From d6d44ecc6208026592d18aa27287c4657afbf898 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Sun, 23 Nov 2025 09:37:25 +0100 Subject: [PATCH 01/44] Update version --- sklearn/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/__init__.py b/sklearn/__init__.py index 2bb31200ed1a5..2bb7e05db568f 100644 --- a/sklearn/__init__.py +++ b/sklearn/__init__.py @@ -42,7 +42,7 @@ # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' # -__version__ = "1.8.dev0" +__version__ = "1.8.0rc1" # On OSX, we can get a runtime error due to multiple OpenMP libraries loaded From b6d6ef1ffcc16d937df1b62f49b870709e4e9b2f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Sun, 23 Nov 2025 09:39:18 +0100 Subject: [PATCH 02/44] Update changelog --- doc/whats_new/v1.8.rst | 585 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 585 insertions(+) diff --git a/doc/whats_new/v1.8.rst b/doc/whats_new/v1.8.rst index 603373824d395..017c9e981cd74 100644 --- a/doc/whats_new/v1.8.rst +++ b/doc/whats_new/v1.8.rst @@ -26,6 +26,591 @@ Version 1.8 .. towncrier release notes start +.. _changes_1_8_0: + +Version 1.8.0 +============= + +**November 2025** + +Changes impacting many modules +------------------------------ + +- |Efficiency| Improved CPU and memory usage in estimators and metric functions that rely on + weighted percentiles and better match NumPy and Scipy (un-weighted) implementations + of percentiles. + By :user:`Lucy Liu ` :pr:`31775` + +Support for Array API +--------------------- + +Additional estimators and functions have been updated to include support for all +`Array API `_ compliant inputs. + +See :ref:`array_api` for more details. + +- |Feature| :class:`sklearn.preprocessing.StandardScaler` now supports Array API compliant inputs. + By :user:`Alexander Fabisch `, :user:`Edoardo Abati `, + :user:`Olivier Grisel ` and :user:`Charles Hill `. :pr:`27113` + +- |Feature| :class:`linear_model.RidgeCV`, :class:`linear_model.RidgeClassifier` and + :class:`linear_model.RidgeClassifierCV` now support array API compatible + inputs with `solver="svd"`. + By :user:`Jérôme Dockès `. :pr:`27961` + +- |Feature| :func:`metrics.pairwise.pairwise_kernels` for any kernel except + "laplacian" and + :func:`metrics.pairwise_distances` for metrics "cosine", + "euclidean" and "l2" now support array API inputs. + By :user:`Emily Chen ` and :user:`Lucy Liu ` :pr:`29822` + +- |Feature| :func:`sklearn.metrics.confusion_matrix` now supports Array API compatible inputs. + By :user:`Stefanie Senger ` :pr:`30562` + +- |Feature| :class:`sklearn.mixture.GaussianMixture` with + `init_params="random"` or `init_params="random_from_data"` and + `warm_start=False` now supports Array API compatible inputs. + By :user:`Stefanie Senger ` and :user:`Loïc Estève ` :pr:`30777` + +- |Feature| :func:`sklearn.metrics.roc_curve` now supports Array API compatible inputs. + By :user:`Thomas Li ` :pr:`30878` + +- |Feature| :class:`preprocessing.PolynomialFeatures` now supports array API compatible inputs. + By :user:`Omar Salman ` :pr:`31580` + +- |Feature| :class:`calibration.CalibratedClassifierCV` now supports array API compatible + inputs with `method="temperature"` and when the underlying `estimator` also + supports the array API. + By :user:`Omar Salman ` :pr:`32246` + +- |Feature| :func:`sklearn.metrics.precision_recall_curve` now supports array API compatible + inputs. + By :user:`Lucy Liu ` :pr:`32249` + +- |Feature| :func:`sklearn.model_selection.cross_val_predict` now supports array API compatible inputs. + By :user:`Omar Salman ` :pr:`32270` + +- |Feature| :func:`sklearn.metrics.brier_score_loss`, :func:`sklearn.metrics.log_loss`, + :func:`sklearn.metrics.d2_brier_score` and :func:`sklearn.metrics.d2_log_loss_score` + now support array API compatible inputs. + By :user:`Omar Salman ` :pr:`32422` + +- |Feature| :class:`naive_bayes.GaussianNB` now supports array API compatible inputs. + By :user:`Omar Salman ` :pr:`32497` + +- |Feature| :func:`sklearn.metrics.det_curve` now supports Array API compliant inputs. + By :user:`Josef Affourtit `. :pr:`32586` + +- |Feature| :func:`sklearn.metrics.pairwise.manhattan_distances` now supports array API compatible inputs. + By :user:`Omar Salman `. :pr:`32597` + +- |Feature| :func:`sklearn.metrics.calinski_harabasz_score` now supports Array API compliant inputs. + By :user:`Josef Affourtit `. :pr:`32600` + +- |Feature| :func:`sklearn.metrics.balanced_accuracy_score` now supports array API compatible inputs. + By :user:`Omar Salman `. :pr:`32604` + +- |Feature| :func:`sklearn.metrics.pairwise.laplacian_kernel` now supports array API compatible inputs. + By :user:`Zubair Shakoor `. :pr:`32613` + +- |Feature| :func:`sklearn.metrics.cohen_kappa_score` now supports array API compatible inputs. + By :user:`Omar Salman `. :pr:`32619` + +Metadata routing +---------------- + +Refer to the :ref:`Metadata Routing User Guide ` for +more details. + +- |Fix| Fixed an issue where passing `sample_weight` to a :class:`Pipeline` inside a + :class:`GridSearchCV` would raise an error with metadata routing enabled. + By `Adrin Jalali`_. :pr:`31898` + +Free-threaded CPython 3.14 support +---------------------------------- + +scikit-learn has support for free-threaded CPython, in particular +free-threaded wheels are available for all of our supported platforms on Python +3.14. + +Free-threaded (also known as nogil) CPython is a version of CPython that aims at +enabling efficient multi-threaded use cases by removing the Global Interpreter +Lock (GIL). + +If you want to try out free-threaded Python, the recommendation is to use +Python 3.14, that has fixed a number of issues compared to Python 3.13. Feel +free to try free-threaded on your use case and report any issues! + +For more details about free-threaded CPython see `py-free-threading doc `_, +in particular `how to install a free-threaded CPython `_ +and `Ecosystem compatibility tracking `_. + +By :user:`Loïc Estève ` and :user:`Olivier Grisel ` and many +other people in the wider Scientific Python and CPython ecosystem, for example +:user:`Nathan Goldbaum `, :user:`Ralf Gommers `, +:user:`Edgar Andrés Margffoy Tuay `. :pr:`custom-top-level-32079` + +:mod:`sklearn.base` +------------------- + +- |Feature| Refactored :meth:`dir` in :class:`BaseEstimator` to recognize condition check in :meth:`available_if`. + By :user:`John Hendricks ` and :user:`Miguel Parece `. :pr:`31928` + +- |Fix| Fixed the handling of pandas missing values in HTML display of all estimators. + By :user: `Dea María Léon `. :pr:`32341` + +:mod:`sklearn.calibration` +-------------------------- + +- |Feature| Added temperature scaling method in :class:`calibration.CalibratedClassifierCV`. + By :user:`Virgil Chan ` and :user:`Christian Lorentzen `. :pr:`31068` + +:mod:`sklearn.cluster` +---------------------- + +- |Efficiency| :func:`cluster.kmeans_plusplus` now uses `np.cumsum` directly without extra + numerical stability checks and without casting to `np.float64`. + By :user:`Tiziano Zito ` :pr:`31991` + +- |Fix| The default value of the `copy` parameter in :class:`cluster.HDBSCAN` + will change from `False` to `True` in 1.10 to avoid data modification + and maintain consistency with other estimators. + By :user:`Sarthak Puri `. :pr:`31973` + +:mod:`sklearn.compose` +---------------------- + +- |Fix| The :class:`compose.ColumnTransformer` now correctly fits on data provided as a + `polars.DataFrame` when any transformer has a sparse output. + By :user:`Phillipp Gnan `. :pr:`32188` + +:mod:`sklearn.covariance` +------------------------- + +- |Efficiency| :class:`sklearn.covariance.GraphicalLasso`, + :class:`sklearn.covariance.GraphicalLassoCV` and + :func:`sklearn.covariance.graphical_lasso` with `mode="cd"` profit from the + fit time performance improvement of :class:`sklearn.linear_model.Lasso` by means of + gap safe screening rules. + By :user:`Christian Lorentzen `. :pr:`31987` + +- |Fix| Fixed uncontrollable randomness in :class:`sklearn.covariance.GraphicalLasso`, + :class:`sklearn.covariance.GraphicalLassoCV` and + :func:`sklearn.covariance.graphical_lasso`. For `mode="cd"`, they now use cyclic + coordinate descent. Before, it was random coordinate descent with uncontrollable + random number seeding. + By :user:`Christian Lorentzen `. :pr:`31987` + +- |Fix| Added correction to :class:`covariance.MinCovDet` to adjust for + consistency at the normal distribution. This reduces the bias present + when applying this method to data that is normally distributed. + By :user:`Daniel Herrera-Esposito ` :pr:`32117` + +:mod:`sklearn.decomposition` +---------------------------- + +- |Efficiency| :class:`sklearn.decomposition.DictionaryLearning` and + :class:`sklearn.decomposition.MiniBatchDictionaryLearning` with `fit_algorithm="cd"`, + :class:`sklearn.decomposition.SparseCoder` with `transform_algorithm="lasso_cd"`, + :class:`sklearn.decomposition.MiniBatchSparsePCA`, + :class:`sklearn.decomposition.SparsePCA`, + :func:`sklearn.decomposition.dict_learning` and + :func:`sklearn.decomposition.dict_learning_online` with `method="cd"`, + :func:`sklearn.decomposition.sparse_encode` with `algorithm="lasso_cd"` + all profit from the fit time performance improvement of + :class:`sklearn.linear_model.Lasso` by means of gap safe screening rules. + By :user:`Christian Lorentzen `. :pr:`31987` + +- |Enhancement| :class:`decomposition.SparseCoder` now follows the transformer API of scikit-learn. + In addition, the :meth:`fit` method now validates the input and parameters. + By :user:`François Paugam `. :pr:`32077` + +- |Fix| Add input checks to the `inverse_transform` method of :class:`decomposition.PCA` + and :class:`decomposition.IncrementalPCA`. + :pr:`29310` by :user:`Ian Faust `. :pr:`29310` + +:mod:`sklearn.discriminant_analysis` +------------------------------------ + +- |Feature| Added `solver`, `covariance_estimator` and `shrinkage` in + :class:`discriminant_analysis.QuadraticDiscriminantAnalysis`. + The resulting class is more similar to + :class:`discriminant_analysis.LinearDiscriminantAnalysis` + and allows for more flexibility in the estimation of the covariance matrices. + By :user:`Daniel Herrera-Esposito `. :pr:`32108` + +:mod:`sklearn.ensemble` +----------------------- + +- |Fix| :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor` + and :class:`ensemble.IsolationForest` now use `sample_weight` to draw + the samples instead of forwarding them multiplied by a uniformly sampled + mask to the underlying estimators. Furthermore, `max_samples` is now + interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]` + when passed as a float. + By :user:`Antoine Baker `. :pr:`31414` + +:mod:`sklearn.feature_selection` +-------------------------------- + +- |Enhancement| :class:`feature_selection.SelectFromModel` now does not force `max_features` to be + less than or equal to the number of input features. + By :user:`Thibault ` :pr:`31939` + +:mod:`sklearn.gaussian_process` +------------------------------- + +- |Efficiency| make :class:`GaussianProcessRegressor.predict` faster when `return_cov` and + `return_std` are both `False`. + By :user:`Rafael Ayllón Gavilán `. :pr:`31431` + +:mod:`sklearn.linear_model` +--------------------------- + +- |Efficiency| :class:`linear_model.ElasticNet` and :class:`linear_model.Lasso` with + `precompute=False` use less memory for dense `X` and are a bit faster. + Previously, they used twice the memory of `X` even for Fortran-contiguous `X`. + By :user:`Christian Lorentzen ` :pr:`31665` + +- |Efficiency| :class:`linear_model.ElasticNet` and :class:`linear_model.Lasso` avoid + double input checking and are therefore a bit faster. + By :user:`Christian Lorentzen `. :pr:`31848` + +- |Efficiency| :class:`linear_model.ElasticNet`, :class:`linear_model.ElasticNetCV`, + :class:`linear_model.Lasso`, :class:`linear_model.LassoCV`, + :class:`linear_model.MultiTaskElasticNet`, + :class:`linear_model.MultiTaskElasticNetCV`, + :class:`linear_model.MultiTaskLasso` and :class:`linear_model.MultiTaskLassoCV` + are faster to fit by avoiding a BLAS level 1 (axpy) call in the innermost loop. + Same for functions :func:`linear_model.enet_path` and + :func:`linear_model.lasso_path`. + By :user:`Christian Lorentzen ` :pr:`31956` and :pr:`31880` + +- |Efficiency| :class:`linear_model.ElasticNetCV`, :class:`linear_model.LassoCV`, + :class:`linear_model.MultiTaskElasticNetCV` and :class:`linear_model.MultiTaskLassoCV` + avoid an additional copy of `X` with default `copy_X=True`. + By :user:`Christian Lorentzen `. :pr:`31946` + +- |Efficiency| :class:`linear_model.ElasticNet`, :class:`linear_model.ElasticNetCV`, + :class:`linear_model.Lasso`, :class:`linear_model.LassoCV`, + :class:`linear_model.MultiTaskElasticNetCV`, :class:`linear_model.MultiTaskLassoCV` + as well as + :func:`linear_model.lasso_path` and :func:`linear_model.enet_path` now implement + gap safe screening rules in the coordinate descent solver for dense and sparse `X`. + The speedup of fitting time is particularly pronounced (10-times is possible) when + computing regularization paths like the \*CV-variants of the above estimators do. + There is now an additional check of the stopping criterion before entering the main + loop of descent steps. As the stopping criterion requires the computation of the dual + gap, the screening happens whenever the dual gap is computed. + By :user:`Christian Lorentzen ` :pr:`31882`, :pr:`31986`, + :pr:`31987` and :pr:`32014` + +- |Enhancement| :class:`linear_model.ElasticNet`, :class:`linear_model.ElasticNetCV`, + :class:`linear_model.Lasso`, :class:`linear_model.LassoCV`, + :class:`MultiTaskElasticNet`, :class:`MultiTaskElasticNetCV`, + :class:`MultiTaskLasso`, :class:`MultiTaskLassoCV`, as well as + :func:`linear_model.enet_path` and :func:`linear_model.lasso_path` + now use `dual gap <= tol` instead of `dual gap < tol` as stopping criterion. + The resulting coefficients might differ to previous versions of scikit-learn in + rare cases. + By :user:`Christian Lorentzen `. :pr:`31906` + +- |Fix| Fix the convergence criteria for SGD models, to avoid premature convergence when + `tol != None`. This primarily impacts :class:`SGDOneClassSVM` but also affects + :class:`SGDClassifier` and :class:`SGDRegressor`. Before this fix, only the loss + function without penalty was used as the convergence check, whereas now, the full + objective with regularization is used. + By :user:`Guillaume Lemaitre ` and :user:`kostayScr ` :pr:`31856` + +- |Fix| The allowed parameter range for the initial learning rate `eta0` in + :class:`linear_model.SGDClassifier`, :class:`linear_model.SGDOneClassSVM`, + :class:`linear_model.SGDRegressor` and :class:`linear_model.Perceptron` + changed from non-negative numbers to strictly positive numbers. + As a consequence, the default `eta0` of :class:`linear_model.SGDClassifier` + and :class:`linear_model.SGDOneClassSVM` changed from 0 to 0.01. But note that + `eta0` is not used by the default learning rate "optimal" of those two estimators. + By :user:`Christian Lorentzen `. :pr:`31933` + +- |Fix| :class:`linear_model.LogisticRegressionCV` is able to handle CV splits where + some class labels are missing in some folds. Before, it raised an error whenever a + class label were missing in a fold. + By :user:`Christian Lorentzen :pr:`32747` + +- |API| :class:`linear_model.PassiveAggressiveClassifier` and + :class:`linear_model.PassiveAggressiveRegressor` are deprecated and will be removed + in 1.10. Equivalent estimators are available with :class:`linear_model.SGDClassifier` + and :class:`SGDRegressor`, both of which expose the options `learning_rate="pa1"` and + `"pa2"`. The parameter `eta0` can be used to specify the aggressiveness parameter of + the Passive-Aggressive-Algorithms, called C in the reference paper. + By :user:`Christian Lorentzen ` :pr:`31932` and :pr:`29097` + +- |API| :class:`linear_model.SGDClassifier`, :class:`linear_model.SGDRegressor`, and + :class:`linear_model.SGDOneClassSVM` now deprecate negative values for the + `power_t` parameter. Using a negative value will raise a warning in version 1.8 + and will raise an error in version 1.10. A value in the range [0.0, inf) must be used + instead. + By :user:`Ritvi Alagusankar ` :pr:`31474` + +- |API| Raising error in :class:`sklearn.linear_model.LogisticRegression` when + liblinear solver is used and input X values are larger than 1e30, + the liblinear solver freezes otherwise. + By :user:`Shruti Nath `. :pr:`31888` + +- |API| :class:`linear_model.LogisticRegressionCV` got a new parameter + `use_legacy_attributes` to control the types and shapes of the fitted attributes + `C_`, `l1_ratio_`, `coefs_paths_`, `scores_` and `n_iter_`. + The current default value `True` keeps the legacy behaviour. If `False` then: + + - ``C_`` is a float. + - ``l1_ratio_`` is a float. + - ``coefs_paths_`` is an ndarray of shape + (n_folds, n_l1_ratios, n_cs, n_classes, n_features). + For binary problems (n_classes=2), the 2nd last dimension is 1. + - ``scores_`` is an ndarray of shape (n_folds, n_l1_ratios, n_cs). + - ``n_iter_`` is an ndarray of shape (n_folds, n_l1_ratios, n_cs). + + In version 1.10, the default will change to `False` and `use_legacy_attributes` will + be deprecated. In 1.12 `use_legacy_attributes` will be removed. + By :user:`Christian Lorentzen `. :pr:`32114` + +- |API| The `n_jobs` parameter of :class:`linear_model.LogisticRegression` is deprecated and + will be removed in 1.10. It has no effect since 1.8. + By :user:`Loïc Estève `. :pr:`32742` + +:mod:`sklearn.manifold` +----------------------- + +- |MajorFeature| :class:`manifold.ClassicalMDS` was implemented to perform classical MDS + (eigendecomposition of the double-centered distance matrix). + By :user:`Dmitry Kobak ` and :user:`Meekail Zain ` :pr:`31322` + +- |Feature| :class:`manifold.MDS` now supports arbitrary distance metrics + (via `metric` and `metric_params` parameters) and + initialization via classical MDS (via `init` parameter). + The `dissimilarity` parameter was deprecated. The old `metric` parameter + was renamed into `metric_mds`. + By :user:`Dmitry Kobak ` :pr:`32229` + +- |Feature| :class:`manifold.TSNE` now supports PCA initialization with sparse input matrices. + By :user:`Arturo Amor `. :pr:`32433` + +:mod:`sklearn.metrics` +---------------------- + +- |Feature| :func:`metrics.d2_brier_score` has been added which calculates the D^2 for the Brier score. + By :user:`Omar Salman `. :pr:`28971` + +- |Feature| Add :func:`metrics.confusion_matrix_at_thresholds` function that returns the number of + true negatives, false positives, false negatives and true positives per threshold. + By :user:`Success Moses `. :pr:`30134` + +- |Efficiency| Avoid redundant input validation in :func:`metrics.d2_log_loss_score` + leading to a 1.2x speedup in large scale benchmarks. + By :user:`Olivier Grisel ` and :user:`Omar Salman ` :pr:`32356` + +- |Enhancement| :func:`metrics.median_absolute_error` now supports Array API compatible inputs. + By :user:`Lucy Liu `. :pr:`31406` + +- |Enhancement| Improved the error message for sparse inputs for the following metrics: + :func:`metrics.accuracy_score`, + :func:`metrics.multilabel_confusion_matrix`, :func:`metrics.jaccard_score`, + :func:`metrics.zero_one_loss`, :func:`metrics.f1_score`, + :func:`metrics.fbeta_score`, :func:`metrics.precision_recall_fscore_support`, + :func:`metrics.class_likelihood_ratios`, :func:`metrics.precision_score`, + :func:`metrics.recall_score`, :func:`metrics.classification_report`, + :func:`metrics.hamming_loss`. + By :user:`Lucy Liu `. :pr:`32047` + +- |Fix| :func:`metrics.median_absolute_error` now uses `_averaged_weighted_percentile` + instead of `_weighted_percentile` to calculate median when `sample_weight` is not + `None`. This is equivalent to using the "averaged_inverted_cdf" instead of + the "inverted_cdf" quantile method, which gives results equivalent to `numpy.median` + if equal weights used. + By :user:`Lucy Liu ` :pr:`30787` + +- |Fix| Additional `sample_weight` checking has been added to + :func:`metrics.accuracy_score`, + :func:`metrics.balanced_accuracy_score`, + :func:`metrics.brier_score_loss`, + :func:`metrics.class_likelihood_ratios`, + :func:`metrics.classification_report`, + :func:`metrics.cohen_kappa_score`, + :func:`metrics.confusion_matrix`, + :func:`metrics.f1_score`, + :func:`metrics.fbeta_score`, + :func:`metrics.hamming_loss`, + :func:`metrics.jaccard_score`, + :func:`metrics.matthews_corrcoef`, + :func:`metrics.multilabel_confusion_matrix`, + :func:`metrics.precision_recall_fscore_support`, + :func:`metrics.precision_score`, + :func:`metrics.recall_score` and + :func:`metrics.zero_one_loss`. + `sample_weight` can only be 1D, consistent to `y_true` and `y_pred` in length,and + all values must be finite and not complex. + By :user:`Lucy Liu `. :pr:`31701` + +- |Fix| `y_pred` is deprecated in favour of `y_score` in + :func:`metrics.DetCurveDisplay.from_predictions` and + :func:`metrics.PrecisionRecallDisplay.from_predictions`. `y_pred` will be removed in + v1.10. + By :user:`Luis ` :pr:`31764` + +- |Fix| `repr` on a scorer which has been created with a `partial` `score_func` now correctly + works and uses the `repr` of the given `partial` object. + By `Adrin Jalali`_. :pr:`31891` + +- |Fix| kwargs specified in the `curve_kwargs` parameter of + :meth:`metrics.RocCurveDisplay.from_cv_results` now only overwrite their corresponding + default value before being passed to Matplotlib's `plot`. Previously, passing any + `curve_kwargs` would overwrite all default kwargs. + By :user:`Lucy Liu `. :pr:`32313` + +- |Fix| Registered named scorer objects for :func:`metrics.d2_brier_score` and + :func:`metrics.d2_log_loss_score` and updated their input validation to be + consistent with related metric functions. + By :user:`Olivier Grisel ` and :user:`Omar Salman ` :pr:`32356` + +- |Fix| :meth:`metrics.RocCurveDisplay.from_cv_results` will now infer `pos_label` as + `estimator.classes_[-1]`, using the estimator from `cv_results`, when + `pos_label=None`. Previously, an error was raised when `pos_label=None`. + By :user:`Lucy Liu `. :pr:`32372` + +- |Fix| All classification metrics now raise a `ValueError` when required input arrays + (`y_pred`, `y_true`, `y1`, `y2`, `pred_decision`, or `y_proba`) are empty. + Previously, `accuracy_score`, `class_likelihood_ratios`, `classification_report`, + `confusion_matrix`, `hamming_loss`, `jaccard_score`, `matthews_corrcoef`, + `multilabel_confusion_matrix`, and `precision_recall_fscore_support` did not raise + this error consistently. + By :user:`Stefanie Senger `. :pr:`32549` + +- |API| :func:`metrics.cluster.entropy` is deprecated and will be removed in v1.10. + By :user:`Lucy Liu ` :pr:`31294` + +- |API| The `estimator_name` parameter is deprecated in favour of `name` in + :class:`metrics.PrecisionRecallDisplay` and will be removed in 1.10. + By :user:`Lucy Liu `. :pr:`32310` + +:mod:`sklearn.model_selection` +------------------------------ + +- |Enhancement| :class:`model_selection.StratifiedShuffleSplit` will now specify which classes + have too few members when raising a ``ValueError`` if any class has less than 2 members. + This is useful to identify which classes are causing the error. + By :user:`Marc Bresson ` :pr:`32265` + +- |Fix| Fix shuffle behaviour in :class:`model_selection.StratifiedGroupKFold`. Now + stratification among folds is also preserved when `shuffle=True`. + By :user:`Pau Folch `. :pr:`32540` + +:mod:`sklearn.multiclass` +------------------------- + +- |Fix| Fix tie-breaking behavior in :class:`multiclass.OneVsRestClassifier` to match + `np.argmax` tie-breaking behavior. + By :user:`Lakshmi Krishnan `. :pr:`15504` + +:mod:`sklearn.naive_bayes` +-------------------------- + +- |Fix| :class:`naive_bayes.GaussianNB` preserves the dtype of the fitted attributes + according to the dtype of `X`. + By :user:`Omar Salman ` :pr:`32497` + +:mod:`sklearn.preprocessing` +---------------------------- + +- |Enhancement| :class:`preprocessing.SplineTransformer` can now handle missing values with the + parameter `handle_missing`. By :user:`Stefanie Senger `. :pr:`28043` + +- |Enhancement| The :class:`preprocessing.PowerTransformer` now returns a warning + when NaN values are encountered in the inverse transform, `inverse_transform`, typically + caused by extremely skewed data. + By :user:`Roberto Mourao ` :pr:`29307` + +- |Enhancement| :class:`preprocessing.MaxAbsScaler` can now clip out-of-range values in held-out data + with the parameter `clip`. + By :user:`Hleb Levitski `. :pr:`31790` + +:mod:`sklearn.semi_supervised` +------------------------------ + +- |Fix| User written kernel results are now normalized in + :class:`semi_supervised.LabelPropagation` + so all row sums equal 1 even if kernel gives asymmetric or non-uniform row sums. + By :user:`Dan Schult `. :pr:`31924` + +:mod:`sklearn.tree` +------------------- + +- |Efficiency| :class:`tree.DecisionTreeRegressor` with `criterion="absolute_error"` + now runs much faster: O(n log n) complexity against previous O(n^2) + allowing to scale to millions of data points, even hundred of millions. + By :user:`Arthur Lacote ` :pr:`32100` + +- |Fix| Make :func:`tree.export_text` thread-safe. + By :user:`Olivier Grisel `. :pr:`30041` + +- |Fix| :func:`~sklearn.tree.export_graphviz` now raises a `ValueError` if given feature + names are not all strings. + By :user:`Guilherme Peixoto ` :pr:`31036` + +- |Fix| :class:`tree.DecisionTreeRegressor` with `criterion="absolute_error"` + would sometimes make sub-optimal splits + (i.e. splits that don't minimize the absolute error). + Now it's fixed. Hence retraining trees might gives slightly different + results. + By :user:`Arthur Lacote ` :pr:`32100` + +- |Fix| Fixed a regression in :ref:`decision trees ` where almost constant features were + not handled properly. + By :user:`Sercan Turkmen `. :pr:`32259` + +- |Fix| Fix handling of missing values in method :func:`decision_path` of trees + (:class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, + :class:`tree.ExtraTreeClassifier` and :class:`tree.ExtraTreeRegressor`) + By :user:`Arthur Lacote `. :pr:`32280` + +- |Fix| Fix decision tree splitting with missing values present in some features. In some cases the last + non-missing sample would not be partitioned correctly. + By :user:`Tim Head ` and :user:`Arthur Lacote `. :pr:`32351` + +:mod:`sklearn.utils` +-------------------- + +- |Efficiency| The function :func:`sklearn.utils.extmath.safe_sparse_dot` was improved by a dedicated + Cython routine for the case of `a @ b` with sparse 2-dimensional `a` and `b` and when + a dense output is required, i.e., `dense_output=True`. This improves several + algorithms in scikit-learn when dealing with sparse arrays (or matrices). + By :user:`Christian Lorentzen `. :pr:`31952` + +- |Enhancement| The parameter table in the HTML representation of all scikit-learn estimators and + more generally of estimators inheriting from :class:`base.BaseEstimator` + now displays the parameter description as a tooltip and has a link to the online + documentation for each parameter. + By :user:`Dea María Léon `. :pr:`31564` + +- |Enhancement| ``sklearn.utils._check_sample_weight`` now raises a clearer error message when the + provided weights are neither a scalar nor a 1-D array-like of the same size as the + input data. + By :user:`Kapil Parekh `. :pr:`31873` + +- |Enhancement| :func:`sklearn.utils.estimator_checks.parametrize_with_checks` now lets you configure + strict mode for xfailing checks. Tests that unexpectedly pass will lead to a test + failure. The default behaviour is unchanged. + By :user:`Tim Head `. :pr:`31951` + +- |Enhancement| Fixed the alignment of the "?" and "i" symbols and improved the color style of the + HTML representation of estimators. + By :user:`Guillaume Lemaitre `. :pr:`31969` + +- |Fix| Changes the way color are chosen when displaying an estimator as an HTML representation. Colors are not adapted anymore to the user's theme, but chosen based on theme declared color scheme (light or dark) for VSCode and JupyterLab. If theme does not declare a color scheme, scheme is chosen according to default text color of the page, if it fails fallbacks to a media query. + By :user:`Matt J. `. :pr:`32330` + +- |API| :func:`utils.extmath.stable_cumsum` is deprecated and will be removed + in v1.10. Use `np.cumulative_sum` with the desired dtype directly instead. + By :user:`Tiziano Zito `. :pr:`32258` + .. rubric:: Code and documentation contributors Thanks to everyone who has contributed to the maintenance and improvement of From f7d06e45a8210a956c9d3f99bd8daeba477bd41f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 25 Nov 2025 18:07:38 +0100 Subject: [PATCH 03/44] [cd build] Add build dependencies upper bounds --- pyproject.toml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 4e0e4417c2d7f..dbc65fe543181 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -95,10 +95,10 @@ maintenance = ["conda-lock==3.0.1"] build-backend = "mesonpy" # Minimum requirements for the build system to execute. requires = [ - "meson-python>=0.17.1", - "cython>=3.1.2", - "numpy>=2", - "scipy>=1.10.0", + "meson-python>=0.17.1,<0.19.0", + "cython>=3.1.2,<3.3.0", + "numpy>=2,<2.4.0", + "scipy>=1.10.0,<1.17.0", ] [tool.pytest.ini_options] From fc3bc55d3671163edd5676851623944121ecba89 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Thu, 27 Nov 2025 00:26:29 +1100 Subject: [PATCH 04/44] DOC Add info on 'array-like' array API inputs when `array_api_dispatch=False` (#32676) --- doc/glossary.rst | 6 ++++++ doc/modules/array_api.rst | 21 ++++++++++++++++----- 2 files changed, 22 insertions(+), 5 deletions(-) diff --git a/doc/glossary.rst b/doc/glossary.rst index 9ff1eb001c8e5..1f214a11b7320 100644 --- a/doc/glossary.rst +++ b/doc/glossary.rst @@ -63,6 +63,12 @@ General Concepts * a :class:`pandas.DataFrame` with all columns numeric * a numeric :class:`pandas.Series` + Other array API inputs, but see :ref:`array_api` for the preferred way of + using these: + + * a `PyTorch `_ tensor on 'cpu' device + * a `JAX `_ array + It excludes: * a :term:`sparse matrix` diff --git a/doc/modules/array_api.rst b/doc/modules/array_api.rst index b9b46f99f3cae..cf3d9d5890c3a 100644 --- a/doc/modules/array_api.rst +++ b/doc/modules/array_api.rst @@ -42,15 +42,26 @@ and how it facilitates interoperability between array libraries: - `Scikit-learn on GPUs with Array API `_ by :user:`Thomas Fan ` at PyData NYC 2023. -Example usage -============= +Enabling array API support +========================== The configuration `array_api_dispatch=True` needs to be set to `True` to enable array API support. We recommend setting this configuration globally to ensure consistent behaviour and prevent accidental mixing of array namespaces. -Note that we set it with :func:`config_context` below to avoid having to call -:func:`set_config(array_api_dispatch=False)` at the end of every code snippet -that uses the array API. +Note that in the examples below, we use a context manager (:func:`config_context`) +to avoid having to reset it to `False` at the end of every code snippet, so as to +not affect the rest of the documentation. + +Scikit-learn accepts :term:`array-like` inputs for all :mod:`metrics` +and some estimators. When `array_api_dispatch=False`, these inputs are converted +into NumPy arrays using :func:`numpy.asarray` (or :func:`numpy.array`). +While this will successfully convert some array API inputs (e.g., JAX array), +we generally recommend setting `array_api_dispatch=True` when using array API inputs. +This is because NumPy conversion can often fail, e.g., torch tensor allocated on GPU. + +Example usage +============= + The example code snippet below demonstrates how to use `CuPy `_ to run :class:`~discriminant_analysis.LinearDiscriminantAnalysis` on a GPU:: From dbb7d84def49185fb46a29f7984961c3bf1aea5a Mon Sep 17 00:00:00 2001 From: Arthur Date: Fri, 21 Nov 2025 11:38:34 +0100 Subject: [PATCH 05/44] TST: increase tolerance in `test_mcd` --- sklearn/covariance/tests/test_robust_covariance.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/covariance/tests/test_robust_covariance.py b/sklearn/covariance/tests/test_robust_covariance.py index 4a7590ef2c18c..f1aa5da88655e 100644 --- a/sklearn/covariance/tests/test_robust_covariance.py +++ b/sklearn/covariance/tests/test_robust_covariance.py @@ -19,7 +19,7 @@ def test_mcd(global_random_seed): # Tests the FastMCD algorithm implementation # Small data set # test without outliers (random independent normal data) - launch_mcd_on_dataset(100, 5, 0, 0.02, 0.1, 75, global_random_seed) + launch_mcd_on_dataset(100, 5, 0, 0.02, 0.1, 74, global_random_seed) # test with a contaminated data set (medium contamination) launch_mcd_on_dataset(100, 5, 20, 0.3, 0.3, 65, global_random_seed) # test with a contaminated data set (strong contamination) From 1c18fa90187ecf074f815ac1249d22677560e173 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Paugam?