API Replace n_iter in Bayesian Ridge and ARDRegression#25697
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n_iter in Bayesian Ridge and ARDRegressionn_iter in Bayesian Ridge and ARDRegression
Micky774
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Feb 28, 2023
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Hey there @jpangas, thanks for the PR! I know it is still a draft and not-yet marked "ready for review", but I wanted to quickly offer some feedback anyways, some of which may already be planned by you :)
Let me know if you have any questions, and feel free to ping me when you're ready for review!
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| if max_iter is None: | ||
| if self.n_iter != "deprecated": | ||
| warnings.warn( | ||
| "'n_iter' was renamed to 'max_iter' in version 1.2 and " | ||
| "will be removed in 1.4", | ||
| FutureWarning, | ||
| ) | ||
| max_iter = self.n_iter | ||
| else: | ||
| max_iter = 300 | ||
| else: | ||
| # Still generate a warning when n_iter is used with max_iter | ||
| # n_iter is ignored and max_iter is used | ||
| if self.n_iter != "deprecated": | ||
| warnings.warn( | ||
| "'n_iter' was renamed to 'max_iter' in version 1.2 and " | ||
| "will be removed in 1.4", | ||
| FutureWarning, | ||
| ) |
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This makes the code flow a bit simpler
Suggested change
| if max_iter is None: | |
| if self.n_iter != "deprecated": | |
| warnings.warn( | |
| "'n_iter' was renamed to 'max_iter' in version 1.2 and " | |
| "will be removed in 1.4", | |
| FutureWarning, | |
| ) | |
| max_iter = self.n_iter | |
| else: | |
| max_iter = 300 | |
| else: | |
| # Still generate a warning when n_iter is used with max_iter | |
| # n_iter is ignored and max_iter is used | |
| if self.n_iter != "deprecated": | |
| warnings.warn( | |
| "'n_iter' was renamed to 'max_iter' in version 1.2 and " | |
| "will be removed in 1.4", | |
| FutureWarning, | |
| ) | |
| # TODO(1.5) Remove | |
| if self.n_iter != "deprecated": | |
| if max_iter is not None: | |
| raise ValueError( | |
| "Both `n_iter` and `max_iter` attributes were set. Attribute" | |
| " `n_iter` was deprecated in version 1.3 and will be removed in" | |
| " 1.5. To avoid this error, only set the `max_iter` attribute." | |
| ) | |
| warnings.warn( | |
| "'n_iter' was renamed to 'max_iter' in version 1.2 and " | |
| "will be removed in 1.4", | |
| FutureWarning, | |
| ) | |
| max_iter = self.n_iter | |
| elif max_iter is None: | |
| max_iter = 300 |
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Thanks @Micky774, I had initially done it this way to only update max_iter if max_iter is None and self.n_iter != 'deprecated'
I thought it would be consistent ito favour max_iter over n_iter and we ignore n_iter when max_iter is set. (Still generating a warning)
I think the change you have suggested will use the user's n_iter over max_iter if both of them are set.
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I've updated the suggestion. Generally, if both are set I prefer to throw a ValueError and require the user to explicitly choose one or the other, rather than letting it off with a warning. What do you think?
Edit: if you accept the suggestion, the tests will need to be updated accordingly
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This is a better approach. I agree with your suggestion. I will make the necessary changes in both the code and tests. Thanks
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Indeed, we don't want to allow setting both parameters at the same time.
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glemaitre
reviewed
Mar 6, 2023
| Parameters | ||
| ---------- | ||
| n_iter : int, default=300 | ||
| n_iter : int |
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You can then move this parameter at the end of the parameter list.
