError: cross_val_predict(method='predict_proba') with MultiOutputClassifier (swev-id: scikit-learn__scikit-learn-14629) | {
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} | ## User Request
AttributeError with `cross_val_predict(method='predict_proba')` when using `MultiOutputClassifier`.
Description:
Using `cross_val_predict(method='predict_proba')` with `MultiOutputClassifier` triggers `AttributeError` because `estimator.classes_` is accessed; `MultiOutputClassifier` exposes `classes_` per sub-estimator as `estimators_[i].classes_`. Proposed fix: store `classes_` at the wrapper level like `ClassifierChain` does, and adjust validation code paths.
Repro:
```python
from sklearn.datasets import make_multilabel_classification
from sklearn.multioutput import MultiOutputClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_val_predict
X, Y = make_multilabel_classification()
mo_lda = MultiOutputClassifier(LinearDiscriminantAnalysis())
pred = cross_val_predict(mo_lda, X, Y, cv=5) # Works
pred_proba = cross_val_predict(mo_lda, X, Y, cv=5, method='predict_proba') # Error
```
Observed failure (branch: `scikit-learn__scikit-learn-14629`):
```
Traceback (most recent call last):
File "<stdin>", line 8, in <module>
File "sklearn/model_selection/_validation.py", line 625, in cross_val_predict
# ... setup omitted ...
File "sklearn/model_selection/_validation.py", line 859, in _fit_and_predict
predictions = [_enforce_prediction_order(
estimator.classes_[i_label], predictions[i_label],
n_classes=len(set(y[:, i_label])), method=method)
for i_label in range(len(predictions))]
AttributeError: 'MultiOutputClassifier' object has no attribute 'classes_'
```
## Researcher Specification
- cross_val_predict handling for `method='predict_proba'` enforces class order via `_enforce_prediction_order`, expecting a top-level `estimator.classes_` that is indexable per output when predictions is a list.
- In this branch, `_validation.py` does:
```python
predictions = [_enforce_prediction_order(
estimator.classes_[i_label], predictions[i_label],
n_classes=len(set(y[:, i_label])), method=method)
for i_label in range(len(predictions))]
```
which requires `estimator.classes_` at the wrapper level.
- `MultiOutputClassifier` (in `sklearn/multioutput.py`) currently does not define `self.classes_` at the wrapper level after `fit`. Each underlying estimator has its own `.classes_`.
- `ClassifierChain` in the same module sets `self.classes_ = [estimator.classes_ for estimator in self.estimators_]` during `fit`.
### Proposed Fix
- Modify `MultiOutputClassifier` to set a wrapper-level `classes_` after `fit` (and update on `partial_fit` if applicable), mirroring `ClassifierChain`:
- In `sklearn/multioutput.py`, within `class MultiOutputClassifier`:
- Add to Attributes docstring:
- `classes_ : list` — list of length `n_outputs`; each element is an array of class labels for the corresponding output.
- Implement:
```python
def fit(self, X, y, sample_weight=None):
super().fit(X, y, sample_weight=sample_weight)
self.classes_ = [estimator.classes_ for estimator in self.estimators_]
return self
```
- Optionally:
```python
@if_delegate_has_method('estimator')
def partial_fit(self, X, y, classes=None, sample_weight=None):
super().partial_fit(X, y, classes=classes, sample_weight=sample_weight)
if all(hasattr(e, 'classes_') for e in self.estimators_):
self.classes_ = [e.classes_ for e in self.estimators_]
return self
```
- No changes needed in `cross_val_predict` validation: it already handles list predictions and expects `wrapper.classes_[i]`.
### Tests
- Add regression tests (e.g., in `sklearn/model_selection/tests/test_validation.py`):
1) LDA + `MultiOutputClassifier` with `cross_val_predict(method='predict_proba')` should return a list of length `n_outputs`; each array shape `(n_samples, n_classes_i)` with rows summing to 1.
2) Mixed binary/multiclass outputs (e.g., `LogisticRegression` base) maintain correct shapes and order.
3) `cross_val_predict` without `method` remains working for multi-output classifiers.
### Risks/Notes
- If any sub-estimator lacks `predict_proba`, `MultiOutputClassifier.predict_proba` raises `ValueError`; `cross_val_predict(method='predict_proba')` will propagate accordingly.
- Class order differences across folds are handled by `_enforce_prediction_order` (existing behavior).
- Multi-output with different class sets per target is represented naturally by list-of-arrays `classes_`.
We will implement this fix on a dedicated branch and open a single PR targeting `scikit-learn__scikit-learn-14629`. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32949 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32949/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32949/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32949/events | https://github.com/scikit-learn/scikit-learn/issues/32949 | 3,762,549,153 | I_kwDOAAzd1s7gQ_Gh | 32,949 | BUG: HuberRegressor fails with boolean feature matrix | {
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} | ### Summary
`HuberRegressor.fit` raises a `TypeError` when the feature matrix is boolean because the internal gradient helper negates a boolean mask with the `-` operator. In NumPy >=1.13 this triggers `TypeError: The numpy boolean negative, the '-' operator, is not supported, use the '~' operator or the logical_not function instead.`
### Steps to reproduce
```python
import numpy as np
from sklearn.linear_model import HuberRegressor
X = np.array([[True, False], [False, True], [True, True]])
y = np.array([1.0, -1.0, 0.5])
HuberRegressor().fit(X, y)
```
### Current behavior
The call fails while computing the gradient due to negation of a boolean mask inside `_huber_loss_and_gradient`.
### Expected behavior
The estimator should accept boolean feature matrices just like other linear models do, internally casting them to floating point types before optimization.
### Environment
- agyn-sandbox/scikit-learn legacy toolchain (Python 3.8.20, NumPy 1.19.5, SciPy 1.7.1)
- master branch as of today | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32948 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32948/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32948/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32948/events | https://github.com/scikit-learn/scikit-learn/pull/32948 | 3,762,419,891 | PR_kwDOAAzd1s66lRpT | 32,948 | Fix multinomial CV scoring uses softmax | {
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] | closed | false | [] | null | 1 | 2025-12-25T22:48:58 | 2026-01-05T09:37:20 | 2025-12-25T22:49:14 | null | NONE | null | null | null | null | ## Summary
- ensure _log_reg_scoring_path propagates solver and multi_class to the temporary LogisticRegression scorer
- add regression tests covering multinomial neg_log_loss scoring for scoring path and LogisticRegressionCV
## Testing
- blocked: python setup.py develop (fails linking legacy ATLAS cblas headers with GCC 14; see build.log for details)
- blocked: pytest sklearn/linear_model/tests/test_logistic.py -k multinomial (requires compiled extensions that fail to build on this toolchain)
Closes #13 | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32947 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32947/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32947/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32947/events | https://github.com/scikit-learn/scikit-learn/issues/32947 | 3,762,410,863 | I_kwDOAAzd1s7gQdVv | 32,947 | clone: treat class-valued parameters as non-estimators (avoid TypeError) [swev-id: scikit-learn__scikit-learn-12585] | {
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} | ## User request
Clone fails for parameters that are estimator types.
Key: clone should not error when a parameter is a class; adjust clone to treat classes as non-estimators (e.g., check isinstance(estimator, type) or ismethod(get_params)).
## Research specification
- Location: sklearn/base.py, function `clone`.
- Problem: When an estimator has a parameter whose value is a class/type (e.g., `LogisticRegression` rather than an instance), `clone` attempts to call `get_params` on the class object, leading to `TypeError: get_params() missing 1 required positional argument: 'self'`.
- Proposed change: Add an early guard in `clone` after container-type handling:
- `elif isinstance(estimator, type): return estimator`
- Treat Python classes/types as non-estimators and leave them unchanged, both at top level and within parameter traversal.
- Edge cases:
- Estimator parameter set to a scikit-learn estimator class → preserved unchanged.
- Parameter set to a user-defined class type (not `BaseEstimator`) → preserved unchanged.
- numpy scalar types (e.g., `np.float64`) → preserved unchanged as types.
- typing/dataclass types → preserved unchanged.
- Tests:
- New tests to ensure cloning succeeds and preserves identity for class-valued parameters; estimator instances continue to be cloned.
- Risk & API consistency:
- Minimal, backward-compatible change; relaxes previous erroneous behavior when encountering class objects. Normal estimator instance cloning behavior is unchanged. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32946 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32946/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32946/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32946/events | https://github.com/scikit-learn/scikit-learn/issues/32946 | 3,762,349,288 | I_kwDOAAzd1s7gQOTo | 32,946 | GridSearchCV.verbose documentation does not match actual behavior (values >3, >10) | {
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} | ### Describe the issue linked to the documentation
[`GridSearchCV` docs ](https://scikit-learn.org/1.7/modules/generated/sklearn.model_selection.GridSearchCV.html) state
> **verbose** int
> Controls the verbosity: the higher, the more messages.
> \>1 : the computation time for each fold and parameter candidate is displayed;
> \>2 : the score is also displayed;
> \>3 : the fold and candidate parameter indexes are also displayed together with the starting time of the computation.
In reality, this param is passed to joblib [here](https://github.com/scikit-learn/scikit-learn/blob/d3898d9d57aeb1e960d266613a2e31b07bca39d7/sklearn/model_selection/_search.py#L1009), whose [docs](https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html) state:
> **verbose**: int, default=0
> The verbosity level: if non zero, progress messages are printed. Above 50, the output is sent to stdout. The frequency of the messages increases with the verbosity level. If it more than 10, all iterations are reported.
Testing with the MRE below shows that 0 is silent, 1 prints one line, 2 prints a line for each CV split. 3-9 are all the same. 10-inf are all the same
### MRE:
```python
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
X, y = make_classification()
clf = LogisticRegression(max_iter=100)
param_grid = {"C": [1, 2]}
for v in [0, 1, 2, 3, 9, 10, 1000]:
print(f"\n======= verbose={v} ======= ")
gs = GridSearchCV(
clf,
param_grid,
cv=2,
n_jobs=1,
verbose=v,
)
gs.fit(X, y)
```
It is possible taht the same issue exists elsewhere, I've not looked thoroughly
### Suggest a potential alternative/fix
Update docs to match observed behaviour, e.g.:
> **verbose** int
> Controls the verbosity: the higher, the more messages.
> 0 : no messages;
> 1 : a single line describing the number of folds, the number of candidates, and the number of fits;
> 2 : the above, plus when each fold ends, the computation time for that fold and parameter candidate is displayed;
> 3-9 : the above, plus the fold index and the score is also displayed;
> ≥10 : an additional message for the start of each fold, and the candidate parameter indexes are also displayed. | {
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Currently, in Ridge regression, the current_alpha used in the SAG and SAGA solvers does not correctly account for the sum of sample weights when provided. This can lead to incorrect regularization strength compared to other solvers like cholesky or lsqr.
Changes
This PR updates the internal _ridge_regression function to properly scale alpha when sample_weight is present for sag and saga solvers. Specifically:
It computes the sum of sample weights (sw_sum).
It adjusts current_alpha as alpha_i * n_samples / sw_sum, ensuring consistency with the objective function's weight normalization.
It fixes the unpacking of returns from the sag_solver to correctly handle the intercept when return_intercept=True.
Verification
Verified by running: pytest sklearn/linear_model/tests/test_ridge.py -k "test_ridge_regression_sample_weights"
All tests passed (104 passed, some skipped as expected).
Manually compared coefficients with the cholesky solver to ensure convergence to the same solution when weights are applied. | {
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} | 5 | 2025-12-24T16:22:02 | 2026-01-06T08:30:40 | 2025-12-31T11:47:22 | null | MEMBER | null | null | null | null | This PR fixes some missing calls to `html.escape`.
#### AI usage disclosure
I found the problem when prototyping a test refactoring with copilot but the final content of this PR was manually edited in the end. | {
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When sample_weight zeros out an entire class, weighted_class_counts becomes zero for that class, causing division by zero and producing inf values that silently propagate through the training pipeline.
This fix adds explicit validation to detect classes with zero total sample_weight and raises a clear ValueError instead of silently returning inf values.
Includes regression tests for both single and multiple zero-weight classes.
---
#### Reference Issues/PRs
None (discovered during code review)
---
#### What does this implement/fix? Explain your changes.
**Problem:** In `compute_class_weight()`, when `class_weight="balanced"` and `sample_weight` zeros out an entire class, the weighted class count becomes zero. The subsequent division:
```python
recip_freq = weighted_class_counts.sum() / (len(le.classes_) * weighted_class_counts)
```
produces `inf` values that silently propagate through training, causing incorrect model behavior.
**Reproduction:**
```python
from sklearn.utils.class_weight import compute_class_weight
import numpy as np
classes = np.array([0, 1])
y = np.array([0, 0, 1, 1])
sample_weight = np.array([1.0, 1.0, 0.0, 0.0]) # Class 1 zeroed out
# Before fix: silently returns [0.5, inf]
# After fix: raises ValueError with clear message
compute_class_weight("balanced", classes=classes, y=y, sample_weight=sample_weight)
```
**Solution:** Added explicit validation after computing `weighted_class_counts` to check for zero values and raise a descriptive `ValueError` listing all affected classes:
```python
zero_weight_classes_mask = weighted_class_counts == 0
if np.any(zero_weight_classes_mask):
zero_weight_classes = le.classes_[zero_weight_classes_mask]
raise ValueError(
f"The following classes have zero total sample_weight, which would "
f"result in undefined class weights: {zero_weight_classes.tolist()}. "
f"Ensure all classes have at least some positive sample_weight."
)
```
**Files changed:**
- `sklearn/utils/class_weight.py` - Added zero-weight validation before division
- `sklearn/utils/tests/test_class_weight.py` - Added 2 regression tests
---
#### AI usage disclosure
I used AI assistance for:
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [x] Test/benchmark generation
- [ ] Documentation (including examples)
- [ ] Research and understanding
---
#### Any other comments?
This is a small, isolated fix that prevents silent data corruption. The error message is actionable and lists all affected classes to help users debug their `sample_weight` arrays.
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] | closed | false | [] | null | 0 | 2025-12-24T07:57:47 | 2025-12-25T00:04:40 | 2025-12-25T00:04:40 | null | NONE | null | null | null | null | ### Summary
This PR removes the `<1.9.0` version constraint for `numpydoc` in the CI environment configuration.