= <35327799+FrancoisPgm@users.noreply.github.com> Date: Thu, 27 Nov 2025 14:08:09 +0100 Subject: [PATCH 06/44] CI Run unit-tests with random seed on schedule and open automated issue (#32797) --- .github/workflows/unit-tests.yml | 18 ++++++++++++++++++ maint_tools/update_tracking_issue.py | 1 + 2 files changed, 19 insertions(+) diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml index 2a2ce57eaefb7..01801e1d4df6b 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows/unit-tests.yml @@ -5,6 +5,11 @@ permissions: on: push: pull_request: + schedule: + # Nightly build at 02:30 UTC + - cron: "30 2 * * *" + # Manual run + workflow_dispatch: concurrency: group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }} @@ -152,6 +157,10 @@ jobs: - name: Build scikit-learn run: bash -l build_tools/azure/install.sh + - name: Set random seed for nightly/manual runs + if: github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' + run: echo "SKLEARN_TESTS_GLOBAL_RANDOM_SEED=$((RANDOM % 100))" >> $GITHUB_ENV + - name: Run tests env: COMMIT_MESSAGE: ${{ needs.retrieve-commit-message.outputs.message }} @@ -178,3 +187,12 @@ jobs: files: ./coverage.xml token: ${{ secrets.CODECOV_TOKEN }} disable_search: true + + update-tracker: + uses: ./.github/workflows/update_tracking_issue.yml + if: ${{ always() }} + needs: [unit-tests] + with: + job_status: ${{ needs.unit-tests.result }} + secrets: + BOT_GITHUB_TOKEN: ${{ secrets.BOT_GITHUB_TOKEN }} diff --git a/maint_tools/update_tracking_issue.py b/maint_tools/update_tracking_issue.py index b40e8222fefae..6a02c48d24bf6 100644 --- a/maint_tools/update_tracking_issue.py +++ b/maint_tools/update_tracking_issue.py @@ -67,6 +67,7 @@ def get_issue(): login = gh.get_user().login issues = gh.search_issues( f"repo:{args.issue_repo} {title_query} in:title state:open author:{login}" + " is:issue" ) first_page = issues.get_page(0) # Return issue if it exist From bb59538f9346072d27b9b932e91cf710778656aa Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Fri, 28 Nov 2025 00:16:59 +1100 Subject: [PATCH 07/44] DOC Shorten PR template and improve PR attention FAQ (#32734) --- .github/PULL_REQUEST_TEMPLATE.md | 11 +++------ doc/faq.rst | 42 ++++++++++++++++++++++---------- 2 files changed, 33 insertions(+), 20 deletions(-) diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index dda65568b4a29..df26bf21907c3 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -29,13 +29,10 @@ is merged. See https://github.com/blog/1506-closing-issues-via-pull-requests +I used AI assistance for: +- [ ] Code generation (e.g., when writing an implementation or fixing a bug) +- [ ] Test/benchmark generation +- [ ] Documentation (including examples) +- [ ] Research and understanding + + #### Any other comments? @@ -37,18 +50,3 @@ https://scikit-learn.org/dev/faq.html#why-is-my-pull-request-not-getting-any-att Thanks for contributing! --> - - diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index 1d582255f6c11..f722b035618d8 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -152,22 +152,26 @@ look. Automated Contributions Policy ============================== +Contributing to scikit-learn requires human judgment, contextual understanding, and +familiarity with scikit-learn's structure and goals. It is not suitable for +automatic processing by AI tools. + Please refrain from submitting issues or pull requests generated by fully-automated tools. Maintainers reserve the right, at their sole discretion, to close such submissions and to block any account responsible for them. -Ideally, contributions should follow from a human-to-human discussion in the -form of an issue. In particular, please do not paste AI generated text in the -description of issues, PRs or in comments as it makes it significantly harder for -reviewers to assess the relevance of your contribution and the potential value it -brings to future end-users of the library. Note that it's fine to use AI tools -to proofread or improve your draft text if you are not a native English speaker, -but reviewers are not interested in unknowingly interacting back and forth with -automated chatbots that fundamentally do not care about the value of our open -source project. - -Please self review all code or documentation changes made by AI tools before -submitting them under your name. +Review all code or documentation changes made by AI tools and +make sure you understand all changes and can explain them on request, before +submitting them under your name. Do not submit any AI-generated code that you haven't +personally reviewed, understood and tested, as this wastes maintainers' time. + +Please do not paste AI generated text in the description of issues, PRs or in comments +as this makes it harder for reviewers to assess your contribution. We are happy for it +to be used to improve grammar or if you are not a native English speaker. + +If you used AI tools, please state so in your PR description. + +PRs that appear to violate this policy will be closed without review. Submitting a bug report or a feature request ============================================ From c098a62cad234a358617e9004af9a28928a1293d Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 1 Dec 2025 10:08:02 +0100 Subject: [PATCH 20/44] :lock: :robot: CI Update lock files for array-api CI build(s) :lock: :robot: (#32823) Co-authored-by: Lock file bot --- ...a_forge_cuda_array-api_linux-64_conda.lock | 118 +++++++++--------- 1 file changed, 59 insertions(+), 59 deletions(-) diff --git a/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock b/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock index d3a632653ce31..392461bfb0ceb 100644 --- a/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock +++ b/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock @@ -15,15 +15,14 @@ https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda#9430 https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.11.12-hbd8a1cb_0.conda#f0991f0f84902f6b6009b4d2350a83aa https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda#a7970cd949a077b7cb9696379d338681 -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-bootstrap_ha15bf96_3.conda#3036ca5b895b7f5146c5a25486234a68 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-21.1.6-h4922eb0_0.conda#7a0b9ce502e0ed62195e02891dfcd704 https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_8.conda#1bad93f0aa428d618875ef3a588a889e -https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-6_kmp_llvm.conda#197811678264cb9da0d2ea0726a70661 +https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda#887b70e1d607fba7957aa02f9ee0d939 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-h767d61c_7.conda#c0374badb3a5d4b1372db28d19462c53 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_14.conda#550dceb769d23bcf0e2f97fd4062d720 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.12.0-hb9d3cd8_0.conda#f65c946f28f0518f41ced702f44c52b7 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda#51a19bba1b8ebfb60df25cde030b7ebc @@ -33,15 +32,15 @@ https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb03c661_4 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda#6c77a605a7a689d17d4819c0f8ac9a00 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda#8b09ae86839581147ef2e5c5e229d164 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h9ec8514_0.conda#35f29eec58405aaf55e01cb470d8c26a -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_7.conda#280ea6eee9e2ddefde25ff799c4f0363 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-hcd61629_7.conda#f116940d825ffc9104400f0d7f1a4551 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_14.conda#6c13aaae36d7514f28bd5544da1a7bb8 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_14.conda#3078a2a9a58566a54e579b41b9e88c84 https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda#915f5995e94f60e9a4826e0b0920ee88 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.2-hb03c661_0.conda#8397539e3a0bbd1695584fb4f927485a https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda#c7e925f37e3b40d893459e625f6a53f1 https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda#7c7927b404672409d9917d49bff5f2d6 https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda#70e3400cbbfa03e96dcde7fc13e38c7b -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h8f9b012_7.conda#5b767048b1b3ee9a954b06f4084f93dc +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_14.conda#8e96fe9b17d5871b5cf9d312cab832f6 https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.10.0-h202a827_0.conda#0f98f3e95272d118f7931b6bef69bfe5 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.2-he9a06e4_0.conda#80c07c68d2f6870250959dcc95b209d1 https://conda.anaconda.org/conda-forge/linux-64/libuv-1.51.0-hb03c661_1.conda#0f03292cc56bf91a077a134ea8747118 @@ -68,10 +67,11 @@ https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.125-hb03c661_1.conda# https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda#c277e0a4d549b03ac1e9d6cbbe3d017b https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d 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https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip babel @ https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl#sha256=4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2 # pip certifi @ https://files.pythonhosted.org/packages/70/7d/9bc192684cea499815ff478dfcdc13835ddf401365057044fb721ec6bddb/certifi-2025.11.12-py3-none-any.whl#sha256=97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b # pip charset-normalizer @ https://files.pythonhosted.org/packages/67/ff/f6b948ca32e4f2a4576aa129d8bed61f2e0543bf9f5f2b7fc3758ed005c9/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl#sha256=ecaae4149d99b1c9e7b88bb03e3221956f68fd6d50be2ef061b2381b61d20838 # pip coverage @ 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https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl#sha256=771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b @@ -51,7 +50,7 @@ https://conda.anaconda.org/conda-forge/noarch/pip-25.3-pyh145f28c_0.conda#bf4787 # pip platformdirs @ https://files.pythonhosted.org/packages/73/cb/ac7874b3e5d58441674fb70742e6c374b28b0c7cb988d37d991cde47166c/platformdirs-4.5.0-py3-none-any.whl#sha256=e578a81bb873cbb89a41fcc904c7ef523cc18284b7e3b3ccf06aca1403b7ebd3 # pip pluggy @ https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl#sha256=e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746 # pip pygments @ https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl#sha256=86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b -# pip roman-numerals-py @ https://files.pythonhosted.org/packages/53/97/d2cbbaa10c9b826af0e10fdf836e1bf344d9f0abb873ebc34d1f49642d3f/roman_numerals_py-3.1.0-py3-none-any.whl#sha256=9da2ad2fb670bcf24e81070ceb3be72f6c11c440d73bd579fbeca1e9f330954c +# pip roman-numerals @ https://files.pythonhosted.org/packages/82/1d/7356f115a0e5faf8dc59894a3e9fc8b1821ab949163458b0072db0a12a68/roman_numerals-3.1.0-py3-none-any.whl#sha256=842ae5fd12912d62720c9aad8cab706e8c692556d01a38443e051ee6cc158d90 # pip six @ https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl#sha256=4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 # pip snowballstemmer @ 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https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-2_h4a7cf45_openblas.conda#6146bf1b7f58113d54614c6ec683c14a https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.0-hee844dc_0.conda#729a572a3ebb8c43933b30edcc628ceb -https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a -https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-2_h0358290_openblas.conda#a84b2b7ed34206d14739fb8d29cd2799 -https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-2_h47877c9_openblas.conda#9fb20e74a7436dc94dd39d9a9decddc3 https://conda.anaconda.org/conda-forge/linux-64/python-3.14.0-he1279bd_2_cp314t.conda#f82ece6dbaba8c6bf8ed6122eb273b9d +https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.0-py314hd8ed1ab_2.conda#86fdc2e15c6f0efb98804a2c461f30b6 -https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.1-py314h3f98dc2_0.conda#eebd4c060e488edb97488858f1293190 +https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.2-py314h3f98dc2_0.conda#af078f15b212d1414633e888166dd2bf https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda#9614359868482abba1bd15ce465e3c42 +https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-2_h0358290_openblas.conda#a84b2b7ed34206d14739fb8d29cd2799 +https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-2_h47877c9_openblas.conda#9fb20e74a7436dc94dd39d9a9decddc3 https://conda.anaconda.org/conda-forge/noarch/meson-1.9.1-pyhcf101f3_0.conda#ef2b132f3e216b5bf6c2f3c36cfd4c89 -https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.5-py314hd4f4903_0.conda#f9c8cd3ab6c388232550c806379856d5 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pip-25.3-pyh145f28c_0.conda#bf47878473e5ab9fdb4115735230e191 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 @@ -54,9 +52,10 @@ https://conda.anaconda.org/conda-forge/noarch/tomli-2.3.0-pyhcf101f3_0.conda#d27 https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda#0caa1af407ecff61170c9437a808404d https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda#8e662bd460bda79b1ea39194e3c4c9ab https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.2-pyhd8ed1ab_0.conda#4e717929cfa0d49cef92d911e31d0e90 +https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.5-py314hd4f4903_0.conda#f9c8cd3ab6c388232550c806379856d5 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.10.0-pyhd8ed1ab_0.conda#d9998bf52ced268eb83749ad65a2e061 https://conda.anaconda.org/conda-forge/noarch/python-freethreading-3.14.0-h92d6c8b_2.conda#bbd6d97a4f90042d5ae148217d3110a6 -https://conda.anaconda.org/conda-forge/linux-64/scipy-1.16.3-py314hf5b80f4_1.conda#b010b4d97f99c579c759996db97e53c0 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.1-pyhcf101f3_0.conda#fa7f71faa234947d9c520f89b4bda1a2 -https://conda.anaconda.org/conda-forge/noarch/pytest-run-parallel-0.7.1-pyhd8ed1ab_0.conda#1277cda67d2764e7b19d6b0bed02c812 +https://conda.anaconda.org/conda-forge/linux-64/scipy-1.16.3-py314hf5b80f4_1.conda#b010b4d97f99c579c759996db97e53c0 +https://conda.anaconda.org/conda-forge/noarch/pytest-run-parallel-0.8.0-pyhd8ed1ab_0.conda#7d545e76cfc231cf246f99963bcd27b0 From 68b16c67d2454d12a194d4de82e1e7caf8edeac6 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Mon, 1 Dec 2025 14:43:37 +0100 Subject: [PATCH 23/44] CI Move pylatest_pip_openblas_pandas to Github Actions (#32810) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- .github/workflows/unit-tests.yml | 19 ++++++++++++++++++- azure-pipelines.yml | 14 -------------- 2 files changed, 18 insertions(+), 15 deletions(-) diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml index 01801e1d4df6b..008e32b1acb48 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows/unit-tests.yml @@ -125,6 +125,23 @@ jobs: os: ubuntu-24.04-arm DISTRIB: conda LOCK_FILE: build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock + + # Linux environment to test the latest available dependencies. + # It runs tests requiring lightgbm, pandas and PyAMG. + - name: Linux pylatest_pip_openblas_pandas + os: ubuntu-24.04 + DISTRIB: conda + LOCK_FILE: build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock + SKLEARN_TESTS_GLOBAL_RANDOM_SEED: 3 # non-default seed + SCIPY_ARRAY_API: 1 + CHECK_PYTEST_SOFT_DEPENDENCY: true + SKLEARN_WARNINGS_AS_ERRORS: 1 + # disable pytest-xdist to have 1 job where OpenMP and BLAS are not single + # threaded because by default the tests configuration (sklearn/conftest.py) + # makes sure that they are single threaded in each xdist subprocess. + PYTEST_XDIST_VERSION: none + PIP_BUILD_ISOLATION: true + - name: macOS pylatest_conda_forge_arm os: macOS-15 DISTRIB: conda @@ -132,7 +149,7 @@ jobs: SKLEARN_TESTS_GLOBAL_RANDOM_SEED: 5 # non-default seed SCIPY_ARRAY_API: 1 PYTORCH_ENABLE_MPS_FALLBACK: 1 - CHECK_PYTEST_SOFT_DEPENDENCY: 'true' + CHECK_PYTEST_SOFT_DEPENDENCY: true env: ${{ matrix }} diff --git a/azure-pipelines.yml b/azure-pipelines.yml index eca3683253ff7..74f2f7ea5ef82 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -183,20 +183,6 @@ jobs: SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES: '1' SKLEARN_RUN_FLOAT32_TESTS: '1' SKLEARN_TESTS_GLOBAL_RANDOM_SEED: '2' # non-default seed - # Linux environment to test the latest available dependencies. - # It runs tests requiring lightgbm, pandas and PyAMG. - pylatest_pip_openblas_pandas: - DISTRIB: 'conda-pip-latest' - LOCK_FILE: './build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock' - CHECK_PYTEST_SOFT_DEPENDENCY: 'true' - SKLEARN_WARNINGS_AS_ERRORS: '1' - SKLEARN_TESTS_GLOBAL_RANDOM_SEED: '3' # non-default seed - # disable pytest-xdist to have 1 job where OpenMP and BLAS are not single - # threaded because by default the tests configuration (sklearn/conftest.py) - # makes sure that they are single threaded in each xdist subprocess. - PYTEST_XDIST_VERSION: 'none' - PIP_BUILD_ISOLATION: 'true' - SCIPY_ARRAY_API: '1' - template: build_tools/azure/posix-docker.yml parameters: From 349fb5c6897be1ebbb73436b6fa77cb7f54142d1 Mon Sep 17 00:00:00 2001 From: "Christine P. Chai" Date: Tue, 2 Dec 2025 00:25:24 -0800 Subject: [PATCH 24/44] DOC: Correct many grammatical issues (#32820) --- doc/computing/computational_performance.rst | 2 +- doc/developers/tips.rst | 6 +++--- doc/modules/array_api.rst | 2 +- doc/modules/model_evaluation.rst | 4 ++-- examples/applications/plot_face_recognition.py | 2 +- examples/compose/plot_column_transformer.py | 2 +- examples/miscellaneous/plot_roc_curve_visualization_api.py | 4 ++-- examples/model_selection/plot_learning_curve.py | 4 ++-- examples/svm/plot_separating_hyperplane_unbalanced.py | 2 +- sklearn/cluster/_hierarchical_fast.pyx | 4 ++-- sklearn/cluster/tests/test_spectral.py | 2 +- sklearn/cross_decomposition/_pls.py | 2 +- sklearn/ensemble/_forest.py | 2 +- sklearn/ensemble/_gb.py | 2 +- .../_hist_gradient_boosting/tests/test_compare_lightgbm.py | 6 +++--- sklearn/ensemble/_iforest.py | 6 +++--- sklearn/ensemble/tests/test_gradient_boosting.py | 2 +- sklearn/linear_model/tests/test_sgd.py | 2 +- .../_pairwise_distances_reduction/_datasets_pair.pyx.tp | 4 ++-- .../_middle_term_computer.pyx.tp | 6 +++--- .../_pairwise_distances_reduction/_radius_neighbors.pyx.tp | 2 +- sklearn/metrics/pairwise.py | 2 +- sklearn/metrics/tests/test_classification.py | 2 +- sklearn/metrics/tests/test_ranking.py | 2 +- sklearn/metrics/tests/test_score_objects.py | 2 +- sklearn/model_selection/tests/test_split.py | 2 +- sklearn/preprocessing/tests/test_encoders.py | 2 +- sklearn/preprocessing/tests/test_function_transformer.py | 2 +- sklearn/tests/test_base.py | 4 ++-- sklearn/tree/tests/test_tree.py | 2 +- sklearn/utils/_arpack.py | 2 +- sklearn/utils/_random.pxd | 2 +- sklearn/utils/sparsefuncs_fast.pyx | 4 ++-- sklearn/utils/validation.py | 2 +- 34 files changed, 49 insertions(+), 49 deletions(-) diff --git a/doc/computing/computational_performance.rst b/doc/computing/computational_performance.rst index 6aa0865b54c35..d1df34551e157 100644 --- a/doc/computing/computational_performance.rst +++ b/doc/computing/computational_performance.rst @@ -178,7 +178,7 @@ non-zero coefficients. For the :mod:`sklearn.svm` family of algorithms with a non-linear kernel, the latency is tied to the number of support vectors (the fewer the faster). Latency and throughput should (asymptotically) grow linearly with the number -of support vectors in a SVC or SVR model. The kernel will also influence the +of support vectors in an SVC or SVR model. The kernel will also influence the latency as it is used to compute the projection of the input vector once per support vector. In the following graph the ``nu`` parameter of :class:`~svm.NuSVR` was used to influence the number of diff --git a/doc/developers/tips.rst b/doc/developers/tips.rst index e4f67a08a08c8..52c8ad682572b 100644 --- a/doc/developers/tips.rst +++ b/doc/developers/tips.rst @@ -339,14 +339,14 @@ tutorials and documentation on the `valgrind web site `_. .. _arm64_dev_env: -Building and testing for the ARM64 platform on a x86_64 machine -=============================================================== +Building and testing for the ARM64 platform on an x86_64 machine +================================================================ ARM-based machines are a popular target for mobile, edge or other low-energy deployments (including in the cloud, for instance on Scaleway or AWS Graviton). Here are instructions to setup a local dev environment to reproduce -ARM-specific bugs or test failures on a x86_64 host laptop or workstation. This +ARM-specific bugs or test failures on an x86_64 host laptop or workstation. This is based on QEMU user mode emulation using docker for convenience (see https://github.com/multiarch/qemu-user-static). diff --git a/doc/modules/array_api.rst b/doc/modules/array_api.rst index c820621d5b313..7771cc92f338c 100644 --- a/doc/modules/array_api.rst +++ b/doc/modules/array_api.rst @@ -87,7 +87,7 @@ After the model is trained, fitted attributes that are arrays will also be from the same Array API namespace as the training data. For example, if CuPy's Array API namespace was used for training, then fitted attributes will be on the GPU. We provide an experimental `_estimator_with_converted_arrays` utility that -transfers an estimator attributes from Array API to a ndarray:: +transfers an estimator attributes from Array API to an ndarray:: >>> from sklearn.utils._array_api import _estimator_with_converted_arrays >>> cupy_to_ndarray = lambda array : array.get() diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst index c86fae1b6688b..a5e32336da38c 100644 --- a/doc/modules/model_evaluation.rst +++ b/doc/modules/model_evaluation.rst @@ -1302,7 +1302,7 @@ is defined by: - w_{i, y_i}, 0\right\} Here is a small example demonstrating the use of the :func:`hinge_loss` function -with a svm classifier in a binary class problem:: +with an svm classifier in a binary class problem:: >>> from sklearn import svm >>> from sklearn.metrics import hinge_loss @@ -1318,7 +1318,7 @@ with a svm classifier in a binary class problem:: 0.3 Here is an example demonstrating the use of the :func:`hinge_loss` function -with a svm classifier in a multiclass problem:: +with an svm classifier in a multiclass problem:: >>> X = np.array([[0], [1], [2], [3]]) >>> Y = np.array([0, 1, 2, 3]) diff --git a/examples/applications/plot_face_recognition.py b/examples/applications/plot_face_recognition.py index add219aed1610..e14c2686514ef 100644 --- a/examples/applications/plot_face_recognition.py +++ b/examples/applications/plot_face_recognition.py @@ -83,7 +83,7 @@ # %% -# Train a SVM classification model +# Train an SVM classification model print("Fitting the classifier to the training set") t0 = time() diff --git a/examples/compose/plot_column_transformer.py b/examples/compose/plot_column_transformer.py index 8f779d085614a..f61b3b04b0195 100644 --- a/examples/compose/plot_column_transformer.py +++ b/examples/compose/plot_column_transformer.py @@ -171,7 +171,7 @@ def text_stats(posts): }, ), ), - # Use a SVC classifier on the combined features + # Use an SVC classifier on the combined features ("svc", LinearSVC(dual=False)), ], verbose=True, diff --git a/examples/miscellaneous/plot_roc_curve_visualization_api.py b/examples/miscellaneous/plot_roc_curve_visualization_api.py index 1aacbd9de3631..2a9b14fdeabcf 100644 --- a/examples/miscellaneous/plot_roc_curve_visualization_api.py +++ b/examples/miscellaneous/plot_roc_curve_visualization_api.py @@ -13,8 +13,8 @@ # SPDX-License-Identifier: BSD-3-Clause # %% -# Load Data and Train a SVC -# ------------------------- +# Load Data and Train an SVC +# -------------------------- # First, we load the wine dataset and convert it to a binary classification # problem. Then, we train a support vector classifier on a training dataset. import matplotlib.pyplot as plt diff --git a/examples/model_selection/plot_learning_curve.py b/examples/model_selection/plot_learning_curve.py index d8060c67cbe15..876c70c0d901e 100644 --- a/examples/model_selection/plot_learning_curve.py +++ b/examples/model_selection/plot_learning_curve.py @@ -24,8 +24,8 @@ # process. The effect is depicted by checking the statistical performance of # the model in terms of training score and testing score. # -# Here, we compute the learning curve of a naive Bayes classifier and a SVM -# classifier with a RBF kernel using the digits dataset. +# Here, we compute the learning curve of a naive Bayes classifier and an SVM +# classifier with an RBF kernel using the digits dataset. from sklearn.datasets import load_digits from sklearn.naive_bayes import GaussianNB from sklearn.svm import SVC diff --git a/examples/svm/plot_separating_hyperplane_unbalanced.py b/examples/svm/plot_separating_hyperplane_unbalanced.py index d0814e1af065f..d92735fc91a82 100644 --- a/examples/svm/plot_separating_hyperplane_unbalanced.py +++ b/examples/svm/plot_separating_hyperplane_unbalanced.py @@ -17,7 +17,7 @@ This example will also work by replacing ``SVC(kernel="linear")`` with ``SGDClassifier(loss="hinge")``. Setting the ``loss`` parameter of the :class:`SGDClassifier` equal to ``hinge`` will yield behaviour - such as that of a SVC with a linear kernel. + such as that of an SVC with a linear kernel. For example try instead of the ``SVC``:: diff --git a/sklearn/cluster/_hierarchical_fast.pyx b/sklearn/cluster/_hierarchical_fast.pyx index f20b1359f46e2..8d7c363daef37 100644 --- a/sklearn/cluster/_hierarchical_fast.pyx +++ b/sklearn/cluster/_hierarchical_fast.pyx @@ -351,7 +351,7 @@ cdef class UnionFind(object): def _single_linkage_label(const float64_t[:, :] L): """ - Convert an linkage array or MST to a tree by labelling clusters at merges. + Convert a linkage array or MST to a tree by labelling clusters at merges. This is done by using a Union find structure to keep track of merges efficiently. This is the private version of the function that assumes that ``L`` has been properly validated. See ``single_linkage_label`` for the @@ -399,7 +399,7 @@ def _single_linkage_label(const float64_t[:, :] L): @cython.wraparound(True) def single_linkage_label(L): """ - Convert an linkage array or MST to a tree by labelling clusters at merges. + Convert a linkage array or MST to a tree by labelling clusters at merges. This is done by using a Union find structure to keep track of merges efficiently. diff --git a/sklearn/cluster/tests/test_spectral.py b/sklearn/cluster/tests/test_spectral.py index 71b11c9fe151c..85c9c1c04d9ab 100644 --- a/sklearn/cluster/tests/test_spectral.py +++ b/sklearn/cluster/tests/test_spectral.py @@ -311,7 +311,7 @@ def test_verbose(assign_labels, capsys): def test_spectral_clustering_np_matrix_raises(): """Check that spectral_clustering raises an informative error when passed - a np.matrix. See #10993""" + an np.matrix. See #10993""" X = np.matrix([[0.0, 2.0], [2.0, 0.0]]) msg = r"np\.matrix is not supported. Please convert to a numpy array" diff --git a/sklearn/cross_decomposition/_pls.py b/sklearn/cross_decomposition/_pls.py index 756af41e97290..bb720c9ab503b 100644 --- a/sklearn/cross_decomposition/_pls.py +++ b/sklearn/cross_decomposition/_pls.py @@ -903,7 +903,7 @@ def __init__( class PLSSVD(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator): """Partial Least Square SVD. - This transformer simply performs a SVD on the cross-covariance matrix + This transformer simply performs an SVD on the cross-covariance matrix `X'y`. It is able to project both the training data `X` and the targets `y`. The training data `X` is projected on the left singular vectors, while the targets are projected on the right singular vectors. diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py index 54ecdec5e977e..dd72224107f37 100644 --- a/sklearn/ensemble/_forest.py +++ b/sklearn/ensemble/_forest.py @@ -578,7 +578,7 @@ def _compute_oob_predictions(self, X, y): n_samples = y.shape[0] n_outputs = self.n_outputs_ if is_classifier(self) and hasattr(self, "n_classes_"): - # n_classes_ is a ndarray at this stage + # n_classes_ is an ndarray at this stage # all the supported type of target will have the same number of # classes in all outputs oob_pred_shape = (n_samples, self.n_classes_[0], n_outputs) diff --git a/sklearn/ensemble/_gb.py b/sklearn/ensemble/_gb.py index e64763123f270..ba515b4bbcea9 100644 --- a/sklearn/ensemble/_gb.py +++ b/sklearn/ensemble/_gb.py @@ -274,7 +274,7 @@ def compute_update(y_, indices, neg_gradient, raw_prediction, k): def set_huber_delta(loss, y_true, raw_prediction, sample_weight=None): """Calculate and set self.closs.delta based on self.quantile.""" abserr = np.abs(y_true - raw_prediction.squeeze()) - # sample_weight is always a ndarray, never None. + # sample_weight is always an ndarray, never None. delta = _weighted_percentile(abserr, sample_weight, 100 * loss.quantile) loss.closs.delta = float(delta) diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py index bbdcb38ef013a..0891457a0475d 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py +++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py @@ -93,7 +93,7 @@ def test_same_predictions_regression( est_lightgbm.fit(X_train, y_train) est_sklearn.fit(X_train, y_train) - # We need X to be treated an numerical data, not pre-binned data. + # We need X to be treated a numerical data, not pre-binned data. X_train, X_test = X_train.astype(np.float32), X_test.astype(np.float32) pred_lightgbm = est_lightgbm.predict(X_train) @@ -170,7 +170,7 @@ def test_same_predictions_classification( est_lightgbm.fit(X_train, y_train) est_sklearn.fit(X_train, y_train) - # We need X to be treated an numerical data, not pre-binned data. + # We need X to be treated a numerical data, not pre-binned data. X_train, X_test = X_train.astype(np.float32), X_test.astype(np.float32) pred_lightgbm = est_lightgbm.predict(X_train) @@ -245,7 +245,7 @@ def test_same_predictions_multiclass_classification( est_lightgbm.fit(X_train, y_train) est_sklearn.fit(X_train, y_train) - # We need X to be treated an numerical data, not pre-binned data. + # We need X to be treated a numerical data, not pre-binned data. X_train, X_test = X_train.astype(np.float32), X_test.astype(np.float32) pred_lightgbm = est_lightgbm.predict(X_train) diff --git a/sklearn/ensemble/_iforest.py b/sklearn/ensemble/_iforest.py index 9c709927d7bbc..578fbd1fab073 100644 --- a/sklearn/ensemble/_iforest.py +++ b/sklearn/ensemble/_iforest.py @@ -441,7 +441,7 @@ def decision_function(self, X): of the leaf containing this observation, which is equivalent to the number of splittings required to isolate this point. In case of several observations n_left in the leaf, the average path length of - a n_left samples isolation tree is added. + an n_left samples isolation tree is added. Parameters ---------- @@ -492,7 +492,7 @@ def score_samples(self, X): of the leaf containing this observation, which is equivalent to the number of splittings required to isolate this point. In case of several observations n_left in the leaf, the average path length of - a n_left samples isolation tree is added. + an n_left samples isolation tree is added. Parameters ---------- @@ -647,7 +647,7 @@ def __sklearn_tags__(self): def _average_path_length(n_samples_leaf): """ - The average path length in a n_samples iTree, which is equal to + The average path length in an n_samples iTree, which is equal to the average path length of an unsuccessful BST search since the latter has the same structure as an isolation tree. Parameters diff --git a/sklearn/ensemble/tests/test_gradient_boosting.py b/sklearn/ensemble/tests/test_gradient_boosting.py index 20866348697f6..22b455f4adbb5 100644 --- a/sklearn/ensemble/tests/test_gradient_boosting.py +++ b/sklearn/ensemble/tests/test_gradient_boosting.py @@ -963,7 +963,7 @@ def test_warm_start_sparse(Cls, sparse_container): @pytest.mark.parametrize("Cls", GRADIENT_BOOSTING_ESTIMATORS) def test_warm_start_fortran(Cls, global_random_seed): - # Test that feeding a X in Fortran-ordered is giving the same results as + # Test that feeding an X in Fortran-ordered is giving the same results as # in C-ordered X, y = datasets.make_hastie_10_2(n_samples=100, random_state=global_random_seed) est_c = Cls(n_estimators=1, random_state=global_random_seed, warm_start=True) diff --git a/sklearn/linear_model/tests/test_sgd.py b/sklearn/linear_model/tests/test_sgd.py index 87284f117d0e4..ad48cfec3938c 100644 --- a/sklearn/linear_model/tests/test_sgd.py +++ b/sklearn/linear_model/tests/test_sgd.py @@ -1006,7 +1006,7 @@ def test_balanced_weight(klass): # to use "balanced" assert_array_almost_equal(clf.coef_, clf_balanced.coef_, 6) - # build an very very imbalanced dataset out of iris data + # build a very very imbalanced dataset out of iris data X_0 = X[y == 0, :] y_0 = y[y == 0] diff --git a/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp index 67ed362c05884..f5615b49fb01a 100644 --- a/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp +++ b/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp @@ -64,12 +64,12 @@ cdef class DatasetsPair{{name_suffix}}: ---------- X : {ndarray, sparse matrix} of shape (n_samples_X, n_features) Input data. - If provided as a ndarray, it must