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| if max_iter is None: | ||
| if self.n_iter != "deprecated": | ||
| warnings.warn( | ||
| "'n_iter' was renamed to 'max_iter' in version 1.2 and " | ||
| "will be removed in 1.4", | ||
| FutureWarning, | ||
| ) | ||
| max_iter = self.n_iter | ||
| else: | ||
| max_iter = 300 | ||
| else: | ||
| # Still generate a warning when n_iter is used with max_iter | ||
| # n_iter is ignored and max_iter is used | ||
| if self.n_iter != "deprecated": | ||
| warnings.warn( | ||
| "'n_iter' was renamed to 'max_iter' in version 1.2 and " | ||
| "will be removed in 1.4", | ||
| FutureWarning, | ||
| ) |
Member
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Indeed, we don't want to allow setting both parameters at the same time.
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|
Looks like you may have merged with an out-of-date version of |
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Author
|
Yes, I am working on that. |
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Done @Micky774 , Does this need a changelog entry? I don't think so. |
n_iter in Bayesian Ridge and ARDRegressionn_iter in Bayesian Ridge and ARDRegression
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|
This PR is changing public API, which means it requires a change log entry. A change log entry will notify users to update their code to use the new parameter. |
glemaitre
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Mar 7, 2023
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Only a couple of nitpicks. It looks good. We only miss an entry in the changelog as you previously mentioned.
| self : object | ||
| Returns the instance itself. | ||
| """ | ||
|
|
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Probably some black I assume as well.
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@glemaitre, I realized I have to deprecate |
glemaitre
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Mar 20, 2023
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Oh Indeed. Completely forgot this one.
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jpangas
commented
Mar 20, 2023
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| .. versionadded:: 1.0 | ||
|
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| n_iter_ : int |
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Have I included this attribute correctly? It previously wasn't there in ARDRegression but is required for estimators that have the max_iter attribute.
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Let's put it under scores_ to be consistent with BayesRidge.
jpangas
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Mar 20, 2023
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ping @glemaitre and @Micky774 |
glemaitre
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Mar 20, 2023
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Looks good. I think we can remove some duplicated code using a function.
|
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| .. versionadded:: 1.0 | ||
|
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| n_iter_ : int |
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Let's put it under scores_ to be consistent with BayesRidge.
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| max_iter = self.max_iter | ||
| # TODO(1.5) Remove | ||
| if self.n_iter != "deprecated": | ||
| if max_iter is not None: | ||
| raise ValueError( | ||
| "Both `n_iter` and `max_iter` attributes were set. Attribute" | ||
| " `n_iter` was deprecated in version 1.3 and will be removed in" | ||
| " 1.5. To avoid this error, only set the `max_iter` attribute." | ||
| ) | ||
| warnings.warn( | ||
| "'n_iter' was renamed to 'max_iter' in version 1.3 and " | ||
| "will be removed in 1.5", | ||
| FutureWarning, | ||
| ) | ||
| max_iter = self.n_iter | ||
| elif max_iter is None: | ||
| max_iter = 300 |
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Let's create a small function called _deprecate_max_iter that take an estimator and return max_iter. Like this we can use it in both class without code duplication.
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Among, _bayes.py or _base.py? Where would be the ideal file to include this function?
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I'd say in _bayes.py just to keep it local
| - |API| Deprecates `n_iter` in favor of `max_iter` in | ||
| :class:`linear_model.BayesianRidge` and :class:`linear_model.ARDRegression`. | ||
| `n_iter` will be removed in scikit-learn 1.5. This change makes those | ||
| estimators consistent with the rest of estimators. |
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We need to also mention that n_iter_ was added to ARDRegression.
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I have done so. Please check and see if it's looking good. Is there another particular reason I have missed behind including n_iter_ attribute in ARDRegression?
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Would it be beneficial to include them as two entries? It's a bit clunky as a single entry
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I think it could work if it was on it’s own. WDYT @glemaitre ?
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On it. I will push the change before EOD.
glemaitre
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Mar 29, 2023
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LGTM on my side once the entry is split for readibility.