### Rationale
The constraint was originally added due to an issue in `numpydoc` (issue #638). Since that issue has been resolved and a new version has been released, we can now allow the CI to use the latest version of `numpydoc`.
### Changes
- Modified `build_tools/update_environments_and_lock_files.py` to remove `numpydoc` from `default_package_constraints`.
Closes #32884 | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32939 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32939/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32939/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32939/events | https://github.com/scikit-learn/scikit-learn/pull/32939 | 3,758,505,931 | PR_kwDOAAzd1s66Y2FB | 32,939 | CI Make test_predict_joint_proba more stable for different random seeds | {
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"node_id": "MDU6TGFiZWwyNjU5OD... | closed | false | [] | null | 6 | 2025-12-23T20:36:41 | 2025-12-25T02:36:33 | 2025-12-24T04:52:28 | null | CONTRIBUTOR | null | null | null | null | Close #32906
Hi @lesteve ,
This PR fixes the flaky failure in the min-dependencies OpenBLAS CI job for test_predict_joint_proba (#32906 and #29909). With the default rtol and atol, I hit intermittent assert_allclose failures.
<img width="1113" height="443" alt="image" src="https://github.com/user-attachments/assets/c73f6b52-9474-46ee-a0ef-bd6c381583cd" />
I first tried atol=1e-14, but still reproduced a failure with max abs diff ~1e-13. Bumping the assertion to use atol=1e-12 fixed it. Here's the run [link ](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=83661&view=logs&jobId=85dc54f1-b746-54c6-aaa8-29d3ad6201c9&j=85dc54f1-b746-54c6-aaa8-29d3ad6201c9&t=29b4082b-c348-5d75-9100-2eb67283d8d6)
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32933 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32933/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32933/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32933/events | https://github.com/scikit-learn/scikit-learn/pull/32933 | 3,757,768,397 | PR_kwDOAAzd1s66WZ3b | 32,933 | DOC Add release highlights link in 1.8 changelog | {
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"node_id": "LA_kwDOAAzd1s7eW... | closed | false | [] | null | 1 | 2025-12-23T15:43:14 | 2025-12-23T20:33:03 | 2025-12-23T20:11:44 | null | MEMBER | null | null | null | null | Looks like I skipped this when following the release process documentation :sweat_smile:. The release highlights are not in the [dev website changelog](https://scikit-learn.org/dev/whats_new/v1.8.html) and not in the [stable one](https://scikit-learn.org/stable/whats_new/v1.8.html). Compare to [1.7 changelog](https://scikit-learn.org/dev/whats_new/v1.7.html) which has the link to the release highlights at the beginning.
I will backport this in 1.8.X branch once this PR is merged.
The [rendered doc](https://output.circle-artifacts.com/output/job/911294a3-def7-4b9f-9645-745e35570543/artifacts/0/doc/whats_new/v1.8.html) looks good with the release highlights link at the top. | {
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} | 9 | 2025-12-23T09:05:22 | 2025-12-23T17:10:50 | 2025-12-23T16:28:28 | null | MEMBER | null | null | null | null | Fix #32927. cc @lorentzenchr. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32931 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32931/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32931/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32931/events | https://github.com/scikit-learn/scikit-learn/pull/32931 | 3,756,059,932 | PR_kwDOAAzd1s66QqQV | 32,931 | DOC Add glossary entry for one-vs-rest and one-vs-one | {
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"node_id": "LA_kwDOAAzd1s8AA... | closed | false | [] | null | 0 | 2025-12-23T05:08:12 | 2025-12-24T17:49:35 | 2025-12-24T16:25:08 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Mentioned here: https://github.com/scikit-learn/scikit-learn/pull/32733#discussion_r2540045497
#### What does this implement/fix? Explain your changes.
Adds a glossary entry for one-vs-one and one-vs-rest.
Happy to amend wording!
I have not added links from our docs to this entry, I think this can be done later, maybe even an 'easy' issue.
#### AI usage disclosure
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cc @ArturoAmorQ
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32930 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32930/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32930/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32930/events | https://github.com/scikit-learn/scikit-learn/issues/32930 | 3,755,917,481 | I_kwDOAAzd1s7f3sCp | 32,930 | DOC Supervised clustering metrics specify array input dtype | {
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} | Several of our supervised clustering metrics specify/suggest (??) that the array input should be of type `int` or `integral`, e.g.:
`pair_confusion_matrix`:
https://github.com/scikit-learn/scikit-learn/blob/b0ba8b029c298e0cc545206d2df4757be0ec2ac2/sklearn/metrics/cluster/_supervised.py#L212-L216
`rand_score`:
https://github.com/scikit-learn/scikit-learn/blob/b0ba8b029c298e0cc545206d2df4757be0ec2ac2/sklearn/metrics/cluster/_supervised.py#L298-L302
`mutual_info_score`:
https://github.com/scikit-learn/scikit-learn/blob/b0ba8b029c298e0cc545206d2df4757be0ec2ac2/sklearn/metrics/cluster/_supervised.py#L858-L864
`fowlkes_mallows_score`:
https://github.com/scikit-learn/scikit-learn/blob/b0ba8b029c298e0cc545206d2df4757be0ec2ac2/sklearn/metrics/cluster/_supervised.py#L1221-L1225
Looking at the code/testing a few - we don't seem to enforce this. I also have not seen specification of dtype of array input much elsewhere in the codebase. Is this dytpe specification needed in the docs?
Edit: I mean is it worth clarifying it is a suggestion and not enforced? | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32929 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32929/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32929/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32929/events | https://github.com/scikit-learn/scikit-learn/issues/32929 | 3,755,413,093 | I_kwDOAAzd1s7f1w5l | 32,929 | [Solved] Picard library modifies sklearn FastICA causing it to reject a default value for the `fun` parameter | {
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} | ### Describe the bug
I wrote some code that did not specify a `fun` parameter to FastICA and somehow this default value was rejected
```
File "/home/louis/dev/arxiv_explorer/benchmark_ica_impls.py", line 84, in <module>
S_sklearn = ica_sklearn.fit_transform(X_whitened2)
File "/home/louis/dev/arxiv_explorer/.venv/lib/python3.13/site-packages/sklearn/utils/_set_output.py", line 316, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/home/louis/dev/arxiv_explorer/.venv/lib/python3.13/site-packages/sklearn/base.py", line 1329, in wrapper
estimator._validate_params()
~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/home/louis/dev/arxiv_explorer/.venv/lib/python3.13/site-packages/sklearn/base.py", line 492, in _validate_params
validate_parameter_constraints(
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
self._parameter_constraints,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
self.get_params(deep=False),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
caller_name=self.__class__.__name__,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/home/louis/dev/arxiv_explorer/.venv/lib/python3.13/site-packages/sklearn/utils/_param_validation.py", line 98, in validate_parameter_constraints
raise InvalidParameterError(
...<2 lines>...
)
sklearn.utils._param_validation.InvalidParameterError: The 'fun' parameter of FastICA must be a str among {'exp', 'cube', 'tanh'} or a callable. Got 'logcosh' instead.
```
I don't want to dwell on it but I find it very confusing because it is a default value and the value that is documented as of 1.8.0, and changing it to the implied `fun` value of "tanh" causes an unset variable error because the `g` variable is not set for a value "tanh"!
### Steps/Code to Reproduce
```py
from sklearn.decomposition import FastICA
t0 = time.perf_counter()
pca2 = PCA(n_components=N_PCA_COMPONENTS, whiten=True, svd_solver='randomized')
X_whitened2 = pca2.fit_transform(X)
t_pca2 = time.perf_counter() - t0
t0 = time.perf_counter()
ica_sklearn = FastICA(n_components=N_TOPICS, max_iter=200, random_state=42)
S_sklearn = ica_sklearn.fit_transform(X_whitened2)
t_ica_sklearn = time.perf_counter() - t0
print(f"PCA whitening: {t_pca2:.3f}s")
print(f"sklearn FastICA: {t_ica_sklearn:.3f}s")
print(f"Total: {t_pca2 + t_ica_sklearn:.3f}s")
```
### Expected Results
I was comparing the value against picard and my own implementation of FastICA, it works with `fun="exp"` just fine.
### Actual Results
Above
### Versions
```shell
(arxiv_explorer) louis 🌟 ~/dev/arxiv_explorer $ python -c 'import sklearn; sklearn.show_versions()'
System:
python: 3.13.3 (main, Apr 9 2025, 04:03:52) [Clang 20.1.0 ]
executable: /home/louis/dev/arxiv_explorer/.venv/bin/python3
machine: Linux-6.8.0-57-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.8.0
pip: None
setuptools: None
numpy: 2.3.5
scipy: 1.16.3
Cython: None
pandas: 2.3.3
matplotlib: None
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 20
prefix: libscipy_openblas
filepath: /home/louis/dev/arxiv_explorer/.venv/lib/python3.13/site-packages/numpy.libs/libscipy_openblas64_-fdde5778.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 20
prefix: libscipy_openblas
filepath: /home/louis/dev/arxiv_explorer/.venv/lib/python3.13/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: 20
prefix: libgomp
filepath: /home/louis/dev/arxiv_explorer/.venv/lib/python3.13/site-packages/scikit_learn.libs/libgomp-e985bcbb.so.1.0.0
version: None
``` | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32928 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32928/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32928/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32928/events | https://github.com/scikit-learn/scikit-learn/pull/32928 | 3,754,464,777 | PR_kwDOAAzd1s66LXuc | 32,928 | FIX html_repr: remove redundant text dropdown (Fixes #32146) | {
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Replace estimator_label_details with an empty string in the HTML output.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32927 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32927/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32927/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32927/events | https://github.com/scikit-learn/scikit-learn/issues/32927 | 3,754,248,538 | I_kwDOAAzd1s7fxUla | 32,927 | `LogisticRegression`: Conflicting warnings when following deprecation advice for `penalty=None` (sklearn 1.8.0) | {
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} | ### Describe the bug
I am encountering a catch-22 situation with `LogisticRegression` warnings in scikit-learn 1.8.0.
When I use `LogisticRegression(penalty=None)`, I get a `FutureWarning` advising me to use `C=np.inf` instead. However, when I follow this advice and use `LogisticRegression(C=np.inf)` (leaving `penalty` at its default), I get **both** the `FutureWarning` again and a `UserWarning` stating that `C` is ignored because `penalty=None`.
It seems that `C=np.inf` correctly disables regularization (effectively setting `penalty=None` internally), but this internal state then triggers the warning intended for users who explicitly pass conflicting arguments.
### Steps/Code to Reproduce
```python
import sys
import warnings
import numpy as np
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
X, y = make_classification(random_state=42)
print("LogisticRegression default params:", LogisticRegression().get_params())
print()
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
print("--- Case 1: penalty=None ---")
LogisticRegression(penalty=None).fit(X, y)
for warning in w:
print(f"{warning.category.__name__}: {warning.message}")
print("\n--- Case 2: l1_ratio=0, C=np.inf ---")
LogisticRegression(l1_ratio=0, C=np.inf).fit(X, y)
for warning in w:
print(f"{warning.category.__name__}: {warning.message}")
```
### Expected Results
Using `C=np.inf` (with default `penalty`) should be the correct way to specify no regularization without triggering warnings, as per the `FutureWarning` message. It should definitely not trigger a `UserWarning` claiming that `C` is ignored, since `C` was the parameter used to request this behavior.
### Actual Results
```text
LogisticRegression default params: {'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': 0.0, 'max_iter': 100, 'n_jobs': None, 'penalty': 'deprecated', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}
--- Case 1: penalty=None ---
FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', and C=np.inf instead of penalty=None.
--- Case 2: l1_ratio=0, C=np.inf ---
FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', and C=np.inf instead of penalty=None.
UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters
```
### Versions
```shell
System:
python: 3.13.3 (main, Apr 9 2025, 03:47:57) [Clang 20.1.0 ]
executable: /Users/rubens.filho/projects/pers/robust/.venv/bin/python
machine: macOS-15.7.3-arm64-arm-64bit-Mach-O
Python dependencies:
sklearn: 1.8.0
pip: None
setuptools: None
numpy: 2.3.5
scipy: 1.16.3
Cython: None
pandas: 2.3.3
matplotlib: 3.10.8
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: openmp
internal_api: openmp
num_threads: 10
prefix: libomp
filepath: /Users/rubens.filho/projects/pers/robust/.venv/lib/python3.13/site-packages/sklearn/.dylibs/libomp.dylib
version: None
``` | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32925 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32925/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32925/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32925/events | https://github.com/scikit-learn/scikit-learn/pull/32925 | 3,746,820,425 | PR_kwDOAAzd1s65yZqh | 32,925 | MNT Ensure classes are sorted in _check_pos_label_consistency | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32924 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32924/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32924/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32924/events | https://github.com/scikit-learn/scikit-learn/issues/32924 | 3,746,197,390 | I_kwDOAAzd1s7fSm-O | 32,924 | Array API spec for `unique_values` does not specify order of unique elements | {
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} | Noticed when working on #32923
I don't think we need to do anything about this, (and indeed all our tests pass), I just wanted to document this for posterity, in case we encounter any problems later.
Array API spec for `unique_values` does not specify what the order of unique elements should be.
From https://data-apis.org/array-api/latest/API_specification/generated/array_api.unique_values.html#array_api.unique_values :
> The order of unique elements is not specified and may vary between implementations.
Searching for `xp.unique_values` in our codebase, I can see 2 places where order would matter.
In `_check_pos_label_consistency` we do:
https://github.com/scikit-learn/scikit-learn/blob/86acf4547ed8e183cb75c07bcd68ef186a223f06/sklearn/utils/validation.py#L2578-L2582
which assumes a specific order of unique values.
Also in `_unique_np` (used in `_encode.py`), we do:
https://github.com/scikit-learn/scikit-learn/blob/86acf4547ed8e183cb75c07bcd68ef186a223f06/sklearn/utils/_encode.py#L71-L73
assuming NaNs get sorted to the end. | null | {
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#### Reference Issues/PRs
#### What does this implement/fix? Explain your changes.
As discussed in https://github.com/scikit-learn/scikit-learn/pull/32422/changes#r2575666004 , add array API handling to the case when `pos_label=None` and `y_true` is not string.