be C-contiguous. + If provided as an ndarray, it must be C-contiguous. If provided as a sparse matrix, it must be in CSR format. Y : {ndarray, sparse matrix} of shape (n_samples_Y, n_features) Input data. - If provided as a ndarray, it must be C-contiguous. + If provided as an ndarray, it must be C-contiguous. If provided as a sparse matrix, it must be in CSR format. metric : str or DistanceMetric object, default='euclidean' diff --git a/sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.pyx.tp index 04c1b61310bb7..48216f27f4261 100644 --- a/sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.pyx.tp +++ b/sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.pyx.tp @@ -129,11 +129,11 @@ cdef class MiddleTermComputer{{name_suffix}}: ---------- X : ndarray or CSR sparse matrix of shape (n_samples_X, n_features) Input data. - If provided as a ndarray, it must be C-contiguous. + If provided as an ndarray, it must be C-contiguous. Y : ndarray or CSR sparse matrix of shape (n_samples_Y, n_features) Input data. - If provided as a ndarray, it must be C-contiguous. + If provided as an ndarray, it must be C-contiguous. Returns ------- @@ -534,7 +534,7 @@ cdef class SparseSparseMiddleTermComputer{{name_suffix}}(MiddleTermComputer{{nam return dist_middle_terms cdef class SparseDenseMiddleTermComputer{{name_suffix}}(MiddleTermComputer{{name_suffix}}): - """Middle term of the Euclidean distance between chunks of a CSR matrix and a np.ndarray. + """Middle term of the Euclidean distance between chunks of a CSR matrix and an np.ndarray. The logic of the computation is wrapped in the routine _middle_term_sparse_dense_{{name_suffix}}. This routine iterates over the data, indices and indptr arrays of the sparse matrices diff --git a/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp index 5e56cde30e5cd..6003e570ef003 100644 --- a/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp +++ b/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp @@ -26,7 +26,7 @@ cnp.import_array() cdef cnp.ndarray[object, ndim=1] coerce_vectors_to_nd_arrays( shared_ptr[vector_vector_double_intp_t] vecs ): - """Coerce a std::vector of std::vector to a ndarray of ndarray.""" + """Coerce a std::vector of std::vector to an ndarray of ndarray.""" cdef: intp_t n = deref(vecs).size() cnp.ndarray[object, ndim=1] nd_arrays_of_nd_arrays = np.empty(n, dtype=np.ndarray) diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py index 005a353b8d778..80ce9fd89bfd6 100644 --- a/sklearn/metrics/pairwise.py +++ b/sklearn/metrics/pairwise.py @@ -973,7 +973,7 @@ def pairwise_distances_argmin(X, Y, *, axis=1, metric="euclidean", metric_kwargs with config_context(assume_finite=True): indices = np.concatenate( list( - # This returns a np.ndarray generator whose arrays we need + # This returns an np.ndarray generator whose arrays we need # to flatten into one. pairwise_distances_chunked( X, Y, reduce_func=_argmin_reduce, metric=metric, **metric_kwargs diff --git a/sklearn/metrics/tests/test_classification.py b/sklearn/metrics/tests/test_classification.py index b8dc67b298be7..958dfa5fd86f6 100644 --- a/sklearn/metrics/tests/test_classification.py +++ b/sklearn/metrics/tests/test_classification.py @@ -69,7 +69,7 @@ def make_prediction(dataset=None, binary=False): - """Make some classification predictions on a toy dataset using a SVC + """Make some classification predictions on a toy dataset using an SVC If binary is True restrict to a binary classification problem instead of a multiclass classification problem diff --git a/sklearn/metrics/tests/test_ranking.py b/sklearn/metrics/tests/test_ranking.py index 81d14b0265276..0a108ecfb1cca 100644 --- a/sklearn/metrics/tests/test_ranking.py +++ b/sklearn/metrics/tests/test_ranking.py @@ -56,7 +56,7 @@ def make_prediction(dataset=None, binary=False): - """Make some classification predictions on a toy dataset using a SVC + """Make some classification predictions on a toy dataset using an SVC If binary is True restrict to a binary classification problem instead of a multiclass classification problem diff --git a/sklearn/metrics/tests/test_score_objects.py b/sklearn/metrics/tests/test_score_objects.py index 17df56846a664..7f1c60c691c43 100644 --- a/sklearn/metrics/tests/test_score_objects.py +++ b/sklearn/metrics/tests/test_score_objects.py @@ -1521,7 +1521,7 @@ def raising_scorer(estimator, X, y): X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf = LogisticRegression().fit(X_train, y_train) - # "raising_scorer" is raising ValueError and should return an string representation + # "raising_scorer" is raising ValueError and should return a string representation # of the error of the last scorer: scoring = { "accuracy": make_scorer(accuracy_score), diff --git a/sklearn/model_selection/tests/test_split.py b/sklearn/model_selection/tests/test_split.py index a4b6b21470061..c4be05b695dd7 100644 --- a/sklearn/model_selection/tests/test_split.py +++ b/sklearn/model_selection/tests/test_split.py @@ -249,7 +249,7 @@ def check_valid_split(train, test, n_samples=None): assert train.intersection(test) == set() if n_samples is not None: - # Check that the union of train an test split cover all the indices + # Check that the union of train and test split cover all the indices assert train.union(test) == set(range(n_samples)) diff --git a/sklearn/preprocessing/tests/test_encoders.py b/sklearn/preprocessing/tests/test_encoders.py index 9dbd9952bc017..d7632ab2f09d1 100644 --- a/sklearn/preprocessing/tests/test_encoders.py +++ b/sklearn/preprocessing/tests/test_encoders.py @@ -1942,7 +1942,7 @@ def test_ordinal_encoder_unknown_missing_interaction(): @pytest.mark.parametrize("with_pandas", [True, False]) def test_ordinal_encoder_encoded_missing_value_error(with_pandas): """Check OrdinalEncoder errors when encoded_missing_value is used by - an known category.""" + a known category.""" X = np.array([["a", "dog"], ["b", "cat"], ["c", np.nan]], dtype=object) # The 0-th feature has no missing values so it is not included in the list of diff --git a/sklearn/preprocessing/tests/test_function_transformer.py b/sklearn/preprocessing/tests/test_function_transformer.py index 6bfb5d1367c8d..c4d6867fc66ab 100644 --- a/sklearn/preprocessing/tests/test_function_transformer.py +++ b/sklearn/preprocessing/tests/test_function_transformer.py @@ -468,7 +468,7 @@ def test_set_output_func(): assert isinstance(X_trans, pd.DataFrame) assert_array_equal(X_trans.columns, ["a", "b"]) - # Warning is raised when func returns a ndarray + # Warning is raised when func returns an ndarray ft_np = FunctionTransformer(lambda x: np.asarray(x)) for transform in ("pandas", "polars"): diff --git a/sklearn/tests/test_base.py b/sklearn/tests/test_base.py index cf55bb71c6987..66830a3d57b21 100644 --- a/sklearn/tests/test_base.py +++ b/sklearn/tests/test_base.py @@ -760,7 +760,7 @@ def transform(self, X): with pytest.raises(ValueError, match=msg): trans.transform(df_bad) - # warns when fitted on dataframe and transforming a ndarray + # warns when fitted on dataframe and transforming an ndarray msg = ( "X does not have valid feature names, but NoOpTransformer was " "fitted with feature names" @@ -768,7 +768,7 @@ def transform(self, X): with pytest.warns(UserWarning, match=msg): trans.transform(X_np) - # warns when fitted on a ndarray and transforming dataframe + # warns when fitted on an ndarray and transforming dataframe msg = "X has feature names, but NoOpTransformer was fitted without feature names" trans = NoOpTransformer().fit(X_np) with pytest.warns(UserWarning, match=msg): diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index bb5dcde356d32..fc1094d2555b9 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -291,7 +291,7 @@ def test_regression_toy(Tree, criterion): def test_xor(): - # Check on a XOR problem + # Check on an XOR problem y = np.zeros((10, 10)) y[:5, :5] = 1 y[5:, 5:] = 1 diff --git a/sklearn/utils/_arpack.py b/sklearn/utils/_arpack.py index 04457b71db10a..227de76c006c0 100644 --- a/sklearn/utils/_arpack.py +++ b/sklearn/utils/_arpack.py @@ -7,7 +7,7 @@ def _init_arpack_v0(size, random_state): """Initialize the starting vector for iteration in ARPACK functions. - Initialize a ndarray with values sampled from the uniform distribution on + Initialize an ndarray with values sampled from the uniform distribution on [-1, 1]. This initialization model has been chosen to be consistent with the ARPACK one as another initialization can lead to convergence issues. diff --git a/sklearn/utils/_random.pxd b/sklearn/utils/_random.pxd index ecb9f80361409..376446b066ad1 100644 --- a/sklearn/utils/_random.pxd +++ b/sklearn/utils/_random.pxd @@ -18,7 +18,7 @@ cdef enum: # rand_r replacement using a 32bit XorShift generator # See http://www.jstatsoft.org/v08/i14/paper for details cdef inline uint32_t our_rand_r(uint32_t* seed) nogil: - """Generate a pseudo-random np.uint32 from a np.uint32 seed""" + """Generate a pseudo-random np.uint32 from an np.uint32 seed""" # seed shouldn't ever be 0. if (seed[0] == 0): seed[0] = DEFAULT_SEED diff --git a/sklearn/utils/sparsefuncs_fast.pyx b/sklearn/utils/sparsefuncs_fast.pyx index 0e9f75a18a542..2859b4d127f11 100644 --- a/sklearn/utils/sparsefuncs_fast.pyx +++ b/sklearn/utils/sparsefuncs_fast.pyx @@ -50,7 +50,7 @@ def _sqeuclidean_row_norms_sparse( def csr_mean_variance_axis0(X, weights=None, return_sum_weights=False): """Compute mean and variance along axis 0 on a CSR matrix - Uses a np.float64 accumulator. + Uses an np.float64 accumulator. Parameters ---------- @@ -184,7 +184,7 @@ def _csr_mean_variance_axis0( def csc_mean_variance_axis0(X, weights=None, return_sum_weights=False): """Compute mean and variance along axis 0 on a CSC matrix - Uses a np.float64 accumulator. + Uses an np.float64 accumulator. Parameters ---------- diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py index ed9b5e20e40bb..a112ec4adba61 100644 --- a/sklearn/utils/validation.py +++ b/sklearn/utils/validation.py @@ -1437,7 +1437,7 @@ def column_or_1d(y, *, dtype=None, input_name="y", warn=False, device=None): def check_random_state(seed): - """Turn seed into a np.random.RandomState instance. + """Turn seed into an np.random.RandomState instance. Parameters ---------- From e4f996f6e14b7437cfdc9a5fef7114b955894110 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Tue, 2 Dec 2025 11:03:18 +0100 Subject: [PATCH 25/44] FIX: move_to should handle BufferError and ValueError to support PyTorch 2.9 with array API enabled (#32827) --- sklearn/utils/_array_api.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/sklearn/utils/_array_api.py b/sklearn/utils/_array_api.py index 46f4bb576fa44..e3ba6b58149c5 100644 --- a/sklearn/utils/_array_api.py +++ b/sklearn/utils/_array_api.py @@ -527,7 +527,15 @@ def move_to(*arrays, xp, device): # kwargs in the from_dlpack method and their expected # meaning by namespaces implementing the array API spec. # TODO: try removing this once DLPack v1 more widely supported - except (AttributeError, TypeError, NotImplementedError): + # TODO: ValueError should not be needed but is in practice: + # https://github.com/numpy/numpy/issues/30341 + except ( + AttributeError, + TypeError, + NotImplementedError, + BufferError, + ValueError, + ): # Converting to numpy is tricky, handle this via dedicated function if _is_numpy_namespace(xp): array_converted = _convert_to_numpy(array, xp_array) From 8685aa9fea0f9c3d71eb23d70cd891b86a65b449 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Tue, 2 Dec 2025 12:38:14 +0100 Subject: [PATCH 26/44] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#32824) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Lock file bot Co-authored-by: Loïc Estève --- build_tools/azure/debian_32bit_lock.txt | 2 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 167 ++++++++-------- ...onda_forge_mkl_no_openmp_osx-64_conda.lock | 42 ++-- .../pylatest_conda_forge_osx-arm64_conda.lock | 102 +++++----- ...st_pip_openblas_pandas_linux-64_conda.lock | 35 ++-- ...nblas_min_dependencies_linux-64_conda.lock | 114 +++++------ ...e_openblas_ubuntu_2204_linux-64_conda.lock | 51 +++-- ...min_conda_forge_openblas_win-64_conda.lock | 30 +-- build_tools/circle/doc_linux-64_conda.lock | 184 +++++++++--------- .../doc_min_dependencies_linux-64_conda.lock | 157 ++++++++------- ...n_conda_forge_arm_linux-aarch64_conda.lock | 48 ++--- 11 files changed, 467 insertions(+), 465 deletions(-) diff --git a/build_tools/azure/debian_32bit_lock.txt b/build_tools/azure/debian_32bit_lock.txt index d78b1d3cde84f..545879de75099 100644 --- a/build_tools/azure/debian_32bit_lock.txt +++ b/build_tools/azure/debian_32bit_lock.txt @@ -6,7 +6,7 @@ # coverage[toml]==7.12.0 # via pytest-cov -cython==3.2.1 +cython==3.2.2 # via -r build_tools/azure/debian_32bit_requirements.txt execnet==2.1.2 # via pytest-xdist diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 9f3b309640118..b39c5c199a0a6 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -9,38 +9,37 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.co https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-headers-1.21.0-ha770c72_1.conda#9e298d76f543deb06eb0f3413675e13a https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2025.3.0-hf2ce2f3_462.conda#0ec3505e9b16acc124d1ec6e5ae8207c https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h54a6638_1.conda#16c2a0e9c4a166e53632cfca4f68d020 -https://conda.anaconda.org/conda-forge/noarch/pybind11-abi-4-hd8ed1ab_3.tar.bz2#878f923dd6acc8aeb47a75da6c4098be +https://conda.anaconda.org/conda-forge/noarch/pybind11-abi-11-hc364b38_1.conda#f0599959a2447c1e544e216bddf393fa https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda#94305520c52a4aa3f6c2b1ff6008d9f8 https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.11.12-hbd8a1cb_0.conda#f0991f0f84902f6b6009b4d2350a83aa https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda#a7970cd949a077b7cb9696379d338681 -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-bootstrap_ha15bf96_3.conda#3036ca5b895b7f5146c5a25486234a68 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-21.1.6-h4922eb0_0.conda#7a0b9ce502e0ed62195e02891dfcd704 -https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-6_kmp_llvm.conda#197811678264cb9da0d2ea0726a70661 +https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda#887b70e1d607fba7957aa02f9ee0d939 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab 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+https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.2.0-h69a702a_14.conda#ab557953cdcf9c483e1d088e0d8ab238 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.0-hee844dc_0.conda#729a572a3ebb8c43933b30edcc628ceb -https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a https://conda.anaconda.org/conda-forge/linux-64/python-3.13.9-hc97d973_101_cp313.conda#4780fe896e961722d0623fa91d0d3378 +https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a https://conda.anaconda.org/conda-forge/noarch/pip-25.3-pyh145f28c_0.conda#bf47878473e5ab9fdb4115735230e191 # pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip babel @ https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl#sha256=4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2 @@ -40,10 +39,10 @@ https://conda.anaconda.org/conda-forge/noarch/pip-25.3-pyh145f28c_0.conda#bf4787 # pip charset-normalizer @ https://files.pythonhosted.org/packages/f5/83/6ab5883f57c9c801ce5e5677242328aa45592be8a00644310a008d04f922/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl#sha256=a8a8b89589086a25749f471e6a900d3f662d1d3b6e2e59dcecf787b1cc3a1894 # pip coverage @ https://files.pythonhosted.org/packages/76/b6/67d7c0e1f400b32c883e9342de4a8c2ae7c1a0b57c5de87622b7262e2309/coverage-7.12.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl#sha256=bc13baf85cd8a4cfcf4a35c7bc9d795837ad809775f782f697bf630b7e200211 # pip cycler @ https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 -# pip cython @ https://files.pythonhosted.org/packages/f9/33/5d9ca6abba0e77e1851b843dd1b3c4095fbc6373166935e83c4414f80e88/cython-3.2.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl#sha256=f5a54a757d01ca6a260b02ce5baf17d9db1c2253566ab5844ee4966ff2a69c19 -# pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 +# pip cython @ https://files.pythonhosted.org/packages/57/c1/76928c07176a4402c74d5b304936ad8ee167dd04a07cf7dca545e8c25f9b/cython-3.2.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl#sha256=a473df474ba89e9fee81ee82b31062a267f9e598096b222783477e56d02ad12c +# pip docutils @ https://files.pythonhosted.org/packages/11/a8/c6a4b901d17399c77cd81fb001ce8961e9f5e04d3daf27e8925cb012e163/docutils-0.22.3-py3-none-any.whl#sha256=bd772e4aca73aff037958d44f2be5229ded4c09927fcf8690c577b66234d6ceb # pip execnet @ https://files.pythonhosted.org/packages/ab/84/02fc1827e8cdded4aa65baef11296a9bbe595c474f0d6d758af082d849fd/execnet-2.1.2-py3-none-any.whl#sha256=67fba928dd5a544b783f6056f449e5e3931a5c378b128bc18501f7ea79e296ec -# pip fonttools @ https://files.pythonhosted.org/packages/2d/8b/371ab3cec97ee3fe1126b3406b7abd60c8fec8975fd79a3c75cdea0c3d83/fonttools-4.60.1-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl#sha256=b33a7884fabd72bdf5f910d0cf46be50dce86a0362a65cfc746a4168c67eb96c +# pip fonttools @ https://files.pythonhosted.org/packages/4e/80/c87bc524a90dbeb2a390eea23eae448286983da59b7e02c67fa0ca96a8c5/fonttools-4.61.0-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl#sha256=b2b734d8391afe3c682320840c8191de9bd24e7eb85768dd4dc06ed1b63dbb1b # pip idna @ https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl#sha256=771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl#sha256=f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12 @@ -59,7 +58,7 @@ https://conda.anaconda.org/conda-forge/noarch/pip-25.3-pyh145f28c_0.conda#bf4787 # pip pygments @ https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl#sha256=86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b # pip pyparsing @ https://files.pythonhosted.org/packages/10/5e/1aa9a93198c6b64513c9d7752de7422c06402de6600a8767da1524f9570b/pyparsing-3.2.5-py3-none-any.whl#sha256=e38a4f02064cf41fe6593d328d0512495ad1f3d8a91c4f73fc401b3079a59a5e # pip pytz @ https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl#sha256=5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00 -# pip roman-numerals-py @ https://files.pythonhosted.org/packages/53/97/d2cbbaa10c9b826af0e10fdf836e1bf344d9f0abb873ebc34d1f49642d3f/roman_numerals_py-3.1.0-py3-none-any.whl#sha256=9da2ad2fb670bcf24e81070ceb3be72f6c11c440d73bd579fbeca1e9f330954c +# pip roman-numerals @ https://files.pythonhosted.org/packages/82/1d/7356f115a0e5faf8dc59894a3e9fc8b1821ab949163458b0072db0a12a68/roman_numerals-3.1.0-py3-none-any.whl#sha256=842ae5fd12912d62720c9aad8cab706e8c692556d01a38443e051ee6cc158d90 # pip six @ https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl#sha256=4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 # pip snowballstemmer @ https://files.pythonhosted.org/packages/c8/78/3565d011c61f5a43488987ee32b6f3f656e7f107ac2782dd57bdd7d91d9a/snowballstemmer-3.0.1-py3-none-any.whl#sha256=6cd7b3897da8d6c9ffb968a6781fa6532dce9c3618a4b127d920dab764a19064 # pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/5d/85/9ebeae2f76e9e77b952f4b274c27238156eae7979c5421fba91a28f4970d/sphinxcontrib_applehelp-2.0.0-py3-none-any.whl#sha256=4cd3f0ec4ac5dd9c17ec65e9ab272c9b867ea77425228e68ecf08d6b28ddbdb5 @@ -88,5 +87,5 @@ https://conda.anaconda.org/conda-forge/noarch/pip-25.3-pyh145f28c_0.conda#bf4787 # pip pytest-cov @ https://files.pythonhosted.org/packages/80/b4/bb7263e12aade3842b938bc5c6958cae79c5ee18992f9b9349019579da0f/pytest_cov-6.3.0-py3-none-any.whl#sha256=440db28156d2468cafc0415b4f8e50856a0d11faefa38f30906048fe490f1749 # pip pytest-xdist @ https://files.pythonhosted.org/packages/ca/31/d4e37e9e550c2b92a9cbc2e4d0b7420a27224968580b5a447f420847c975/pytest_xdist-3.8.0-py3-none-any.whl#sha256=202ca578cfeb7370784a8c33d6d05bc6e13b4f25b5053c30a152269fd10f0b88 # pip scipy-doctest @ https://files.pythonhosted.org/packages/f5/99/a17f725f45e57efcf5a84494687bba7176e0b5cba7ca0f69161a063fa86d/scipy_doctest-2.0.1-py3-none-any.whl#sha256=7725b1cb5f4722ab2a77b39f0aadd39726266e682b19e40f96663d7afb2d46b1 -# pip sphinx @ https://files.pythonhosted.org/packages/31/53/136e9eca6e0b9dc0e1962e2c908fbea2e5ac000c2a2fbd9a35797958c48b/sphinx-8.2.3-py3-none-any.whl#sha256=4405915165f13521d875a8c29c8970800a0141c14cc5416a38feca4ea5d9b9c3 +# pip sphinx @ https://files.pythonhosted.org/packages/fe/8b/76e2a1ce12b915399365873eef2b1197da9d032c99e661a71fd7e1490333/sphinx-9.0.0-py3-none-any.whl#sha256=3442bf635d378da2ba4e88aa8496f3a61b2d59ef145aeaf34353ab93fd79f1bf # pip numpydoc @ https://files.pythonhosted.org/packages/6c/45/56d99ba9366476cd8548527667f01869279cedb9e66b28eb4dfb27701679/numpydoc-1.8.0-py3-none-any.whl#sha256=72024c7fd5e17375dec3608a27c03303e8ad00c81292667955c6fea7a3ccf541 diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock index 9f881ff559fc7..ed5d6b562fb93 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock @@ -10,14 +10,13 @@ https://conda.anaconda.org/conda-forge/noarch/python_abi-3.11-8_cp311.conda#8fcb https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.11.12-hbd8a1cb_0.conda#f0991f0f84902f6b6009b4d2350a83aa https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda#a7970cd949a077b7cb9696379d338681 -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-bootstrap_ha15bf96_3.conda#3036ca5b895b7f5146c5a25486234a68 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-21.1.6-h4922eb0_0.conda#7a0b9ce502e0ed62195e02891dfcd704 -https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-6_kmp_llvm.conda#197811678264cb9da0d2ea0726a70661 +https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda#887b70e1d607fba7957aa02f9ee0d939 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-h767d61c_7.conda#c0374badb3a5d4b1372db28d19462c53 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_14.conda#550dceb769d23bcf0e2f97fd4062d720 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.2-h39aace5_0.conda#791365c5f65975051e4e017b5da3abf5 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda#51a19bba1b8ebfb60df25cde030b7ebc @@ -26,8 +25,8 @@ https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda# https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda#6c77a605a7a689d17d4819c0f8ac9a00 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda#8b09ae86839581147ef2e5c5e229d164 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h9ec8514_0.conda#35f29eec58405aaf55e01cb470d8c26a -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_7.conda#280ea6eee9e2ddefde25ff799c4f0363 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-hcd61629_7.conda#f116940d825ffc9104400f0d7f1a4551 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_14.conda#6c13aaae36d7514f28bd5544da1a7bb8 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_14.conda#3078a2a9a58566a54e579b41b9e88c84 https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda#915f5995e94f60e9a4826e0b0920ee88 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.2-hb03c661_0.conda#8397539e3a0bbd1695584fb4f927485a https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc @@ -37,7 +36,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libnuma-2.0.18-hb9d3cd8_3.conda# https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.5-hd0c01bc_1.conda#68e52064ed3897463c0e958ab5c8f91b https://conda.anaconda.org/conda-forge/linux-64/libopus-1.5.2-hd0c01bc_0.conda#b64523fb87ac6f87f0790f324ad43046 https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda#70e3400cbbfa03e96dcde7fc13e38c7b -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h8f9b012_7.conda#5b767048b1b3ee9a954b06f4084f93dc +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_14.conda#8e96fe9b17d5871b5cf9d312cab832f6 https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-hf23e847_1.conda#b1aa0faa95017bca11369bd080487ec4 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.2-he9a06e4_0.conda#80c07c68d2f6870250959dcc95b209d1 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9 @@ -63,10 +62,11 @@ https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.25.1-h3f43e3d_1.conda#2f4de899028319b27eb7a4023be5dfd2 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_7.conda#8621a450add4e231f676646880703f49 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_14.conda#fa9d91abc5a9db36fa8dcd1b9a602e61 https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.51-h421ea60_0.conda#d8b81203d08435eb999baa249427884e +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.1-h0c1763c_0.conda#2e1b84d273b01835256e53fd938de355 https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda#eecce068c7e4eddeb169591baac20ac4 -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-h4852527_7.conda#f627678cf829bd70bccf141a19c3ad3e +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_14.conda#9531f671a13eec0597941fa19e489b96 https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h54a6638_2.conda#b4ecbefe517ed0157c37f8182768271c https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda#92ed62436b625154323d40d5f2f11dd7 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc @@ -80,8 +80,8 @@ https://conda.anaconda.org/conda-forge/linux-64/s2n-1.3.46-h06160fa_0.conda#413d https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_ha0e22de_103.conda#86bc20552bf46075e3d92b67f089172d https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.6-he73a12e_0.conda#1c74ff8c35dcadf952a16f752ca5aa49 https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda#c9f075ab2f33b3bbee9e62d4ad0a6cd8 -https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.5-hde8ca8f_0.conda#1920c3502e7f6688d650ab81cd3775fd -https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda#6432cb5d4ac0046c3ac0a8a0f95842f9 +https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.1-hde8ca8f_0.conda#49c832bff803d95a56190e7992b4b230 +https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-h3691f8a_4.conda#af7715829219de9043fcc5575e66d22e https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.6.0-h93469e0_0.conda#580a52a05f5be28ce00764149017c6d4 https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.17-h862ab75_1.conda#0013fcee7acb3cfc801c5929824feb3c https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.11-h862ab75_1.conda#6fbc9bd49434eb36d3a59c5020f4af95 @@ -89,6 +89,7 @@ https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.16-h862ab75_1. https://conda.anaconda.org/conda-forge/linux-64/glog-0.6.0-h6f12383_0.tar.bz2#b31f3565cb84435407594e548a2fb7b2 https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda#8b189310083baabfb622af68fd9d3ae3 https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda#3f43953b7d3fb3aaa1d0d0723d91e368 +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_104.conda#a6abd2796fc332536735f68ba23f7901 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-https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-bin-1.2.0-hf3d421d_0.conda#c43264ebd8b93281d09d3a9ad145f753 +https://conda.anaconda.org/conda-forge/linux-aarch64/zlib-ng-2.3.1-h92288e7_0.conda#56f8acceedc8c4de5f7a6e1418e74eee +https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.7-hefd9da6_4.conda#68a63e1ba896c15344e33eacb11e1311 +https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-bin-1.2.0-he30d5cf_1.conda#b31f6f3a888c3f8f4c5a9dafc2575187 https://conda.anaconda.org/conda-forge/linux-aarch64/icu-75.1-hf9b3779_0.conda#268203e8b983fddb6412b36f2024e75c https://conda.anaconda.org/conda-forge/linux-aarch64/krb5-1.21.3-h50a48e9_0.conda#29c10432a2ca1472b53f299ffb2ffa37 -https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.45-default_1234567_3.conda#cafa05c86759c42f9eb1e8398b41a1a3 +https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.45-default_h1979696_104.conda#28035705fe0c977ea33963489cd008ad https://conda.anaconda.org/conda-forge/linux-aarch64/libfreetype6-2.14.1-hdae7a39_0.conda#9c2f56b6e011c6d8010ff43b796aab2f -https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-ng-15.2.0-he9431aa_7.conda#e810efad68f395154237c4dce83aa482 +https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-ng-15.2.0-he9431aa_14.conda#640d27c96b59e2af7d440728cbb9e437 https://conda.anaconda.org/conda-forge/linux-aarch64/libglib-2.86.2-he84ff74_0.conda#d184d68eaa57125062786e10440ff461 https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.30-pthreads_h9d3fd7e_4.conda#11d7d57b7bdd01da745bbf2b67020b2e https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.7.1-hdb009f0_1.conda#8c6fd84f9c87ac00636007c6131e457d @@ -79,9 +79,9 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-keysyms-0.4.1-h5c7 https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-renderutil-0.3.10-h5c728e9_0.conda#7beeda4223c5484ef72d89fb66b7e8c1 https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-wm-0.4.2-h5c728e9_0.conda#f14dcda6894722e421da2b7dcffb0b78 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libx11-1.8.12-hca56bd8_0.conda#3df132f0048b9639bc091ef22937c111 -https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.2.0-hec30622_0.conda#5005bf1c06def246408b73d65f0d3de9 +https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.2.0-hd651790_1.conda#5c933384d588a06cd8dac78ca2864aab https://conda.anaconda.org/conda-forge/linux-aarch64/cyrus-sasl-2.1.28-h6c5dea3_0.conda#b6d06b46e791add99cc39fbbc34530d5 -https://conda.anaconda.org/conda-forge/linux-aarch64/dbus-1.16.2-heda779d_0.conda#9203b74bb1f3fa0d6f308094b3b44c1e +https://conda.anaconda.org/conda-forge/linux-aarch64/dbus-1.16.2-h70963c4_1.conda#a4b6b82427d15f0489cef0df2d82f926 https://conda.anaconda.org/conda-forge/linux-aarch64/lcms2-2.17-hc88f144_0.conda#b87b1abd2542cf65a00ad2e2461a3083 https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.11.0-2_haddc8a3_openblas.conda#1a4b8fba71eb980ac7fb0f2ab86f295d https://conda.anaconda.org/conda-forge/linux-aarch64/libcups-2.3.3-h5cdc715_5.conda#ac0333d338076ef19170938bbaf97582 @@ -99,8 +99,8 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxfixes-6.0.2-he30d5 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrender-0.9.12-h86ecc28_0.conda#ae2c2dd0e2d38d249887727db2af960e https://conda.anaconda.org/conda-forge/linux-aarch64/ccache-4.11.3-h4889ad1_0.conda#e0b9e519da2bf0fb8c48381daf87a194 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 -https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda#44600c4667a319d67dbe0681fc0bc833 -https://conda.anaconda.org/conda-forge/linux-aarch64/cython-3.2.1-py311hdc11669_0.conda#4e9072696f84a95df4aa562e2732d332 +https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda#4c2a8fef270f6c69591889b93f9f55c1 +https://conda.anaconda.org/conda-forge/linux-aarch64/cython-3.2.2-py311hdc11669_0.conda#324e329aea785abb32429a383f1f151d https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.2-pyhd8ed1ab_0.conda#a57b4be42619213a94f31d2c69c5dda7 https://conda.anaconda.org/conda-forge/linux-aarch64/freetype-2.14.1-h8af1aa0_0.conda#0c8f36ebd3678eed1685f0fc93fc2175 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda#9614359868482abba1bd15ce465e3c42 @@ -113,14 +113,14 @@ https://conda.anaconda.org/conda-forge/noarch/meson-1.9.1-pyhcf101f3_0.conda#ef2 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda#37293a85a0f4f77bbd9cf7aaefc62609 https://conda.anaconda.org/conda-forge/linux-aarch64/openldap-2.6.10-h30c48ee_0.conda#48f31a61be512ec1929f4b4a9cedf4bd https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 -https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-12.0.0-py311h9a6517a_0.conda#2dcc43f9f47cb65f1ebcbdc96183f6d2 +https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-12.0.0-py311h8e17b9e_2.conda#b86d6e26631d730f43732ade7e510a3f https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda#6b6ece66ebcae2d5f326c77ef2c5a066 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.5-pyhcf101f3_0.conda#6c8979be6d7a17692793114fa26916e8 https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda#3339e3b65d58accf4ca4fb8748ab16b3 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f -https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_2.conda#00d80af3a7bf27729484e786a68aafff +https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhcf101f3_3.conda#d0fc809fa4c4d85e959ce4ab6e1de800 https://conda.anaconda.org/conda-forge/noarch/tomli-2.3.0-pyhcf101f3_0.conda#d2732eb636c264dc9aa4cbee404b1a53 https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.5.2-py311hb9158a3_2.conda#6d68a78b162d9823e5abe63001c6df36 https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda#0caa1af407ecff61170c9437a808404d @@ -136,11 +136,11 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxxf86vm-1.1.6-h86ec https://conda.anaconda.org/conda-forge/linux-aarch64/coverage-7.12.0-py311h2dad8b0_0.conda#ddb3e5a915ecebd167f576268083c50b https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda#8e662bd460bda79b1ea39194e3c4c9ab https://conda.anaconda.org/conda-forge/linux-aarch64/fontconfig-2.15.0-h8dda3cd_1.conda#112b71b6af28b47c624bcbeefeea685b -https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.60.1-py311h164a683_0.conda#e15201d7a1ed08ce5b85beca0d4a0131 +https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.61.0-py311h164a683_0.conda#3c533754d7ceb31f50f1f9bea8f2cb8f https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.2-pyhd8ed1ab_0.conda#4e717929cfa0d49cef92d911e31d0e90 https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.11.0-2_hb558247_openblas.conda#498aa2a8940c8c26c141dd4ce99e7843 https://conda.anaconda.org/conda-forge/linux-aarch64/libllvm21-21.1.6-hfd2ba90_0.conda#54e87a913eeaa2b27f2e7b491860f612 -https://conda.anaconda.org/conda-forge/linux-aarch64/libpq-18.1-haf03d9f_1.conda#11a55df5dc2234fcd4135e73fb5737d7 +https://conda.anaconda.org/conda-forge/linux-aarch64/libpq-18.1-haf03d9f_2.conda#8b0d66c4db91b3ef64daad7f61a569d0 https://conda.anaconda.org/conda-forge/linux-aarch64/libvulkan-loader-1.4.328.1-h8b8848b_0.conda#e5a3ff3a266b68398bd28ed1d4363e65 https://conda.anaconda.org/conda-forge/linux-aarch64/libxkbcommon-1.13.0-h3c6a4c8_0.conda#a7c78be36bf59b4ba44ad2f2f8b92b37 https://conda.anaconda.org/conda-forge/linux-aarch64/libxslt-1.1.43-h6700d25_1.conda#0f31501ccd51a40f0a91381080ae7368 From c124805a250e6bfb50096aa35020510ba4c6c218 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Tue, 2 Dec 2025 14:52:41 +0100 Subject: [PATCH 27/44] FIX screening in enet_coordinate_descent_multi_task (#32811) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- sklearn/linear_model/_cd_fast.