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Thank you @jpangas for the contribution! |
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|
Thanks for the guidance and feedback too. |
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…learn#25697) Co-authored-by: Guillaume Lemaitre <[email protected]>
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* MAINT Clean deprecated losses in (hist) gradient boosting for 1.3 (scikit-learn#25834) * MAINT Clean deprecation of normalize in calibration_curve for 1.3 (scikit-learn#25833) * BLD Clean command removes generated from cython templates (scikit-learn#25839) * PERF Implement `PairwiseDistancesReduction` backend for `KNeighbors.predict_proba` (scikit-learn#24076) Signed-off-by: Julien Jerphanion <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> * MAINT Added Parameter Validation for datasets.make_circles (scikit-learn#25848) Co-authored-by: jeremiedbb <[email protected]> * MNT use a single job by default with sphinx build (scikit-learn#25836) * BLD Generate warning automatically for templated cython files (scikit-learn#25842) * MAINT parameter validation for sklearn.datasets.fetch_lfw_people (scikit-learn#25820) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for metrics.fbeta_score (scikit-learn#25841) * TST add global_random_seed fixture to sklearn/covariance/tests/test_robust_covariance.py (scikit-learn#25821) * MAINT Parameter validation for linear_model.orthogonal_mp (scikit-learn#25817) * TST activate common tests for TSNE (scikit-learn#25374) * CI Update lock files (scikit-learn#25849) * MAINT Added Parameter Validation for metrics.mean_gamma_deviance (scikit-learn#25853) * MAINT Parameters validation for feature_selection.mutual_info_regression (scikit-learn#25850) * MAINT parameter validation metrics.class_likelihood_ratios (scikit-learn#25863) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Ensure disjoint interval constraints (scikit-learn#25797) * MAINT Parameters validation for utils.gen_batches (scikit-learn#25864) * TST use global_random_seed in test_dict_vectorizer.py (scikit-learn#24533) * TST use global_random_seed in test_pls.py (scikit-learn#24526) Co-authored-by: jeremiedbb <[email protected]> * TST use global_random_seed in test_gpc.py (scikit-learn#24600) Co-authored-by: jeremiedbb <[email protected]> * DOC Fix overlapping plot axis in bench_sample_without_replacement.py (scikit-learn#25870) * MAINT Use contiguous memoryviews in _random.pyx (scikit-learn#25871) * MAINT parameter validation sklearn.datasets.fetch_lfw_pair (scikit-learn#25857) * MAINT Parameters validation for metrics.classification_report (scikit-learn#25868) * Empty commit * DOC fix docstring dtype parameter in OrdinalEncoder (scikit-learn#25877) * MAINT Clean up depreacted "log" loss of SGDClassifier for 1.3 (scikit-learn#25865) * ENH Adds TargetEncoder (scikit-learn#25334) Co-authored-by: Andreas Mueller <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Jovan Stojanovic <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> * CI make it possible to cancel running Azure jobs (scikit-learn#25876) * MAINT Clean-up deprecated if_delegate_has_method for 1.3 (scikit-learn#25879) * MAINT Parameter validation for tree.export_text (scikit-learn#25867) * DOC impact of `tol` for solvers in RidgeClassifier (scikit-learn#25530) * MAINT Parameters validation for metrics.hinge_loss (scikit-learn#25880) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for metrics.ndcg_score (scikit-learn#25885) * ENH KMeans initialization account for sample weights (scikit-learn#25752) Co-authored-by: jeremiedbb <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> * TST use global_random_seed in sklearn/tests/test_dummy.py (scikit-learn#25884) * DOC improve calibration user guide (scikit-learn#25687) * ENH Support for sparse matrices added to `sklearn.metrics.silhouette_samples` (scikit-learn#24677) Co-authored-by: Sahil Gupta <[email protected]> Co-authored-by: Thomas J. 