Also adds test.
#### AI usage disclosure
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#### Any other comments?
This highlights again the inconsistency of `pos_label` handling, see longstanding issue #10010 - details here: https://github.com/scikit-learn/scikit-learn/issues/10010#issuecomment-1826133710 - would be great to get any movement/thoughts here!
cc @OmarManzoor, @ogrisel
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32922 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32922/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32922/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32922/events | https://github.com/scikit-learn/scikit-learn/pull/32922 | 3,743,869,747 | PR_kwDOAAzd1s65oi10 | 32,922 | DOC: Clarify LinearRegression intercept in documentation | {
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] | open | false | [] | null | 1 | 2025-12-18T16:32:15 | 2025-12-22T08:59:37 | null | null | NONE | null | null | null | null | Fixes #22551.
Updated LinearRegression documentation to clarify that the model includes an intercept term, addressing the confusion noted in the issue.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32921 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32921/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32921/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32921/events | https://github.com/scikit-learn/scikit-learn/pull/32921 | 3,742,317,674 | PR_kwDOAAzd1s65jNFm | 32,921 | MNT Remove unused `class_of_interest` from `_check_boundary_response_method` | {
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"node_id": "MDU6TGFi... | closed | false | [] | null | 0 | 2025-12-18T09:49:14 | 2026-01-05T11:13:32 | 2025-12-18T23:08:40 | null | CONTRIBUTOR | null | null | null | null | <!--
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Discovered while working on #32872, follow-up to #29797
#### What does this implement/fix? Explain your changes.
When multiclass plotting was enabled in #29797, the line using this variable (line 51 in the old version of `sklearn/inspection/_plot/decision_boundary.py`) was removed, so this variable is no longer used in this function.
@lucyleeow
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32920 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32920/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32920/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32920/events | https://github.com/scikit-learn/scikit-learn/pull/32920 | 3,740,530,318 | PR_kwDOAAzd1s65dRRX | 32,920 | address logcosh bottleneck | {
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#### Reference Issues/PRs
Implements #32914.
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#### What does this implement/fix? Explain your changes.
Implements proposed change to `_logcosh` function.
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Test cases pass: `pytest sklearn/decomposition/tests/test_fastica.py`
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32919 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32919/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32919/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32919/events | https://github.com/scikit-learn/scikit-learn/issues/32919 | 3,739,834,889 | I_kwDOAAzd1s7e6VoJ | 32,919 | Kernel crash on BayesianGaussianMixture | {
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} | ### Describe the bug
I learn BayesianGaussianMixture right now and get kernel crash on sklearn documentation example.
The error is 100% reproducible and wont go away after OS restrat, kernel restrat. I even try to read Goodfellow, no effect.
Initially i got the error on version chosen by `conda install scikit-learn` but then i tried to upgrade to `conda install scikit-learn==1.8.0` (pkgs/main::scikit-learn-1.7.1-py311h3~ --> conda-forge::scikit-learn-1.8.0-np2py311hd01f973_1) but got the same error.
### Steps/Code to Reproduce
import numpy as np
from sklearn.mixture import BayesianGaussianMixture
from sklearn import show_versions
print(np.__version__)
print(show_versions())
X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [12, 4], [10, 7]])
bgm = BayesianGaussianMixture(n_components=2, random_state=42).fit(X)
### Expected Results
I dont know since I never managed to run the code
### Actual Results
2.0.1
System:
python: 3.11.13 | packaged by Anaconda, Inc. | (main, Jun 5 2025, 13:03:15) [MSC v.1929 64 bit (AMD64)]
executable: [c:\Users\my_user\AppData\Local\anaconda3\envs\vdl\python.exe](file:///C:/Users/my_user/AppData/Local/anaconda3/envs/vdl/python.exe)
machine: Windows-10-10.0.19045-SP0
Python dependencies:
sklearn: 1.8.0
pip: 25.2
setuptools: 80.9.0
numpy: 2.0.1
scipy: 1.16.0
Cython: None
pandas: 2.3.3
matplotlib: 3.10.7
joblib: 1.5.2
threadpoolctl: 3.5.0
Built with OpenMP: True
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
Cell In[4], [line 5](vscode-notebook-cell:?execution_count=4&line=5)
3 from sklearn import show_versions
4 print(np.__version__)
----> [5](vscode-notebook-cell:?execution_count=4&line=5) print(show_versions())
6 X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [12, 4], [10, 7]])
7 bgm = BayesianGaussianMixture(n_components=2, random_state=42).fit(X)
File c:\Users\my_user\AppData\Local\anaconda3\envs\vdl\Lib\site-packages\sklearn\utils\_show_versions.py:106, in show_versions()
99 print(
100 "\n{k}: {stat}".format(
101 k="Built with OpenMP", stat=_openmp_parallelism_enabled()
102 )
103 )
105 # show threadpoolctl results
--> [106](file:///C:/Users/my_user/AppData/Local/anaconda3/envs/vdl/Lib/site-packages/sklearn/utils/_show_versions.py:106) threadpool_results = threadpool_info()
107 if threadpool_results:
108 print()
File c:\Users\my_user\AppData\Local\anaconda3\envs\vdl\Lib\site-packages\threadpoolctl.py:567, in threadpool_info()
551 @_format_docstring(USER_APIS=list(_ALL_USER_APIS), INTERNAL_APIS=_ALL_INTERNAL_APIS)
552 def threadpool_info():
553 """Return the maximal number of threads for each detected library.
554
555 Return a list with all the supported libraries that have been found. Each
(...) 565 In addition, each library may contain internal_api specific entries.
566 """
--> [567](file:///C:/Users/my_user/AppData/Local/anaconda3/envs/vdl/Lib/site-packages/threadpoolctl.py:567) return ThreadpoolController().info()
File c:\Users\my_user\AppData\Local\anaconda3\envs\vdl\Lib\site-packages\threadpoolctl.py:829, in ThreadpoolController.info(self)
827 def info(self):
828 """Return lib_controllers info as a list of dicts"""
--> [829](file:///C:/Users/my_user/AppData/Local/anaconda3/envs/vdl/Lib/site-packages/threadpoolctl.py:829) return [lib_controller.info() for lib_controller in self.lib_controllers]
File c:\Users\my_user\AppData\Local\anaconda3\envs\vdl\Lib\site-packages\threadpoolctl.py:829, in <listcomp>(.0)
827 def info(self):
828 """Return lib_controllers info as a list of dicts"""
--> [829](file:///C:/Users/my_user/AppData/Local/anaconda3/envs/vdl/Lib/site-packages/threadpoolctl.py:829) return [lib_controller.info() for lib_controller in self.lib_controllers]
File c:\Users\my_user\AppData\Local\anaconda3\envs\vdl\Lib\site-packages\threadpoolctl.py:125, in LibController.info(self)
120 """Return relevant info wrapped in a dict"""
121 hidden_attrs = ("dynlib", "parent", "_symbol_prefix", "_symbol_suffix")
122 return {
123 "user_api": self.user_api,
124 "internal_api": self.internal_api,
--> [125](file:///C:/Users/my_user/AppData/Local/anaconda3/envs/vdl/Lib/site-packages/threadpoolctl.py:125) "num_threads": self.num_threads,
126 **{k: v for k, v in vars(self).items() if k not in hidden_attrs},
127 }
File c:\Users\my_user\AppData\Local\anaconda3\envs\vdl\Lib\site-packages\threadpoolctl.py:138, in LibController.num_threads(self)
132 @property
133 def num_threads(self):
134 """Exposes the current thread limit as a dynamic property
135
136 This is not meant to be used or overriden by subclasses.
137 """
--> [138](file:///C:/Users/my_user/AppData/Local/anaconda3/envs/vdl/Lib/site-packages/threadpoolctl.py:138) return self.get_num_threads()
File c:\Users\my_user\AppData\Local\anaconda3\envs\vdl\Lib\site-packages\threadpoolctl.py:444, in MKLController.get_num_threads(self)
442 def get_num_threads(self):
443 get_func = getattr(self.dynlib, "MKL_Get_Max_Threads", lambda: None)
--> [444](file:///C:/Users/my_user/AppData/Local/anaconda3/envs/vdl/Lib/site-packages/threadpoolctl.py:444) return get_func()
OSError: [WinError -1066598274] Windows Error 0xc06d007e
### Versions
```shell
System:
python: 3.11.13 | packaged by Anaconda, Inc. | (main, Jun 5 2025, 13:03:15) [MSC v.1929 64 bit (AMD64)]
executable: c:\Users\my_user\AppData\Local\anaconda3\envs\vdl\python.exe
machine: Windows-10-10.0.19045-SP0
Python dependencies:
sklearn: 1.8.0
pip: 25.2
setuptools: 80.9.0
numpy: 2.0.1
scipy: 1.16.0
Cython: None
pandas: 2.3.3
matplotlib: 3.10.7
joblib: 1.5.2
threadpoolctl: 3.5.0
Built with OpenMP: True
``` | {
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 0 | 2025-12-17T15:00:58 | 2026-01-05T11:13:48 | 2025-12-22T16:03:50 | null | CONTRIBUTOR | null | null | null | null | <!--
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Towards #32872
#### What does this implement/fix? Explain your changes.
The problem in #32872 arises from the assumption that multiclass classification will have a different output shape than binary classification, which is not the case when `response_method`="predict".
To make sure, I added a test for all output shapes and updated the docstring according to the result.
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I have not checked if this assumption is used anywhere else.
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"node_id": "MDU6TGFiZWwyNj... | closed | false | [] | null | 4 | 2025-12-17T14:28:15 | 2025-12-18T17:01:40 | 2025-12-18T10:22:37 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
discovered while working on #32909
#### What does this implement/fix? Explain your changes.
This cleans up an old workaround, that had been fixed in numpy three years ago, see https://github.com/numpy/numpy/issues/6784#issuecomment-1341719384. I think this fix went into numpy 1.24.1 or earlier.
It makes sense to keep `present_labels` as an array instead of turning it into a list.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32916 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32916/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32916/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32916/events | https://github.com/scikit-learn/scikit-learn/pull/32916 | 3,739,099,636 | PR_kwDOAAzd1s65YbGQ | 32,916 | FIX: Ensure deterministic results in FastICA for float32 by honoring random_state (#25038) | {
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"node_id": "LA_kwDOAAzd1s8AA... | closed | false | [] | null | 2 | 2025-12-17T14:16:32 | 2025-12-19T10:19:17 | 2025-12-19T10:18:35 | null | NONE | null | null | null | null | This PR addresses https://github.com/scikit-learn/scikit-learn/issues/25038
This PR fixes non-deterministic behavior in `FastICA` when input data is `float32`.
previously `FastICA` ignored the provided `random_state` in some execution paths for `float32` causing variability across runs
With this patch `random_state` is now consistently respected ensuring deterministic initialization and reproducible outputs for `float32` matching the expected behavior for `float64`.
what was changed:
Updated `sklearn/decomposition/_fastica.py` to ensure initialization seeds use the provided `random_state`
added a regression test in `sklearn/decomposition/tests/test_fastica.py` to verify that multiple runs with the same `random_state` return identical components for `float32`
Behavior Before:
```
ica = FastICA(random_state=0)
X32 = X.astype(np.float32)
# components differed between runs
```
Behavior After:
```
ica = FastICA(random_state=0)
# deterministic for both float32 and float64
```
Impact:
Reproducible stochastic behavior across dtypes
Improved reliability for GPU users and ML pipelines
Better consistency with sklearn design principles
Feedback welcome especially if maintainers prefer extending the check to `_ica_def` in future cleanup. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32915 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32915/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32915/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32915/events | https://github.com/scikit-learn/scikit-learn/issues/32915 | 3,739,008,396 | I_kwDOAAzd1s7e3L2M | 32,915 | [for discussion]: adding link to Contributing Guide to top menu | {
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] | open | false | [] | null | 5 | 2025-12-17T13:52:28 | 2026-01-10T16:00:48 | null | null | MEMBER | null | null | {
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"blocked_by": 0,
"total_blocked_by": 0,
"blocking": 0,
"total_blocking": 0
} | ### Describe the issue linked to the documentation
This issue is a **discussion** item.
## Questions:
1. Can we add a link to "Contributing" to the top level menu? It seems a bit hidden, and the questions on contributing come up quite often.
- Contributing: https://scikit-learn.org/stable/developers/contributing.html#contributing
I usually find the link for it via the FAQs: Contributing: https://scikit-learn.org/stable/faq.html#contributing
<img width="836" height="366" alt="Image" src="https://github.com/user-attachments/assets/c7582fd5-4937-466a-bf34-3a5db3bdaf11" />
2. Should we add a section indicating that scikit-learn no longer participates in GSOC?
## Note for New Contributors
> [!WARNING]
> This issue is for discussion amongst the maintainers. Please do not ask if you can work on it or to be assigned to it.
### Suggest a potential alternative/fix
_No response_ | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32914 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32914/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32914/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32914/events | https://github.com/scikit-learn/scikit-learn/issues/32914 | 3,738,123,695 | I_kwDOAAzd1s7ezz2v | 32,914 | Addressed: FastICA logcosh bottleneck | {
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} | ### Describe the workflow you want to enable
For: sklearn/decomposition/_fastica.py
Hello,
Firstly thank you for the amazing library :D
I see in the FastICA there was a bottleneck that was messing a lot with my run time, so i optimized it and compared my implementation with the original and the results are exactly the same. I think you may find it useful.