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/linear_model/_cd_fast.pyx b/sklearn/linear_model/_cd_fast.pyx index 578d7f7fe2338..c227100ec066e 100644 --- a/sklearn/linear_model/_cd_fast.pyx +++ b/sklearn/linear_model/_cd_fast.pyx @@ -1434,7 +1434,7 @@ def enet_coordinate_descent_multi_task( if do_screening: n_active = 0 for j in range(n_features): - if norm2_cols_X[j] == 0: + if excluded_set[j]: continue # Xj_theta = ||X[:,j] @ dual_theta||_2 Xj_theta = XtA_row_norms[j] / fmax(l1_reg, dual_norm_XtA) From 0ba882f716035ca3f836e651744eeb4c8f690947 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Tue, 2 Dec 2025 15:36:26 +0100 Subject: [PATCH 28/44] TST Enable pytest faulthandler functionality (#32776) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- azure-pipelines.yml | 1 - pyproject.toml | 6 ++++++ sklearn/conftest.py | 6 ------ 3 files changed, 6 insertions(+), 7 deletions(-) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 74f2f7ea5ef82..95d0d104036af 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -89,7 +89,6 @@ jobs: COVERAGE: 'false' # Disable pytest-xdist to use multiple cores for stress-testing with pytest-run-parallel PYTEST_XDIST_VERSION: 'none' - SKLEARN_FAULTHANDLER_TIMEOUT: '1800' # 30 * 60 seconds # Will run all the time regardless of linting outcome. - template: build_tools/azure/posix.yml diff --git a/pyproject.toml b/pyproject.toml index dbc65fe543181..a867cda19e769 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -115,6 +115,12 @@ thread_unsafe_fixtures = [ "tmp_path", # does not isolate temporary directories across threads "pyplot", # some tests might mutate some shared state of pyplot. ] +# 10 min timeout per test: in case of timeout, dump the tracebacks of all +# threads and terminate the whole test session if a test hangs for more than 10 +# min (likely due to a deadlock). +# The second option requires pytest 9.0+ to be active. +faulthandler_timeout = 600 +faulthandler_exit_on_timeout = true [tool.ruff] diff --git a/sklearn/conftest.py b/sklearn/conftest.py index 5699392ba2505..0d7cd01b60258 100644 --- a/sklearn/conftest.py +++ b/sklearn/conftest.py @@ -2,7 +2,6 @@ # SPDX-License-Identifier: BSD-3-Clause import builtins -import faulthandler import platform import sys from contextlib import suppress @@ -346,11 +345,6 @@ def pytest_configure(config): for line in get_pytest_filterwarning_lines(): config.addinivalue_line("filterwarnings", line) - faulthandler_timeout = int(environ.get("SKLEARN_FAULTHANDLER_TIMEOUT", "0")) - if faulthandler_timeout > 0: - faulthandler.enable() - faulthandler.dump_traceback_later(faulthandler_timeout, exit=True) - if not PARALLEL_RUN_AVAILABLE: config.addinivalue_line( "markers", From 4d5376bb9265fe2b390b44926b1775e1d52c4064 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Wed, 3 Dec 2025 02:14:53 +1100 Subject: [PATCH 29/44] DOC Update Contributing intro and Ways to contribute (#32792) --- doc/developers/contributing.rst | 106 +++++++++++++++++--------------- 1 file changed, 58 insertions(+), 48 deletions(-) diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index f722b035618d8..7e79b6bc19d33 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -24,15 +24,16 @@ Contributing .. currentmodule:: sklearn -This project is a community effort, and everyone is welcome to -contribute. It is hosted on https://github.com/scikit-learn/scikit-learn. +This project is a community effort, shaped by a large number of contributors from +across the world. For more information on the history and people behind scikit-learn +see :ref:`about`. It is hosted on https://github.com/scikit-learn/scikit-learn. The decision making process and governance structure of scikit-learn is laid out in :ref:`governance`. Scikit-learn is :ref:`selective ` when it comes to adding new algorithms and features. This means the best way to contribute and help the project is to start working on known issues. -See :ref:`new_contributors` to get started. +See :ref:`ways_to_contribute` to learn how to make meaningful contributions. .. topic:: **Our community, our values** @@ -54,49 +55,33 @@ See :ref:`new_contributors` to get started. Communications on all channels should respect our `Code of Conduct `_. - - -In case you experience issues using this package, do not hesitate to submit a -ticket to the -`GitHub issue tracker -`_. You are also -welcome to post feature requests or pull requests. - .. _ways_to_contribute: Ways to contribute ================== -There are many ways to contribute to scikit-learn. Improving the -documentation is no less important than improving the code of the library -itself. If you find a typo in the documentation, or have made improvements, do -not hesitate to create a GitHub issue or preferably submit a GitHub pull request. - -There are many ways to help. In particular helping to -:ref:`improve, triage, and investigate issues ` and -:ref:`reviewing other developers' pull requests ` are very -valuable contributions that move the project forward. - -Another way to contribute is to report issues you are facing, and give a "thumbs -up" on issues that others reported and that are relevant to you. It also helps -us if you spread the word: reference the project from your blog and articles, -link to it from your website, or simply star to say "I use it": - -.. raw:: html - -

- - -

- -In case a contribution/issue involves changes to the API principles -or changes to dependencies or supported versions, it must be backed by a -:ref:`slep`, where a SLEP must be submitted as a pull-request to -`enhancement proposals `_ -using the `SLEP template `_ -and follows the decision-making process outlined in :ref:`governance`. +There are many ways to contribute to scikit-learn. These include: + +* referencing scikit-learn from your blog and articles, linking to it from your website, + or simply + `staring it `__ + to say "I use it"; this helps us promote the project +* :ref:`improving and investigating issues ` +* :ref:`reviewing other developers' pull requests ` +* reporting difficulties when using this package by submitting an + `issue `__, and giving a + "thumbs up" on issues that others reported and that are relevant to you (see + :ref:`submitting_bug_feature` for details) +* improving the :ref:`contribute_documentation` +* making a code contribution + +There are many ways to contribute without writing code, and we value these +contributions just as highly as code contributions. If you are interested in making +a code contribution, please keep in mind that scikit-learn has evolved into a mature +and complex project since its inception in 2007. Contributing to the project code +generally requires advanced skills, and it may not be the best place to begin if you +are new to open source contribution. In this case we suggest you follow the suggestions +in :ref:`new_contributors`. .. dropdown:: Contributing to related projects @@ -125,16 +110,32 @@ New Contributors ---------------- We recommend new contributors start by reading this contributing guide, in -particular :ref:`ways_to_contribute`, :ref:`automated_contributions_policy` -and :ref:`pr_checklist`. For expected etiquette around which issues and stalled PRs +particular :ref:`ways_to_contribute`, :ref:`automated_contributions_policy`. + +Next, we advise new contributors gain foundational knowledge on +scikit-learn and open source by: + +* :ref:`improving and investigating issues ` + + * confirming that a problem reported can be reproduced and providing a + :ref:`minimal reproducible code ` (if missing), can help you + learn about different use cases and user needs + * investigating the root cause of an issue will aid you in familiarising yourself + with the scikit-learn codebase + +* :ref:`reviewing other developers' pull requests ` will help you + develop an understanding of the requirements and quality expected of contributions +* improving the :ref:`contribute_documentation` can help deepen your knowledge + of the statistical concepts behind models and functions, and scikit-learn API + +If you wish to make code contributions after building your foundational knowledge, we +recommend you start by looking for an issue that is of interest to you, in an area you +are already familiar with as a user or have background knowledge of. We recommend +starting with smaller pull requests and following our :ref:`pr_checklist`. +For expected etiquette around which issues and stalled PRs to work on, please read :ref:`stalled_pull_request`, :ref:`stalled_unclaimed_issues` and :ref:`issues_tagged_needs_triage`. -We understand that everyone has different interests and backgrounds, thus we recommend -you start by looking for an issue that is of interest to you, in an area you are -already familiar with as a user or have background knowledge of. We recommend starting -with smaller pull requests, to get used to the contribution process. - We rarely use the "good first issue" label because it is difficult to make assumptions about new contributors and these issues often prove more complex than originally anticipated. It is still useful to check if there are @@ -173,6 +174,8 @@ If you used AI tools, please state so in your PR description. PRs that appear to violate this policy will be closed without review. +.. _submitting_bug_feature: + Submitting a bug report or a feature request ============================================ @@ -199,6 +202,13 @@ following rules before submitting: - If you are submitting a bug report, we strongly encourage you to follow the guidelines in :ref:`filing_bugs`. +When a feature request involves changes to the API principles +or changes to dependencies or supported versions, it must be backed by a +:ref:`SLEP `, which must be submitted as a pull-request to +`enhancement proposals `_ +using the `SLEP template `_ +and follows the decision-making process outlined in :ref:`governance`. + .. _filing_bugs: How to make a good bug report From 894b3fff8411ec1a940954fd5e049d0839be01fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois=20Paugam?= <35327799+FrancoisPgm@users.noreply.github.com> Date: Thu, 4 Dec 2025 05:39:03 +0100 Subject: [PATCH 30/44] CI Make linting bot comment only on failure (#32804) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- .github/workflows/bot-lint-comment.yml | 2 +- build_tools/get_comment.py | 229 ++++++++++--------------- 2 files changed, 91 insertions(+), 140 deletions(-) diff --git a/.github/workflows/bot-lint-comment.yml b/.github/workflows/bot-lint-comment.yml index 36c29ad3e0b84..8832d583ca7d2 100644 --- a/.github/workflows/bot-lint-comment.yml +++ b/.github/workflows/bot-lint-comment.yml @@ -58,7 +58,7 @@ jobs: python-version: 3.11 - name: Install dependencies - run: python -m pip install requests + run: python -m pip install PyGithub - name: Create/update GitHub comment env: diff --git a/build_tools/get_comment.py b/build_tools/get_comment.py index 2c25ae9da8605..1725865e6dba5 100644 --- a/build_tools/get_comment.py +++ b/build_tools/get_comment.py @@ -1,11 +1,10 @@ # This script is used to generate a comment for a PR when linting issues are # detected. It is used by the `Comment on failed linting` GitHub Action. -# This script fails if there are not comments to be posted. import os import re -import requests +from github import Auth, Github, GithubException def get_versions(versions_file): @@ -67,15 +66,15 @@ def get_step_message(log, start, end, title, message, details): return res -def get_message(log_file, repo, pr_number, sha, run_id, details, versions): +def get_message(log_file, repo_str, pr_number, sha, run_id, details, versions): with open(log_file, "r") as f: log = f.read() sub_text = ( "\n\n _Generated for commit:" - f" [{sha[:7]}](https://github.com/{repo}/pull/{pr_number}/commits/{sha}). " + f" [{sha[:7]}](https://github.com/{repo_str}/pull/{pr_number}/commits/{sha}). " "Link to the linter CI: [here]" - f"(https://github.com/{repo}/actions/runs/{run_id})_ " + f"(https://github.com/{repo_str}/actions/runs/{run_id})_ " ) if "### Linting completed ###" not in log: @@ -189,12 +188,8 @@ def get_message(log_file, repo, pr_number, sha, run_id, details, versions): ) if not message: - # no issues detected, so this script "fails" - return ( - "## ✔️ Linting Passed\n" - "All linting checks passed. Your pull request is in excellent shape! ☀️" - + sub_text - ) + # no issues detected, the linting succeeded + return None if not details: # This happens if posting the log fails, which happens if the log is too @@ -216,7 +211,7 @@ def get_message(log_file, repo, pr_number, sha, run_id, details, versions): + "https://scikit-learn.org/dev/developers/development_setup.html#set-up-pre-commit)" + ".\n\n" + "You can see the details of the linting issues under the `lint` job [here]" - + f"(https://github.com/{repo}/actions/runs/{run_id})\n\n" + + f"(https://github.com/{repo_str}/actions/runs/{run_id})\n\n" + message + sub_text ) @@ -224,96 +219,50 @@ def get_message(log_file, repo, pr_number, sha, run_id, details, versions): return message -def get_headers(token): - """Get the headers for the GitHub API.""" - return { - "Accept": "application/vnd.github+json", - "Authorization": f"Bearer {token}", - "X-GitHub-Api-Version": "2022-11-28", - } - - -def find_lint_bot_comments(repo, token, pr_number): +def find_lint_bot_comments(issue): """Get the comment from the linting bot.""" - # repo is in the form of "org/repo" - # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#list-issue-comments - response = requests.get( - f"https://api.github.com/repos/{repo}/issues/{pr_number}/comments", - headers=get_headers(token), - ) - response.raise_for_status() - all_comments = response.json() failed_comment = "❌ Linting issues" - success_comment = "✔️ Linting Passed" - - # Find all comments that match the linting bot, and return the first one. - # There should always be only one such comment, or none, if the PR is - # just created. - comments = [ - comment - for comment in all_comments - if comment["user"]["login"] == "github-actions[bot]" - and (failed_comment in comment["body"] or success_comment in comment["body"]) - ] - - if len(all_comments) > 25 and not comments: - # By default the API returns the first 30 comments. If we can't find the - # comment created by the bot in those, then we raise and we skip creating - # a comment in the first place. - raise RuntimeError("Comment not found in the first 30 comments.") - - return comments[0] if comments else None - - -def create_or_update_comment(comment, message, repo, pr_number, token): - """Create a new comment or update existing one.""" - # repo is in the form of "org/repo" + + for comment in issue.get_comments(): + if comment.user.login == "github-actions[bot]": + if failed_comment in comment.body: + return comment + + return None + + +def create_or_update_comment(comment, message, issue): + """Create a new comment or update the existing linting comment.""" + if comment is not None: - print("updating existing comment") - # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#update-an-issue-comment - response = requests.patch( - f"https://api.github.com/repos/{repo}/issues/comments/{comment['id']}", - headers=get_headers(token), - json={"body": message}, - ) + print("Updating existing comment") + comment.edit(message) else: - print("creating new comment") - # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#create-an-issue-comment - response = requests.post( - f"https://api.github.com/repos/{repo}/issues/{pr_number}/comments", - headers=get_headers(token), - json={"body": message}, - ) + print("Creating new comment") + issue.create_comment(message) - response.raise_for_status() +def update_linter_fails_label(linting_failed, issue): + """Add or remove the label indicating that the linting has failed.""" -def update_linter_fails_label(message, repo, pr_number, token): - """ "Add or remove the label indicating that the linting has failed.""" + label = "CI:Linter failure" + + if linting_failed: + issue.add_to_labels(label) - if "❌ Linting issues" in message: - # API doc: https://docs.github.com/en/rest/issues/labels?apiVersion=2022-11-28#add-labels-to-an-issue - response = requests.post( - f"https://api.github.com/repos/{repo}/issues/{pr_number}/labels", - headers=get_headers(token), - json={"labels": ["CI:Linter failure"]}, - ) - response.raise_for_status() else: - # API doc: https://docs.github.com/en/rest/issues/labels?apiVersion=2022-11-28#remove-a-label-from-an-issue - response = requests.delete( - f"https://api.github.com/repos/{repo}/issues/{pr_number}/labels/CI:Linter" - " failure", - headers=get_headers(token), - ) - # If the label was not set, trying to remove it returns a 404 error - if response.status_code != 404: - response.raise_for_status() + try: + issue.remove_from_labels(label) + except GithubException as exception: + # The exception is ignored if raised because the issue did not have the + # label already + if not exception.message == "Label does not exist": + raise if __name__ == "__main__": - repo = os.environ["GITHUB_REPOSITORY"] + repo_str = os.environ["GITHUB_REPOSITORY"] token = os.environ["GITHUB_TOKEN"] pr_number = os.environ["PR_NUMBER"] sha = os.environ["BRANCH_SHA"] @@ -323,58 +272,60 @@ def update_linter_fails_label(message, repo, pr_number, token): versions = get_versions(versions_file) - if not repo or not token or not pr_number or not log_file or not run_id: - raise ValueError( - "One of the following environment variables is not set: " - "GITHUB_REPOSITORY, GITHUB_TOKEN, PR_NUMBER, LOG_FILE, RUN_ID" - ) + for var, val in [ + ("GITHUB_REPOSITORY", repo_str), + ("GITHUB_TOKEN", token), + ("PR_NUMBER", pr_number), + ("LOG_FILE", log_file), + ("RUN_ID", run_id), + ]: + if not val: + raise ValueError(f"The following environment variable is not set: {var}") if not re.match(r"\d+$", pr_number): raise ValueError(f"PR_NUMBER should be a number, got {pr_number!r} instead") + pr_number = int(pr_number) + + gh = Github(auth=Auth.Token(token)) + repo = gh.get_repo(repo_str) + issue = repo.get_issue(number=pr_number) + + message = get_message( + log_file, + repo_str=repo_str, + pr_number=pr_number, + sha=sha, + run_id=run_id, + details=True, + versions=versions, + ) - try: - comment = find_lint_bot_comments(repo, token, pr_number) - except RuntimeError: - print("Comment not found in the first 30 comments. Skipping!") - exit(0) - - try: - message = get_message( - log_file, - repo=repo, - pr_number=pr_number, - sha=sha, - run_id=run_id, - details=True, - versions=versions, - ) - create_or_update_comment( - comment=comment, - message=message, - repo=repo, - pr_number=pr_number, - token=token, - ) - print(message) - except requests.HTTPError: - # The above fails if the message is too long. In that case, we - # try again without the details. - message = get_message( - log_file, - repo=repo, - pr_number=pr_number, - sha=sha, - run_id=run_id, - details=False, - versions=versions, - ) - create_or_update_comment( - comment=comment, - message=message, - repo=repo, - pr_number=pr_number, - token=token, - ) - print(message) + update_linter_fails_label( + linting_failed=message is not None, + issue=issue, + ) + + comment = find_lint_bot_comments(issue) - update_linter_fails_label(message, repo, pr_number, token) + if message is None: # linting succeeded + if comment is not None: + print("Deleting existing comment.") + comment.delete() + else: + try: + create_or_update_comment(comment, message, issue) + print(message) + except GithubException: + # The above fails if the message is too long. In that case, we + # try again without the details. + message = get_message( + log_file, + repo=repo, + pr_number=pr_number, + sha=sha, + run_id=run_id, + details=False, + versions=versions, + ) + create_or_update_comment(comment, message, issue) + print(message) From f025323b4c1a306731941792e2b23b3302a606b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Thu, 4 Dec 2025 07:34:27 +0100 Subject: [PATCH 31/44] DOC Add HTML repr related PR to existing changelog (#32831) --- .../upcoming_changes/sklearn.utils/31564.enhancement.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/31564.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.utils/31564.enhancement.rst index 6b9ef89fdd01f..d0082d8335eac 100644 --- a/doc/whats_new/upcoming_changes/sklearn.utils/31564.enhancement.rst +++ b/doc/whats_new/upcoming_changes/sklearn.utils/31564.enhancement.rst @@ -2,4 +2,4 @@ more generally of estimators inheriting from :class:`base.BaseEstimator` now displays the parameter description as a tooltip and has a link to the online documentation for each parameter. - By :user:`Dea María Léon `. + By :user:`Dea María Léon `. :pr:`30763` and From b268746a4602f41a33a23a5710c20240d6341b8e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Thu, 4 Dec 2025 12:01:20 +0100 Subject: [PATCH 32/44] MNT Use Pyodide 0.29 version to avoid micropip error inside JupyterLite (#32835) --- doc/jupyter-lite.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/jupyter-lite.json b/doc/jupyter-lite.json index 9ad29615decb6..63a4ad485b310 100644 --- a/doc/jupyter-lite.json +++ b/doc/jupyter-lite.json @@ -3,7 +3,7 @@ "jupyter-config-data": { "litePluginSettings": { "@jupyterlite/pyodide-kernel-extension:kernel": { - "pyodideUrl": "https://cdn.jsdelivr.net/pyodide/v0.27.2/full/pyodide.js" + "pyodideUrl": "https://cdn.jsdelivr.net/pyodide/v0.29.0/full/pyodide.js" } } } From 776dfcde658ee04e4aca7ba210df3e528f4636ca Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dea=20Mar=C3=ADa=20L=C3=A9on?= Date: Fri, 5 Dec 2025 06:04:31 +0100 Subject: [PATCH 33/44] DOC Remove PR from changelog that was already in 1.7 (#32841) --- .../upcoming_changes/sklearn.utils/31564.enhancement.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/31564.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.utils/31564.enhancement.rst index d0082d8335eac..6b9ef89fdd01f 100644 --- a/doc/whats_new/upcoming_changes/sklearn.utils/31564.enhancement.rst +++ b/doc/whats_new/upcoming_changes/sklearn.utils/31564.enhancement.rst @@ -2,4 +2,4 @@ more generally of estimators inheriting from :class:`base.BaseEstimator` now displays the parameter description as a tooltip and has a link to the online documentation for each parameter. - By :user:`Dea María Léon `. :pr:`30763` and + By :user:`Dea María Léon `. From eca5e0ab2a16844c57fff675c2d017af2d8a570b Mon Sep 17 00:00:00 2001 From: antoinebaker Date: Fri, 5 Dec 2025 11:30:43 +0100 Subject: [PATCH 34/44] FIX Add new default max_samples=None in Bagging estimators (#32825) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Arthur Lacote Co-authored-by: Olivier Grisel Co-authored-by: Loïc Estève --- .../sklearn.ensemble/31414.fix.rst | 7 -- .../sklearn.ensemble/32825.fix.rst | 8 ++ sklearn/ensemble/_bagging.py | 96 ++++++++++++++----- sklearn/ensemble/tests/test_bagging.py | 64 +++++++++++-- 4 files changed, 139 insertions(+), 36 deletions(-) delete mode 100644 doc/whats_new/upcoming_changes/sklearn.ensemble/31414.fix.rst create mode 100644 doc/whats_new/upcoming_changes/sklearn.ensemble/32825.fix.rst diff --git a/doc/whats_new/upcoming_changes/sklearn.ensemble/31414.fix.rst b/doc/whats_new/upcoming_changes/sklearn.ensemble/31414.fix.rst deleted file mode 100644 index 17c2f765d4b7c..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.ensemble/31414.fix.rst +++ /dev/null @@ -1,7 +0,0 @@ -- :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor` - and :class:`ensemble.IsolationForest` now use `sample_weight` to draw - the samples instead of forwarding them multiplied by a uniformly sampled - mask to the underlying estimators. Furthermore, `max_samples` is now - interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]` - when passed as a float. - By :user:`Antoine Baker `. diff --git a/doc/whats_new/upcoming_changes/sklearn.ensemble/32825.fix.rst b/doc/whats_new/upcoming_changes/sklearn.ensemble/32825.fix.rst new file mode 100644 index 0000000000000..604ec9421a424 --- /dev/null +++ b/doc/whats_new/upcoming_changes/sklearn.ensemble/32825.fix.rst @@ -0,0 +1,8 @@ +- :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor` and + :class:`ensemble.IsolationForest` now use `sample_weight` to draw the samples + instead of forwarding them multiplied by a uniformly sampled mask to the + underlying estimators. Furthermore, when `max_samples` is a float, it is now + interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]`. + The new default `max_samples=None` draws `X.shape[0]` samples, irrespective + of `sample_weight`. + By :user:`Antoine Baker `. :pr:`31414` and diff --git a/sklearn/ensemble/_bagging.py b/sklearn/ensemble/_bagging.py index 067bdb9e7db0e..a3d0b2bc931c7 100644 --- a/sklearn/ensemble/_bagging.py +++ b/sklearn/ensemble/_bagging.py @@ -46,6 +46,63 @@ MAX_INT = np.iinfo(np.int32).max +def _get_n_samples_bootstrap(n_samples, max_samples, sample_weight): + """ + Get the number of samples in a bootstrap sample. + + Parameters + ---------- + n_samples : int + Number of samples in the dataset. + + max_samples : None, int or float + The maximum number of samples to draw. + + - If None, then draw `n_samples` samples. + - If int, then draw `max_samples` samples. + - If float, then draw `max_samples * n_samples` unweighted samples or + `max_samples * sample_weight.sum()` weighted samples. + + sample_weight : array of shape (n_samples,) or None + Sample weights with frequency semantics when `max_samples` is explicitly + set to a float or integer value. When keeping the `max_samples=None` default + value, the equivalence between fitting with integer weighted data points or + integer repeated data points is no longer guaranteed because the effective + bootstrap size is no longer guaranteed to be equivalent. + + Returns + ------- + n_samples_bootstrap : int + The total number of samples to draw for the bootstrap sample. + """ + if max_samples is None: + return n_samples + elif isinstance(max_samples, Integral): + return max_samples + + if sample_weight is None: + weighted_n_samples = n_samples + weighted_n_samples_msg = f"the number of samples is {weighted_n_samples} " + else: + weighted_n_samples = sample_weight.sum() + weighted_n_samples_msg = ( + f"the total sum of sample weights is {weighted_n_samples} " + ) + + # max_samples Real fractional value relative to weighted_n_samples + n_samples_bootstrap = max(int(max_samples * weighted_n_samples), 1) + # Warn when number of bootstrap samples is suspiciously small + # This heuristic for "suspiciously small" might be adapted if found + # unsuitable in practice + if n_samples_bootstrap < max(10, n_samples ** (1 / 3)): + warn( + f"Using the fractional value {max_samples=} when {weighted_n_samples_msg}" + f"results in a low number ({n_samples_bootstrap}) of bootstrap samples. " + "We recommend passing `max_samples` as an integer instead." + ) + return n_samples_bootstrap + + def _generate_indices(random_state, bootstrap, n_population, n_samples): """Draw randomly sampled indices.""" # Draw sample indices @@ -273,6 +330,7 @@ class BaseBagging(BaseEnsemble, metaclass=ABCMeta): "estimator": [HasMethods(["fit", "predict"]), None], "n_estimators": [Interval(Integral, 1, None, closed="left")], "max_samples": [ + None, Interval(Integral, 1, None, closed="left"), Interval(RealNotInt, 0, 1, closed="right"), ], @@ -295,7 +353,7 @@ def __init__( estimator=None, n_estimators=10, *, - max_samples=1.0, + max_samples=None, max_features=1.0, bootstrap=True, bootstrap_features=False, @@ -340,7 +398,9 @@ def fit(self, X, y, sample_weight=None, **fit_params): Sample weights. If None, then samples are equally weighted. Used as probabilities to sample the training set. Note that the expected frequency semantics for the `sample_weight` parameter are only - fulfilled when sampling with replacement `bootstrap=True`. + fulfilled when sampling with replacement `bootstrap=True` and using + a float or integer `max_samples` (instead of the default + `max_samples=None`). **fit_params : dict Parameters to pass to the underlying estimators. @@ -462,20 +522,7 @@ def _fit( if max_samples is None: max_samples = self.max_samples - if not isinstance(max_samples, numbers.Integral): - if sample_weight is None: - max_samples = max(int(max_samples * X.shape[0]), 1) - else: - sw_sum = np.sum(sample_weight) - if sw_sum <= 1: - raise ValueError( - f"The total sum of sample weights is {sw_sum}, which prevents " - "resampling with a fractional value for max_samples=" - f"{max_samples}. Either pass max_samples as an integer or " - "use a larger sample_weight." - ) - max_samples = max(int(max_samples * sw_sum), 1) - + max_samples = _get_n_samples_bootstrap(X.shape[0], max_samples, sample_weight) if not self.bootstrap and max_samples > X.shape[0]: raise ValueError( f"Effective max_samples={max_samples} must be <= n_samples=" @@ -728,13 +775,14 @@ class BaggingClassifier(ClassifierMixin, BaseBagging): n_estimators : int, default=10 The number of base estimators in the ensemble. - max_samples : int or float, default=1.0 + max_samples : int or float, default=None The number of samples to draw from X to train each base estimator (with replacement by default, see `bootstrap` for more details). + - If None, then draw `X.shape[0]` samples irrespective of `sample_weight`. - If int, then draw `max_samples` samples. - - If float, then draw `max_samples * X.shape[0]` unweighted samples - or `max_samples * sample_weight.sum()` weighted samples. + - If float, then draw `max_samples * X.shape[0]` unweighted samples or + `max_samples * sample_weight.sum()` weighted samples. max_features : int or float, default=1.0 The number of features to draw from X to train each base estimator ( @@ -867,7 +915,7 @@ def __init__( estimator=None, n_estimators=10, *, - max_samples=1.0, + max_samples=None, max_features=1.0, bootstrap=True, bootstrap_features=False, @@ -1239,12 +1287,14 @@ class BaggingRegressor(RegressorMixin, BaseBagging): n_estimators : int, default=10 The number of base estimators in the ensemble. - max_samples : int or float, default=1.0 + max_samples : int or float, default=None The number of samples to draw from X to train each base estimator (with replacement by default, see `bootstrap` for more details). + - If None, then draw `X.shape[0]` samples irrespective of `sample_weight`. - If int, then draw `max_samples` samples. - - If float, then draw `max_samples * X.shape[0]` samples. + - If float, then draw `max_samples * X.shape[0]` unweighted samples or + `max_samples * sample_weight.sum()` weighted samples. max_features : int or float, default=1.0 The number of features to draw from X to train each base estimator ( @@ -1368,7 +1418,7 @@ def __init__( estimator=None, n_estimators=10, *, - max_samples=1.0, + max_samples=None, max_features=1.0, bootstrap=True, bootstrap_features=False, diff --git a/sklearn/ensemble/tests/test_bagging.py b/sklearn/ensemble/tests/test_bagging.py index 611ea271b3f91..0b73499467da6 100644 --- a/sklearn/ensemble/tests/test_bagging.py +++ b/sklearn/ensemble/tests/test_bagging.py @@ -6,6 +6,7 @@ # SPDX-License-Identifier: BSD-3-Clause import re +import warnings from itertools import cycle, product import joblib @@ -26,6 +27,7 @@ RandomForestClassifier, RandomForestRegressor, ) +from sklearn.ensemble._bagging import _get_n_samples_bootstrap from sklearn.feature_selection import SelectKBest from sklearn.linear_model import LogisticRegression, Perceptron from sklearn.model_selection import GridSearchCV, ParameterGrid, train_test_split @@ -706,16 +708,17 @@ def test_warning_bootstrap_sample_weight(): def test_invalid_sample_weight_max_samples_bootstrap_combinations(): X, y = iris.data, iris.target - # Case 1: small weights and fractional max_samples would lead to sampling - # less than 1 sample, which is not allowed. + # Case 1: small weights and fractional max_samples lead to a small + # number of bootstrap samples, which raises a UserWarning. clf = BaggingClassifier(max_samples=1.0) sample_weight = np.ones_like(y) / (2 * len(y)) expected_msg = ( - r"The total sum of sample weights is 0.5(\d*), which prevents resampling with " - r"a fractional value for max_samples=1\.0\. Either pass max_samples as an " - r"integer or use a larger sample_weight\." + "Using the fractional value max_samples=1.0 when " + r"the total sum of sample weights is 0.5(\d*) " + r"results in a low number \(1\) of bootstrap samples. " + "We recommend passing `max_samples` as an integer." ) - with pytest.raises(ValueError, match=expected_msg): + with pytest.warns(UserWarning, match=expected_msg): clf.fit(X, y, sample_weight=sample_weight) # Case 2: large weights and bootstrap=False would lead to sampling without @@ -813,6 +816,55 @@ def test_draw_indices_using_sample_weight( assert_allclose(estimator.y_, y[samples]) +def test_get_n_samples_bootstrap(): + n_samples, max_samples, sample_weight = 10, None, "not_used" + assert _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == n_samples + + n_samples, max_samples, sample_weight = 10, 5, "not_used" + assert ( + _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == max_samples + ) + + n_samples, max_samples, sample_weight = 10, 1e-5, None + assert _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == 1 + + n_samples, max_samples, sample_weight = 10, 0.66, None + warning_msg = ".