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MAINT Parameters validation for metrics.explained_variance_score (scikit-learn#26079) * DOC use correct template name for displays (scikit-learn#26081) * MAINT Parameters validation for sklearn.preprocessing.maxabs_scale (scikit-learn#26077) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.preprocessing.label_binarize (scikit-learn#26078) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT parameter validation for d2_absolute_error_score (scikit-learn#26066) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameter validation for roc_auc_score (scikit-learn#26007) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for sklearn.preprocessing.normalize (scikit-learn#26069) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameter validation for metrics.cluster.fowlkes_mallows_score (scikit-learn#26080) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for compose.make_column_transformer (scikit-learn#25897) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for sklearn.metrics.pairwise.polynomial_kernel (scikit-learn#26070) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.pairwise.rbf_kernel (scikit-learn#26071) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.pairwise.sigmoid_kernel (scikit-learn#26072) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Param validation: constraint for numeric missing values (scikit-learn#26085) * FIX Adds support for negative values in categorical features in gradient boosting (scikit-learn#25629) Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Tim Head <[email protected]> * MAINT Fix C warning in Cython module splitting.pyx (scikit-learn#26051) * MNT Updates _isotonic.pyx to use memoryviews instead of `cnp.ndarray` (scikit-learn#26068) * FIX Fixes memory regression for inspecting extension arrays (scikit-learn#26106) * PERF set openmp to use only physical cores by default (scikit-learn#26082) * MNT Update black to 23.3.0 (scikit-learn#26110) * MNT Adds black commit to git-blame-ignore-revs (scikit-learn#26111) * MAINT Parameters validation for sklearn.metrics.pair_confusion_matrix (scikit-learn#26107) * MAINT Parameters validation for sklearn.metrics.mean_poisson_deviance (scikit-learn#26104) * DOC Use notebook style in plot_lof_outlier_detection.py (scikit-learn#26017) Co-authored-by: Jérémie du Boisberranger <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> * MAINT utils._fast_dict uses types from utils._typedefs (scikit-learn#26025) * DOC remove sparse-matrix for `y` in ElasticNet (scikit-learn#26127) * ENH add exponential loss (scikit-learn#25965) * MAINT Parameters validation for sklearn.preprocessing.robust_scale (scikit-learn#26086) * MAINT Parameters validation for sklearn.datasets.fetch_rcv1 (scikit-learn#26126) * MAINT Parameters validation for sklearn.metrics.adjusted_rand_score (scikit-learn#26134) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.calinski_harabasz_score (scikit-learn#26135) * MAINT Parameters validation for sklearn.metrics.davies_bouldin_score (scikit-learn#26136) * MAINT: remove `from numpy.math cimport` statements (scikit-learn#26143) * MAINT Parameters validation for sklearn.inspection.permutation_importance (scikit-learn#26145) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.cluster.homogeneity_completeness_v_measure (scikit-learn#26137) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.rand_score (scikit-learn#26138) Co-authored-by: Jérémie du Boisberranger <[email protected]> * DOC update comment in metrics/tests/test_classification.py (scikit-learn#26150) * CI small cleanup of Cirrus CI test script (scikit-learn#26168) * MAINT remove deprecated is_categorical_dtype (scikit-learn#26156) * DOC Add skforecast to related projects page (scikit-learn#26133) Co-authored-by: Thomas J. Fan <[email protected]> * FIX Keeps namedtuple's class when transform returns a tuple (scikit-learn#26121) * DOC corrected letter case for better readability in sklearn/metrics/_classification.py / (scikit-learn#26169) * MAINT Parameters validation for sklearn.preprocessing.power_transform (scikit-learn#26142) * FIX `roc_auc_score` now uses `y_prob` instead of `y_pred` (scikit-learn#26155) * MAINT Parameters validation for sklearn.datasets.load_iris (scikit-learn#26177) * MAINT Parameters validation for sklearn.datasets.load_diabetes (scikit-learn#26166) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.datasets.load_breast_cancer (scikit-learn#26165) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.cluster.entropy (scikit-learn#26162) * MAINT Parameters validation for sklearn.datasets.fetch_species_distributions (scikit-learn#26161) Co-authored-by: Jérémie du Boisberranger <[email protected]> * ASV Fix tol in SGDRegressorBenchmark (scikit-learn#26146) Co-authored-by: jeremie du boisberranger <[email protected]> * MNT use api.openml.org URLs for fetch_openml (scikit-learn#26171) * MAINT Parameters validation for sklearn.utils.resample (scikit-learn#26139) * MAINT make it explicit that additive_chi2_kernel does not accept sparse matrix (scikit-learn#26178) * MNT fix circleci link in README.rst (scikit-learn#26183) * CI Fix circleci artifact redirector action (scikit-learn#26181) * GOV introduce rights for groups as discussed in SLEP019 (scikit-learn#25753) Co-authored-by: Julien <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> * MAINT Parameters validation for sklearn.neighbors.sort_graph_by_row_values (scikit-learn#26173) Co-authored-by: Jérémie du Boisberranger <[email protected]> * FIX improve convergence criterion for LogisticRegression(penalty="l1", solver='liblinear') (scikit-learn#25214) Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> * MAINT Fix several typos in src and doc files (scikit-learn#26187) * PERF fix overhead of _rescale_data in LinearRegression (scikit-learn#26207) * ENH add Huber loss (scikit-learn#25966) * MAINT Refactor GraphicalLasso and graphical_lasso (scikit-learn#26033) Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Cython linting (scikit-learn#25861) * DOC Add JupyterLite button in example gallery (scikit-learn#25887) * MAINT Parameters validation for sklearn.covariance.ledoit_wolf_shrinkage (scikit-learn#26200) * MAINT Parameters validation for sklearn.datasets.load_linnerud (scikit-learn#26199) * MAINT Parameters validation for sklearn.datasets.load_wine (scikit-learn#26196) * DOC Added redirect to Provost paper + minor refactor (scikit-learn#26223) * MAINT Parameter Validation for `covariance.graphical_lasso` (scikit-learn#25053) Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.datasets.load_digits (scikit-learn#26195) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.preprocessing.quantile_transform (scikit-learn#26144) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.model_selection.cross_validate (scikit-learn#26129) Co-authored-by: jeremiedbb <[email protected]> * DOC Adds TargetEncoder example explaining the internal CV (scikit-learn#26185) Co-authored-by: Tim Head <[email protected]> * spelling mistake corrected in documentation for script `plot_document_clustering.py` (scikit-learn#26228) Co-authored-by: Olivier Grisel <[email protected]> * FIX possible UnboundLocalError in fetch_openml (scikit-learn#26236) * ENH Adds PyTorch support to LinearDiscriminantAnalysis (scikit-learn#25956) Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Tim Head <[email protected]> * MNT Use fixed version of Pyodide (scikit-learn#26247) * MNT Reset transform_output default in example to fix doc build build (scikit-learn#26269) * DOC Update example plot_nearest_centroid.py (scikit-learn#26263) * MNT reduce JupyterLite build size (scikit-learn#26246) * DOC term -> meth in GradientBoosting (scikit-learn#26225) * MNT speed-up html-noplot build (scikit-learn#26245) Co-authored-by: Thomas J. Fan <[email protected]> * MNT Use copy=False when creating DataFrames (scikit-learn#26272) * MAINT Parameters validation for sklearn.model_selection.permutation_test_score (scikit-learn#26230) * MAINT Parameters validation for sklearn.datasets.clear_data_home (scikit-learn#26259) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.datasets.load_files (scikit-learn#26203) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.datasets.get_data_home (scikit-learn#26260) Co-authored-by: Jérémie du Boisberranger <[email protected]> * DOC Fix y-axis plot labels in permutation test score example (scikit-learn#26240) * MAINT cython-lint ignores asv_benchmarks (scikit-learn#26282) * MAINT Parameter validation for metrics.cluster._supervised (scikit-learn#26258) Co-authored-by: Jérémie du Boisberranger <[email protected]> * DOC Improve docstring for tol in SequentialFeatureSelector (scikit-learn#26271) * MAINT Parameters validation for sklearn.datasets.load_sample_image (scikit-learn#26226) Co-authored-by: Jérémie du Boisberranger <[email protected]> * DOC Consistent param type for pos_label (scikit-learn#26237) * DOC Minor grammar fix to imputation docs (scikit-learn#26283) * MAINT Parameters validation for sklearn.calibration.calibration_curve (scikit-learn#26198) Co-authored-by: jeremie du boisberranger <[email protected]> * MAINT Parameters validation for sklearn.inspection.partial_dependence (scikit-learn#26209) Co-authored-by: jeremie du boisberranger <[email protected]> * MAINT Parameters validation for sklearn.model_selection.validation_curve (scikit-learn#26229) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.model_selection.learning_curve (scikit-learn#26227) Co-authored-by: jeremie du boisberranger <[email protected]> * MNT Remove deprecated pandas.api.types.is_sparse (scikit-learn#26287) * CI Use Trusted Publishers for uploading wheels to PyPI (scikit-learn#26249) * MAINT Parameters validation for sklearn.metrics.pairwise.manhattan_distances (scikit-learn#26122) * PERF revert openmp use in csr_row_norms (scikit-learn#26275) * MAINT Parameters validation for metrics.check_scoring (scikit-learn#26041) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MNT Improve error message when checking classification target is of a non-regression type (scikit-learn#26281) Co-authored-by: Adrin Jalali <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> * DOC fix link to User Guide encoder_infrequent_categories (scikit-learn#26309) * MNT remove unused args in _predict_regression_tree_inplace_fast_dense (scikit-learn#26314) * ENH Adds missing value support for trees (scikit-learn#23595) Co-authored-by: Tim Head <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> * CLN Clean up logic in validate_data and cast_to_ndarray (scikit-learn#26300) * MAINT refactor scorer using _get_response_values (scikit-learn#26037) Co-authored-by: Jérémie du Boisberranger <[email protected]> Co-authored-by: Adrin Jalali <[email protected]> * DOC Add HGBDT to "see also" section of random forests (scikit-learn#26319) Co-authored-by: ArturoAmorQ <[email protected]> Co-authored-by: Tim Head <[email protected]> * MNT Bump Github Action labeler version to use newer Node (scikit-learn#26302) * FIX thresholds should not exceed 1.0 with probabilities in `roc_curve` (scikit-learn#26194) Co-authored-by: Olivier Grisel <[email protected]> * ENH Allow for appropriate dtype us in `preprocessing.PolynomialFeatures` for sparse matrices (scikit-learn#23731) Co-authored-by: Aleksandr Kokhaniukov <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> * DOC Fix minor typo (scikit-learn#26327) * MAINT bump minimum version for pytest (scikit-learn#26184) Co-authored-by: Loïc Estève <[email protected]> Co-authored-by: Adrin Jalali <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> * DOC fix return type in isotonic_regression (scikit-learn#26332) * FIX fix available_if for MultiOutputRegressor.partial_fit (scikit-learn#26333) Co-authored-by: Guillaume Lemaitre <[email protected]> * FIX make pipeline pass check_estimator (scikit-learn#26325) * FEA Add multiclass support to `average_precision_score` (scikit-learn#24769) Co-authored-by: Geoffrey <[email protected]> Co-authored-by: gbolmier <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> --------- Signed-off-by: Julien Jerphanion <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> Co-authored-by: Meekail Zain <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: zeeshan lone <[email protected]> Co-authored-by: jeremiedbb <[email protected]> Co-authored-by: Adrin Jalali <[email protected]> Co-authored-by: Shiva chauhan <[email protected]> Co-authored-by: AymericBasset <[email protected]> Co-authored-by: Maren Westermann <[email protected]> Co-authored-by: Nishu Choudhary <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Loïc Estève <[email protected]> Co-authored-by: Benedek Harsanyi <[email protected]> Co-authored-by: Pooja Subramaniam <[email protected]> Co-authored-by: Rushil Desai <[email protected]> Co-authored-by: Xiao Yuan <[email protected]> Co-authored-by: Omar Salman <[email protected]> Co-authored-by: 2357juan <[email protected]> Co-authored-by: Théophile Baranger <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Andreas Mueller <[email protected]> Co-authored-by: Jovan Stojanovic <[email protected]> Co-authored-by: Rahil Parikh <[email protected]> Co-authored-by: Bharat Raghunathan <[email protected]> Co-authored-by: Sortofamudkip <[email protected]> Co-authored-by: Gleb Levitski <[email protected]> Co-authored-by: Christian Lorentzen <[email protected]> Co-authored-by: Ashwin Mathur <[email protected]> Co-authored-by: Sahil Gupta <[email protected]> Co-authored-by: Veghit <[email protected]> Co-authored-by: Itay <[email protected]> Co-authored-by: precondition <[email protected]> Co-authored-by: Marc Torrellas Socastro <[email protected]> Co-authored-by: Dominic Fox <[email protected]> Co-authored-by: futurewarning <[email protected]> Co-authored-by: Yao Xiao <[email protected]> Co-authored-by: Joey Ortiz <[email protected]> Co-authored-by: Tim Head <[email protected]> Co-authored-by: Christian Veenhuis <[email protected]> Co-authored-by: adienes <[email protected]> Co-authored-by: Dave Berenbaum <[email protected]> Co-authored-by: Lene Preuss <[email protected]> Co-authored-by: A.H.Mansouri <[email protected]> Co-authored-by: Boris Feld <[email protected]> Co-authored-by: Carla J <[email protected]> Co-authored-by: windiana42 <[email protected]> Co-authored-by: mdarii <[email protected]> Co-authored-by: murezzda <[email protected]> Co-authored-by: Peter Piontek <[email protected]> Co-authored-by: John Pangas <[email protected]> Co-authored-by: Dmitry Nesterov <[email protected]> Co-authored-by: Yuchen Zhou <[email protected]> Co-authored-by: Ekaterina Butyugina <[email protected]> Co-authored-by: Jiawei Zhang <[email protected]> Co-authored-by: Ansam Zedan <[email protected]> Co-authored-by: genvalen <[email protected]> Co-authored-by: farhan khan <[email protected]> Co-authored-by: Arturo Amor <[email protected]> Co-authored-by: Jiawei Zhang <[email protected]> Co-authored-by: Ralf Gommers <[email protected]> Co-authored-by: Jessicakk0711 <[email protected]> Co-authored-by: Ankur Singh <[email protected]> Co-authored-by: Seoeun(Sun☀️) Hong <[email protected]> Co-authored-by: Nightwalkx <[email protected]> Co-authored-by: VIGNESH D <[email protected]> Co-authored-by: Vincent-violet <[email protected]> Co-authored-by: Elabonga Atuo <[email protected]> Co-authored-by: Tom Dupré la Tour <[email protected]> Co-authored-by: André Pedersen <[email protected]> Co-authored-by: Ashish Dutt <[email protected]> Co-authored-by: Phil <[email protected]> Co-authored-by: Stanislav (Stanley) Modrak <[email protected]> Co-authored-by: hujiahong726 <[email protected]> Co-authored-by: James Dean <[email protected]> Co-authored-by: ArturoAmorQ <[email protected]> Co-authored-by: Aleksandr Kokhaniukov <[email protected]> Co-authored-by: c-git <[email protected]> Co-authored-by: annegnx <[email protected]> Co-authored-by: Geoffrey <[email protected]> Co-authored-by: gbolmier <[email protected]>
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Reference Issues/PRs
Fixes #25518
What does this implement/fix? Explain your changes.
This PR deprecates the
n_iterattribute in favour ofmax_iterinBayesianRidgeandARDRegression.Any other comments?