### Describe your proposed solution
In my implementation I use logcocsh as a callable
Original Scikit:
```python
def _logcosh(x, fun_args=None):
alpha = fun_args.get("alpha", 1.0) # comment it out?
x *= alpha
gx = np.tanh(x, x) # apply the tanh inplace
g_x = np.empty(x.shape[0], dtype=x.dtype)
# XXX compute in chunks to avoid extra allocation
for i, gx_i in enumerate(gx): # please don't vectorize.
g_x[i] = (alpha * (1 - gx_i**2)).mean()
return gx, g_x
```
My implementation:
```python
def opt_logcosh(x):
"""
we create our own logcosh, which is modernized and avoids the bottleneck of the original logcosh
our logcosh vs the original logcosh from sklearn:
same output, but faster execution time
"""
tanh = np.tanh(x)
return tanh, ((1 - tanh**2)).mean(axis=-1)
```
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
_No response_ | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32913 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32913/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32913/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32913/events | https://github.com/scikit-learn/scikit-learn/pull/32913 | 3,736,914,982 | PR_kwDOAAzd1s65RCTF | 32,913 | Fix: RANSAC warning on small subsets | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32912 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32912/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32912/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32912/events | https://github.com/scikit-learn/scikit-learn/pull/32912 | 3,735,339,358 | PR_kwDOAAzd1s65Lq2l | 32,912 | FIX Error handling in ranking metrics supporting multiclass: `average_precision_score`, `roc_auc_score` and `top_k_accuracy_score` | {
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"node_id": "MDU6TGFiZWwyNj... | closed | false | [] | null | 5 | 2025-12-16T15:43:51 | 2026-01-02T13:02:44 | 2026-01-02T13:01:58 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
discovered while working in #32909
#### What does this implement/fix? Explain your changes.
This adds a proper error handling if a multiclass `y_true` is passed into `average_precision_score`, `roc_auc_score` and `top_k_accuracy_score` while `y_score` has only shape `(n_classes, )` (aka lacks information on the non-dominant classes).
`y_true` is internally label-binarized to make it match a multi-label case.
There is no way to calculate `y_score` from a binarised `y_true`, since we lack information on the scores of the other classes.
Thus, `y_score` needs to be passed with shape `(n_samples, n_classes)`.
The previous error raised in `average_precision_score` from `check_array` for instance was
```
E ValueError: Expected 2D array, got 1D array instead:
E array=[0.5 0.4 0.8 0.9 0.8 0.7].
E Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
```
which gives wrong advise. Users cannot deal with that by reshaping `y_score`.
#### AI usage disclosure
<!--
If AI tools were involved in creating this PR, please check all boxes that apply
below and make sure that you adhere to our Automated Contributions Policy:
https://scikit-learn.org/dev/developers/contributing.html#automated-contributions-policy
-->
I used AI assistance for:
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [x] Research and understanding
#### Any other comments?
Discovered while adding the `check_array_api_multiclass_classification_metric` test case in #32909.
Also added some documentation on the multiclass case for `average_precision_score`, since it was lacking. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32911 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32911/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32911/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32911/events | https://github.com/scikit-learn/scikit-learn/pull/32911 | 3,734,978,850 | PR_kwDOAAzd1s65Kbvp | 32,911 | PERF: `GradientBoosting*` optimize line-search from O(n * n_leaves) to O(n) for classification losses | {
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"node_id": "LA_kwDOAAzd1... | open | false | [] | null | 2 | 2025-12-16T14:15:35 | 2026-01-29T12:36:40 | null | null | CONTRIBUTOR | null | null | null | null | ### Why current complexity is O(n * n_leaves)
Currently the line-search is implemented this way:
```Python
for leaf in leaves:
(indices,) = np.nonzero(masked_terminal_regions == leaf)
```
`np.nonzero(masked_terminal_regions == leaf)` is O(`n_sample`) and we do it `n_leaves` times. This can quickly become prohibitively slow for trees with many leaves: typically, if trees are grown fully, it becomes O(n^2).
### Why this can be damaging
While trees are usually shallow for GB, nothing prevents users from making deep trees, and especially nothing prevents them from doing some hyper-parameter search that will in some cases create deep trees. Such a search could suffer a lot from this O(n^2) in some part of the search-space.
### What does this implement/fix? Explain your changes.
**For the classification losses**, it was possible to rewrite the calculation without a loop using `np.bincount(leaves_idx, weights=w)` to efficiently compute sums and averages grouped by leaves. This is a classic trick to do a "group by" in numpy.
It can result in a 10x speed-up on the total training duration when trees are grown fully.
**For the MSE**, line search is not needed, so it doesn't need optimization
**For the other regression losses (AE, huber, pinball)**, `loss.fit_intercept_only` is called for each leaf; hence it's more complicated:
- It's possible to avoid the O(n^2) with sparse matrices (see commit https://github.com/scikit-learn/scikit-learn/pull/32911/commits/27f647f1f276fb76fe273b1759dba7a8f748a5f3) but you still need a O(n) Python loop with numpy calls in it, so the speed-up ends-up being very minimal. Conclusion: it's not worth the increased code complexity, and I reverted the commit.
- We could implement a fully vectorized `loss.fit_intercept_only_by_idx`. It would require a `weighted_quantile_per_idx`, I tried implementing that, and it's very fast: see PR https://github.com/cakedev0/scikit-learn/pull/4.It's quite a lot of code, and so I've put it in a separate PR.
### AI usage
Not that I remember (but I promise I do use AI sometimes).
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32910 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32910/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32910/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32910/events | https://github.com/scikit-learn/scikit-learn/issues/32910 | 3,734,591,349 | I_kwDOAAzd1s7emVd1 | 32,910 | bug: Nested Pipeline is not fitted (sklearn 1.8) | {
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} | EDIT: The specific issue described here is actually caused by skrub, and solved in https://github.com/skrub-data/skrub/pull/1813.
However, this poses a more general question on how easy it is to abide by sklearn's conventions: skip directly to [this comment](https://github.com/scikit-learn/scikit-learn/issues/32910#issuecomment-3665534057) for that discussion.
---
### Describe the bug
With sklearn==1.8.0 and skrub==0.6.0, the following script fails:
```python
# test_failure.py
from sklearn.linear_model import Ridge
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
from skrub import DatetimeEncoder, ToDatetime, DropCols
from skrub.datasets import fetch_employee_salaries
datasets = fetch_employee_salaries()
X, y = datasets.X, datasets.y
date_encoder = make_pipeline(
ToDatetime(),
DatetimeEncoder(),
DropCols("date_first_hired_year"),
)
preprocessing = make_column_transformer(
(date_encoder, "date_first_hired"),
)
model = make_pipeline(preprocessing, Ridge())
model.fit(X, y)
model.predict(X)
```
with stacktrace:
<details>
<summary>Stacktrace</summary>
```
/home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.36439e-16): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
Traceback (most recent call last):
File "/home/auguste/Desktop/work/skore/examples/use_cases/test_failure.py", line 24, in <module>
model.predict(X)
~~~~~~~~~~~~~^^^
File "/home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/sklearn/pipeline.py", line 745, in predict
Xt = transform.transform(Xt)
File "/home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/sklearn/utils/_set_output.py", line 316, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
File "/home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/sklearn/compose/_column_transformer.py", line 1099, in transform
Xs = self._call_func_on_transformers(
X,
...<3 lines>...
routed_params=routed_params,
)
File "/home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/sklearn/compose/_column_transformer.py", line 901, in _call_func_on_transformers
return Parallel(n_jobs=self.n_jobs)(jobs)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
File "/home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/sklearn/utils/parallel.py", line 91, in __call__
return super().__call__(iterable_with_config_and_warning_filters)
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/joblib/parallel.py", line 1986, in __call__
return output if self.return_generator else list(output)
~~~~^^^^^^^^
File "/home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/joblib/parallel.py", line 1914, in _get_sequential_output
res = func(*args, **kwargs)
File "/home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/sklearn/utils/parallel.py", line 184, in __call__
return self.function(*args, **kwargs)
~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/sklearn/pipeline.py", line 1464, in _transform_one
res = transformer.transform(X, **params.transform)
File "/home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/sklearn/pipeline.py", line 1035, in transform
check_is_fitted(self)
~~~~~~~~~~~~~~~^^^^^^
File "/home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/sklearn/utils/validation.py", line 1705, in check_is_fitted
raise NotFittedError(msg % {"name": type(estimator).__name__})
sklearn.exceptions.NotFittedError: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.
```
</details>
Indeed, if we look at the model after `.fit()` it seems some internal steps are not fitted:
```python
>>> model
Pipeline(steps=[('columntransformer',
ColumnTransformer(transformers=[('pipeline',
Pipeline(steps=[('todatetime',
ToDatetime()),
('datetimeencoder',
DatetimeEncoder()),
('dropcols',
DropCols(cols='date_first_hired_year'))]),
'date_first_hired')])),
('ridge', Ridge())])
>>> model.__sklearn_is_fitted__()
True
>>> model.steps[0][1]
ColumnTransformer(transformers=[('pipeline',
Pipeline(steps=[('todatetime', ToDatetime()),
('datetimeencoder',
DatetimeEncoder()),
('dropcols',
DropCols(cols='date_first_hired_year'))]),
'date_first_hired')])
>>> model.steps[0][1].transformers[0][1].__sklearn_is_fitted__()
False
```
---
I used the debugger to dig a bit more inside `model.fit`, and I ended up in `sklearn.compose._column_transformer.py` at line 892.
<https://github.com/scikit-learn/scikit-learn/blob/1.8.0/sklearn/compose/_column_transformer.py#L892>
Notice that at this line, `trans` is *cloned* if `fitted` is False. If it's a clone of the transformer that gets fitted, I guess it makes sense that the original is not fitted?
Indeed the state when I reach this line is
```python
>>> self
ColumnTransformer(transformers=[('pipeline',
Pipeline(steps=[('todatetime', ToDatetime()),
('datetimeencoder',
DatetimeEncoder()),
('dropcols',
DropCols(cols='date_first_hired_year'))]),
'date_first_hired')])
>>> trans
Pipeline(steps=[('todatetime', ToDatetime()),
('datetimeencoder', DatetimeEncoder()),
('dropcols', DropCols(cols='date_first_hired_year'))])
>>> fitted
False
>>> func
<function _fit_transform_one at 0x7f0997d335e0>
```
---
However, if you replace the datetime pipeline with a scaling (skrub-less) pipeline, there is no error:
```python
# test_success.py
from sklearn.linear_model import Ridge
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
from sklearn.datasets import make_classification
from sklearn.preprocessing import StandardScaler
X, y = make_classification()
scaling = make_pipeline(StandardScaler())
preprocessing = make_column_transformer((scaling, [1]))
model = make_pipeline(preprocessing, Ridge())
model.fit(X, y)
model.predict(X)
```
Likewise, the error does not happen with sklearn 1.7.
For what it's worth, this might be linked to https://github.com/scikit-learn/scikit-learn/issues/29983 and https://github.com/scikit-learn/scikit-learn/issues/8157
### Versions
```shell
System:
python: 3.14.0 (main, Nov 19 2025, 22:48:15) [Clang 21.1.4 ]
executable: /home/auguste/Desktop/work/skore/.venv/bin/python3
machine: Linux-6.12.59-x86_64-with-glibc2.40
Python dependencies:
sklearn: 1.8.0
pip: None
setuptools: 80.9.0
numpy: 2.3.5
scipy: 1.16.3
Cython: None
pandas: 2.3.3
matplotlib: 3.10.8
joblib: 1.5.3
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: /home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/numpy.libs/libscipy_openblas64_-fdde5778.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: /home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/scipy.libs/libscipy_openblas-b75cc656.so
version: 0.3.29.dev
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 12
prefix: libgomp
filepath: /home/auguste/Desktop/work/skore/.venv/lib/python3.14/site-packages/scikit_learn.libs/libgomp-e985bcbb.so.1.0.0
version: None
``` | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32909 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32909/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32909/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32909/events | https://github.com/scikit-learn/scikit-learn/pull/32909 | 3,734,504,762 | PR_kwDOAAzd1s65Izz8 | 32,909 | FEA Add array API support for `average_precision_score` | {
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"node_id": "LA_kwDOAAzd1s8... | closed | false | [] | null | 6 | 2025-12-16T12:04:56 | 2026-01-23T07:43:13 | 2026-01-23T01:58:16 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
towards #26024
#### What does this implement/fix? Explain your changes.
Adds array API support to `average_precision_score`
#### AI usage disclosure
<!--
If AI tools were involved in creating this PR, please check all boxes that apply
below and make sure that you adhere to our Automated Contributions Policy:
https://scikit-learn.org/dev/developers/contributing.html#automated-contributions-policy
-->
I used AI assistance for:
- [ ] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [x] Research and understanding
#### Any other comments?
WIP
TODO:
- [x] add array API in `average_precision_score`
- [x] add common metrics test
- [x] add average_precision_score to the array API docs
- [x] removed old fix for numpy<= 1.24.1 from `average_precision_score` as well as from `_check_set_wise_labels` which allows to have `present_labels` as an array instead of a list
- [x] use `LabelBinarizer` instead of `label_binarize` # TODO: check if in fact necessary
- [x] add array API in `_average_binary_score`
- [x] fix remaining test failures
- [x] add changelog
- [x] make it accept mixed namespace inputs
- [ ] (only necessary if #32755 is merged first) add new test case for `average_precision_score` in `METRICS_SUPPORTING_MIXED_NAMESPACE` in `sklearn/metrics/tests/test_common.py` | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32908 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32908/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32908/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32908/events | https://github.com/scikit-learn/scikit-learn/issues/32908 | 3,734,481,596 | I_kwDOAAzd1s7el6q8 | 32,908 | Call to estimator.score() in RANSAC even if the produced input data set is too small - fix proposed | {
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"blocked_by": 0,
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"total_blocking": 0
} | ### Describe the bug
By sampling radomly from a distribution of x,y values, the RANSAC algorithm tries to find the line which describes best most of the data (while ignoring outliers). For deciding, how well a certain line describes the data, estimator.score() is called in line 526 of _ransac.py.
```pytb
525 # score of inlier data set
--> 526 score_subset = estimator.score(
527 X_inlier_subset,
528 y_inlier_subset,
529 **score_params_inlier_subset,
530 )
```
However, estimator.score() is called even if the inlier data set contains only a single data point, which then triggers a warning down the road:
```pytb
...\Lib\site-packages\sklearn\metrics\_regression.py:1283, in r2_score(y_true, y_pred, sample_weight, multioutput, force_finite)
1281 if _num_samples(y_pred) < 2:
1282 msg = "R^2 score is not well-defined with less than two samples."
-> 1283 warnings.warn(msg, UndefinedMetricWarning)
1284 return float("nan")
```
From my point of view, the code should not call this function if the input data set is not valid.