+the number of samples.+low number.+max_samples.+as an integer" + with pytest.warns(UserWarning, match=warning_msg): + assert _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == int( + max_samples * n_samples + ) + + n_samples, max_samples, sample_weight = 10, 1e-5, None + with pytest.warns(UserWarning, match=warning_msg): + assert _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == 1 + + warning_msg_with_weights = ( + ".+the total sum of sample weights.+low number.+max_samples.+as an integer" + ) + rng = np.random.default_rng(0) + n_samples, max_samples, sample_weight = 1_000_000, 1e-5, rng.uniform(size=1_000_000) + with pytest.warns(UserWarning, match=warning_msg_with_weights): + assert _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == int( + max_samples * sample_weight.sum() + ) + + sample_weight = np.ones(3) + with warnings.catch_warnings(): + warnings.simplefilter("error") + + n_samples, max_samples, sample_weight = 100, 30, None + assert ( + _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) + == max_samples + ) + + n_samples, max_samples, sample_weight = 100, 0.5, rng.uniform(size=100) + assert _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == int( + max_samples * sample_weight.sum() + ) + + def test_oob_score_removed_on_warm_start(): X, y = make_hastie_10_2(n_samples=100, random_state=1) From 764249a255ba4110685a772836f06477c40cf8a3 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Fri, 5 Dec 2025 15:09:16 +0100 Subject: [PATCH 35/44] Fix `_safe_indexing` with non integer arrays on array API inputs (#32840) --- sklearn/utils/_array_api.py | 2 +- sklearn/utils/_indexing.py | 20 +++++++++-- sklearn/utils/tests/test_indexing.py | 52 ++++++++++++++++++++++++---- 3 files changed, 65 insertions(+), 9 deletions(-) diff --git a/sklearn/utils/_array_api.py b/sklearn/utils/_array_api.py index e3ba6b58149c5..03e3e18db4f84 100644 --- a/sklearn/utils/_array_api.py +++ b/sklearn/utils/_array_api.py @@ -469,7 +469,7 @@ def move_to(*arrays, xp, device): `array` may contain `None` entries, these are left unchanged. Sparse arrays are accepted (as pass through) if the reference namespace is - Numpy, in which case they are returned unchanged. Otherwise a `TypeError` + NumPy, in which case they are returned unchanged. Otherwise a `TypeError` is raised. Parameters diff --git a/sklearn/utils/_indexing.py b/sklearn/utils/_indexing.py index de983f2f3adb0..f4202ac7586c0 100644 --- a/sklearn/utils/_indexing.py +++ b/sklearn/utils/_indexing.py @@ -36,8 +36,24 @@ def _array_indexing(array, key, key_dtype, axis): """Index an array or scipy.sparse consistently across NumPy version.""" xp, is_array_api, device_ = get_namespace_and_device(array) if is_array_api: - key = move_to(key, xp=xp, device=device_) - return xp.take(array, key, axis=axis) + if hasattr(key, "shape"): + key = move_to(key, xp=xp, device=device_) + elif isinstance(key, (int, slice)): + # Passthrough for valid __getitem__ inputs as noted in the array + # API spec. + pass + else: + key = xp.asarray(key, device=device_) + + if hasattr(key, "dtype"): + if xp.isdtype(key.dtype, "integral"): + return xp.take(array, key, axis=axis) + elif xp.isdtype(key.dtype, "bool"): + # Array API does not support boolean indexing for n-dim arrays + # yet hence the need to turn to equivalent integer indexing. + indices = xp.arange(array.shape[axis], device=device_) + return xp.take(array, indices[key], axis=axis) + if issparse(array) and key_dtype == "bool": key = np.asarray(key) if isinstance(key, tuple): diff --git a/sklearn/utils/tests/test_indexing.py b/sklearn/utils/tests/test_indexing.py index 8934b5ef5a98d..4d4b5a4a7bf78 100644 --- a/sklearn/utils/tests/test_indexing.py +++ b/sklearn/utils/tests/test_indexing.py @@ -10,7 +10,10 @@ from sklearn.externals._packaging.version import parse as parse_version from sklearn.utils import _safe_indexing, resample, shuffle from sklearn.utils._array_api import ( + _convert_to_numpy, _get_namespace_device_dtype_ids, + device, + move_to, yield_namespace_device_dtype_combinations, ) from sklearn.utils._indexing import ( @@ -22,6 +25,7 @@ from sklearn.utils._testing import ( _array_api_for_tests, _convert_container, + assert_allclose, assert_allclose_dense_sparse, assert_array_equal, skip_if_array_api_compat_not_configured, @@ -108,22 +112,22 @@ def test_determine_key_type_slice_error(): @skip_if_array_api_compat_not_configured @pytest.mark.parametrize( - "array_namespace, device, dtype_name", + "array_namespace, device_, dtype_name", yield_namespace_device_dtype_combinations(), ids=_get_namespace_device_dtype_ids, ) -def test_determine_key_type_array_api(array_namespace, device, dtype_name): - xp = _array_api_for_tests(array_namespace, device) +def test_determine_key_type_array_api(array_namespace, device_, dtype_name): + xp = _array_api_for_tests(array_namespace, device_) with sklearn.config_context(array_api_dispatch=True): - int_array_key = xp.asarray([1, 2, 3]) + int_array_key = xp.asarray([1, 2, 3], device=device_) assert _determine_key_type(int_array_key) == "int" - bool_array_key = xp.asarray([True, False, True]) + bool_array_key = xp.asarray([True, False, True], device=device_) assert _determine_key_type(bool_array_key) == "bool" try: - complex_array_key = xp.asarray([1 + 1j, 2 + 2j, 3 + 3j]) + complex_array_key = xp.asarray([1 + 1j, 2 + 2j, 3 + 3j], device=device_) except TypeError: # Complex numbers are not supported by all Array API libraries. complex_array_key = None @@ -133,6 +137,42 @@ def test_determine_key_type_array_api(array_namespace, device, dtype_name): _determine_key_type(complex_array_key) +@skip_if_array_api_compat_not_configured +@pytest.mark.parametrize( + "array_namespace, device_, dtype_name", + yield_namespace_device_dtype_combinations(), + ids=_get_namespace_device_dtype_ids, +) +@pytest.mark.parametrize( + "indexing_key", + ( + 0, + -1, + [1, 3], + np.array([1, 3]), + slice(1, 2), + [True, False, True, True], + np.asarray([False, False, False, False]), + ), +) +@pytest.mark.parametrize("axis", [0, 1]) +def test_safe_indexing_array_api_support( + array_namespace, device_, dtype_name, indexing_key, axis +): + xp = _array_api_for_tests(array_namespace, device_) + + array_to_index_np = np.arange(16).reshape(4, 4) + expected_result = _safe_indexing(array_to_index_np, indexing_key, axis=axis) + array_to_index_xp = move_to(array_to_index_np, xp=xp, device=device_) + + with sklearn.config_context(array_api_dispatch=True): + indexed_array_xp = _safe_indexing(array_to_index_xp, indexing_key, axis=axis) + assert device(indexed_array_xp) == device(array_to_index_xp) + assert indexed_array_xp.dtype == array_to_index_xp.dtype + + assert_allclose(_convert_to_numpy(indexed_array_xp, xp=xp), expected_result) + + @pytest.mark.parametrize( "array_type", ["list", "array", "sparse", "dataframe", "polars", "pyarrow"] ) From c1de8fc595fce930c5ce1792faeab8d80a4fecc2 Mon Sep 17 00:00:00 2001 From: Tim Head Date: Fri, 5 Dec 2025 17:59:35 +0100 Subject: [PATCH 36/44] FIX Make `get_namespace` handle pandas dataframe input (#32838) --- .../upcoming_changes/array-api/32838.fix.rst | 2 + sklearn/compose/_column_transformer.py | 6 +- .../gradient_boosting.py | 4 +- sklearn/feature_selection/_base.py | 4 +- sklearn/inspection/_plot/decision_boundary.py | 5 +- .../preprocessing/_function_transformer.py | 7 +- sklearn/utils/_array_api.py | 3 + sklearn/utils/_dataframe.py | 123 ++++++++++++++++++ sklearn/utils/_indexing.py | 16 ++- sklearn/utils/tests/test_array_api.py | 30 +++++ sklearn/utils/tests/test_dataframe.py | 84 ++++++++++++ sklearn/utils/tests/test_validation.py | 71 ---------- sklearn/utils/validation.py | 53 +------- 13 files changed, 267 insertions(+), 141 deletions(-) create mode 100644 doc/whats_new/upcoming_changes/array-api/32838.fix.rst create mode 100644 sklearn/utils/_dataframe.py create mode 100644 sklearn/utils/tests/test_dataframe.py diff --git a/doc/whats_new/upcoming_changes/array-api/32838.fix.rst b/doc/whats_new/upcoming_changes/array-api/32838.fix.rst new file mode 100644 index 0000000000000..ae689f8816841 --- /dev/null +++ b/doc/whats_new/upcoming_changes/array-api/32838.fix.rst @@ -0,0 +1,2 @@ +- Estimators with array API support no longer reject dataframe inputs when array API support is enabled. + By :user:`Tim Head ` diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py index 4e052399d36f5..e2c196f84d313 100644 --- a/sklearn/compose/_column_transformer.py +++ b/sklearn/compose/_column_transformer.py @@ -20,6 +20,7 @@ from sklearn.pipeline import _fit_transform_one, _name_estimators, _transform_one from sklearn.preprocessing import FunctionTransformer from sklearn.utils import Bunch +from sklearn.utils._dataframe import is_pandas_df from sklearn.utils._indexing import ( _determine_key_type, _get_column_indices, @@ -47,7 +48,6 @@ _check_feature_names_in, _check_n_features, _get_feature_names, - _is_pandas_df, _num_samples, check_array, check_is_fitted, @@ -773,7 +773,7 @@ def _validate_output(self, result): except ImportError: return for Xs, name in zip(result, names): - if not _is_pandas_df(Xs): + if not is_pandas_df(Xs): continue for col_name, dtype in Xs.dtypes.to_dict().items(): if getattr(dtype, "na_value", None) is not pd.NA: @@ -1064,7 +1064,7 @@ def transform(self, X, **params): # were not present in fit time, and the order of the columns doesn't # matter. fit_dataframe_and_transform_dataframe = hasattr(self, "feature_names_in_") and ( - _is_pandas_df(X) or hasattr(X, "__dataframe__") + is_pandas_df(X) or hasattr(X, "__dataframe__") ) n_samples = _num_samples(X) diff --git a/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py b/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py index 4bbc46d9ae135..4a4fa319f4ab7 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py +++ b/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py @@ -40,6 +40,7 @@ from sklearn.model_selection import train_test_split from sklearn.preprocessing import FunctionTransformer, LabelEncoder, OrdinalEncoder from sklearn.utils import check_random_state, compute_sample_weight, resample +from sklearn.utils._dataframe import is_pandas_df from sklearn.utils._missing import is_scalar_nan from sklearn.utils._openmp_helpers import _openmp_effective_n_threads from sklearn.utils._param_validation import Interval, RealNotInt, StrOptions @@ -48,7 +49,6 @@ _check_monotonic_cst, _check_sample_weight, _check_y, - _is_pandas_df, check_array, check_consistent_length, check_is_fitted, @@ -371,7 +371,7 @@ def _check_categorical_features(self, X): # fixed in main and maybe included in 2.2.1, see # https://github.com/pandas-dev/pandas/pull/57173. # Also pandas versions < 1.5.1 do not support the dataframe interchange - if _is_pandas_df(X): + if is_pandas_df(X): X_is_dataframe = True categorical_columns_mask = np.asarray(X.dtypes == "category") elif hasattr(X, "__dataframe__"): diff --git a/sklearn/feature_selection/_base.py b/sklearn/feature_selection/_base.py index 3c12cd035d5c8..4527cfd0cd815 100644 --- a/sklearn/feature_selection/_base.py +++ b/sklearn/feature_selection/_base.py @@ -12,11 +12,11 @@ from sklearn.base import TransformerMixin from sklearn.utils import _safe_indexing, check_array, safe_sqr +from sklearn.utils._dataframe import is_pandas_df from sklearn.utils._set_output import _get_output_config from sklearn.utils._tags import get_tags from sklearn.utils.validation import ( _check_feature_names_in, - _is_pandas_df, check_is_fitted, validate_data, ) @@ -100,7 +100,7 @@ def transform(self, X): # Preserve X when X is a dataframe and the output is configured to # be pandas. output_config_dense = _get_output_config("transform", estimator=self)["dense"] - preserve_X = output_config_dense != "default" and _is_pandas_df(X) + preserve_X = output_config_dense != "default" and is_pandas_df(X) # note: we use get_tags instead of __sklearn_tags__ because this is a # public Mixin. diff --git a/sklearn/inspection/_plot/decision_boundary.py b/sklearn/inspection/_plot/decision_boundary.py index 22292053f7867..33c8a15bb4fe7 100644 --- a/sklearn/inspection/_plot/decision_boundary.py +++ b/sklearn/inspection/_plot/decision_boundary.py @@ -8,13 +8,12 @@ from sklearn.base import is_regressor from sklearn.preprocessing import LabelEncoder from sklearn.utils import _safe_indexing +from sklearn.utils._dataframe import is_pandas_df, is_polars_df from sklearn.utils._optional_dependencies import check_matplotlib_support from sklearn.utils._response import _get_response_values from sklearn.utils._set_output import _get_adapter_from_container from sklearn.utils.validation import ( _is_arraylike_not_scalar, - _is_pandas_df, - _is_polars_df, _num_features, check_is_fitted, ) @@ -496,7 +495,7 @@ def from_estimator( ) X_grid = np.c_[xx0.ravel(), xx1.ravel()] - if _is_pandas_df(X) or _is_polars_df(X): + if is_pandas_df(X) or is_polars_df(X): adapter = _get_adapter_from_container(X) X_grid = adapter.create_container( X_grid, diff --git a/sklearn/preprocessing/_function_transformer.py b/sklearn/preprocessing/_function_transformer.py index 7c56758d249a2..b3a64508e906c 100644 --- a/sklearn/preprocessing/_function_transformer.py +++ b/sklearn/preprocessing/_function_transformer.py @@ -7,6 +7,7 @@ import numpy as np from sklearn.base import BaseEstimator, TransformerMixin, _fit_context +from sklearn.utils._dataframe import is_pandas_df, is_polars_df from sklearn.utils._param_validation import StrOptions from sklearn.utils._repr_html.estimator import _VisualBlock from sklearn.utils._set_output import _get_adapter_from_container, _get_output_config @@ -15,8 +16,6 @@ _allclose_dense_sparse, _check_feature_names_in, _get_feature_names, - _is_pandas_df, - _is_polars_df, check_array, validate_data, ) @@ -302,9 +301,9 @@ def transform(self, X): "a {0} DataFrame to follow the `set_output` API or `feature_names_out`" " should be defined." ) - if output_config == "pandas" and not _is_pandas_df(out): + if output_config == "pandas" and not is_pandas_df(out): warnings.warn(warn_msg.format("pandas")) - elif output_config == "polars" and not _is_polars_df(out): + elif output_config == "polars" and not is_polars_df(out): warnings.warn(warn_msg.format("polars")) return out diff --git a/sklearn/utils/_array_api.py b/sklearn/utils/_array_api.py index 03e3e18db4f84..9b6cc6d9774ba 100644 --- a/sklearn/utils/_array_api.py +++ b/sklearn/utils/_array_api.py @@ -16,6 +16,7 @@ from sklearn.externals import array_api_compat from sklearn.externals import array_api_extra as xpx from sklearn.externals.array_api_compat import numpy as np_compat +from sklearn.utils._dataframe import is_df_or_series from sklearn.utils.fixes import parse_version # TODO: complete __all__ @@ -320,6 +321,8 @@ def _remove_non_arrays(*arrays, remove_none=True, remove_types=(str,)): continue if sp.issparse(array): continue + if is_df_or_series(array): + continue filtered_arrays.append(array) return filtered_arrays diff --git a/sklearn/utils/_dataframe.py b/sklearn/utils/_dataframe.py new file mode 100644 index 0000000000000..2d77e098aefbb --- /dev/null +++ b/sklearn/utils/_dataframe.py @@ -0,0 +1,123 @@ +"""Functions to determine if an object is a dataframe or series.""" + +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause + +import sys + + +def is_df_or_series(X): + """Return True if the X is a dataframe or series. + + Parameters + ---------- + X : {array-like, dataframe} + The array-like or dataframe object to check. + + Returns + ------- + bool + True if the X is a dataframe or series, False otherwise. + """ + return is_pandas_df_or_series(X) or is_polars_df_or_series(X) or is_pyarrow_data(X) + + +def is_pandas_df_or_series(X): + """Return True if the X is a pandas dataframe or series. + + Parameters + ---------- + X : {array-like, dataframe} + The array-like or dataframe object to check. + + Returns + ------- + bool + True if the X is a pandas dataframe or series, False otherwise. + """ + try: + pd = sys.modules["pandas"] + except KeyError: + return False + return isinstance(X, (pd.DataFrame, pd.Series)) + + +def is_pandas_df(X): + """Return True if the X is a pandas dataframe. + + Parameters + ---------- + X : {array-like, dataframe} + The array-like or dataframe object to check. + + Returns + ------- + bool + True if the X is a pandas dataframe, False otherwise. + """ + try: + pd = sys.modules["pandas"] + except KeyError: + return False + return isinstance(X, pd.DataFrame) + + +def is_pyarrow_data(X): + """Return True if the X is a pyarrow Table, RecordBatch, Array or ChunkedArray. + + Parameters + ---------- + X : {array-like, dataframe} + The array-like or dataframe object to check. + + Returns + ------- + bool + True if the X is a pyarrow Table, RecordBatch, Array or ChunkedArray, + False otherwise. + """ + try: + pa = sys.modules["pyarrow"] + except KeyError: + return False + return isinstance(X, (pa.Table, pa.RecordBatch, pa.Array, pa.ChunkedArray)) + + +def is_polars_df_or_series(X): + """Return True if the X is a polars dataframe or series. + + Parameters + ---------- + X : {array-like, dataframe} + The array-like or dataframe object to check. + + Returns + ------- + bool + True if the X is a polars dataframe or series, False otherwise. + """ + try: + pl = sys.modules["polars"] + except KeyError: + return False + return isinstance(X, (pl.DataFrame, pl.Series)) + + +def is_polars_df(X): + """Return True if the X is a polars dataframe. + + Parameters + ---------- + X : {array-like, dataframe} + The array-like or dataframe object to check. + + Returns + ------- + bool + True if the X is a polarsdataframe, False otherwise. + """ + try: + pl = sys.modules["polars"] + except KeyError: + return False + return isinstance(X, pl.DataFrame) diff --git a/sklearn/utils/_indexing.py b/sklearn/utils/_indexing.py index f4202ac7586c0..484e716bc1170 100644 --- a/sklearn/utils/_indexing.py +++ b/sklearn/utils/_indexing.py @@ -16,15 +16,17 @@ get_namespace_and_device, move_to, ) +from sklearn.utils._dataframe import ( + is_pandas_df, + is_polars_df_or_series, + is_pyarrow_data, +) from sklearn.utils._param_validation import Interval, validate_params from sklearn.utils.extmath import _approximate_mode from sklearn.utils.fixes import PYARROW_VERSION_BELOW_17 from sklearn.utils.validation import ( _check_sample_weight, _is_arraylike_not_scalar, - _is_pandas_df, - _is_polars_df_or_series, - _is_pyarrow_data, _use_interchange_protocol, check_array, check_consistent_length, @@ -341,21 +343,21 @@ def _safe_indexing(X, indices, *, axis=0): if ( axis == 1 and indices_dtype == "str" - and not (_is_pandas_df(X) or _use_interchange_protocol(X)) + and not (is_pandas_df(X) or _use_interchange_protocol(X)) ): raise ValueError( "Specifying the columns using strings is only supported for dataframes." ) if hasattr(X, "iloc"): - # TODO: we should probably use _is_pandas_df_or_series(X) instead but: + # TODO: we should probably use is_pandas_df_or_series(X) instead but: # 1) Currently, it (probably) works for dataframes compliant to pandas' API. # 2) Updating would require updating some tests such as # test_train_test_split_mock_pandas. return _pandas_indexing(X, indices, indices_dtype, axis=axis) - elif _is_polars_df_or_series(X): + elif is_polars_df_or_series(X): return _polars_indexing(X, indices, indices_dtype, axis=axis) - elif _is_pyarrow_data(X): + elif is_pyarrow_data(X): return _pyarrow_indexing(X, indices, indices_dtype, axis=axis) elif _use_interchange_protocol(X): # pragma: no cover # Once the dataframe X is converted into its dataframe interchange protocol diff --git a/sklearn/utils/tests/test_array_api.py b/sklearn/utils/tests/test_array_api.py index 8bb17c8a4fa08..a1fc81c109af8 100644 --- a/sklearn/utils/tests/test_array_api.py +++ b/sklearn/utils/tests/test_array_api.py @@ -43,6 +43,7 @@ from sklearn.utils._testing import ( SkipTest, _array_api_for_tests, + _convert_container, assert_array_equal, skip_if_array_api_compat_not_configured, ) @@ -84,6 +85,35 @@ def test_get_namespace_ndarray_with_dispatch(): assert xp_out is np_compat +@skip_if_array_api_compat_not_configured +@pytest.mark.parametrize( + "constructor_name", ["pyarrow", "dataframe", "polars", "series"] +) +def test_get_namespace_df_with_dispatch(constructor_name): + """Test get_namespace on dataframes and series.""" + + df = _convert_container([[1, 4, 2], [3, 3, 6]], constructor_name) + with config_context(array_api_dispatch=True): + xp_out, is_array_api_compliant = get_namespace(df) + assert not is_array_api_compliant + + # When operating on dataframes or series the Numpy namespace is + # the right thing to use. + assert xp_out is np_compat + + +@skip_if_array_api_compat_not_configured +def test_get_namespace_sparse_with_dispatch(): + """Test get_namespace on sparse arrays.""" + with config_context(array_api_dispatch=True): + xp_out, is_array_api_compliant = get_namespace(sp.csr_array([[1, 2, 3]])) + assert not is_array_api_compliant + + # When operating on sparse arrays the Numpy namespace is + # the right thing to use. + assert xp_out is np_compat + + @skip_if_array_api_compat_not_configured def test_get_namespace_array_api(monkeypatch): """Test get_namespace for ArrayAPI arrays.""" diff --git a/sklearn/utils/tests/test_dataframe.py b/sklearn/utils/tests/test_dataframe.py new file mode 100644 index 0000000000000..49e5296590c34 --- /dev/null +++ b/sklearn/utils/tests/test_dataframe.py @@ -0,0 +1,84 @@ +"""Tests for dataframe detection functions.""" + +import numpy as np +import pytest + +from sklearn._min_dependencies import dependent_packages +from sklearn.utils._dataframe import is_df_or_series, is_pandas_df, is_polars_df +from sklearn.utils._testing import _convert_container + + +@pytest.mark.parametrize("constructor_name", ["pyarrow", "dataframe", "polars"]) +def test_is_df_or_series(constructor_name): + df = _convert_container([[1, 4, 2], [3, 3, 6]], constructor_name) + + assert is_df_or_series(df) + assert not is_df_or_series(np.asarray([1, 2, 3])) + + +@pytest.mark.parametrize("constructor_name", ["pyarrow", "dataframe", "polars"]) +def test_is_pandas_df_other_libraries(constructor_name): + df = _convert_container([[1, 4, 2], [3, 3, 6]], constructor_name) + if constructor_name in ("pyarrow", "polars"): + assert not is_pandas_df(df) + else: + assert is_pandas_df(df) + + +def test_is_pandas_df(): + """Check behavior of is_pandas_df when pandas is installed.""" + pd = pytest.importorskip("pandas") + df = pd.DataFrame([[1, 2, 3]]) + assert is_pandas_df(df) + assert not is_pandas_df(np.asarray([1, 2, 3])) + assert not is_pandas_df(1) + + +def test_is_pandas_df_pandas_not_installed(hide_available_pandas): + """Check is_pandas_df when pandas is not installed.""" + + assert not is_pandas_df(np.asarray([1, 2, 3])) + assert not is_pandas_df(1) + + +@pytest.mark.parametrize( + "constructor_name, minversion", + [ + ("pyarrow", dependent_packages["pyarrow"][0]), + ("dataframe", dependent_packages["pandas"][0]), + ("polars", dependent_packages["polars"][0]), + ], +) +def test_is_polars_df_other_libraries(constructor_name, minversion): + df = _convert_container( + [[1, 4, 2], [3, 3, 6]], + constructor_name, + minversion=minversion, + ) + if constructor_name in ("pyarrow", "dataframe"): + assert not is_polars_df(df) + else: + assert is_polars_df(df) + + +def test_is_polars_df_for_duck_typed_polars_dataframe(): + """Check is_polars_df for object that looks like a polars dataframe""" + + class NotAPolarsDataFrame: + def __init__(self): + self.columns = [1, 2, 3] + self.schema = "my_schema" + + not_a_polars_df = NotAPolarsDataFrame() + assert not is_polars_df(not_a_polars_df) + + +def test_is_polars_df(): + """Check that is_polars_df return False for non-dataframe objects.""" + + class LooksLikePolars: + def __init__(self): + self.columns = ["a", "b"] + self.schema = ["a", "b"] + + assert not is_polars_df(LooksLikePolars()) diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py index 3aafe4ce625b9..b029cab433eb9 100644 --- a/sklearn/utils/tests/test_validation.py +++ b/sklearn/utils/tests/test_validation.py @@ -14,7 +14,6 @@ import sklearn from sklearn._config import config_context -from sklearn._min_dependencies import dependent_packages from sklearn.base import BaseEstimator from sklearn.datasets import make_blobs from sklearn.ensemble import RandomForestRegressor @@ -77,8 +76,6 @@ _estimator_has, _get_feature_names, _is_fitted, - _is_pandas_df, - _is_polars_df, _num_features, _num_samples, _to_object_array, @@ -1995,63 +1992,6 @@ def test_get_feature_names_dataframe_protocol(constructor_name, minversion): assert_array_equal(feature_names, columns) -@pytest.mark.parametrize("constructor_name", ["pyarrow", "dataframe", "polars"]) -def test_is_pandas_df_other_libraries(constructor_name): - df = _convert_container([[1, 4, 2], [3, 3, 6]], constructor_name) - if constructor_name in ("pyarrow", "polars"): - assert not _is_pandas_df(df) - else: - assert _is_pandas_df(df) - - -def test_is_pandas_df(): - """Check behavior of is_pandas_df when pandas is installed.""" - pd = pytest.importorskip("pandas") - df = pd.DataFrame([[1, 2, 3]]) - assert _is_pandas_df(df) - assert not _is_pandas_df(np.asarray([1, 2, 3])) - assert not _is_pandas_df(1) - - -def test_is_pandas_df_pandas_not_installed(hide_available_pandas): - """Check _is_pandas_df when pandas is not installed.""" - - assert not _is_pandas_df(np.asarray([1, 2, 3])) - assert not _is_pandas_df(1) - - -@pytest.mark.parametrize( - "constructor_name, minversion", - [ - ("pyarrow", dependent_packages["pyarrow"][0]), - ("dataframe", dependent_packages["pandas"][0]), - ("polars", dependent_packages["polars"][0]), - ], -) -def test_is_polars_df_other_libraries(constructor_name, minversion): - df = _convert_container( - [[1, 4, 2], [3, 3, 6]], - constructor_name, - minversion=minversion, - ) - if constructor_name in ("pyarrow", "dataframe"): - assert not _is_polars_df(df) - else: - assert _is_polars_df(df) - - -def test_is_polars_df_for_duck_typed_polars_dataframe(): - """Check _is_polars_df for object that looks like a polars dataframe""" - - class NotAPolarsDataFrame: - def __init__(self): - self.columns = [1, 2, 3] - self.schema = "my_schema" - - not_a_polars_df = NotAPolarsDataFrame() - assert not _is_polars_df(not_a_polars_df) - - def test_get_feature_names_numpy(): """Get feature names return None for numpy arrays.""" X = np.array([[1, 2, 3], [4, 5, 6]]) @@ -2322,17 +2262,6 @@ def test_column_or_1d(): column_or_1d(y) -def test__is_polars_df(): - """Check that _is_polars_df return False for non-dataframe objects.""" - - class LooksLikePolars: - def __init__(self): - self.columns = ["a", "b"] - self.schema = ["a", "b"] - - assert not _is_polars_df(LooksLikePolars()) - - def test_check_array_writeable_np(): """Check the behavior of check_array when a writeable array is requested without copy if possible, on numpy arrays. diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py index a112ec4adba61..0e59bde2c02dc 100644 --- a/sklearn/utils/validation.py +++ b/sklearn/utils/validation.py @@ -5,7 +5,6 @@ import numbers import operator -import sys import warnings from collections.abc import Sequence from contextlib import suppress @@ -30,6 +29,7 @@ get_namespace, get_namespace_and_device, ) +from sklearn.utils._dataframe import is_pandas_df, is_pandas_df_or_series from sklearn.utils._isfinite import FiniteStatus, cy_isfinite from sklearn.utils._tags import get_tags from sklearn.utils.fixes import ( @@ -312,7 +312,7 @@ def _use_interchange_protocol(X): to ensure strict behavioral backward compatibility with older versions of scikit-learn. """ - return not _is_pandas_df(X) and hasattr(X, "__dataframe__") + return not is_pandas_df(X) and hasattr(X, "__dataframe__") def _num_features(X): @@ -1129,7 +1129,7 @@ def is_sparse(dtype): # ensure that the output is writeable, even if avoidable, to not overwrite # the user's data by surprise. - if _is_pandas_df_or_series(array_orig): + if is_pandas_df_or_series(array_orig): try: # In pandas >= 3, np.asarray(df), called earlier in check_array, # returns a read-only intermediate array. It can be made writeable @@ -2307,51 +2307,6 @@ def _check_method_params(X, params, indices=None): return method_params_validated -def _is_pandas_df_or_series(X): - """Return True if the X is a pandas dataframe or series.""" - try: - pd = sys.modules["pandas"] - except KeyError: - return False - return isinstance(X, (pd.DataFrame, pd.Series)) - - -def _is_pandas_df(X): - """Return True if the X is a pandas dataframe.""" - try: - pd = sys.modules["pandas"] - except KeyError: - return False - return isinstance(X, pd.DataFrame) - - -def _is_pyarrow_data(X): - """Return True if the X is a pyarrow Table, RecordBatch, Array or ChunkedArray.""" - try: - pa = sys.modules["pyarrow"] - except KeyError: - return False - return isinstance(X, (pa.Table, pa.RecordBatch, pa.Array, pa.ChunkedArray)) - - -def _is_polars_df_or_series(X): - """Return True if the X is a polars dataframe or series.""" - try: - pl = sys.modules["polars"] - except KeyError: - return False - return isinstance(X, (pl.DataFrame, pl.Series)) - - -def _is_polars_df(X): - """Return True if the X is a polars dataframe.""" - try: - pl = sys.modules["polars"] - except KeyError: - return False - return isinstance(X, pl.DataFrame) - - def _get_feature_names(X): """Get feature names from X. @@ -2375,7 +2330,7 @@ def _get_feature_names(X): feature_names = None # extract feature names for support array containers - if _is_pandas_df(X): + if is_pandas_df(X): # Make sure we can inspect columns names from pandas, even with # versions too old to expose a working implementation of # __dataframe__.column_names() and avoid introducing any From a0f6d88d05d52594785455b570ed142c912568e7 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 8 Dec 2025 14:49:05 +0100 Subject: [PATCH 37/44] :lock: :robot: CI Update lock files for array-api CI build(s) :lock: :robot: (#32858) Co-authored-by: Lock file bot --- ...a_forge_cuda_array-api_linux-64_conda.lock | 40 +++++++++---------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock b/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock index 392461bfb0ceb..130e53ea4b032 100644 --- a/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock +++ b/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock @@ -16,13 +16,13 @@ https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222 https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.11.12-hbd8a1cb_0.conda#f0991f0f84902f6b6009b4d2350a83aa https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda#a7970cd949a077b7cb9696379d338681 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-21.1.6-h4922eb0_0.conda#7a0b9ce502e0ed62195e02891dfcd704 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https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.12.0-hb9d3cd8_0.conda#f65c946f28f0518f41ced702f44c52b7 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda#51a19bba1b8ebfb60df25cde030b7ebc @@ -32,17 +32,17 @@ https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb03c661_4 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda#6c77a605a7a689d17d4819c0f8ac9a00 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda#8b09ae86839581147ef2e5c5e229d164 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h9ec8514_0.conda#35f29eec58405aaf55e01cb470d8c26a -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_14.conda#6c13aaae36d7514f28bd5544da1a7bb8 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https://conda.anaconda.org/conda-forge/noarch/pytest-run-parallel-0.8.0-pyhd8ed1ab_0.conda#7d545e76cfc231cf246f99963bcd27b0 From 5645b27b985486d6b75bb75e4929faa4da574173 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 8 Dec 2025 14:52:28 +0100 Subject: [PATCH 39/44] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#32859) Co-authored-by: Lock file bot --- build_tools/azure/debian_32bit_lock.txt | 4 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 108 +++++++------ ...onda_forge_mkl_no_openmp_osx-64_conda.lock | 28 ++-- .../pylatest_conda_forge_osx-arm64_conda.lock | 59 +++---- ...st_pip_openblas_pandas_linux-64_conda.lock | 30 ++-- ...nblas_min_dependencies_linux-64_conda.lock | 40 ++--- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 58 ++++--- ...min_conda_forge_openblas_win-64_conda.lock | 30 ++-- build_tools/azure/ubuntu_atlas_lock.txt | 4 +- build_tools/circle/doc_linux-64_conda.lock | 144 +++++++++--------- .../doc_min_dependencies_linux-64_conda.lock | 99 ++++++------ ...n_conda_forge_arm_linux-aarch64_conda.lock | 50 +++--- 12 files changed, 324 insertions(+), 330 deletions(-) diff --git a/build_tools/azure/debian_32bit_lock.txt b/build_tools/azure/debian_32bit_lock.txt index 545879de75099..69fff8cc96d64 100644 --- a/build_tools/azure/debian_32bit_lock.txt +++ b/build_tools/azure/debian_32bit_lock.txt @@ -14,7 +14,7 @@ iniconfig==2.3.0 # via pytest joblib==1.5.2 # via -r build_tools/azure/debian_32bit_requirements.txt -meson==1.9.1 +meson==1.9.2 # via meson-python meson-python==0.18.0 # via -r build_tools/azure/debian_32bit_requirements.txt @@ -33,7 +33,7 @@ pygments==2.19.2 # via pytest pyproject-metadata==0.10.0 # via meson-python -pytest==9.0.1 +pytest==9.0.2 # via # -r build_tools/azure/debian_32bit_requirements.txt # pytest-cov diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 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https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b @@ -49,7 +49,7 @@ https://conda.anaconda.org/conda-forge/noarch/pip-25.3-pyh145f28c_0.conda#bf4787 # pip joblib @ https://files.pythonhosted.org/packages/1e/e8/685f47e0d754320684db4425a0967f7d3fa70126bffd76110b7009a0090f/joblib-1.5.2-py3-none-any.whl#sha256=4e1f0bdbb987e6d843c70cf43714cb276623def372df3c22fe5266b2670bc241 # pip kiwisolver @ https://files.pythonhosted.org/packages/e9/e9/f218a2cb3a9ffbe324ca29a9e399fa2d2866d7f348ec3a88df87fc248fc5/kiwisolver-1.4.