Unfortunately, I cannot provide a minimum example code as due to the random sampling the warning does only rarely occur. As far as I understand, the situation occurs if in previous iterations there was already a data set with more than 1 inlier data point drawn. Otherwise, line 526 is not reached due to:
```python
510 # less inliers -> skip current random sample
511 if n_inliers_subset < n_inliers_best:
512 self.n_skips_no_inliers_ += 1
513 continue
```
If propose to modify this line to:
```python
511 if ((n_inliers_subset < n_inliers_best) or (n_inliers_subset < 2)):
```
At least for me, this does fix the situation that generates the warnings.
### Steps/Code to Reproduce
```python
# less inliers -> skip current random sample
if n_inliers_subset < n_inliers_best: # proposed fix: if ((n_inliers_subset < n_inliers_best) or (n_inliers_subset < 2)):
self.n_skips_no_inliers_ += 1
continue
# extract inlier data set
inlier_idxs_subset = sample_idxs[inlier_mask_subset]
X_inlier_subset = X[inlier_idxs_subset]
y_inlier_subset = y[inlier_idxs_subset]
# cut `fit_params` down to `inlier_idxs_subset`
score_params_inlier_subset = _check_method_params(
X, params=routed_params.estimator.score, indices=inlier_idxs_subset
)
# score of inlier data set
score_subset = estimator.score(
X_inlier_subset,
y_inlier_subset,
**score_params_inlier_subset,
)
```
### Expected Results
Expected result: RANSAC algorithm should not make call to functions with invalid data that cause warnings.
### Actual Results
```pytb
...\Lib\site-packages\sklearn\metrics\_regression.py:1283, in r2_score(y_true, y_pred, sample_weight, multioutput, force_finite)
1281 if _num_samples(y_pred) < 2:
1282 msg = "R^2 score is not well-defined with less than two samples."
-> 1283 warnings.warn(msg, UndefinedMetricWarning)
1284 return float("nan")
```
### Versions
```shell
import sklearn; sklearn.show_versions()
System:
python: 3.13.5 | packaged by Anaconda, Inc. | (main, Jun 12 2025, 16:37:03) [MSC v.1929 64 bit (AMD64)]
executable: C:\ProgramData\anaconda3\envs\E134\python.exe
machine: Windows-11-10.0.26200-SP0
Python dependencies:
sklearn: 1.7.2
pip: 25.2
setuptools: 78.1.1
numpy: 2.3.2
scipy: 1.16.1
Cython: None
pandas: 2.3.2
matplotlib: 3.10.6
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: C:\ProgramData\anaconda3\envs\E134\Lib\site-packages\numpy.libs\libscipy_openblas64_-860d95b1c38e637ce4509f5fa24fbf2a.dll
version: 0.3.30
threading_layer: pthreads
architecture: SkylakeX
user_api: blas
internal_api: openblas
num_threads: 12
prefix: libscipy_openblas
filepath: C:\ProgramData\anaconda3\envs\E134\Lib\site-packages\scipy.libs\libscipy_openblas-6b2103f2ae4d8547998b5d188e9801fb.dll
version: 0.3.28
threading_layer: pthreads
architecture: SkylakeX
user_api: openmp
internal_api: openmp
num_threads: 12
prefix: vcomp
filepath: C:\ProgramData\anaconda3\envs\E134\Lib\site-packages\sklearn\.libs\vcomp140.dll
version: None
``` | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32907 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32907/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32907/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32907/events | https://github.com/scikit-learn/scikit-learn/pull/32907 | 3,734,323,006 | PR_kwDOAAzd1s65IM_H | 32,907 | DOC add link to brand guidelines | {
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] | closed | false | [] | null | 2 | 2025-12-16T11:09:46 | 2025-12-22T19:33:00 | 2025-12-22T19:33:00 | null | MEMBER | null | null | null | null | While renewing the trademark, a guideline is therefore available (PyTorch as the same exact thing on their website). However, we never published it.
I therefore make a PR in the blog website since it seems that it is the location where we have this information: https://github.com/scikit-learn/blog/pull/220
I just propose to refer to this page when it comes to terms of use. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32906 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32906/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32906/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32906/events | https://github.com/scikit-learn/scikit-learn/issues/32906 | 3,733,943,455 | I_kwDOAAzd1s7ej3Sf | 32,906 | CI Instable test in min-dependencies build test_naive_bayes.py::test_predict_joint_proba with different random seeds | {
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create the env from lock-file (only works on Linux)
```
conda-lock install -n scikit-learn-min-dependencies build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock
conda activate scikit-learn-min-dependencies
```
```
SKLEARN_TESTS_GLOBAL_RANDOM_SEED=all pytest sklearn/tests/test_naive_bayes.py -k test_predict_join
```
```
FAILED sklearn/tests/test_naive_bayes.py::test_predict_joint_proba[49-ComplementNB] - AssertionError:
FAILED sklearn/tests/test_naive_bayes.py::test_predict_joint_proba[86-BernoulliNB] - AssertionError:
FAILED sklearn/tests/test_naive_bayes.py::test_predict_joint_proba[87-BernoulliNB] - AssertionError:
FAILED sklearn/tests/test_naive_bayes.py::test_predict_joint_proba[99-BernoulliNB] - AssertionError:
```
Probably adding a small `atol` like `atol=1e-14` would fix it. How to test it in the CI with custom commit message: https://github.com/scikit-learn/scikit-learn/issues/28959. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32905 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32905/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32905/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32905/events | https://github.com/scikit-learn/scikit-learn/pull/32905 | 3,733,757,007 | PR_kwDOAAzd1s65GVKx | 32,905 | MNT Remove `unnasign.yml` file to avoid automatic `help wanted` label | {
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#### What does this implement/fix? Explain your changes.
Removes `.github/workflows/unassign.yml` that is currently adding `help wanted` label automatically.
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I manually removed the `help wanted` label from some of the work I'm doing.
Not complaining. Just sharing. ;-)
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32904 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32904/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32904/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32904/events | https://github.com/scikit-learn/scikit-learn/pull/32904 | 3,733,724,495 | PR_kwDOAAzd1s65GOWT | 32,904 | MAINT: Remove inconsistent warnings from Regressors (ElasticNetCV, LassoCV etc) | {
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Closes #32898
#### What does this implement/fix? Explain your changes.
Removes inconsistent warnings added for a lot of `Regressors` (ElasticNetCV, LassoCV, MLPRegressor, SVR, BaseBagging). See the linked issue for more details.
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- [x] Test/benchmark removal
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I realize this may need maintainer decision as to where warnings are still necessary, still raising the PR to facilitate that cleanup discussion.
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"node_id": "MDU6TGFiZWwyOTExMDU3... | open | false | [] | null | 5 | 2025-12-15T21:03:22 | 2026-01-28T21:41:55 | null | null | CONTRIBUTOR | null | null | null | null | #### Reference Issues/PRs
Follow up to PR https://github.com/scikit-learn/scikit-learn/pull/32100 and esp. discussion here: https://github.com/scikit-learn/scikit-learn/pull/32100#discussion_r2331679833
#### Parameter naming
For now, I went for `criterion="quantile"` and `quantile=0.9` (to match HGB).
Other possibilities:
- `criterion="quantile_loss"`, `criterion="pinball"`, `criterion="pinball_loss"`
- `quantile=0.9`, `q=0.9`, `alpha=0.9`, ...?
#### What does this implement/fix? Explain your changes.
The real core changes are simple: just adapt the algorithm to support an arbitrary quantile level instead of just the median. It happens that the core changes required for that are quite minimal:
- instead of `target_weight = 0.5 * tree.total_w`, do `target_weight = alpha * tree.total_w`
- instead of:
```Python
abs_errors[p] += (
0.5 * (wy_right - median * w_right)
+ 0.5 * (median * w_left - wy_left)
)
```
do
```Python
pinball_losses[p] += (
alpha * (wy_right - quantile * w_right)
+ (1.0 - alpha) * (quantile * w_left - wy_left)
)
```
For more mathematical details, you can refer to this [technical report](https://github.com/cakedev0/fast-mae-split/blob/main/report.ipynb)
**In addition to those core changes, you'll see a lot of "simple" changes:**
- renaming of variables
- changes to the API to add the quantile-level parameter
- tests: though I'd like avoiding including quantile in `test_split.py` for now, because modifications will conflict a lot with my work on categorical features and this will be tedious... | null | {
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} | ### Describe the bug
ElasticNetCV and LassoCV generate warnings when y is a (n,1) vector but doesn't when y is a (n,) vector. This behavior is not in LinearRegression or RidgeCV. Can they be made consistent to avoid confusion in applications?
### Steps/Code to Reproduce
```
from sklearn.linear_model import ElasticNetCV, LinearRegression, LassoCV, RidgeCV
import numpy as np
y = np.random.rand(30, 1)
X = np.random.rand(30, 2)
# no warnings
ypred = LinearRegression().fit(X,y)
# no warnings
ypred = RidgeCV().fit(X,y)
# warnings
ypred = ElasticNetCV().fit(X,y)
# warnings
ypred = LassoCV().fit(X,y)
```
### Expected Results
No warnings are displayed.
### Actual Results
from sklearn.linear_model import ElasticNetCV, LinearRegression, LassoCV, RidgeCV
import numpy as np
y = np.random.rand(30, 1)
X = np.random.rand(30, 2)
ypred = LinearRegression().fit(X,y)
ypred = RidgeCV().fit(X,y)
ypred = ElasticNetCV().fit(X,y)
e:\data\SAndo\venvs\myenv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:1714: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
y = column_or_1d(y, warn=True)
ypred = LassoCV().fit(X,y)
e:\data\SAndo\venvs\myenv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:1714: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
y = column_or_1d(y, warn=True)
### Versions
```shell
System:
python: 3.12.10 (tags/v3.12.10:0cc8128, Apr 8 2025, 12:21:36) [MSC v.1943 64 bit (AMD64)]
executable: ...\venvs\myenv\Scripts\python.exe
machine: Windows-2016Server-10.0.14393-SP0
Python dependencies:
sklearn: 1.7.2
pip: 24.0
setuptools: 72.1.0
numpy: 2.2.6
scipy: 1.14.0
Cython: 3.0.11
pandas: 2.2.2
matplotlib: 3.10.7
joblib: 1.4.2
threadpoolctl: 3.5.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 24
prefix: libscipy_openblas
filepath: ...\venvs\myenv\Lib\site-packages\numpy.libs\libscipy_openblas64_-13e2df515630b4a41f92893938845698.dll
version: 0.3.29
threading_layer: pthreads
architecture: SkylakeX
user_api: openmp
internal_api: openmp
num_threads: 32
prefix: vcomp
filepath: ...\venvs\myenv\Lib\site-packages\sklearn\.libs\vcomp140.dll
version: None
user_api: blas
internal_api: openblas
num_threads: 24
prefix: libscipy_openblas
filepath: ...\venvs\myenv\Lib\site-packages\scipy.libs\libscipy_openblas-5b1ec8b915dfb81d11cebc0788069d2d.dll
version: 0.3.27.dev
threading_layer: pthreads
architecture: SkylakeX
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Fixed in numpy/numpydoc#622, which is included in the latest release: https://github.com/numpy/numpydoc/releases/tag/v1.10.0 | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32896 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32896/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32896/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32896/events | https://github.com/scikit-learn/scikit-learn/pull/32896 | 3,723,144,481 | PR_kwDOAAzd1s64jYzX | 32,896 | fix: Correct impurity decrease calculation for friedman_mse criterion | {
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"node_id": "MDU6TGFiZWwyOTExMDU3... | closed | false | [] | null | 1 | 2025-12-12T11:52:17 | 2025-12-13T18:03:17 | 2025-12-13T18:03:16 | null | NONE | null | null | null | null | ## Problem
The `FriedmanMSE.impurity_improvement()` method was incorrectly computing the impurity decrease by not normalizing by the total weighted samples (`weighted_n_samples`).
## Root Cause
The method was computing:
```python
diff * diff / (weighted_n_left * weighted_n_right * weighted_n_node_samples)
```
But it should compute (following the standard Criterion pattern):
```python
(weighted_n_node_samples / weighted_n_samples) * diff * diff / (weighted_n_left * weighted_n_right)
```
## Impact
This bug caused the `min_impurity_decrease` parameter to not work correctly with the `friedman_mse" criterion in decision tree and forest regressors, as the calculated impurity decrease values were systematically too large.
## Testing
- Fixes issue #32707
- Should pass `test_min_impurity_decrease` test with `criterion="friedman_mse"`
- Applies to DecisionTreeRegressor, RandomForestRegressor, ExtraTreesRegressor, and other tree-based regressors using friedman_mse
## Related Issues
- #32707 - Original bug report
- #32699 - PR to run test_min_impurity_decrease for all criteria
- #32700 - Related maintenance issue
- #32708 - PR to deprecate friedman_mse criterion
## Note
The friedman_mse criterion is planned for deprecation (see #32708), but this fix ensures it works correctly in the meantime. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32895 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32895/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32895/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32895/events | https://github.com/scikit-learn/scikit-learn/pull/32895 | 3,722,778,645 | PR_kwDOAAzd1s64iJi5 | 32,895 | FIX/TST make pandas error message regex more generic | {
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Fixes #32891
Update to https://github.com/scikit-learn/scikit-learn/issues/32829, as the pandas error message changed slightly in wording
#### What does this implement/fix? Explain your changes.
Add wildcards to regex to catch different formulations.
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32894 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32894/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32894/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32894/events | https://github.com/scikit-learn/scikit-learn/pull/32894 | 3,721,995,069 | PR_kwDOAAzd1s64fjvU | 32,894 | DOC Add reference to user guide for `MultiLabelBinarizer` | {
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#### Reference Issues/PRs
Noticed when working on #32893
#### What does this implement/fix? Explain your changes.
Add reference to user guide for `MultiLabelBinarizer`
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"node_id": "MDU6TGFiZWwxODE1... | closed | false | [] | null | 0 | 2025-12-12T05:04:00 | 2025-12-22T06:10:49 | 2025-12-22T03:57:11 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
Noticed while working on #32755
#### What does this implement/fix? Explain your changes.
Adds reference to the glossary term https://scikit-learn.org/dev/glossary.html#term-label-indicator-matrix for classification metrics
Not sure about the best term to use.
* Avoided use of 'binary' i.e., binary indicator - binary here is talking about the indicator being 0 or 1, not about the data being binary. This *may* be confusing and this term is not included as an alias in the glossary so I have avoided it
* when talking about binary data, I have used the term "label indicator format“ / ”label indicator matrix“ (depending on how best the wording fits into the sentence), to avoid use of 'multilabel' (as the data is not multilabel).
* used 'multilabel indicator matrix' when talking about multilabel data
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32892 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32892/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32892/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32892/events | https://github.com/scikit-learn/scikit-learn/pull/32892 | 3,721,946,634 | PR_kwDOAAzd1s64fZUW | 32,892 | Fix #30310: Allow sparse intermediate outputs in ColumnTransformer with pandas output | {
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## Problem
When using ColumnTransformer.set_output(transform='pandas'), scikit-learn raises a ValueError if OneHotEncoder is configured with sparse_output=True, even when downstream transformers (like TruncatedSVD) convert the sparse output to dense.
> ValueError: Pandas output does not support sparse data.
> Set sparse_output=False to output pandas dataframes or
> disable Pandas output via `ohe.set_output(transform="default")`.
## Root Cause
The sparse compatibility check in OneHotEncoder.transform() happens at the start and doesn't account for pipeline composition where sparse intermediate outputs are normal.
## Solution
Moved the check to the END of the method (after sparse matrix construction)
Auto-convert sparse to dense using .toarray() instead of raising error
Added UserWarning guiding users to use sparse_output=False for better performance
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Fix #30310
#### What does this implement/fix? Explain your changes.
File: sklearn/preprocessing/_encoders.py
* Removed: Error-raising code that checked if transform_output != "default" and self.sparse_output:
* Added (after sparse matrix construction): Auto-conversion logic that converts sparse to dense when pandas output is configured
* Added: UserWarning with guidance on using sparse_output=False for better performance
File: sklearn/preprocessing/tests/test_encoders.py
Added 5 comprehensive regression tests for issue #30310:
* test_onehotencoder_sparse_output_with_pandas_set_output() - Standalone OneHotEncoder with sparse + pandas output
* test_onehotencoder_in_pipeline_with_sparse_and_pandas_output() - OneHotEncoder in Pipeline
* test_onehotencoder_in_columntransformer_with_sparse_and_pandas_output() - Main use case from issue (ColumnTransformer)
* test_onehotencoder_sparse_false_no_warning() - No warning when sparse_output=False
* test_onehotencoder_sparse_output_default_transform_output() - Behavior comparison between pandas and default output
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"node_id": "MDU6TGFiZWw1NTUyMz... | closed | false | [] | null | 1 | 2025-12-11T14:55:17 | 2025-12-29T11:04:18 | 2025-12-22T15:04:08 | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
- fixes https://github.com/scikit-learn/scikit-learn/pull/32743#issuecomment-3641069962
- fixes https://github.com/scikit-learn/scikit-learn/pull/32660#issuecomment-3546620178
- implements ideas from discussion with @lucyleeow
#### What does this implement/fix? Explain your changes.
This PR fixes the `autoclose-schedule` workflow to not run on forks, links to a central list of how to improve a PR, and implements some changes in the wording. Specifically, it makes it more clear to new contributors where the autoclose label was set, that they are responsible to fix the issues and advises them to not request the label getting removed.
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Fixes https://github.com/scikit-learn/scikit-learn/issues/32719
#### What does this implement/fix? Explain your changes.
It adds a simple check to `Pipeline`, `FeatureUnion` and `ColumnTransformer` to raise a clearer error message if the user accidentally passed a class instead of an instance, and provides corresponding non-regression test.
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#### Any other comments?
As noted in my comment [here](https://github.com/scikit-learn/scikit-learn/issues/32719#issuecomment-3638023309), it could also be added to the existing `_validate_estimator()` function in `_BaseHeterogeneousEnsemble` and its version in `StackingClassifier` in a follow-up PR. If I followed the class hierarchy correctly, then this would also catch this mistake in `StackingClassifier`, `StackingRegressor`, `VotingClassifier` and `VotingRegressor`. I didn't check the other meta-estimators yet.
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The documentation tooltip that we introduced with https://github.com/scikit-learn/scikit-learn/pull/31564 is hidden if the parameter name that it belongs to, is at the bottom of the table. It's visible only after scrolling down the cell.
In this case, when we hover over `n_jobs`, we get:
<img width="300" height="250" alt="Screenshot 2025-12-11 at 14 23 33" src="https://github.com/user-attachments/assets/a8b5445a-20e9-402f-b126-2c19689efc38" />
Using [CSS anchor positioning:](https://developer.mozilla.org/en-US/docs/Web/CSS/Guides/Anchor_positioning/Using), the result is the following (when hovering over `n_jobs`). No scroll-down needed:
<img width="400" height="250" alt="Screenshot 2025-12-11 at 14 21 36" src="https://github.com/user-attachments/assets/e4576a7b-b006-4855-a77b-116285566241" />
There is no opened issue for this.
#### What does this implement/fix? Explain your changes.
For the record: The specs for `anchor-name` and `position-anchor` can't be placed in the `params.css` file. When I did that, the tooltip was always visible at the bottom of the table - not next to the parameter name.
(It was @glemaitre's idea that the tooltip was getting fixed to the last parameter when using Chrome.)
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I used AI assistance for:
- [x] Moving the style to `params.py` instead of keeping it on `params.css`.
- [ ] Test/benchmark generation
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- [ ] Research and understanding
#### Any other comments?
I tried to set [Fallback options](https://developer.mozilla.org/en-US/docs/Web/CSS/Guides/Anchor_positioning/Try_options_hiding) ~~but I haven't found the correct rule.~~
Edit: I think the UX would be annoying if I add the fallback options.
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} | Mentioned somewhere in the release activity the closest I could find was https://github.com/scikit-learn/scikit-learn/pull/32809#discussion_r2587809537. It would probably be doable to add pytorch-cpu in the doc lock-file and add an example with a somewhat non trivial pipeline.
One idea could be to have some commented code saying `device = 'cuda'` and explain that uncommenting it will run on GPU if you have one.
Questions:
- not sure which version to pick for doc-min-dependencies build, I think it doesn't really matter and convenience (for us as maintainers) is the main thing here, since array API is still experimental
- some other things I did not think of ...
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} | MEMBER | null | null | null | null | #### Reference Issues/PRs
#### What does this implement/fix? Explain your changes.
`confusion_matrix_at_thresholds` was amended to a public function added in #30134. I think it would be worth adding to the common metric tests in `test_common.py`.
This would also fix one of the test failures in #32755
Also fixes `test_binary_sample_weight_invariance` - the curve metrics, as well as `CONTINUOUS_CLASSIFICATION_METRICS`, should also take `y_score` (i.e. unthresholded scores) NOT `y_pred`
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#### Any other comments?
cc @ogrisel because this is related to #32755 and @adrinjalali and @jeremiedbb who reviewed #30134 | {
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#### What does this implement/fix? Explain your changes.
This pull request fixes conflicting information between the User Guide and the API regarding DBSCAN.
In the User Guide, the current wording implies that points exactly `eps` distance away from any core sample are considered outliers, but this is contradicted by the API, which refers to `eps` as "The maximum distance between two samples for one to be considered as in the neighborhood of the other." DBSCAN uses the `radius_neighbors` method in sklearn.neighbors.NearestNeighbors, which states in its API documentation, "Points lying on the boundary are included in the results," implying that points exactly `eps` distance away are not outliers.
I edited the wording in the User Guide so that it agrees with the wording in both API pages.
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#### Any other comments?
It would be helpful for readers with a background in mathematics if the User Guide stated that neighborhoods used in the DBSCAN algorithm are closed, and that this contrasts with neighborhoods in the theory of topological spaces, which are open.
Example wording: "Neighborhoods in the DBSCAN algorithm are closed, unlike neighborhoods in mathematics which are open."
This is not included in my pull request because I believe this merits further discussion.
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"node_id": "MDU6TGFiZWwyOT... | closed | false | [] | null | 0 | 2025-12-09T19:02:08 | 2025-12-13T18:13:33 | 2025-12-13T17:32:52 | null | NONE | null | null | null | null | ### Changes
1. **Cython Backend (`sklearn/metrics/_pairwise_fast.pyx`):**
- Refactored `_chi2_kernel_fast` and `_sparse_manhattan` to use `floating` fused types for internal accumulators.
- Updated `_sparse_manhattan` to accept a `floating[:, ::1]` output buffer, preventing implicit casting to double.
2. **Python Frontend (`sklearn/metrics/pairwise.py`):**
- Updated `manhattan_distances` to allocate the output buffer `D` using `dtype=X.dtype` instead of defaulting to `float64`.
- Updated manhattan_distances to allow sparse inputs in check_pairwise_arrays. Previously, the default accept_sparse=False would densify sparse matrices before the optimization check, causing the sparse path to be skipped entirely.
3. **Tests (`sklearn/metrics/tests/test_pairwise.py`):**
- Removed the `pytest.raises(AssertionError)` block in `test_pairwise_distances_for_sparse_data` for Manhattan distance, as it now correctly preserves `float32` precision.
## Verification
- Ran `SKLEARN_RUN_FLOAT32_TESTS=1 pytest sklearn/metrics/tests/test_pairwise.py`
- Result: 360 passed, 0 failed. | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32874 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32874/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32874/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32874/events | https://github.com/scikit-learn/scikit-learn/pull/32874 | 3,712,147,245 | PR_kwDOAAzd1s63-1Cx | 32,874 | Fix nan_euclidean_distances symmetry for self-distances | {
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] | open | false | [] | null | 0 | 2025-12-09T18:28:52 | 2025-12-15T21:20:17 | null | null | NONE | null | null | null | null | Fixes #32848
## Summary
Ensures `nan_euclidean_distances` returns perfectly symmetric matrices when computing self-distances with NaN values, eliminating floating-point rounding asymmetries.
## Problem
When computing distances with NaN values, `nan_euclidean_distances` produced slightly asymmetric matrices (~4e-16 differences) due to floating-point rounding after the sqrt operation. This breaks downstream tools like `scipy.spatial.distance.squareform` that expect exact symmetry.
## Solution
- Enforce perfect bitwise symmetry by averaging `(distances + distances.T) / 2` after sqrt
- Carefully preserve NaN diagonal values for rows with all missing features
- Only zero out non-NaN diagonal entries
## Testing
- Added comprehensive regression test with ~10% NaN coverage
- Uses `assert_array_equal` for strict bitwise symmetry check
- Verifies diagonal handling for both zero and NaN cases
- All 29 existing nan_euclidean tests pass
## Note
Developed independently alongside PR #32851. This approach guarantees perfect symmetry rather than relying on `allclose` tolerance.
| null | {
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"node_id": "LA_kwDOAAzd1s8AAAA... | open | false | [] | null | 1 | 2025-12-09T17:40:29 | 2026-03-06T11:33:19 | null | null | MEMBER | null | null | null | null | I think this kind of test would have help identify some of the problem discovered during the release process earlier.
Note that it still fails because `Ridge` and `LinearDiscriminantAnalysis` still do not accept mixed-array inputs (see #28668).
Not sure if we should fix that here or in a PR that introduces a common test for that particular problem.
EDIT: I started to push a fix for the regression case to investigate all the possibly cascading bugs to fix (hiding one after the other). For the regression case we need both #28668 and #32755 and this is enough.
For classification, it seems that there is extra work needed in the private scorer code on top of updating the classifier and the metrics functions themselves. | null | {
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} | ### Describe the bug
This issue was discovered while reviewing #32867, but since it's not directly related, let's open a dedicated issue to avoid derailing the original discussion.
As can be seen in the plots below, passing `multiclass_colors="gist_rainbow"` has no impact when `response_method="predict"` but I don't see any reason why it wouldn't be the case.
<img width="1211" height="1211" alt="Image" src="https://github.com/user-attachments/assets/fce86173-f63b-487d-b19a-53a460ed9621" />
I think we should refactor the code to have consistent colormap configuration for all the response methods (in particular for the multiclass case).
### Steps/Code to Reproduce
```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.inspection import DecisionBoundaryDisplay
data = np.array(
[
[-1, -1],
[-2, -1],
[1, 1],
[2, 1],
[2, 2],
[3, 2],
[3, 3],
]
)
target = np.arange(data.shape[0])
clf = LogisticRegression().fit(data, target)
cmap = "gist_rainbow"
_, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12), constrained_layout=True)
for plot_method_idx, plot_method in enumerate(["contourf", "contour", "pcolormesh"]):
for response_method_idx, response_method in enumerate(
["predict_proba", "decision_function", "predict"]
):
ax = axes[plot_method_idx, response_method_idx]
display = DecisionBoundaryDisplay.from_estimator(
clf,
data,
multiclass_colors=cmap,
response_method=response_method,
plot_method=plot_method,
ax=ax,
alpha=0.5,
)
ax.scatter(
data[:, 0],
data[:, 1],
c=target.astype(int),
edgecolors="black",
cmap=cmap,
)
ax.set_title(f"plot_method={plot_method}\nresponse_method={response_method}")
```
### Expected Results
I would expect the colored areas of the right most columns to also use the `"gist_rainbow"` colormap as the other columns.
### Actual Results
The right most column seems to ignore the `multiclass_colors` argument and falls back to the default "viridis" colormap which is not very well suited for multiclass problems with a large number of classes.