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl#sha256=b67e6efbf68e077dd71d1a6b37e43e1a99d0bff1a3d51867d45ee8908b931098 # pip markupsafe @ 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https://files.pythonhosted.org/packages/f5/10/ca162f45a102738958dcec8023062dad0cbc17d1ab99d68c4e4a6c45fb2b/numpy-2.3.5-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl#sha256=11e06aa0af8c0f05104d56450d6093ee639e15f24ecf62d417329d06e522e017 # pip packaging @ https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl#sha256=29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484 @@ -70,12 +70,12 @@ https://conda.anaconda.org/conda-forge/noarch/pip-25.3-pyh145f28c_0.conda#bf4787 # pip tabulate @ https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl#sha256=024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f # pip threadpoolctl @ https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl#sha256=43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb # pip tzdata @ https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl#sha256=1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8 -# pip urllib3 @ https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl#sha256=e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc +# pip urllib3 @ https://files.pythonhosted.org/packages/56/1a/9ffe814d317c5224166b23e7c47f606d6e473712a2fad0f704ea9b99f246/urllib3-2.6.0-py3-none-any.whl#sha256=c90f7a39f716c572c4e3e58509581ebd83f9b59cced005b7db7ad2d22b0db99f # pip array-api-strict @ https://files.pythonhosted.org/packages/e1/7b/81bef4348db9705d829c58b9e563c78eddca24438f1ce1108d709e6eed55/array_api_strict-2.4.1-py3-none-any.whl#sha256=22198ceb47cd3d9c0534c50650d265848d0da6ff71707171215e6678ce811ca5 # pip contourpy @ https://files.pythonhosted.org/packages/4b/32/e0f13a1c5b0f8572d0ec6ae2f6c677b7991fafd95da523159c19eff0696a/contourpy-1.3.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl#sha256=4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9 # pip jinja2 @ https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl#sha256=85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 # pip pyproject-metadata @ https://files.pythonhosted.org/packages/c0/57/e69a1de45ec7a99a707e9f1a5defa035a48de0cae2d8582451c72d2db456/pyproject_metadata-0.10.0-py3-none-any.whl#sha256=b1e439a9f7560f9792ee5975dcf5e89d2510b1fc84a922d7e5d665aa9102d966 -# pip pytest @ https://files.pythonhosted.org/packages/0b/8b/6300fb80f858cda1c51ffa17075df5d846757081d11ab4aa35cef9e6258b/pytest-9.0.1-py3-none-any.whl#sha256=67be0030d194df2dfa7b556f2e56fb3c3315bd5c8822c6951162b92b32ce7dad +# pip pytest @ https://files.pythonhosted.org/packages/3b/ab/b3226f0bd7cdcf710fbede2b3548584366da3b19b5021e74f5bde2a8fa3f/pytest-9.0.2-py3-none-any.whl#sha256=711ffd45bf766d5264d487b917733b453d917afd2b0ad65223959f59089f875b # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl#sha256=2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6 # pip scipy @ https://files.pythonhosted.org/packages/21/f6/4bfb5695d8941e5c570a04d9fcd0d36bce7511b7d78e6e75c8f9791f82d0/scipy-1.16.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl#sha256=7dc1360c06535ea6116a2220f760ae572db9f661aba2d88074fe30ec2aa1ff88 @@ -87,5 +87,5 @@ https://conda.anaconda.org/conda-forge/noarch/pip-25.3-pyh145f28c_0.conda#bf4787 # pip pytest-cov @ https://files.pythonhosted.org/packages/80/b4/bb7263e12aade3842b938bc5c6958cae79c5ee18992f9b9349019579da0f/pytest_cov-6.3.0-py3-none-any.whl#sha256=440db28156d2468cafc0415b4f8e50856a0d11faefa38f30906048fe490f1749 # pip pytest-xdist @ https://files.pythonhosted.org/packages/ca/31/d4e37e9e550c2b92a9cbc2e4d0b7420a27224968580b5a447f420847c975/pytest_xdist-3.8.0-py3-none-any.whl#sha256=202ca578cfeb7370784a8c33d6d05bc6e13b4f25b5053c30a152269fd10f0b88 # pip scipy-doctest @ https://files.pythonhosted.org/packages/f5/99/a17f725f45e57efcf5a84494687bba7176e0b5cba7ca0f69161a063fa86d/scipy_doctest-2.0.1-py3-none-any.whl#sha256=7725b1cb5f4722ab2a77b39f0aadd39726266e682b19e40f96663d7afb2d46b1 -# pip sphinx @ https://files.pythonhosted.org/packages/fe/8b/76e2a1ce12b915399365873eef2b1197da9d032c99e661a71fd7e1490333/sphinx-9.0.0-py3-none-any.whl#sha256=3442bf635d378da2ba4e88aa8496f3a61b2d59ef145aeaf34353ab93fd79f1bf +# pip sphinx @ https://files.pythonhosted.org/packages/c6/3f/4bbd76424c393caead2e1eb89777f575dee5c8653e2d4b6afd7a564f5974/sphinx-9.0.4-py3-none-any.whl#sha256=5bebc595a5e943ea248b99c13814c1c5e10b3ece718976824ffa7959ff95fffb # pip numpydoc @ https://files.pythonhosted.org/packages/6c/45/56d99ba9366476cd8548527667f01869279cedb9e66b28eb4dfb27701679/numpydoc-1.8.0-py3-none-any.whl#sha256=72024c7fd5e17375dec3608a27c03303e8ad00c81292667955c6fea7a3ccf541 diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock index ed5d6b562fb93..e94fdf6ae3dec 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock @@ -11,12 +11,12 @@ https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222 https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.11.12-hbd8a1cb_0.conda#f0991f0f84902f6b6009b4d2350a83aa https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda#a7970cd949a077b7cb9696379d338681 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-21.1.6-h4922eb0_0.conda#7a0b9ce502e0ed62195e02891dfcd704 +https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-21.1.7-h4922eb0_0.conda#ec29f865968a81e1961b3c2f2765eebb https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda#887b70e1d607fba7957aa02f9ee0d939 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_14.conda#550dceb769d23bcf0e2f97fd4062d720 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_15.conda#a5d86b0496174a412d531eac03af9174 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.2-h39aace5_0.conda#791365c5f65975051e4e017b5da3abf5 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda#51a19bba1b8ebfb60df25cde030b7ebc @@ -25,8 +25,8 @@ https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda# https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda#6c77a605a7a689d17d4819c0f8ac9a00 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda#8b09ae86839581147ef2e5c5e229d164 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h9ec8514_0.conda#35f29eec58405aaf55e01cb470d8c26a -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_14.conda#6c13aaae36d7514f28bd5544da1a7bb8 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_14.conda#3078a2a9a58566a54e579b41b9e88c84 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_15.conda#7b742943660c5173bb6a5c823021c9a0 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_15.conda#356b7358fcd6df32ad50d07cdfadd27d https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda#915f5995e94f60e9a4826e0b0920ee88 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.2-hb03c661_0.conda#8397539e3a0bbd1695584fb4f927485a https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc @@ -36,9 +36,9 @@ https://conda.anaconda.org/conda-forge/linux-64/libnuma-2.0.18-hb9d3cd8_3.conda# https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.5-hd0c01bc_1.conda#68e52064ed3897463c0e958ab5c8f91b https://conda.anaconda.org/conda-forge/linux-64/libopus-1.5.2-hd0c01bc_0.conda#b64523fb87ac6f87f0790f324ad43046 https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda#70e3400cbbfa03e96dcde7fc13e38c7b -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_14.conda#8e96fe9b17d5871b5cf9d312cab832f6 +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_15.conda#fccfb26375ec5e4a2192dee6604b6d02 https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-hf23e847_1.conda#b1aa0faa95017bca11369bd080487ec4 -https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.2-he9a06e4_0.conda#80c07c68d2f6870250959dcc95b209d1 +https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.2-h5347b49_1.conda#41f5c09a211985c3ce642d60721e7c3e https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 @@ -62,11 +62,11 @@ https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.25.1-h3f43e3d_1.conda#2f4de899028319b27eb7a4023be5dfd2 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_14.conda#fa9d91abc5a9db36fa8dcd1b9a602e61 -https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.51-h421ea60_0.conda#d8b81203d08435eb999baa249427884e +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_15.conda#7deffdc77cda3d2bbc9c558efa33d3ed +https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.53-h421ea60_0.conda#00d4e66b1f746cb14944cad23fffb405 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.1-h0c1763c_0.conda#2e1b84d273b01835256e53fd938de355 https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda#eecce068c7e4eddeb169591baac20ac4 -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_14.conda#9531f671a13eec0597941fa19e489b96 +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_15.conda#20a8584ff8677ac9d724345b9d4eb757 https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h54a6638_2.conda#b4ecbefe517ed0157c37f8182768271c https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda#92ed62436b625154323d40d5f2f11dd7 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc @@ -80,8 +80,8 @@ https://conda.anaconda.org/conda-forge/linux-64/s2n-1.3.46-h06160fa_0.conda#413d https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_ha0e22de_103.conda#86bc20552bf46075e3d92b67f089172d https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.6-he73a12e_0.conda#1c74ff8c35dcadf952a16f752ca5aa49 https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda#c9f075ab2f33b3bbee9e62d4ad0a6cd8 -https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.1-hde8ca8f_0.conda#49c832bff803d95a56190e7992b4b230 -https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-h3691f8a_4.conda#af7715829219de9043fcc5575e66d22e +https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.2-h54a6638_0.conda#0faadd01896315ceea58bcc3479b1d21 +https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda#4a13eeac0b5c8e5b8ab496e6c4ddd829 https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.6.0-h93469e0_0.conda#580a52a05f5be28ce00764149017c6d4 https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.17-h862ab75_1.conda#0013fcee7acb3cfc801c5929824feb3c 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https://conda.anaconda.org/conda-forge/win-64/pthread-stubs-0.4-h0e40799_1002.co https://conda.anaconda.org/conda-forge/win-64/python-3.11.14-h0159041_2_cpython.conda#02a9ba5950d8b78e6c9862d6ba7a5045 https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.12-hba3369d_1.conda#8436cab9a76015dfe7208d3c9f97c156 https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.5-hba3369d_1.conda#a7c03e38aa9c0e84d41881b9236eacfb -https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h1b5488d_4.conda#4fcccc053a113f5711dddf64401e9010 +https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda#053b84beec00b71ea8ff7a4f84b55207 https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.2.0-hfd05255_1.conda#6abd7089eb3f0c790235fe469558d190 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda#4c2a8fef270f6c69591889b93f9f55c1 @@ -63,18 +63,18 @@ https://conda.anaconda.org/conda-forge/win-64/cython-3.2.2-py311h9990397_0.conda https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.2-pyhd8ed1ab_0.conda#a57b4be42619213a94f31d2c69c5dda7 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda#9614359868482abba1bd15ce465e3c42 https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.4.9-py311h275cad7_2.conda#e9eb24a8d111be48179bf82a9e0e13ca -https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-2_h2a8eebe_openblas.conda#ffc9f6913d7436e558b9d85a1c380591 -https://conda.anaconda.org/conda-forge/win-64/libclang13-21.1.6-default_ha2db4b5_0.conda#32b0f9f52f859396db50d738d50b4a82 +https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-4_h2a8eebe_openblas.conda#464b3434e245c2967c0e226f1611f6f9 +https://conda.anaconda.org/conda-forge/win-64/libclang13-21.1.7-default_ha2db4b5_1.conda#065bcc5d1a29de06d4566b7b9ac89882 https://conda.anaconda.org/conda-forge/win-64/libfreetype6-2.14.1-hdbac1cb_0.conda#6e7c5c5ab485057b5d07fd8188ba5c28 https://conda.anaconda.org/conda-forge/win-64/libglib-2.86.2-hd9c3897_0.conda#fbd144e60009d93f129f0014a76512d3 -https://conda.anaconda.org/conda-forge/win-64/liblapack-3.11.0-2_hd232482_openblas.conda#b42a971e4cef38ee91a7a42cdb224be4 +https://conda.anaconda.org/conda-forge/win-64/liblapack-3.11.0-4_hd232482_openblas.conda#808ae0372f0b1495c41a1ce2064f291f https://conda.anaconda.org/conda-forge/win-64/libtiff-4.7.1-h8f73337_1.conda#549845d5133100142452812feb9ba2e8 https://conda.anaconda.org/conda-forge/win-64/libxcb-1.17.0-h0e4246c_0.conda#a69bbf778a462da324489976c84cfc8c https://conda.anaconda.org/conda-forge/win-64/libxml2-2.15.1-ha29bfb0_0.conda#87116b9de9c1825c3fd4ef92c984877b -https://conda.anaconda.org/conda-forge/noarch/meson-1.9.1-pyhcf101f3_0.conda#ef2b132f3e216b5bf6c2f3c36cfd4c89 +https://conda.anaconda.org/conda-forge/noarch/meson-1.9.2-pyhcf101f3_0.conda#7920269f1b2d2f49c49616ac5b507aae https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda#37293a85a0f4f77bbd9cf7aaefc62609 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 -https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 +https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda#d7585b6550ad04c8c5e21097ada2888e https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda#6b6ece66ebcae2d5f326c77ef2c5a066 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.5-pyhcf101f3_0.conda#6c8979be6d7a17692793114fa26916e8 https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e @@ -92,22 +92,22 @@ https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0. https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.2-pyhd8ed1ab_0.conda#4e717929cfa0d49cef92d911e31d0e90 https://conda.anaconda.org/conda-forge/win-64/lcms2-2.17-hbcf6048_0.conda#3538827f77b82a837fa681a4579e37a1 https://conda.anaconda.org/conda-forge/win-64/libfreetype-2.14.1-h57928b3_0.conda#3235024fe48d4087721797ebd6c9d28c -https://conda.anaconda.org/conda-forge/win-64/liblapacke-3.11.0-2_hbb0e6ff_openblas.conda#d0bc7a5338ff7d95e210a3f7e1264ed9 +https://conda.anaconda.org/conda-forge/win-64/liblapacke-3.11.0-4_hbb0e6ff_openblas.conda#cf28f3b4945a9b76dbb64c60d534d7b7 https://conda.anaconda.org/conda-forge/win-64/libxslt-1.1.43-h0fbe4c1_1.conda#46034d9d983edc21e84c0b36f1b4ba61 https://conda.anaconda.org/conda-forge/win-64/numpy-2.3.5-py311h80b3fa1_0.conda#1e0fb210584b09130000c4404b77f0f6 https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.4-h24db6dd_0.conda#5af852046226bb3cb15c7f61c2ac020a https://conda.anaconda.org/conda-forge/noarch/pip-25.3-pyh8b19718_0.conda#c55515ca43c6444d2572e0f0d93cb6b9 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.10.0-pyhd8ed1ab_0.conda#d9998bf52ced268eb83749ad65a2e061 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 -https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.11.0-2_ha590de0_openblas.conda#2faff8da7caa95fedbebd4029c815910 +https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.11.0-4_ha590de0_openblas.conda#e3e893858dd88ee0351f1a5b9cb140f3 https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.3-py311h3fd045d_3.conda#5e7e380c470e9f4683b3129fedafbcdf https://conda.anaconda.org/conda-forge/win-64/fonttools-4.61.0-py311h3f79411_0.conda#448f4a9f042eec9a840e3a0090e9a6d8 https://conda.anaconda.org/conda-forge/win-64/freetype-2.14.1-h57928b3_0.conda#d69c21967f35eb2ce7f1f85d6b6022d3 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/win-64/pillow-12.0.0-py311h17b8079_2.conda#a80f6ec79f4ea2bf7572f4f8e8b467f7 -https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.1-pyhcf101f3_0.conda#fa7f71faa234947d9c520f89b4bda1a2 +https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda#2b694bad8a50dc2f712f5368de866480 https://conda.anaconda.org/conda-forge/win-64/scipy-1.16.3-py311hf127856_1.conda#48d562b3a3fb120d7c3f5e6af6d4b3e9 -https://conda.anaconda.org/conda-forge/win-64/blas-2.302-openblas.conda#9a3d6e4359ba0ce36b6dea7b6c32bd94 +https://conda.anaconda.org/conda-forge/win-64/blas-2.304-openblas.conda#a17d4405929291b138f4fb3068b44b2a https://conda.anaconda.org/conda-forge/win-64/fontconfig-2.15.0-h765892d_1.conda#9bb0026a2131b09404c59c4290c697cd https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.10.8-py311h1675fdf_0.conda#57671b98b86015c8b28551cdb09ee294 https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.3.0-pyhd8ed1ab_0.conda#50d191b852fccb4bf9ab7b59b030c99d diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index 6db4c2cd12771..b7e46d69c03b0 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -12,7 +12,7 @@ iniconfig==2.3.0 # via pytest joblib==1.3.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt -meson==1.9.1 +meson==1.9.2 # via meson-python meson-python==0.18.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt @@ -29,7 +29,7 @@ pygments==2.19.2 # via pytest pyproject-metadata==0.10.0 # via meson-python -pytest==9.0.1 +pytest==9.0.2 # via # -r build_tools/azure/ubuntu_atlas_requirements.txt # pytest-xdist diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index c037ab9ddde16..5776d859d9d67 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -2,27 +2,26 @@ # platform: linux-64 # input_hash: ca6b5567d8c939295b5b4408ecaa611380022818d7f626c2732e529c500271e7 @EXPLICIT +https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda#49023d73832ef61042f6a237cb2687e7 https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_8.conda#ff007ab0f0fdc53d245972bba8a6d40c -https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2025.3.0-hf2ce2f3_462.conda#0ec3505e9b16acc124d1ec6e5ae8207c https://conda.anaconda.org/conda-forge/noarch/python_abi-3.11-8_cp311.conda#8fcb6b0e2161850556231336dae58358 https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.11.12-hbd8a1cb_0.conda#f0991f0f84902f6b6009b4d2350a83aa https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda#a7970cd949a077b7cb9696379d338681 -https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_114.conda#5addcb22be964dc0df75bbd4649fee59 +https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_115.conda#eedcd688f597873e1d16f0529f4d6d10 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 -https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_14.conda#91349c276f84f590487e4c7f6e90e077 -https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_114.conda#c88929e13f56dac9233cdf615502e5f3 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-21.1.6-h4922eb0_0.conda#7a0b9ce502e0ed62195e02891dfcd704 +https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_15.conda#a90d6983da0757f4c09bb8fcfaf34e71 +https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_115.conda#1ef1a0376c610365eb26e67f5da5e48d https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_8.conda#1bad93f0aa428d618875ef3a588a889e -https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda#887b70e1d607fba7957aa02f9ee0d939 +https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_14.conda#550dceb769d23bcf0e2f97fd4062d720 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_15.conda#a5d86b0496174a412d531eac03af9174 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda#51a19bba1b8ebfb60df25cde030b7ebc 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00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 9 Dec 2025 14:25:05 +0100 Subject: [PATCH 40/44] DOC Release highlights for 1.8 (#32809) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Christian Lorentzen Co-authored-by: Olivier Grisel Co-authored-by: Virgil Chan Co-authored-by: Omar Salman Co-authored-by: Tim Head Co-authored-by: Jérémie du Boisberranger --- .../plot_release_highlights_1_8_0.py | 288 ++++++++++++++++++ 1 file changed, 288 insertions(+) create mode 100644 examples/release_highlights/plot_release_highlights_1_8_0.py diff --git a/examples/release_highlights/plot_release_highlights_1_8_0.py b/examples/release_highlights/plot_release_highlights_1_8_0.py new file mode 100644 index 0000000000000..3414a512724f4 --- /dev/null +++ b/examples/release_highlights/plot_release_highlights_1_8_0.py @@ -0,0 +1,288 @@ +# ruff: noqa: CPY001 +""" +======================================= +Release Highlights for scikit-learn 1.8 +======================================= + +.. currentmodule:: sklearn + +We are pleased to announce the release of scikit-learn 1.8! Many bug fixes +and improvements were added, as well as some key new features. Below we +detail the highlights of this release. **For an exhaustive list of +all the changes**, please refer to the :ref:`release notes `. + +To install the latest version (with pip):: + + pip install --upgrade scikit-learn + +or with conda:: + + conda install -c conda-forge scikit-learn + +""" + +# %% +# Array API support (enables GPU computations) +# -------------------------------------------- +# The progressive adoption of the Python array API standard in +# scikit-learn means that PyTorch and CuPy input arrays +# are used directly. This means that in scikit-learn estimators +# and functions non-CPU devices, such as GPUs, can be used +# to perform the computation. As a result performance is improved +# and integration with these libraries is easier. +# +# In scikit-learn 1.8, several estimators and functions have been updated to +# support array API compatible inputs, for example PyTorch tensors and CuPy +# arrays. +# +# Array API support was added to the following estimators: +# :class:`preprocessing.StandardScaler`, +# :class:`preprocessing.PolynomialFeatures`, :class:`linear_model.RidgeCV`, +# :class:`linear_model.RidgeClassifierCV`, :class:`mixture.GaussianMixture` and +# :class:`calibration.CalibratedClassifierCV`. +# +# Array API support was also added to several metrics in :mod:`sklearn.metrics` +# module, see :ref:`array_api_supported` for more details. +# +# Please refer to the :ref:`array API support` page for instructions +# to use scikit-learn with array API compatible libraries such as PyTorch or CuPy. +# Note: Array API support is experimental and must be explicitly enabled both +# in SciPy and scikit-learn. +# +# Here is an excerpt of using a feature engineering preprocessor on the CPU, +# followed by :class:`calibration.CalibratedClassifierCV` +# and :class:`linear_model.RidgeCV` together on a GPU with the help of PyTorch: +# +# .. code-block:: python +# +# ridge_pipeline_gpu = make_pipeline( +# # Ensure that all features (including categorical features) are preprocessed +# # on the CPU and mapped to a numerical representation. +# feature_preprocessor, +# # Move the results to the GPU and perform computations there +# FunctionTransformer( +# lambda x: torch.tensor(x.to_numpy().astype(np.float32), device="cuda")) +# , +# CalibratedClassifierCV( +# RidgeClassifierCV(alphas=alphas), method="temperature" +# ), +# ) +# with sklearn.config_context(array_api_dispatch=True): +# cv_results = cross_validate(ridge_pipeline_gpu, features, target) +# +# +# See the `full notebook on Google Colab +# `_ +# for more details. On this particular example, using the Colab GPU vs using a +# single CPU core leads to a 10x speedup which is quite typical for such workloads. + +# %% +# Free-threaded CPython 3.14 support +# ---------------------------------- +# +# scikit-learn has support for free-threaded CPython, in particular +# free-threaded wheels are available for all of our supported platforms on Python +# 3.14. +# +# We would be very interested by user feedback. Here are a few things you can +# try: +# +# - install free-threaded CPython 3.14, run your favourite +# scikit-learn script and check that nothing breaks unexpectedly. +# Note that CPython 3.14 (rather than 3.13) is strongly advised because a +# number of free-threaded bugs have been fixed since CPython 3.13. +# - if you use some estimators with a `n_jobs` parameter, try changing the +# default backend to threading with `joblib.parallel_config` as in the +# snippet below. This could potentially speed-up your code because the +# default joblib backend is process-based and incurs more overhead than +# threads. +# +# .. code-block:: python +# +# grid_search = GridSearchCV(clf, param_grid=param_grid, n_jobs=4) +# with joblib.parallel_config(backend="threading"): +# grid_search.fit(X, y) +# +# - don't hesitate to report any issue or unexpected performance behaviour by +# opening a `GitHub issue `_! +# +# Free-threaded (also known as nogil) CPython is a version of CPython that aims +# to enable efficient multi-threaded use cases by removing the Global +# Interpreter Lock (GIL). +# +# For more details about free-threaded CPython see `py-free-threading doc +# `_, in particular `how to install a +# free-threaded CPython `_ +# and `Ecosystem compatibility tracking `_. +# +# In scikit-learn, one hope with free-threaded Python is to more efficiently +# leverage multi-core CPUs by using thread workers instead of subprocess +# workers for parallel computation when passing `n_jobs>1` in functions or +# estimators. Efficiency gains are expected by removing the need for +# inter-process communication. Be aware that switching the default joblib +# backend and testing that everything works well with free-threaded Python is an +# ongoing long-term effort. + +# %% +# Temperature scaling in `CalibratedClassifierCV` +# ----------------------------------------------- +# Probability calibration of classifiers with temperature scaling is available in +# :class:`calibration.CalibratedClassifierCV` by setting `method="temperature"`. +# This method is particularly well suited for multiclass problems because it provides +# (better) calibrated probabilities with a single free parameter. This is in +# contrast to all the other available calibrations methods +# which use a "One-vs-Rest" scheme that adds more parameters for each class. + +from sklearn.calibration import CalibratedClassifierCV +from sklearn.datasets import make_classification +from sklearn.naive_bayes import GaussianNB + +X, y = make_classification(n_classes=3, n_informative=8, random_state=42) +clf = GaussianNB().fit(X, y) +sig = CalibratedClassifierCV(clf, method="sigmoid", ensemble=False).fit(X, y) +ts = CalibratedClassifierCV(clf, method="temperature", ensemble=False).fit(X, y) + +# %% +# The following example shows that temperature scaling can produce better calibrated +# probabilities than sigmoid calibration in multi-class classification problem +# with 3 classes. + +import matplotlib.pyplot as plt + +from sklearn.calibration import CalibrationDisplay + +fig, axes = plt.subplots( + figsize=(8, 4.5), + ncols=3, + sharey=True, +) +for i, c in enumerate(ts.classes_): + CalibrationDisplay.from_predictions( + y == c, clf.predict_proba(X)[:, i], name="Uncalibrated", ax=axes[i], marker="s" + ) + CalibrationDisplay.from_predictions( + y == c, + ts.predict_proba(X)[:, i], + name="Temperature scaling", + ax=axes[i], + marker="o", + ) + CalibrationDisplay.from_predictions( + y == c, sig.predict_proba(X)[:, i], name="Sigmoid", ax=axes[i], marker="v" + ) + axes[i].set_title(f"Class {c}") + axes[i].set_xlabel(None) + axes[i].set_ylabel(None) + axes[i].get_legend().remove() +fig.suptitle("Reliability Diagrams per Class") +fig.supxlabel("Mean Predicted Probability") +fig.supylabel("Fraction of Class") +fig.legend(*axes[0].get_legend_handles_labels(), loc=(0.72, 0.5)) +plt.subplots_adjust(right=0.7) +_ = fig.show() + +# %% +# Efficiency improvements in linear models +# ---------------------------------------- +# The fit time has been massively reduced for squared error based estimators +# with L1 penalty: `ElasticNet`, `Lasso`, `MultiTaskElasticNet`, +# `MultiTaskLasso` and their CV variants. The fit time improvement is mainly +# achieved by **gap safe screening rules**. They enable the coordinate descent +# solver to set feature coefficients to zero early on and not look at them +# again. The stronger the L1 penalty the earlier features can be excluded from +# further updates. + +from time import time + +from sklearn.datasets import make_regression +from sklearn.linear_model import ElasticNetCV + +X, y = make_regression(n_features=10_000, random_state=0) +model = ElasticNetCV() +tic = time() +model.fit(X, y) +toc = time() +print(f"Fitting ElasticNetCV took {toc - tic:.3} seconds.") + +# %% +# HTML representation of estimators +# --------------------------------- +# Hyperparameters in the dropdown table of the HTML representation now include +# links to the online documentation. Docstring descriptions are also shown as +# tooltips on hover. + +from sklearn.linear_model import LogisticRegression +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler + +clf = make_pipeline(StandardScaler(), LogisticRegression(random_state=0, C=10)) + +# %% +# Expand the estimator diagram below by clicking on "LogisticRegression" and then on +# "Parameters". + +clf + + +# %% +# DecisionTreeRegressor with `criterion="absolute_error"` +# ------------------------------------------------------ +# :class:`tree.DecisionTreeRegressor` with `criterion="absolute_error"` +# now runs much faster. It has now `O(n * log(n))` complexity compared to +# `O(n**2)` previously, which allows to scale to millions of data points. +# +# As an illustration, on a dataset with 100_000 samples and 1 feature, doing a +# single split takes of the order of 100 ms, compared to ~20 seconds before. + +import time + +from sklearn.datasets import make_regression +from sklearn.tree import DecisionTreeRegressor + +X, y = make_regression(n_samples=100_000, n_features=1) +tree = DecisionTreeRegressor(criterion="absolute_error", max_depth=1) + +tic = time.time() +tree.fit(X, y) +elapsed = time.time() - tic +print(f"Fit took {elapsed:.2f} seconds") + +# %% +# ClassicalMDS +# ------------ +# Classical MDS, also known as "Principal Coordinates Analysis" (PCoA) +# or "Torgerson's scaling" is now available within the `sklearn.manifold` +# module. Classical MDS is close to PCA and instead of approximating +# distances, it approximates pairwise scalar products, which has an exact +# analytic solution in terms of eigendecomposition. +# +# Let's illustrate this new addition by using it on an S-curve dataset to +# get a low-dimensional representation of the data. + +import matplotlib.pyplot as plt +from matplotlib import ticker + +from sklearn import datasets, manifold + +n_samples = 1500 +S_points, S_color = datasets.make_s_curve(n_samples, random_state=0) +md_classical = manifold.ClassicalMDS(n_components=2) +S_scaling = md_classical.fit_transform(S_points) + +fig = plt.figure(figsize=(8, 4)) +ax1 = fig.add_subplot(1, 2, 1, projection="3d") +x, y, z = S_points.T +ax1.scatter(x, y, z, c=S_color, s=50, alpha=0.8) +ax1.set_title("Original S-curve samples", size=16) +ax1.view_init(azim=-60, elev=9) +for axis in (ax1.xaxis, ax1.yaxis, ax1.zaxis): + axis.set_major_locator(ticker.MultipleLocator(1)) + +ax2 = fig.add_subplot(1, 2, 2) +x2, y2 = S_scaling.T +ax2.scatter(x2, y2, c=S_color, s=50, alpha=0.8) +ax2.set_title("Classical MDS", size=16) +for axis in (ax2.xaxis, ax2.yaxis): + axis.set_major_formatter(ticker.NullFormatter()) + +plt.show() From 967dcde6dae92a6f9eafa804b5d93e8fdfd08f26 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 9 Dec 2025 11:52:25 +0100 Subject: [PATCH 41/44] Set version --- sklearn/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/__init__.py b/sklearn/__init__.py index 2bb7e05db568f..16aa6d2017a1f 100644 --- a/sklearn/__init__.py +++ b/sklearn/__init__.py @@ -42,7 +42,7 @@ # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' # -__version__ = "1.8.0rc1" +__version__ = "1.8.0" # On OSX, we can get a runtime error due to multiple OpenMP libraries loaded From 4f4f2831de348859efc5d8298cbf37e212a1efac Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= 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a/doc/whats_new/upcoming_changes/array-api/27961.feature.rst b/doc/whats_new/upcoming_changes/array-api/27961.feature.rst deleted file mode 100644 index 3dbea99e0f749..