### Versions
```shell
System:
python: 3.13.7 | packaged by conda-forge | (main, Sep 3 2025, 14:24:46) [Clang 19.1.7 ]
executable: /Users/ogrisel/miniforge3/envs/dev/bin/python3.13
machine: macOS-15.6.1-arm64-arm-64bit-Mach-O
Python dependencies:
sklearn: 1.9.dev0
pip: 25.2
setuptools: 80.9.0
numpy: 2.3.3
scipy: 1.16.3
Cython: 3.1.4
pandas: 3.0.0.dev0+2566.g2bb3fef887
matplotlib: 3.10.6
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 10
prefix: libopenblas
filepath: /Users/ogrisel/miniforge3/envs/dev/lib/libopenblas.0.dylib
version: 0.3.30
threading_layer: openmp
architecture: VORTEX
user_api: openmp
internal_api: openmp
num_threads: 10
prefix: libomp
filepath: /Users/ogrisel/miniforge3/envs/dev/lib/libomp.dylib
version: None
``` | {
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] | closed | false | [] | null | 0 | 2025-12-09T10:44:28 | 2025-12-10T14:34:00 | 2025-12-09T16:12:57 | null | MEMBER | null | null | null | null | * [x] Set the version number in the release branch
* [x] Generate the changelog in the release branch
* [x] Check that the wheels for the release can be built successfully
* [x] Merge the PR with `[cd build]` commit message to upload wheels to the staging repo
* [x] Upload the wheels and source tarball to https://test.pypi.org
* [x] Create tag on the main repo
* [x] Confirm bot detected at https://github.com/conda-forge/scikit-learn-feedstock
and wait for merge
* [x] Upload the wheels and source tarball to PyPI
* [x] Update news and what's new date in main branch
* [x] Backport news and what's new date in release branch
* [x] Update symlink for stable in https://github.com/scikit-learn/scikit-learn.github.io
* [x] Publish to https://github.com/scikit-learn/scikit-learn/releases
* [ ] Announce on mailing list and on social media platforms (LinkedIn, Bluesky, etc.)
* [x] Update SECURITY.md in main branch
<details><summary>Picks and drops</summary>
<p>
I picked everything but the commit changing the version to 1.9.dev0
pick 12fccad3f6 # DOC Add info on 'array-like' array API inputs when `array_api_dispatch=False` (#32676)
drop 4f79388d95 # MNT Bump version to 1.9.dev0 on main (#32794)
pick f78e850342 # TST: increase tolerance in `test_mcd`
pick c915bbeef1 # CI Run unit-tests with random seed on schedule and open automated issue (#32797)
pick 88ae38004c # DOC Shorten PR template and improve PR attention FAQ (#32734)
pick 3c60f0437a # FIX preprocessing: Fix OneHotEncoder handle_unknown='warn' behavior (#32592)
pick 9e77ac6662 # DOC fix typo in 1.8 changelog
pick 5629486a2c # FEA Add array API support to `LabelBinarizer(sparse_output=False)` for numeric labels (#32582)
pick 04e1f00050 # TST: fix `test_mincovdet_bias_on_normal` flakyness by using random seed (#32760)
pick 94f18cefbd # TST: fix `test_absolute_errors_precomputation_function` flakyness (float precision issue) (#32756)
pick c3b0873dfd # MNT clean up double calls to check_array in `log_loss` and `d2_log_loss_score` (#32801)
pick c9e67010ee # DOC fix 32747.fix.api whatsnew (#32812)
pick 33b07ee2d0 # CI Work-around for conda-lock hang in builds with pip packages (#32808)
pick eda342d1aa # Fix spelling in docs: `overfiting` -> `overfitting` (#32816)
pick c411d42ad9 # DOC Fix changelog fragment filename (#32818)
pick 5fe81eceb3 # DOC Fix indentation in _logistic.py docstrings (#32819)
pick 3eecca6d1e # DOC add paragraph on "AI usage disclosure" to Automated Contributions Policy and PR Template (#32566)
pick 7d82819d30 # :lock: :robot: CI Update lock files for array-api CI build(s) :lock: :robot: (#32823)
pick f6d75eae22 # :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#32822)
pick 910a59199d # :lock: :robot: CI Update lock files for free-threaded CI build(s) :lock: :robot: (#32821)
pick d1661f07ad # CI Move pylatest_pip_openblas_pandas to Github Actions (#32810)
pick 045224d753 # DOC: Correct many grammatical issues (#32820)
pick 6d602baf55 # FIX: move_to should handle BufferError and ValueError to support PyTorch 2.9 with array API enabled (#32827)
pick 19dbd540cd # :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#32824)
pick ee50be6441 # FIX screening in enet_coordinate_descent_multi_task (#32811)
pick 90ea990d03 # TST Enable pytest faulthandler functionality (#32776)
pick 473fef0bf8 # DOC Update Contributing intro and Ways to contribute (#32792)
pick 41ceebb66a # CI Make linting bot comment only on failure (#32804)
pick 4a10d0ed8d # DOC Add HTML repr related PR to existing changelog (#32831)
pick 5b9561dfce # MNT Use Pyodide 0.29 version to avoid micropip error inside JupyterLite (#32835)
pick 3300836d26 # DOC Remove PR from changelog that was already in 1.7 (#32841)
pick ec4f93c3f0 # FIX Add new default max_samples=None in Bagging estimators (#32825)
pick f1ad9f9498 # Fix `_safe_indexing` with non integer arrays on array API inputs (#32840)
pick 47621fec9c # FIX Make `get_namespace` handle pandas dataframe input (#32838)
pick 96e2425338 # :lock: :robot: CI Update lock files for array-api CI build(s) :lock: :robot: (#32858)
pick 88b46b5005 # :lock: :robot: CI Update lock files for free-threaded CI build(s) :lock: :robot: (#32856)
pick bd0dd87f79 # :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#32859)
pick e9752287ff # DOC Release highlights for 1.8 (#32809)
</p>
</details>
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32870 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32870/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32870/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32870/events | https://github.com/scikit-learn/scikit-learn/issues/32870 | 3,710,140,402 | I_kwDOAAzd1s7dJD_y | 32,870 | BUG: `DecisionTreeRegressor`: invalid impurity for `criterion="poisson"` with missing values | {
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] | closed | false | [] | null | 2 | 2025-12-09T10:06:41 | 2026-03-05T12:41:33 | 2026-03-05T12:41:33 | null | CONTRIBUTOR | null | null | {
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} | ### Describe the bug
When missing values are present in `X`, `DecisionTreeRegressor(criterion="poisson", ...)` sometimes computes invalid impurities.
Impurity should match with half-poisson deviance according to the documentation. And it is indeed the case when they are no missing values. But when missing values are present, something goes wrong, and you can even get negative impurities.
I found this bug thanks to my big test from this draft PR https://github.com/scikit-learn/scikit-learn/pull/32193. This same test shows that impurity does match `sklearn.metrics.mean_poisson_deviance(...) / 2` when no missing values are present.
### Steps/Code to Reproduce
```Python
from sklearn.tree import DecisionTreeRegressor, plot_tree
X = np.array([np.nan, 1, 2, 3, np.nan]).reshape(-1, 1)
y = [0.49, 0.5, 0.7, 1.5, 0.8]
tree = DecisionTreeRegressor(criterion='poisson', max_depth=1)
tree.fit(X, y)
plot_tree(tree);
```
### Expected Results
Impurities should be positive. Impurity of the right children should be 0, as it has only one sample.
### Actual Results
Some impurities are negative:
<img width="515" height="389" alt="Image" src="https://github.com/user-attachments/assets/90169bc8-1e05-48ab-aac1-b77f599dcef9" />
### 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-6.14.0-36-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.9.dev0
pip: None
setuptools: 80.9.0
numpy: 2.3.5
scipy: 1.16.3
Cython: 3.2.1
pandas: 2.3.3
matplotlib: 3.10.7
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libscipy_openblas
filepath: /home/arthur/dev-perso/scikit-learn/sklearn-env/lib/python3.12/site-packages/numpy.libs/libscipy_openblas64_-fdde5778.so
version: 0.3.30
threading_layer: pthreads
architecture: Haswell
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libscipy_openblas
filepath: /home/arthur/dev-perso/scikit-learn/sklearn-env/lib/python3.12/site-packages/scipy.libs/libscipy_openblas-b75cc656.so
version: 0.3.29.dev
threading_layer: pthreads
architecture: Haswell
user_api: openmp
internal_api: openmp
num_threads: 16
prefix: libgomp
filepath: /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0
version: None
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] | open | false | [] | null | 3 | 2025-12-09T08:11:14 | 2026-01-02T17:21:59 | null | null | MEMBER | null | null | {
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} | ### Describe the workflow you want to enable
For `LogisticRegression` with `n_classes >= 3`, I would like to specify a class-wise (grouped) L21 penalty $\sum_{j=1}^{n_{features}} \Vert\beta_{j,\cdot}\Vert_2 = \sum_{j=1}^{n_{features}} (\sum_{k=1}^{n_{classes}}\beta_{j,k}^2)^{1/2}$ instead of the implemented L1 penalty $\sum_{j=1}^{n_{features}} \sum_{k=1}^{n_{classes}}\Vert\beta\Vert_2$.
The difference is bassically the same as calling `MultiTaskElasticNet().fit(X, y)` vs `ElasticNet().fit(X, y.ravel())`.
[glmnet](https://glmnet.stanford.edu/articles/glmnet.html#multinomial-regression-family-multinomial) has the option `type.multinomial = "grouped"` and (default) `type.multinomial = "ungrouped"`.
### Describe your proposed solution
Introduce a new parameter, e.g. `l1_type` or similar.
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
I could not find any discussion in and around of #8446 (SAGA was the first and so far only solver capable of solving multinomial case with L1 penalties). | null | {
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"node_id": "LA_kwDOAAzd1s8AAAACQaw... | closed | false | [] | null | 1 | 2025-12-09T03:01:15 | 2025-12-09T06:10:22 | 2025-12-09T06:10:22 | null | NONE | null | null | null | null | This PR adds validation checks in `BaseLibSVM.fit` to ensure that
the hyperparameters `gamma`, `degree`, and `coef0` are only used
with kernels that support them.
Currently, these parameters are silently ignored for some kernels
(e.g., `degree` for RBF), which can lead to misconfiguration. This
change raises a `ValueError` when incompatible hyperparameters are
provided, making the behavior explicit.
Summary of changes:
- Add kernel-specific validation for `gamma`, `degree`, and `coef0`.
- Raise `ValueError` when unsupported parameters are set.
- Add tests covering the new validation paths.
Fixes #19614.
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"... | closed | false | [] | null | 9 | 2025-12-08T20:20:03 | 2026-02-18T07:41:11 | 2026-02-18T07:41:11 | null | CONTRIBUTOR | null | null | null | null | This PR aims to fix a bug where `DecisionBoundaryDisplay.from_estimator` only displayed up to 7 distinct colors for classifiers with more than 7 classes when using `plot_method="contourf"` or `plot_method="contour"`
Fixes https://github.com/scikit-learn/scikit-learn/issues/32866 | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32866 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32866/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32866/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32866/events | https://github.com/scikit-learn/scikit-learn/issues/32866 | 3,706,574,907 | I_kwDOAAzd1s7c7dg7 | 32,866 | `DecisionBoundaryDisplay.from_estimator` only displays up to 7 distinct colours | {
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} | ### Describe the bug
I was trying to use `DecisionBoundaryDisplay.from_estimator` to display different regions classified by `NuSVC`:
<img width="455" height="355" alt="Image" src="https://github.com/user-attachments/assets/ffd7d137-b7f1-46a9-bd63-b967903617ce" />
My expectation was to find 11, but by visually counting, I was only able to find 10.
Also, some colours were obviously duplicated in this figure.
This is how it actually should look like:
<img width="455" height="355" alt="Image" src="https://github.com/user-attachments/assets/29b0c239-1a84-47fd-96c1-57d72f0d02c8" />
## Proposed fix
I guess that this will be fixed at some point by https://github.com/matplotlib/matplotlib/commit/0eadaf0effeb424e2b385693fbf6ac8a21da4d84. However, this fix is not yet available in the stable versions of matplotlib.
Manually changing the "default"/hard-wired value [here](https://github.com/matplotlib/matplotlib/blob/v3.10.8/lib/matplotlib/contour.py#L1020) fixes the problem.
The best fix, thus, is probably for scikit-learn to specify the `levels` parameter on its own here:
https://github.com/scikit-learn/scikit-learn/blob/bd0dd87f792ee88c8ec70248299aae779b51ac16/sklearn/inspection/_plot/decision_boundary.py#L220
That line should look something like this:
```py
self.surface_ = plot_func(self.xx0, self.xx1, self.response, <levels>, **kwargs)
```
where `<levels>` is replaced by the appropriate amount of levels.
The case when `self.response.ndim == 3` may also be affected, but I have not yet tried out that one
### Steps/Code to Reproduce
```py
import numpy as np
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.svm import NuSVC
import matplotlib.pyplot as plt
# List of some 11 distinct points
pts = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1],
[2, 2], [3, 2], [3, 3], [4, 3],
[4, 4], [5, 4], [5, 5]])
svc = NuSVC(nu=0.1)
svc.fit(pts, [1,2,3,4,5,6,7,8,9,10,11])
DecisionBoundaryDisplay.from_estimator(
svc,
pts,
response_method="predict",
cmap="gist_rainbow"
)
plt.show()
```
### Expected Results
<img width="455" height="355" alt="Image" src="https://github.com/user-attachments/assets/adbec1fc-5341-4842-a314-98d2d25f3dcd" />
### Actual Results
<img width="455" height="355" alt="Image" src="https://github.com/user-attachments/assets/54b39c7d-c6e8-4e83-ba04-cfb5daf95459" />
### Versions
```shell
System:
python: 3.12.7 | packaged by Anaconda, Inc. | (main, Oct 4 2024, 13:27:36) [GCC 11.2.0]
executable: /home/nico/miniconda3/envs/pca-assessment/bin/python
machine: Linux-6.14.0-36-generic-x86_64-with-glibc2.39
Python dependencies:
sklearn: 1.7.2
pip: 24.3.1
setuptools: 72.1.0
numpy: 2.3.3
scipy: 1.16.2
Cython: None
pandas: 2.3.2
matplotlib: 3.10.6
joblib: 1.4.2
threadpoolctl: 3.5.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 16
prefix: libscipy_openblas
filepath: /home/nico/miniconda3/envs/pca-assessment/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: mkl
num_threads: 12
prefix: libmkl_rt
filepath: /home/nico/miniconda3/envs/pca-assessment/lib/libmkl_rt.so.2
version: 2025.0-Product
threading_layer: gnu
user_api: openmp
internal_api: openmp
num_threads: 16
prefix: libgomp
filepath: /home/nico/miniconda3/envs/pca-assessment/lib/libgomp.so.1.0.0
version: None
``` | {
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Fixes #32829
#### What does this implement/fix? Explain your changes.
Catches the `Pandas4Warning` that causes a CI failure.
#### Any other comments?
I'm not sure if this makes sense, but AFAIU the main thing the test is checking is whether `TargetEncoder` works when `y` is read-only, so maybe the test could even be changed into explicitly creating a read-only `y` without the implicit pandas conversion?
Edit: I changed the version check to use `sklearn.externals._packaging.version`.
@lesteve
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## Issue
Currently, `svm.SVC` silently ignores hyperparameters that do not apply to the chosen kernel (e.g., `gamma` with a `'linear'` kernel), which can confuse users.
## Proposed Change
Adds `_warn_for_unused_kernel_params` in `sklearn/svm/_base.py` to raise `UserWarning` when non-effective hyperparameters are set:
```python
def _warn_for_unused_kernel_params(kernel, *, degree, gamma, coef0):
if callable(kernel):
return
# gamma used by rbf, poly, sigmoid only
if kernel not in ("rbf", "poly", "sigmoid") and gamma != "scale":
warnings.warn(
f"gamma is set to {gamma!r} but unused for kernel={kernel!r}.",
UserWarning,
)
# degree used by poly only
if kernel != "poly" and degree != 3:
warnings.warn(
f"degree is set to {degree!r} but unused for kernel={kernel!r}.",
UserWarning,
)
# coef0 used by poly and sigmoid only
if kernel not in ("poly", "sigmoid") and coef0 != 0.0:
warnings.warn(
f"coef0 is set to {coef0!r} but unused for kernel={kernel!r}.",
UserWarning,
)
## Example Usage
```python
from sklearn.datasets import load_iris
from sklearn.svm import SVC
x, y = load_iris(return_X_y=True)
clf = SVC(kernel='linear', gamma=1e-6)
clf.fit(x, y)
```
### Current Output
```
0.9933333333333333
```
### New output
```
UserWarning: gamma is set to 1e-06 but unused for kernel='linear'.
0.9933333333333333
```
## Motivation
Improves user experience by alerting users to hyperparameters that have no effect, preventing silent mistakes and confusion. | {
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leftover from the changes to the development setup docs https://github.com/scikit-learn/scikit-learn/pull/32509
#### What does this implement/fix? Explain your changes.
update the link in the raised error message to point to the new location on information for building from source (old link location doesn't exist anymore) | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32861 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32861/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32861/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32861/events | https://github.com/scikit-learn/scikit-learn/issues/32861 | 3,705,780,987 | I_kwDOAAzd1s7c4br7 | 32,861 | Pytest -Werror fails with versions <9.0 due to faulthandler | {
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} | When trying to turn warnings into errors with `pytest -Werror`, there is an error with `faulthandler` (`faulthandler` was introduced in #32776) and the tests don't run.
Updating pytest to >=9.0 fixes the problem.
Here's the traceback for when I used pytest==8.3.1:
```bs
➜ scikit-learn git:(TargetEncoder_groups) ✗ pytest sklearn/tests/test_metaestimators_metadata_routing.py -Werror
======================== test session starts ========================
platform linux -- Python 3.12.2, pytest-8.3.1, pluggy-1.5.0
rootdir: /home/stefanie/code/scikit-learn_dev/scikit-learn
configfile: pyproject.toml
plugins: xdist-3.6.1, anyio-4.9.0, cov-5.0.0
collected 271 items
INTERNALERROR> Traceback (most recent call last):
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/_pytest/main.py", line 283, in wrap_session
INTERNALERROR> session.exitstatus = doit(config, session) or 0
INTERNALERROR> ^^^^^^^^^^^^^^^^^^^^^
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/_pytest/main.py", line 336, in _main
INTERNALERROR> config.hook.pytest_collection(session=session)
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/pluggy/_hooks.py", line 513, in __call__
INTERNALERROR> return self._hookexec(self.name, self._hookimpls.copy(), kwargs, firstresult)
INTERNALERROR> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/pluggy/_manager.py", line 120, in _hookexec
INTERNALERROR> return self._inner_hookexec(hook_name, methods, kwargs, firstresult)
INTERNALERROR> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/pluggy/_callers.py", line 139, in _multicall
INTERNALERROR> raise exception.with_traceback(exception.__traceback__)
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/pluggy/_callers.py", line 122, in _multicall
INTERNALERROR> teardown.throw(exception) # type: ignore[union-attr]
INTERNALERROR> ^^^^^^^^^^^^^^^^^^^^^^^^^
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/_pytest/logging.py", line 792, in pytest_collection
INTERNALERROR> return (yield)
INTERNALERROR> ^^^^^
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/pluggy/_callers.py", line 122, in _multicall
INTERNALERROR> teardown.throw(exception) # type: ignore[union-attr]
INTERNALERROR> ^^^^^^^^^^^^^^^^^^^^^^^^^
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/_pytest/warnings.py", line 121, in pytest_collection
INTERNALERROR> return (yield)
INTERNALERROR> ^^^^^
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/pluggy/_callers.py", line 124, in _multicall
INTERNALERROR> teardown.send(result) # type: ignore[union-attr]
INTERNALERROR> ^^^^^^^^^^^^^^^^^^^^^
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/_pytest/config/__init__.py", line 1415, in pytest_collection
INTERNALERROR> self._validate_config_options()
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/_pytest/config/__init__.py", line 1437, in _validate_config_options
INTERNALERROR> self._warn_or_fail_if_strict(f"Unknown config option: {key}\n")
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/_pytest/config/__init__.py", line 1476, in _warn_or_fail_if_strict
INTERNALERROR> self.issue_config_time_warning(PytestConfigWarning(message), stacklevel=3)
INTERNALERROR> File "/home/stefanie/.pyenv/versions/scikit-learn_dev/lib/python3.12/site-packages/_pytest/config/__init__.py", line 1528, in issue_config_time_warning
INTERNALERROR> warnings.warn(warning, stacklevel=stacklevel)
INTERNALERROR> pytest.PytestConfigWarning: Unknown config option: faulthandler_exit_on_timeout
========================= no tests ran in 0.47s =========================
```
In `pyproject.toml`, we mention `faulthandler` to only work with `pytest` newer than 9.0.
We came across this with @AnneBeyer and wondered whether that's a good enough reason to update/bump the min dev depenency to pytest>9.0 or if there is another better way to deal with that?
Maybe @ogrisel or @lesteve , you know more, since you were involved in PR #32776 adding `faulthandler`? | {
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Reverts #32197
#### What does this implement/fix? Explain your changes.
- This reverts the refactoring with respect to the screening in `enet_coordinate_descent_gram`
#### AI usage disclosure
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32855 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32855/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32855/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32855/events | https://github.com/scikit-learn/scikit-learn/issues/32855 | 3,704,475,289 | I_kwDOAAzd1s7czc6Z | 32,855 | Chain classifier customizability | {
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} | ### Describe the workflow you want to enable
Allow users to modify the model or fit_params in each step of the chain dynamically.
For example use the model to dynamically configure fit_params, for example add in Eval_set in light GBM latest prediction to prepare it for the next chain model fit.
### Describe your proposed solution
Enable pre-fit or post-fit callbacks that accept the latest to be trained model in the chain.
This will also allow users to do intermediate logging etc...
### Describe alternatives you've considered, if relevant
Other alternatives are to let users create their own wrappers of estimators, before using chain classifier. But this will reduce the ability to control any intermediate states if necessary.
Example using early stopping on unseen eval set:
```python
import lightgbm as lgb
from sklearn import set_config
from sklearn.multioutput import ClassifierChain
from sklearn.model_selection import train_test_split
set_config(enable_metadata_routing=True)
base_lr = lgb.LGBMClassifier(
callbacks=[lgb.early_stopping(stopping_rounds=5)]
)
base_lr.set_fit_request(eval_set=True)
base_lr.set_fit_request(eval_metric=True)
chain = ClassifierChain(
base_lr,
random_state=42,
cv=5
)
X_train_2, X_eval_2, y_train_2, y_eval_2 = train_test_split(
X_train, Y_train, test_size=0.2
)
changing_eval_set = [[X_eval_2, y_eval_2.iloc[:, 0]]]
n_feature = X_eval_2.shape[1]
def eval_set_updater(latest_model):
eval_set = changing_eval_set[0]
X_eval_2 = eval_set[0]
lastest_cv_eval_pred = latest_model.predict(X_eval_2)
eval_set[0] = pd.concat([X_eval_2, lastest_cv_eval_pred], axis=1)
eval_set[1] = y_eval_2.iloc[:, X_eval_2.shape[1] - n_feature + 1]
chain.fit(
X_train_2,
y_train_2,
eval_set=changing_eval_set,
eval_metric="binary_logloss",
post_fit_callback=eval_set_updater
)
```
### Additional context
In some rare cases, fit_params might depend on the performance and behavior of the previous model. Giving the ability to customize the model and params will increase flexibility of the chain model. | null | {
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} | 7 | 2025-12-08T01:59:07 | 2026-02-18T20:31:22 | null | null | MEMBER | null | null | null | null | #### Reference Issues/PRs
A follow-up to #31068 and https://github.com/scikit-learn/scikit-learn/pull/32809#discussion_r2594800078
#### What does this implement/fix? Explain your changes.
This PR adds an example demonstrating the use of temperature scaling for multi-class probability calibration.
#### Any other comments?
Cc @lorentzenchr for visibility | null | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32853 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32853/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32853/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32853/events | https://github.com/scikit-learn/scikit-learn/pull/32853 | 3,703,820,737 | PR_kwDOAAzd1s63i7lG | 32,853 | FIX: Fixed duplicate column error appearing with FeatureUnion polars | {
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"node_id": "MDU6TGFiZWwx... | closed | false | [] | null | 3 | 2025-12-07T18:40:14 | 2026-02-15T22:25:29 | 2026-01-27T09:00:54 | null | CONTRIBUTOR | null | null | null | null | When using FeatureUnion with set_config(transform_output="polars"), the operation failed with polars.exceptions.DuplicateError because the ContainerAdapterProtocol.hstack() interface was incomplete: it didn't accept a `feature_names` parameter to apply column renaming before concatenation.
Fixes https://github.com/scikit-learn/scikit-learn/issues/32852 | {
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32852 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32852/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32852/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32852/events | https://github.com/scikit-learn/scikit-learn/issues/32852 | 3,703,534,279 | I_kwDOAAzd1s7cv3LH | 32,852 | FeatureUnion with polars output fails due to missing column renaming in adapter interface | {
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} | ### Describe the bug
When using `FeatureUnion` with `set_config(transform_output="polars")`, the operation fails with `polars.exceptions.DuplicateError` because the `ContainerAdapterProtocol.hstack()` interface is incomplete - it doesn't accept `feature_names` parameter to apply column renaming before concatenation.
The `verbose_feature_names_out=True` (default) mechanism should ensure unique column names via `get_feature_names_out()`, but this information cannot be passed to the adapter's `hstack()` method due to interface design limitation.
### Steps/Code to Reproduce
```python
from sklearn.pipeline import FeatureUnion
from sklearn.preprocessing import StandardScaler
from sklearn import set_config
import numpy as np
set_config(transform_output="polars")
X = np.array([[1, 2], [3, 4], [5, 6]])
union = FeatureUnion([
("scaler1", StandardScaler()),
("scaler2", StandardScaler())
])
result = union.fit_transform(X)
```
### Expected Results
The code should work and produce a polars DataFrame with uniquely named columns like `['scaler1__x0', 'scaler1__x1', 'scaler2__x0', 'scaler2__x1']`, consistent with the design of `verbose_feature_names_out=True`.
### Actual Results
```python
Traceback (most recent call last):
File "<stdin>", line 10, in <module>
File "/path/to/sklearn/utils/_set_output.py", line 316, in wrapped
data_to_wrap = f(self, X, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/path/to/sklearn/pipeline.py", line 1971, in fit_transform
return self._hstack(Xs)
^^^^^^^^^^^^^^^^
File "/path/to/sklearn/pipeline.py", line 2052, in _hstack
return adapter.hstack(Xs)
^^^^^^^^^^^^^^^^^^
File "/path/to/sklearn/utils/_set_output.py", line 183, in hstack
return pl.concat(Xs, how="horizontal")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/path/to/polars/functions/eager.py", line 256, in concat
out = wrap_df(plr.concat_df_horizontal(elems))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.DuplicateError: column with name 'x0' has more than one occurrence
```
### Versions
```shell
System:
python: 3.11.14 | packaged by conda-forge | (main, Oct 22 2025, 22:46:25) [GCC 14.3.0]
executable: /opt/conda/envs/sklearn-dev/bin/python
machine: Linux-6.8.0-65-generic-x86_64-with-glibc2.36
Python dependencies:
sklearn: 1.9.dev0
pip: 25.3
setuptools: 80.9.0
numpy: 2.3.5
scipy: 1.16.3
Cython: 3.2.2
pandas: 2.3.3
matplotlib: None
joblib: 1.5.2
threadpoolctl: 3.6.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: openblas
num_threads: 48
prefix: libopenblas
filepath: /opt/conda/envs/sklearn-dev/lib/libopenblasp-r0.3.30.so
version: 0.3.30
threading_layer: pthreads
architecture: SkylakeX
user_api: openmp
internal_api: openmp
num_threads: 48
prefix: libgomp
filepath: /opt/conda/envs/sklearn-dev/lib/libgomp.so.1.0.0
version: None
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https://api.github.com/repos/scikit-learn/scikit-learn/issues/32851 | https://api.github.com/repos/scikit-learn/scikit-learn | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32851/labels{/name} | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32851/comments | https://api.github.com/repos/scikit-learn/scikit-learn/issues/32851/events | https://github.com/scikit-learn/scikit-learn/pull/32851 | 3,703,427,640 | PR_kwDOAAzd1s63htTh | 32,851 | Fix: Ensure nan_euclidean_distances is symmetric for self-distances | {
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The Fix:
When Y is None or Y is X, the code now explicitly symmetrizes the result matrix (D + D.T) / 2 and sets the diagonal to 0, ensuring perfect symmetry.
Closes #32848
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