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/27961.feature.rst +++ /dev/null @@ -1,4 +0,0 @@ -- :class:`linear_model.RidgeCV`, :class:`linear_model.RidgeClassifier` and - :class:`linear_model.RidgeClassifierCV` now support array API compatible - inputs with `solver="svd"`. - By :user:`Jérôme Dockès `. diff --git a/doc/whats_new/upcoming_changes/array-api/29822.feature.rst b/doc/whats_new/upcoming_changes/array-api/29822.feature.rst deleted file mode 100644 index 4cd3dc8d300cb..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/29822.feature.rst +++ /dev/null @@ -1,5 +0,0 @@ -- :func:`metrics.pairwise.pairwise_kernels` for any kernel except - "laplacian" and - :func:`metrics.pairwise_distances` for metrics "cosine", - "euclidean" and "l2" now support array API inputs. - By :user:`Emily Chen ` and :user:`Lucy Liu ` diff --git a/doc/whats_new/upcoming_changes/array-api/30562.feature.rst b/doc/whats_new/upcoming_changes/array-api/30562.feature.rst deleted file mode 100644 index 3c1a58d90bfe5..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/30562.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`sklearn.metrics.confusion_matrix` now supports Array API compatible inputs. - By :user:`Stefanie Senger ` diff --git a/doc/whats_new/upcoming_changes/array-api/30777.feature.rst b/doc/whats_new/upcoming_changes/array-api/30777.feature.rst deleted file mode 100644 index aec9bb4da1e71..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/30777.feature.rst +++ /dev/null @@ -1,4 +0,0 @@ -- :class:`sklearn.mixture.GaussianMixture` with - `init_params="random"` or `init_params="random_from_data"` and - `warm_start=False` now supports Array API compatible inputs. - By :user:`Stefanie Senger ` and :user:`Loïc Estève ` diff --git a/doc/whats_new/upcoming_changes/array-api/30878.feature.rst b/doc/whats_new/upcoming_changes/array-api/30878.feature.rst deleted file mode 100644 index fabb4c80f5713..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/30878.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`sklearn.metrics.roc_curve` now supports Array API compatible inputs. - By :user:`Thomas Li ` diff --git a/doc/whats_new/upcoming_changes/array-api/31580.feature.rst b/doc/whats_new/upcoming_changes/array-api/31580.feature.rst deleted file mode 100644 index 3d7aaa4372109..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/31580.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :class:`preprocessing.PolynomialFeatures` now supports array API compatible inputs. - By :user:`Omar Salman ` diff --git a/doc/whats_new/upcoming_changes/array-api/32246.feature.rst b/doc/whats_new/upcoming_changes/array-api/32246.feature.rst deleted file mode 100644 index aaf015fd3ff79..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32246.feature.rst +++ /dev/null @@ -1,4 +0,0 @@ -- :class:`calibration.CalibratedClassifierCV` now supports array API compatible - inputs with `method="temperature"` and when the underlying `estimator` also - supports the array API. - By :user:`Omar Salman ` diff --git a/doc/whats_new/upcoming_changes/array-api/32249.feature.rst b/doc/whats_new/upcoming_changes/array-api/32249.feature.rst deleted file mode 100644 index f8102a540328f..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32249.feature.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :func:`sklearn.metrics.precision_recall_curve` now supports array API compatible - inputs. - By :user:`Lucy Liu ` diff --git a/doc/whats_new/upcoming_changes/array-api/32270.feature.rst b/doc/whats_new/upcoming_changes/array-api/32270.feature.rst deleted file mode 100644 index 1b2e4ce05090d..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32270.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`sklearn.model_selection.cross_val_predict` now supports array API compatible inputs. - By :user:`Omar Salman ` diff --git a/doc/whats_new/upcoming_changes/array-api/32422.feature.rst b/doc/whats_new/upcoming_changes/array-api/32422.feature.rst deleted file mode 100644 index fa0cfe503d7f7..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32422.feature.rst +++ /dev/null @@ -1,4 +0,0 @@ -- :func:`sklearn.metrics.brier_score_loss`, :func:`sklearn.metrics.log_loss`, - :func:`sklearn.metrics.d2_brier_score` and :func:`sklearn.metrics.d2_log_loss_score` - now support array API compatible inputs. - By :user:`Omar Salman ` diff --git a/doc/whats_new/upcoming_changes/array-api/32497.feature.rst b/doc/whats_new/upcoming_changes/array-api/32497.feature.rst deleted file mode 100644 index 1b02c72f043af..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32497.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :class:`naive_bayes.GaussianNB` now supports array API compatible inputs. - By :user:`Omar Salman ` diff --git a/doc/whats_new/upcoming_changes/array-api/32582.feature.rst b/doc/whats_new/upcoming_changes/array-api/32582.feature.rst deleted file mode 100644 index b3fefc594483b..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32582.feature.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :class:`preprocessing.LabelBinarizer` and :func:`preprocessing.label_binarize` now - support numeric array API compatible inputs with `sparse_output=False`. - By :user:`Virgil Chan `. diff --git a/doc/whats_new/upcoming_changes/array-api/32586.feature.rst b/doc/whats_new/upcoming_changes/array-api/32586.feature.rst deleted file mode 100644 index 8770a2422140b..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32586.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`sklearn.metrics.det_curve` now supports Array API compliant inputs. - By :user:`Josef Affourtit `. diff --git a/doc/whats_new/upcoming_changes/array-api/32597.feature.rst b/doc/whats_new/upcoming_changes/array-api/32597.feature.rst deleted file mode 100644 index 2d22190b4a052..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32597.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`sklearn.metrics.pairwise.manhattan_distances` now supports array API compatible inputs. - By :user:`Omar Salman `. diff --git a/doc/whats_new/upcoming_changes/array-api/32600.feature.rst b/doc/whats_new/upcoming_changes/array-api/32600.feature.rst deleted file mode 100644 index f39aa06a6cb70..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32600.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`sklearn.metrics.calinski_harabasz_score` now supports Array API compliant inputs. - By :user:`Josef Affourtit `. diff --git a/doc/whats_new/upcoming_changes/array-api/32604.feature.rst b/doc/whats_new/upcoming_changes/array-api/32604.feature.rst deleted file mode 100644 index 752ea5b9cb3b5..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32604.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`sklearn.metrics.balanced_accuracy_score` now supports array API compatible inputs. - By :user:`Omar Salman `. diff --git a/doc/whats_new/upcoming_changes/array-api/32613.feature.rst b/doc/whats_new/upcoming_changes/array-api/32613.feature.rst deleted file mode 100644 index 34c73b653f475..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32613.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`sklearn.metrics.pairwise.laplacian_kernel` now supports array API compatible inputs. - By :user:`Zubair Shakoor `. diff --git a/doc/whats_new/upcoming_changes/array-api/32619.feature.rst b/doc/whats_new/upcoming_changes/array-api/32619.feature.rst deleted file mode 100644 index ba3928cea8bce..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32619.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`sklearn.metrics.cohen_kappa_score` now supports array API compatible inputs. - By :user:`Omar Salman `. diff --git a/doc/whats_new/upcoming_changes/array-api/32693.feature.rst b/doc/whats_new/upcoming_changes/array-api/32693.feature.rst deleted file mode 100644 index 466ae99f4e360..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32693.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`sklearn.metrics.cluster.davies_bouldin_score` now supports Array API compliant inputs. - By :user:`Josef Affourtit `. diff --git a/doc/whats_new/upcoming_changes/array-api/32838.fix.rst b/doc/whats_new/upcoming_changes/array-api/32838.fix.rst deleted file mode 100644 index ae689f8816841..0000000000000 --- a/doc/whats_new/upcoming_changes/array-api/32838.fix.rst +++ /dev/null @@ -1,2 +0,0 @@ -- Estimators with array API support no longer reject dataframe inputs when array API support is enabled. - By :user:`Tim Head ` diff --git a/doc/whats_new/upcoming_changes/custom-top-level/32079.other.rst b/doc/whats_new/upcoming_changes/custom-top-level/32079.other.rst deleted file mode 100644 index 0ac966843c075..0000000000000 --- a/doc/whats_new/upcoming_changes/custom-top-level/32079.other.rst +++ /dev/null @@ -1,23 +0,0 @@ -Free-threaded CPython 3.14 support ----------------------------------- - -scikit-learn has support for free-threaded CPython, in particular -free-threaded wheels are available for all of our supported platforms on Python -3.14. - -Free-threaded (also known as nogil) CPython is a version of CPython that aims at -enabling efficient multi-threaded use cases by removing the Global Interpreter -Lock (GIL). - -If you want to try out free-threaded Python, the recommendation is to use -Python 3.14, that has fixed a number of issues compared to Python 3.13. Feel -free to try free-threaded on your use case and report any issues! - -For more details about free-threaded CPython see `py-free-threading doc `_, -in particular `how to install a free-threaded CPython `_ -and `Ecosystem compatibility tracking `_. - -By :user:`Loïc Estève ` and :user:`Olivier Grisel ` and many -other people in the wider Scientific Python and CPython ecosystem, for example -:user:`Nathan Goldbaum `, :user:`Ralf Gommers `, -:user:`Edgar Andrés Margffoy Tuay `. diff --git a/doc/whats_new/upcoming_changes/many-modules/31775.efficiency.rst b/doc/whats_new/upcoming_changes/many-modules/31775.efficiency.rst deleted file mode 100644 index 5aa067aeeb7cf..0000000000000 --- a/doc/whats_new/upcoming_changes/many-modules/31775.efficiency.rst +++ /dev/null @@ -1,4 +0,0 @@ -- Improved CPU and memory usage in estimators and metric functions that rely on - weighted percentiles and better match NumPy and Scipy (un-weighted) implementations - of percentiles. - By :user:`Lucy Liu ` diff --git a/doc/whats_new/upcoming_changes/metadata-routing/31898.fix.rst b/doc/whats_new/upcoming_changes/metadata-routing/31898.fix.rst deleted file mode 100644 index bb4b71974ca60..0000000000000 --- a/doc/whats_new/upcoming_changes/metadata-routing/31898.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- Fixed an issue where passing `sample_weight` to a :class:`Pipeline` inside a - :class:`GridSearchCV` would raise an error with metadata routing enabled. - By `Adrin Jalali`_. diff --git a/doc/whats_new/upcoming_changes/sklearn.base/31928.feature.rst b/doc/whats_new/upcoming_changes/sklearn.base/31928.feature.rst deleted file mode 100644 index 65b94b580f3de..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.base/31928.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- Refactored :meth:`dir` in :class:`BaseEstimator` to recognize condition check in :meth:`available_if`. - By :user:`John Hendricks ` and :user:`Miguel Parece `. diff --git a/doc/whats_new/upcoming_changes/sklearn.base/32341.fix.rst b/doc/whats_new/upcoming_changes/sklearn.base/32341.fix.rst deleted file mode 100644 index 0c43b4cfac930..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.base/32341.fix.rst +++ /dev/null @@ -1,2 +0,0 @@ -- Fixed the handling of pandas missing values in HTML display of all estimators. - By :user:`Dea María Léon `. diff --git a/doc/whats_new/upcoming_changes/sklearn.calibration/31068.feature.rst b/doc/whats_new/upcoming_changes/sklearn.calibration/31068.feature.rst deleted file mode 100644 index 4201db9ad0e59..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.calibration/31068.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- Added temperature scaling method in :class:`calibration.CalibratedClassifierCV`. - By :user:`Virgil Chan ` and :user:`Christian Lorentzen `. diff --git a/doc/whats_new/upcoming_changes/sklearn.cluster/31973.fix.rst b/doc/whats_new/upcoming_changes/sklearn.cluster/31973.fix.rst deleted file mode 100644 index f04abbb889f7d..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.cluster/31973.fix.rst +++ /dev/null @@ -1,4 +0,0 @@ -- The default value of the `copy` parameter in :class:`cluster.HDBSCAN` - will change from `False` to `True` in 1.10 to avoid data modification - and maintain consistency with other estimators. - By :user:`Sarthak Puri `. \ No newline at end of file diff --git a/doc/whats_new/upcoming_changes/sklearn.cluster/31991.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.cluster/31991.efficiency.rst deleted file mode 100644 index 955b8b9ef4c14..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.cluster/31991.efficiency.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :func:`cluster.kmeans_plusplus` now uses `np.cumsum` directly without extra - numerical stability checks and without casting to `np.float64`. - By :user:`Tiziano Zito ` diff --git a/doc/whats_new/upcoming_changes/sklearn.compose/32188.fix.rst b/doc/whats_new/upcoming_changes/sklearn.compose/32188.fix.rst deleted file mode 100644 index 1bd73934a426c..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.compose/32188.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- The :class:`compose.ColumnTransformer` now correctly fits on data provided as a - `polars.DataFrame` when any transformer has a sparse output. - By :user:`Phillipp Gnan `. diff --git a/doc/whats_new/upcoming_changes/sklearn.covariance/31987.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.covariance/31987.efficiency.rst deleted file mode 100644 index a05849fd84ad8..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.covariance/31987.efficiency.rst +++ /dev/null @@ -1,6 +0,0 @@ -- :class:`sklearn.covariance.GraphicalLasso`, - :class:`sklearn.covariance.GraphicalLassoCV` and - :func:`sklearn.covariance.graphical_lasso` with `mode="cd"` profit from the - fit time performance improvement of :class:`sklearn.linear_model.Lasso` by means of - gap safe screening rules. - By :user:`Christian Lorentzen `. diff --git a/doc/whats_new/upcoming_changes/sklearn.covariance/31987.fix.rst b/doc/whats_new/upcoming_changes/sklearn.covariance/31987.fix.rst deleted file mode 100644 index 1728c7f9ead6e..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.covariance/31987.fix.rst +++ /dev/null @@ -1,6 +0,0 @@ -- Fixed uncontrollable randomness in :class:`sklearn.covariance.GraphicalLasso`, - :class:`sklearn.covariance.GraphicalLassoCV` and - :func:`sklearn.covariance.graphical_lasso`. For `mode="cd"`, they now use cyclic - coordinate descent. Before, it was random coordinate descent with uncontrollable - random number seeding. - By :user:`Christian Lorentzen `. diff --git a/doc/whats_new/upcoming_changes/sklearn.covariance/32117.fix.rst b/doc/whats_new/upcoming_changes/sklearn.covariance/32117.fix.rst deleted file mode 100644 index fb8145e22e5ed..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.covariance/32117.fix.rst +++ /dev/null @@ -1,4 +0,0 @@ -- Added correction to :class:`covariance.MinCovDet` to adjust for - consistency at the normal distribution. This reduces the bias present - when applying this method to data that is normally distributed. - By :user:`Daniel Herrera-Esposito ` diff --git a/doc/whats_new/upcoming_changes/sklearn.decomposition/29310.fix.rst b/doc/whats_new/upcoming_changes/sklearn.decomposition/29310.fix.rst deleted file mode 100644 index a6ff94cdac6ab..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.decomposition/29310.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- Add input checks to the `inverse_transform` method of :class:`decomposition.PCA` - and :class:`decomposition.IncrementalPCA`. - :pr:`29310` by :user:`Ian Faust `. diff --git a/doc/whats_new/upcoming_changes/sklearn.decomposition/31987.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.decomposition/31987.efficiency.rst deleted file mode 100644 index 8edfdfcb74d31..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.decomposition/31987.efficiency.rst +++ /dev/null @@ -1,11 +0,0 @@ -- :class:`sklearn.decomposition.DictionaryLearning` and - :class:`sklearn.decomposition.MiniBatchDictionaryLearning` with `fit_algorithm="cd"`, - :class:`sklearn.decomposition.SparseCoder` with `transform_algorithm="lasso_cd"`, - :class:`sklearn.decomposition.MiniBatchSparsePCA`, - :class:`sklearn.decomposition.SparsePCA`, - :func:`sklearn.decomposition.dict_learning` and - :func:`sklearn.decomposition.dict_learning_online` with `method="cd"`, - :func:`sklearn.decomposition.sparse_encode` with `algorithm="lasso_cd"` - all profit from the fit time performance improvement of - :class:`sklearn.linear_model.Lasso` by means of gap safe screening rules. - By :user:`Christian Lorentzen `. diff --git a/doc/whats_new/upcoming_changes/sklearn.decomposition/32077.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.decomposition/32077.enhancement.rst deleted file mode 100644 index aacff8ae1b76c..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.decomposition/32077.enhancement.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :class:`decomposition.SparseCoder` now follows the transformer API of scikit-learn. - In addition, the :meth:`fit` method now validates the input and parameters. - By :user:`François Paugam `. diff --git a/doc/whats_new/upcoming_changes/sklearn.discriminant_analysis/32108.feature.rst b/doc/whats_new/upcoming_changes/sklearn.discriminant_analysis/32108.feature.rst deleted file mode 100644 index 1379a834c63a4..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.discriminant_analysis/32108.feature.rst +++ /dev/null @@ -1,6 +0,0 @@ -- Added `solver`, `covariance_estimator` and `shrinkage` in - :class:`discriminant_analysis.QuadraticDiscriminantAnalysis`. - The resulting class is more similar to - :class:`discriminant_analysis.LinearDiscriminantAnalysis` - and allows for more flexibility in the estimation of the covariance matrices. - By :user:`Daniel Herrera-Esposito `. diff --git a/doc/whats_new/upcoming_changes/sklearn.ensemble/32825.fix.rst b/doc/whats_new/upcoming_changes/sklearn.ensemble/32825.fix.rst deleted file mode 100644 index 604ec9421a424..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.ensemble/32825.fix.rst +++ /dev/null @@ -1,8 +0,0 @@ -- :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor` and - :class:`ensemble.IsolationForest` now use `sample_weight` to draw the samples - instead of forwarding them multiplied by a uniformly sampled mask to the - underlying estimators. Furthermore, when `max_samples` is a float, it is now - interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]`. - The new default `max_samples=None` draws `X.shape[0]` samples, irrespective - of `sample_weight`. - By :user:`Antoine Baker `. :pr:`31414` and diff --git a/doc/whats_new/upcoming_changes/sklearn.feature_selection/31939.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.feature_selection/31939.enhancement.rst deleted file mode 100644 index 8c038c35389ed..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.feature_selection/31939.enhancement.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :class:`feature_selection.SelectFromModel` now does not force `max_features` to be - less than or equal to the number of input features. - By :user:`Thibault ` diff --git a/doc/whats_new/upcoming_changes/sklearn.gaussian_process/31431.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.gaussian_process/31431.efficiency.rst deleted file mode 100644 index 798f2ebb6bd2f..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.gaussian_process/31431.efficiency.rst +++ /dev/null @@ -1,3 +0,0 @@ -- make :class:`GaussianProcessRegressor.predict` faster when `return_cov` and - `return_std` are both `False`. - By :user:`Rafael Ayllón Gavilán `. diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/29097.api.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/29097.api.rst deleted file mode 100644 index 8cb6265a607a5..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/29097.api.rst +++ /dev/null @@ -1,7 +0,0 @@ -- :class:`linear_model.PassiveAggressiveClassifier` and - :class:`linear_model.PassiveAggressiveRegressor` are deprecated and will be removed - in 1.10. Equivalent estimators are available with :class:`linear_model.SGDClassifier` - and :class:`SGDRegressor`, both of which expose the options `learning_rate="pa1"` and - `"pa2"`. The parameter `eta0` can be used to specify the aggressiveness parameter of - the Passive-Aggressive-Algorithms, called C in the reference paper. - By :user:`Christian Lorentzen ` :pr:`31932` and diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31474.api.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31474.api.rst deleted file mode 100644 index 845b9b502b9f1..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31474.api.rst +++ /dev/null @@ -1,6 +0,0 @@ -- :class:`linear_model.SGDClassifier`, :class:`linear_model.SGDRegressor`, and - :class:`linear_model.SGDOneClassSVM` now deprecate negative values for the - `power_t` parameter. Using a negative value will raise a warning in version 1.8 - and will raise an error in version 1.10. A value in the range [0.0, inf) must be used - instead. - By :user:`Ritvi Alagusankar ` \ No newline at end of file diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31665.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31665.efficiency.rst deleted file mode 100644 index 24a8d53f80b23..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31665.efficiency.rst +++ /dev/null @@ -1,4 +0,0 @@ -- :class:`linear_model.ElasticNet` and :class:`linear_model.Lasso` with - `precompute=False` use less memory for dense `X` and are a bit faster. - Previously, they used twice the memory of `X` even for Fortran-contiguous `X`. - By :user:`Christian Lorentzen ` diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31848.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31848.efficiency.rst deleted file mode 100644 index b76b7cacc8328..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31848.efficiency.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :class:`linear_model.ElasticNet` and :class:`linear_model.Lasso` avoid - double input checking and are therefore a bit faster. - By :user:`Christian Lorentzen `. diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31856.fix.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31856.fix.rst deleted file mode 100644 index 8d9138d2b449a..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31856.fix.rst +++ /dev/null @@ -1,6 +0,0 @@ -- Fix the convergence criteria for SGD models, to avoid premature convergence when - `tol != None`. This primarily impacts :class:`SGDOneClassSVM` but also affects - :class:`SGDClassifier` and :class:`SGDRegressor`. Before this fix, only the loss - function without penalty was used as the convergence check, whereas now, the full - objective with regularization is used. - By :user:`Guillaume Lemaitre ` and :user:`kostayScr ` diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31880.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31880.efficiency.rst deleted file mode 100644 index 195eb42d907eb..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31880.efficiency.rst +++ /dev/null @@ -1,9 +0,0 @@ -- :class:`linear_model.ElasticNet`, :class:`linear_model.ElasticNetCV`, - :class:`linear_model.Lasso`, :class:`linear_model.LassoCV`, - :class:`linear_model.MultiTaskElasticNet`, - :class:`linear_model.MultiTaskElasticNetCV`, - :class:`linear_model.MultiTaskLasso` and :class:`linear_model.MultiTaskLassoCV` - are faster to fit by avoiding a BLAS level 1 (axpy) call in the innermost loop. - Same for functions :func:`linear_model.enet_path` and - :func:`linear_model.lasso_path`. - By :user:`Christian Lorentzen ` :pr:`31956` and diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31888.api.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31888.api.rst deleted file mode 100644 index a1ac21999bb09..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31888.api.rst +++ /dev/null @@ -1,4 +0,0 @@ -- Raising error in :class:`sklearn.linear_model.LogisticRegression` when - liblinear solver is used and input X values are larger than 1e30, - the liblinear solver freezes otherwise. - By :user:`Shruti Nath `. diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31906.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31906.enhancement.rst deleted file mode 100644 index 8417c3dd2ac29..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31906.enhancement.rst +++ /dev/null @@ -1,9 +0,0 @@ -- :class:`linear_model.ElasticNet`, :class:`linear_model.ElasticNetCV`, - :class:`linear_model.Lasso`, :class:`linear_model.LassoCV`, - :class:`MultiTaskElasticNet`, :class:`MultiTaskElasticNetCV`, - :class:`MultiTaskLasso`, :class:`MultiTaskLassoCV`, as well as - :func:`linear_model.enet_path` and :func:`linear_model.lasso_path` - now use `dual gap <= tol` instead of `dual gap < tol` as stopping criterion. - The resulting coefficients might differ to previous versions of scikit-learn in - rare cases. - By :user:`Christian Lorentzen `. diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31933.fix.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31933.fix.rst deleted file mode 100644 index b4995b3908c35..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31933.fix.rst +++ /dev/null @@ -1,8 +0,0 @@ -- The allowed parameter range for the initial learning rate `eta0` in - :class:`linear_model.SGDClassifier`, :class:`linear_model.SGDOneClassSVM`, - :class:`linear_model.SGDRegressor` and :class:`linear_model.Perceptron` - changed from non-negative numbers to strictly positive numbers. - As a consequence, the default `eta0` of :class:`linear_model.SGDClassifier` - and :class:`linear_model.SGDOneClassSVM` changed from 0 to 0.01. But note that - `eta0` is not used by the default learning rate "optimal" of those two estimators. - By :user:`Christian Lorentzen `. diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31946.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31946.efficiency.rst deleted file mode 100644 index 0a4fc0bccf2a6..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31946.efficiency.rst +++ /dev/null @@ -1,4 +0,0 @@ -- :class:`linear_model.ElasticNetCV`, :class:`linear_model.LassoCV`, - :class:`linear_model.MultiTaskElasticNetCV` and :class:`linear_model.MultiTaskLassoCV` - avoid an additional copy of `X` with default `copy_X=True`. - By :user:`Christian Lorentzen `. diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/32014.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/32014.efficiency.rst deleted file mode 100644 index 6bb68b2c68c12..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/32014.efficiency.rst +++ /dev/null @@ -1,14 +0,0 @@ -- :class:`linear_model.ElasticNet`, :class:`linear_model.ElasticNetCV`, - :class:`linear_model.Lasso`, :class:`linear_model.LassoCV`, - :class:`linear_model.MultiTaskElasticNet`, :class:`linear_model.MultiTaskElasticNetCV` - :class:`linear_model.MultiTaskLasso`, :class:`linear_model.MultiTaskLassoCV` - as well as - :func:`linear_model.lasso_path` and :func:`linear_model.enet_path` now implement - gap safe screening rules in the coordinate descent solver for dense and sparse `X`. - The speedup of fitting time is particularly pronounced (10-times is possible) when - computing regularization paths like the \*CV-variants of the above estimators do. - There is now an additional check of the stopping criterion before entering the main - loop of descent steps. As the stopping criterion requires the computation of the dual - gap, the screening happens whenever the dual gap is computed. - By :user:`Christian Lorentzen ` :pr:`31882`, :pr:`31986`, - :pr:`31987` and diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/32114.api.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/32114.api.rst deleted file mode 100644 index 7b6768464cf81..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/32114.api.rst +++ /dev/null @@ -1,16 +0,0 @@ -- :class:`linear_model.LogisticRegressionCV` got a new parameter - `use_legacy_attributes` to control the types and shapes of the fitted attributes - `C_`, `l1_ratio_`, `coefs_paths_`, `scores_` and `n_iter_`. - The current default value `True` keeps the legacy behaviour. If `False` then: - - - ``C_`` is a float. - - ``l1_ratio_`` is a float. - - ``coefs_paths_`` is an ndarray of shape - (n_folds, n_l1_ratios, n_cs, n_classes, n_features). - For binary problems (n_classes=2), the 2nd last dimension is 1. - - ``scores_`` is an ndarray of shape (n_folds, n_l1_ratios, n_cs). - - ``n_iter_`` is an ndarray of shape (n_folds, n_l1_ratios, n_cs). - - In version 1.10, the default will change to `False` and `use_legacy_attributes` will - be deprecated. In 1.12 `use_legacy_attributes` will be removed. - By :user:`Christian Lorentzen `. diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/32659.api.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/32659.api.rst deleted file mode 100644 index 00b3cd23a7de3..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/32659.api.rst +++ /dev/null @@ -1,27 +0,0 @@ -- Parameter `penalty` of :class:`linear_model.LogisticRegression` and - :class:`linear_model.LogisticRegressionCV` is deprecated and will be removed in - version 1.10. The equivalent behaviour can be obtained as follows: - - - for :class:`linear_model.LogisticRegression` - - - use `l1_ratio=0` instead of `penalty="l2"` - - use `l1_ratio=1` instead of `penalty="l1"` - - use `0`. diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/32742.api.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/32742.api.rst deleted file mode 100644 index 0fd15ccf7371e..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/32742.api.rst +++ /dev/null @@ -1,3 +0,0 @@ -- The `n_jobs` parameter of :class:`linear_model.LogisticRegression` is deprecated and - will be removed in 1.10. It has no effect since 1.8. - By :user:`Loïc Estève `. diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/32747.fix.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/32747.fix.rst deleted file mode 100644 index 1f83d78aa24de..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/32747.fix.rst +++ /dev/null @@ -1,4 +0,0 @@ -- :class:`linear_model.LogisticRegressionCV` is able to handle CV splits where - some class labels are missing in some folds. Before, it raised an error whenever a - class label were missing in a fold. - By :user:`Christian Lorentzen `. diff --git a/doc/whats_new/upcoming_changes/sklearn.manifold/31322.major-feature.rst b/doc/whats_new/upcoming_changes/sklearn.manifold/31322.major-feature.rst deleted file mode 100644 index 0d1610d69747f..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.manifold/31322.major-feature.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :class:`manifold.ClassicalMDS` was implemented to perform classical MDS - (eigendecomposition of the double-centered distance matrix). - By :user:`Dmitry Kobak ` and :user:`Meekail Zain ` diff --git a/doc/whats_new/upcoming_changes/sklearn.manifold/32229.feature.rst b/doc/whats_new/upcoming_changes/sklearn.manifold/32229.feature.rst deleted file mode 100644 index b1af155f5a1c3..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.manifold/32229.feature.rst +++ /dev/null @@ -1,6 +0,0 @@ -- :class:`manifold.MDS` now supports arbitrary distance metrics - (via `metric` and `metric_params` parameters) and - initialization via classical MDS (via `init` parameter). - The `dissimilarity` parameter was deprecated. The old `metric` parameter - was renamed into `metric_mds`. - By :user:`Dmitry Kobak ` diff --git a/doc/whats_new/upcoming_changes/sklearn.manifold/32433.feature.rst b/doc/whats_new/upcoming_changes/sklearn.manifold/32433.feature.rst deleted file mode 100644 index 6a65dd1ad56d9..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.manifold/32433.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :class:`manifold.TSNE` now supports PCA initialization with sparse input matrices. - By :user:`Arturo Amor `. diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/28971.feature.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/28971.feature.rst deleted file mode 100644 index 9a2379bc31114..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/28971.feature.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`metrics.d2_brier_score` has been added which calculates the D^2 for the Brier score. - By :user:`Omar Salman `. diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/30134.feature.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/30134.feature.rst deleted file mode 100644 index 09f0c99501395..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/30134.feature.rst +++ /dev/null @@ -1,3 +0,0 @@ -- Add :func:`metrics.confusion_matrix_at_thresholds` function that returns the number of - true negatives, false positives, false negatives and true positives per threshold. - By :user:`Success Moses `. diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/30787.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/30787.fix.rst deleted file mode 100644 index 13edbdfc7874d..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/30787.fix.rst +++ /dev/null @@ -1,6 +0,0 @@ -- :func:`metrics.median_absolute_error` now uses `_averaged_weighted_percentile` - instead of `_weighted_percentile` to calculate median when `sample_weight` is not - `None`. This is equivalent to using the "averaged_inverted_cdf" instead of - the "inverted_cdf" quantile method, which gives results equivalent to `numpy.median` - if equal weights used. - By :user:`Lucy Liu ` diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/31294.api.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/31294.api.rst deleted file mode 100644 index d5afd1d46e6e0..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/31294.api.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`metrics.cluster.entropy` is deprecated and will be removed in v1.10. - By :user:`Lucy Liu ` diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/31406.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/31406.enhancement.rst deleted file mode 100644 index 4736c67c80132..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/31406.enhancement.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :func:`metrics.median_absolute_error` now supports Array API compatible inputs. - By :user:`Lucy Liu `. diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/31701.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/31701.fix.rst deleted file mode 100644 index 646cdb544f496..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/31701.fix.rst +++ /dev/null @@ -1,21 +0,0 @@ -- Additional `sample_weight` checking has been added to - :func:`metrics.accuracy_score`, - :func:`metrics.balanced_accuracy_score`, - :func:`metrics.brier_score_loss`, - :func:`metrics.class_likelihood_ratios`, - :func:`metrics.classification_report`, - :func:`metrics.cohen_kappa_score`, - :func:`metrics.confusion_matrix`, - :func:`metrics.f1_score`, - :func:`metrics.fbeta_score`, - :func:`metrics.hamming_loss`, - :func:`metrics.jaccard_score`, - :func:`metrics.matthews_corrcoef`, - :func:`metrics.multilabel_confusion_matrix`, - :func:`metrics.precision_recall_fscore_support`, - :func:`metrics.precision_score`, - :func:`metrics.recall_score` and - :func:`metrics.zero_one_loss`. - `sample_weight` can only be 1D, consistent to `y_true` and `y_pred` in length,and - all values must be finite and not complex. - By :user:`Lucy Liu `. diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/31764.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/31764.fix.rst deleted file mode 100644 index 8dab2fc772563..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/31764.fix.rst +++ /dev/null @@ -1,5 +0,0 @@ -- `y_pred` is deprecated in favour of `y_score` in - :func:`metrics.DetCurveDisplay.from_predictions` and - :func:`metrics.PrecisionRecallDisplay.from_predictions`. `y_pred` will be removed in - v1.10. - By :user:`Luis ` diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/31891.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/31891.fix.rst deleted file mode 100644 index f1f280859a1e5..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/31891.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- `repr` on a scorer which has been created with a `partial` `score_func` now correctly - works and uses the `repr` of the given `partial` object. - By `Adrin Jalali`_. diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/32047.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/32047.enhancement.rst deleted file mode 100644 index 7fcad9a062ce7..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/32047.enhancement.rst +++ /dev/null @@ -1,9 +0,0 @@ -- Improved the error message for sparse inputs for the following metrics: - :func:`metrics.accuracy_score`, - :func:`metrics.multilabel_confusion_matrix`, :func:`metrics.jaccard_score`, - :func:`metrics.zero_one_loss`, :func:`metrics.f1_score`, - :func:`metrics.fbeta_score`, :func:`metrics.precision_recall_fscore_support`, - :func:`metrics.class_likelihood_ratios`, :func:`metrics.precision_score`, - :func:`metrics.recall_score`, :func:`metrics.classification_report`, - :func:`metrics.hamming_loss`. - By :user:`Lucy Liu `. diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/32310.api.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/32310.api.rst deleted file mode 100644 index ae7fc385b3bcc..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/32310.api.rst +++ /dev/null @@ -1,3 +0,0 @@ -- The `estimator_name` parameter is deprecated in favour of `name` in - :class:`metrics.PrecisionRecallDisplay` and will be removed in 1.10. - By :user:`Lucy Liu `. diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/32313.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/32313.fix.rst deleted file mode 100644 index b8f0fc21660da..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/32313.fix.rst +++ /dev/null @@ -1,5 +0,0 @@ -- kwargs specified in the `curve_kwargs` parameter of - :meth:`metrics.RocCurveDisplay.from_cv_results` now only overwrite their corresponding - default value before being passed to Matplotlib's `plot`. Previously, passing any - `curve_kwargs` would overwrite all default kwargs. - By :user:`Lucy Liu `. diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/32356.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/32356.efficiency.rst deleted file mode 100644 index 03b3e41f67911..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/32356.efficiency.rst +++ /dev/null @@ -1,3 +0,0 @@ -- Avoid redundant input validation in :func:`metrics.d2_log_loss_score` - leading to a 1.2x speedup in large scale benchmarks. - By :user:`Olivier Grisel ` and :user:`Omar Salman ` diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/32356.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/32356.fix.rst deleted file mode 100644 index ac611096234b6..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/32356.fix.rst +++ /dev/null @@ -1,4 +0,0 @@ -- Registered named scorer objects for :func:`metrics.d2_brier_score` and - :func:`metrics.d2_log_loss_score` and updated their input validation to be - consistent with related metric functions. - By :user:`Olivier Grisel ` and :user:`Omar Salman ` diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/32372.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/32372.fix.rst deleted file mode 100644 index 5fa8d2204b312..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/32372.fix.rst +++ /dev/null @@ -1,4 +0,0 @@ -- :meth:`metrics.RocCurveDisplay.from_cv_results` will now infer `pos_label` as - `estimator.classes_[-1]`, using the estimator from `cv_results`, when - `pos_label=None`. Previously, an error was raised when `pos_label=None`. - By :user:`Lucy Liu `. diff --git a/doc/whats_new/upcoming_changes/sklearn.metrics/32549.fix.rst b/doc/whats_new/upcoming_changes/sklearn.metrics/32549.fix.rst deleted file mode 100644 index 070e3d1e7fefe..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.metrics/32549.fix.rst +++ /dev/null @@ -1,7 +0,0 @@ -- All classification metrics now raise a `ValueError` when required input arrays - (`y_pred`, `y_true`, `y1`, `y2`, `pred_decision`, or `y_proba`) are empty. - Previously, `accuracy_score`, `class_likelihood_ratios`, `classification_report`, - `confusion_matrix`, `hamming_loss`, `jaccard_score`, `matthews_corrcoef`, - `multilabel_confusion_matrix`, and `precision_recall_fscore_support` did not raise - this error consistently. - By :user:`Stefanie Senger `. diff --git a/doc/whats_new/upcoming_changes/sklearn.model_selection/32265.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.model_selection/32265.enhancement.rst deleted file mode 100644 index b9c87bfec19d9..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.model_selection/32265.enhancement.rst +++ /dev/null @@ -1,4 +0,0 @@ -- :class:`model_selection.StratifiedShuffleSplit` will now specify which classes - have too few members when raising a ``ValueError`` if any class has less than 2 members. - This is useful to identify which classes are causing the error. - By :user:`Marc Bresson ` diff --git a/doc/whats_new/upcoming_changes/sklearn.model_selection/32540.fix.rst b/doc/whats_new/upcoming_changes/sklearn.model_selection/32540.fix.rst deleted file mode 100644 index ec15ecccee161..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.model_selection/32540.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- Fix shuffle behaviour in :class:`model_selection.StratifiedGroupKFold`. Now - stratification among folds is also preserved when `shuffle=True`. - By :user:`Pau Folch `. diff --git a/doc/whats_new/upcoming_changes/sklearn.multiclass/15504.fix.rst b/doc/whats_new/upcoming_changes/sklearn.multiclass/15504.fix.rst deleted file mode 100644 index 177a7309ae3f3..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.multiclass/15504.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- Fix tie-breaking behavior in :class:`multiclass.OneVsRestClassifier` to match - `np.argmax` tie-breaking behavior. - By :user:`Lakshmi Krishnan `. diff --git a/doc/whats_new/upcoming_changes/sklearn.naive_bayes/32497.fix.rst b/doc/whats_new/upcoming_changes/sklearn.naive_bayes/32497.fix.rst deleted file mode 100644 index 855dd8c238f4a..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.naive_bayes/32497.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :class:`naive_bayes.GaussianNB` preserves the dtype of the fitted attributes - according to the dtype of `X`. - By :user:`Omar Salman ` diff --git a/doc/whats_new/upcoming_changes/sklearn.preprocessing/28043.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.preprocessing/28043.enhancement.rst deleted file mode 100644 index 8195352292539..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.preprocessing/28043.enhancement.rst +++ /dev/null @@ -1,2 +0,0 @@ -- :class:`preprocessing.SplineTransformer` can now handle missing values with the - parameter `handle_missing`. By :user:`Stefanie Senger `. diff --git a/doc/whats_new/upcoming_changes/sklearn.preprocessing/29307.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.preprocessing/29307.enhancement.rst deleted file mode 100644 index aa9b02400a0c0..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.preprocessing/29307.enhancement.rst +++ /dev/null @@ -1,4 +0,0 @@ -- The :class:`preprocessing.PowerTransformer` now returns a warning - when NaN values are encountered in the inverse transform, `inverse_transform`, typically - caused by extremely skewed data. - By :user:`Roberto Mourao ` \ No newline at end of file diff --git a/doc/whats_new/upcoming_changes/sklearn.preprocessing/31790.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.preprocessing/31790.enhancement.rst deleted file mode 100644 index caabc96b626fd..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.preprocessing/31790.enhancement.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :class:`preprocessing.MaxAbsScaler` can now clip out-of-range values in held-out data - with the parameter `clip`. - By :user:`Hleb Levitski `. diff --git a/doc/whats_new/upcoming_changes/sklearn.preprocessing/32592.fix.rst b/doc/whats_new/upcoming_changes/sklearn.preprocessing/32592.fix.rst deleted file mode 100644 index f22417a3566fb..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.preprocessing/32592.fix.rst +++ /dev/null @@ -1,2 +0,0 @@ -- Fixed a bug in :class:`preprocessing.OneHotEncoder` where `handle_unknown='warn'` incorrectly behaved like `'ignore'` instead of `'infrequent_if_exist'`. - By :user:`Nithurshen ` diff --git a/doc/whats_new/upcoming_changes/sklearn.semi_supervised/31924.fix.rst b/doc/whats_new/upcoming_changes/sklearn.semi_supervised/31924.fix.rst deleted file mode 100644 index fe21593d99680..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.semi_supervised/31924.fix.rst +++ /dev/null @@ -1,4 +0,0 @@ -- User written kernel results are now normalized in - :class:`semi_supervised.LabelPropagation` - so all row sums equal 1 even if kernel gives asymmetric or non-uniform row sums. - By :user:`Dan Schult `. diff --git a/doc/whats_new/upcoming_changes/sklearn.tree/30041.fix.rst b/doc/whats_new/upcoming_changes/sklearn.tree/30041.fix.rst deleted file mode 100644 index 98c90e31f36eb..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.tree/30041.fix.rst +++ /dev/null @@ -1,2 +0,0 @@ -- Make :func:`tree.export_text` thread-safe. - By :user:`Olivier Grisel `. diff --git a/doc/whats_new/upcoming_changes/sklearn.tree/31036.fix.rst b/doc/whats_new/upcoming_changes/sklearn.tree/31036.fix.rst deleted file mode 100644 index 32e26e180595d..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.tree/31036.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :func:`~sklearn.tree.export_graphviz` now raises a `ValueError` if given feature - names are not all strings. - By :user:`Guilherme Peixoto ` diff --git a/doc/whats_new/upcoming_changes/sklearn.tree/32100.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.tree/32100.efficiency.rst deleted file mode 100644 index 0df37311f22ce..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.tree/32100.efficiency.rst +++ /dev/null @@ -1,4 +0,0 @@ -- :class:`tree.DecisionTreeRegressor` with `criterion="absolute_error"` - now runs much faster: O(n log n) complexity against previous O(n^2) - allowing to scale to millions of data points, even hundred of millions. - By :user:`Arthur Lacote ` diff --git a/doc/whats_new/upcoming_changes/sklearn.tree/32100.fix.rst b/doc/whats_new/upcoming_changes/sklearn.tree/32100.fix.rst deleted file mode 100644 index 7d337131c25e6..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.tree/32100.fix.rst +++ /dev/null @@ -1,6 +0,0 @@ -- :class:`tree.DecisionTreeRegressor` with `criterion="absolute_error"` - would sometimes make sub-optimal splits - (i.e. splits that don't minimize the absolute error). - Now it's fixed. Hence retraining trees might gives slightly different - results. - By :user:`Arthur Lacote ` diff --git a/doc/whats_new/upcoming_changes/sklearn.tree/32259.fix.rst b/doc/whats_new/upcoming_changes/sklearn.tree/32259.fix.rst deleted file mode 100644 index f25f0f2eec483..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.tree/32259.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- Fixed a regression in :ref:`decision trees ` where almost constant features were - not handled properly. - By :user:`Sercan Turkmen `. diff --git a/doc/whats_new/upcoming_changes/sklearn.tree/32274.fix.rst b/doc/whats_new/upcoming_changes/sklearn.tree/32274.fix.rst deleted file mode 100644 index 84c1123cf26c8..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.tree/32274.fix.rst +++ /dev/null @@ -1,6 +0,0 @@ -- Fixed splitting logic during training in :class:`tree.DecisionTree*` - (and consequently in :class:`ensemble.RandomForest*`) - for nodes containing near-constant feature values and missing values. - Beforehand, trees were cut short if a constant feature was found, - even if there was more splitting that could be done on the basis of missing values. - By :user:`Arthur Lacote ` diff --git a/doc/whats_new/upcoming_changes/sklearn.tree/32280.fix.rst b/doc/whats_new/upcoming_changes/sklearn.tree/32280.fix.rst deleted file mode 100644 index 5ff0a9b453e77..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.tree/32280.fix.rst +++ /dev/null @@ -1,4 +0,0 @@ -- Fix handling of missing values in method :func:`decision_path` of trees - (:class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, - :class:`tree.ExtraTreeClassifier` and :class:`tree.ExtraTreeRegressor`) - By :user:`Arthur Lacote `. diff --git a/doc/whats_new/upcoming_changes/sklearn.tree/32351.fix.rst b/doc/whats_new/upcoming_changes/sklearn.tree/32351.fix.rst deleted file mode 100644 index 0c422d7a9e14c..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.tree/32351.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- Fix decision tree splitting with missing values present in some features. In some cases the last - non-missing sample would not be partitioned correctly. - By :user:`Tim Head ` and :user:`Arthur Lacote `. diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/31564.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.utils/31564.enhancement.rst deleted file mode 100644 index 6b9ef89fdd01f..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.utils/31564.enhancement.rst +++ /dev/null @@ -1,5 +0,0 @@ -- The parameter table in the HTML representation of all scikit-learn estimators and - more generally of estimators inheriting from :class:`base.BaseEstimator` - now displays the parameter description as a tooltip and has a link to the online - documentation for each parameter. - By :user:`Dea María Léon `. diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/31873.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.utils/31873.enhancement.rst deleted file mode 100644 index 6e82ce3713f5a..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.utils/31873.enhancement.rst +++ /dev/null @@ -1,4 +0,0 @@ -- ``sklearn.utils._check_sample_weight`` now raises a clearer error message when the - provided weights are neither a scalar nor a 1-D array-like of the same size as the - input data. - By :user:`Kapil Parekh `. diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/31951.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.utils/31951.enhancement.rst deleted file mode 100644 index 556c406bff7b8..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.utils/31951.enhancement.rst +++ /dev/null @@ -1,4 +0,0 @@ -- :func:`sklearn.utils.estimator_checks.parametrize_with_checks` now lets you configure - strict mode for xfailing checks. Tests that unexpectedly pass will lead to a test - failure. The default behaviour is unchanged. - By :user:`Tim Head `. diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/31952.efficiency.rst b/doc/whats_new/upcoming_changes/sklearn.utils/31952.efficiency.rst deleted file mode 100644 index f334bfd81c8dd..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.utils/31952.efficiency.rst +++ /dev/null @@ -1,5 +0,0 @@ -- The function :func:`sklearn.utils.extmath.safe_sparse_dot` was improved by a dedicated - Cython routine for the case of `a @ b` with sparse 2-dimensional `a` and `b` and when - a dense output is required, i.e., `dense_output=True`. This improves several - algorithms in scikit-learn when dealing with sparse arrays (or matrices). - By :user:`Christian Lorentzen `. diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/31969.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.utils/31969.enhancement.rst deleted file mode 100644 index 079b9c589bc91..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.utils/31969.enhancement.rst +++ /dev/null @@ -1,3 +0,0 @@ -- Fixed the alignment of the "?" and "i" symbols and improved the color style of the - HTML representation of estimators. - By :user:`Guillaume Lemaitre `. diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/32258.api.rst b/doc/whats_new/upcoming_changes/sklearn.utils/32258.api.rst deleted file mode 100644 index a8ab5744ddf87..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.utils/32258.api.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :func:`utils.extmath.stable_cumsum` is deprecated and will be removed - in v1.10. Use `np.cumulative_sum` with the desired dtype directly instead. - By :user:`Tiziano Zito `. diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/32330.fix.rst b/doc/whats_new/upcoming_changes/sklearn.utils/32330.fix.rst deleted file mode 100644 index c2243ad2f7c3b..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.utils/32330.fix.rst +++ /dev/null @@ -1,2 +0,0 @@ -- Changes the way color are chosen when displaying an estimator as an HTML representation. Colors are not adapted anymore to the user's theme, but chosen based on theme declared color scheme (light or dark) for VSCode and JupyterLab. If theme does not declare a color scheme, scheme is chosen according to default text color of the page, if it fails fallbacks to a media query. - By :user:`Matt J. `. diff --git a/doc/whats_new/v1.8.rst b/doc/whats_new/v1.8.rst index 017c9e981cd74..fa39c6f1fed43 100644 --- a/doc/whats_new/v1.8.rst +++ b/doc/whats_new/v1.8.rst @@ -31,7 +31,7 @@ Version 1.8 Version 1.8.0 ============= -**November 2025** +**December 2025** Changes impacting many modules ------------------------------ @@ -98,6 +98,10 @@ See :ref:`array_api` for more details. - |Feature| :class:`naive_bayes.GaussianNB` now supports array API compatible inputs. By :user:`Omar Salman ` :pr:`32497` +- |Feature| :class:`preprocessing.LabelBinarizer` and :func:`preprocessing.label_binarize` now + support numeric array API compatible inputs with `sparse_output=False`. + By :user:`Virgil Chan `. :pr:`32582` + - |Feature| :func:`sklearn.metrics.det_curve` now supports Array API compliant inputs. By :user:`Josef Affourtit `. :pr:`32586` @@ -116,6 +120,12 @@ See :ref:`array_api` for more details. - |Feature| :func:`sklearn.metrics.cohen_kappa_score` now supports array API compatible inputs. By :user:`Omar Salman `. :pr:`32619` +- |Feature| :func:`sklearn.metrics.cluster.davies_bouldin_score` now supports Array API compliant inputs. + By :user:`Josef Affourtit `. :pr:`32693` + +- |Fix| Estimators with array API support no longer reject dataframe inputs when array API support is enabled. + By :user:`Tim Head ` :pr:`32838` + Metadata routing ---------------- @@ -148,7 +158,7 @@ and `Ecosystem compatibility tracking ` and :user:`Olivier Grisel ` and many other people in the wider Scientific Python and CPython ecosystem, for example :user:`Nathan Goldbaum `, :user:`Ralf Gommers `, -:user:`Edgar Andrés Margffoy Tuay `. :pr:`custom-top-level-32079` +:user:`Edgar Andrés Margffoy Tuay `. :pr:`32079` :mod:`sklearn.base` ------------------- @@ -157,7 +167,7 @@ other people in the wider Scientific Python and CPython ecosystem, for example By :user:`John Hendricks ` and :user:`Miguel Parece `. :pr:`31928` - |Fix| Fixed the handling of pandas missing values in HTML display of all estimators. - By :user: `Dea María Léon `. :pr:`32341` + By :user:`Dea María Léon `. :pr:`32341` :mod:`sklearn.calibration` -------------------------- @@ -172,7 +182,7 @@ other people in the wider Scientific Python and CPython ecosystem, for example numerical stability checks and without casting to `np.float64`. By :user:`Tiziano Zito ` :pr:`31991` -- |Fix| The default value of the `copy` parameter in :class:`cluster.HDBSCAN` +- |Fix| The default value of the `copy` parameter in :class:`cluster.HDBSCAN` will change from `False` to `True` in 1.10 to avoid data modification and maintain consistency with other estimators. By :user:`Sarthak Puri `. :pr:`31973` @@ -242,13 +252,14 @@ other people in the wider Scientific Python and CPython ecosystem, for example :mod:`sklearn.ensemble` ----------------------- -- |Fix| :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor` - and :class:`ensemble.IsolationForest` now use `sample_weight` to draw - the samples instead of forwarding them multiplied by a uniformly sampled - mask to the underlying estimators. Furthermore, `max_samples` is now - interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]` - when passed as a float. - By :user:`Antoine Baker `. :pr:`31414` +- |Fix| :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor` and + :class:`ensemble.IsolationForest` now use `sample_weight` to draw the samples + instead of forwarding them multiplied by a uniformly sampled mask to the + underlying estimators. Furthermore, when `max_samples` is a float, it is now + interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]`. + The new default `max_samples=None` draws `X.shape[0]` samples, irrespective + of `sample_weight`. + By :user:`Antoine Baker `. :pr:`31414` and :pr:`32825` :mod:`sklearn.feature_selection` -------------------------------- @@ -293,7 +304,8 @@ other people in the wider Scientific Python and CPython ecosystem, for example - |Efficiency| :class:`linear_model.ElasticNet`, :class:`linear_model.ElasticNetCV`, :class:`linear_model.Lasso`, :class:`linear_model.LassoCV`, - :class:`linear_model.MultiTaskElasticNetCV`, :class:`linear_model.MultiTaskLassoCV` + :class:`linear_model.MultiTaskElasticNet`, :class:`linear_model.MultiTaskElasticNetCV` + :class:`linear_model.MultiTaskLasso`, :class:`linear_model.MultiTaskLassoCV` as well as :func:`linear_model.lasso_path` and :func:`linear_model.enet_path` now implement gap safe screening rules in the coordinate descent solver for dense and sparse `X`. @@ -316,7 +328,7 @@ other people in the wider Scientific Python and CPython ecosystem, for example By :user:`Christian Lorentzen `. :pr:`31906` - |Fix| Fix the convergence criteria for SGD models, to avoid premature convergence when - `tol != None`. This primarily impacts :class:`SGDOneClassSVM` but also affects + `tol != None`. This primarily impacts :class:`SGDOneClassSVM` but also affects :class:`SGDClassifier` and :class:`SGDRegressor`. Before this fix, only the loss function without penalty was used as the convergence check, whereas now, the full objective with regularization is used. @@ -334,7 +346,7 @@ other people in the wider Scientific Python and CPython ecosystem, for example - |Fix| :class:`linear_model.LogisticRegressionCV` is able to handle CV splits where some class labels are missing in some folds. Before, it raised an error whenever a class label were missing in a fold. - By :user:`Christian Lorentzen :pr:`32747` + By :user:`Christian Lorentzen `. :pr:`32747` - |API| :class:`linear_model.PassiveAggressiveClassifier` and :class:`linear_model.PassiveAggressiveRegressor` are deprecated and will be removed @@ -373,6 +385,34 @@ other people in the wider Scientific Python and CPython ecosystem, for example be deprecated. In 1.12 `use_legacy_attributes` will be removed. By :user:`Christian Lorentzen `. :pr:`32114` +- |API| Parameter `penalty` of :class:`linear_model.LogisticRegression` and + :class:`linear_model.LogisticRegressionCV` is deprecated and will be removed in + version 1.10. The equivalent behaviour can be obtained as follows: + + - for :class:`linear_model.LogisticRegression` + + - use `l1_ratio=0` instead of `penalty="l2"` + - use `l1_ratio=1` instead of `penalty="l1"` + - use `0`. :pr:`32659` + - |API| The `n_jobs` parameter of :class:`linear_model.LogisticRegression` is deprecated and will be removed in 1.10. It has no effect since 1.8. By :user:`Loïc Estève `. :pr:`32742` @@ -523,8 +563,8 @@ other people in the wider Scientific Python and CPython ecosystem, for example - |Enhancement| :class:`preprocessing.SplineTransformer` can now handle missing values with the parameter `handle_missing`. By :user:`Stefanie Senger `. :pr:`28043` -- |Enhancement| The :class:`preprocessing.PowerTransformer` now returns a warning - when NaN values are encountered in the inverse transform, `inverse_transform`, typically +- |Enhancement| The :class:`preprocessing.PowerTransformer` now returns a warning + when NaN values are encountered in the inverse transform, `inverse_transform`, typically caused by extremely skewed data. By :user:`Roberto Mourao ` :pr:`29307` @@ -532,6 +572,9 @@ other people in the wider Scientific Python and CPython ecosystem, for example with the parameter `clip`. By :user:`Hleb Levitski `. :pr:`31790` +- |Fix| Fixed a bug in :class:`preprocessing.OneHotEncoder` where `handle_unknown='warn'` incorrectly behaved like `'ignore'` instead of `'infrequent_if_exist'`. + By :user:`Nithurshen ` :pr:`32592` + :mod:`sklearn.semi_supervised` ------------------------------ @@ -566,6 +609,13 @@ other people in the wider Scientific Python and CPython ecosystem, for example not handled properly. By :user:`Sercan Turkmen `. :pr:`32259` +- |Fix| Fixed splitting logic during training in :class:`tree.DecisionTree*` + (and consequently in :class:`ensemble.RandomForest*`) + for nodes containing near-constant feature values and missing values. + Beforehand, trees were cut short if a constant feature was found, + even if there was more splitting that could be done on the basis of missing values. + By :user:`Arthur Lacote ` :pr:`32274` + - |Fix| Fix handling of missing values in method :func:`decision_path` of trees (:class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier` and :class:`tree.ExtraTreeRegressor`) @@ -616,4 +666,40 @@ other people in the wider Scientific Python and CPython ecosystem, for example Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.7, including: -TODO: update at the time of the release. +$id, 4hm3d, Acciaro Gennaro Daniele, achyuthan.s, Adam J. Stewart, Adriano +Leão, Adrien Linares, Adrin Jalali, Aitsaid Azzedine Idir, Alexander Fabisch, +Alexandre Abraham, Andrés H. Zapke, Anne Beyer, Anthony Gitter, AnthonyPrudent, +antoinebaker, Arpan Mukherjee, Arthur, Arthur Lacote, Arturo Amor, +ayoub.agouzoul, Ayrat, Ayush, Ayush Tanwar, Basile Jezequel, Bhavya Patwa, +BRYANT MUSI BABILA, Casey Heath, Chems Ben, Christian Lorentzen, Christian +Veenhuis, Christine P. Chai, cstec, C. Titus Brown, Daniel Herrera-Esposito, +Dan Schult, dbXD320, Dea María Léon, Deepyaman Datta, dependabot[bot], Dhyey +Findoriya, Dimitri Papadopoulos Orfanos, Dipak Dhangar, Dmitry Kobak, +elenafillo, Elham Babaei, EmilyXinyi, Emily (Xinyi) Chen, Eugen-Bleck, Evgeni +Burovski, fabarca, Fabrizio Damicelli, Faizan-Ul Huda, François Goupil, +François Paugam, Gaetan, GaetandeCast, Gesa Loof, Gonçalo Guiomar, Gordon Grey, +Gowtham Kumar K., Guilherme Peixoto, Guillaume Lemaitre, hakan çanakçı, Harshil +Sanghvi, Henri Bonamy, Hleb Levitski, HulusiOzy, hvtruong, Ian Faust, Imad +Saddik, Jérémie du Boisberranger, Jérôme Dockès, John Hendricks, Joris Van den +Bossche, Josef Affourtit, Josh, jshn9515, Junaid, KALLA GANASEKHAR, Kapil +Parekh, Kenneth Enevoldsen, Kian Eliasi, kostayScr, Krishnan Vignesh, kryggird, +Kyle S, Lakshmi Krishnan, Leomax, Loic Esteve, Luca Bittarello, Lucas Colley, +Lucy Liu, Luigi Giugliano, Luis, Mahdi Abid, Mahi Dhiman, Maitrey Talware, +Mamduh Zabidi, Manikandan Gobalakrishnan, Marc Bresson, Marco Edward Gorelli, +Marek Pokropiński, Maren Westermann, Marie Sacksick, Marija Vlajic, Matt J., +Mayank Raj, Michael Burkhart, Michael Šimáček, Miguel Fernandes, Miro Hrončok, +Mohamed DHIFALLAH, Muhammad Waseem, MUHAMMED SINAN D, Natalia Mokeeva, Nicholas +Farr, Nicolas Bolle, Nicolas Hug, nithish-74, Nithurshen, Nitin Pratap Singh, +NotAceNinja, Olivier Grisel, omahs, Omar Salman, Patrick Walsh, Peter Holzer, +pfolch, ph-ll-pp, Prashant Bansal, Quan H. Nguyen, Radovenchyk, Rafael Ayllón +Gavilán, Raghvender, Ranjodh Singh, Ravichandranayakar, Remi Gau, Reshama +Shaikh, Richard Harris, RishiP2006, Ritvi Alagusankar, Roberto Mourao, Robert +Pollak, Roshangoli, roychan, R Sagar Shresti, Sarthak Puri, saskra, +scikit-learn-bot, Scott Huberty, Sercan Turkmen, Sergio P, Shashank S, Shaurya +Bisht, Shivam, Shruti Nath, SIKAI ZHANG, sisird864, SiyuJin-1, S. M. Mohiuddin +Khan Shiam, Somdutta Banerjee, sotagg, Sota Goto, Spencer Bradkin, Stefan, +Stefanie Senger, Steffen Rehberg, Steven Hur, Success Moses, Sylvain Combettes, +ThibaultDECO, Thomas J. Fan, Thomas Li, Thomas S., Tim Head, Tingwei Zhu, +Tiziano Zito, TJ Norred, Username46786, Utsab Dahal, Vasanth K, Veghit, +VirenPassi, Virgil Chan, Vivaan Nanavati, Xiao Yuan, xuzhang0327, Yaroslav +Halchenko, Yaswanth Kumar, Zijun yi, zodchi94, Zubair Shakoor From 646da0f072a8afef6a980aa427a710311e67eb9d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 9 Dec 2025 17:13:35 +0100 Subject: [PATCH 43/44] [cd build] From 71cfff33556d30dfc2d999a27fe54daf072db562 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Wed, 10 Dec 2025 12:10:18 +0100 Subject: [PATCH 44/44] DOC Update news in 1.8.X branch (#32880) --- doc/templates/index.html | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/doc/templates/index.html b/doc/templates/index.html index 08abde9895ea0..a7669f9b911b9 100644 --- a/doc/templates/index.html +++ b/doc/templates/index.html @@ -206,15 +206,13 @@

News

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  • On-going development: scikit-learn 1.8 (Changelog).
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  • On-going development: scikit-learn 1.9 (Changelog).
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  • December 2025. scikit-learn 1.8.0 is available for download (Changelog).
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