API Replace `n_iter` in `Bayesian Ridge` and `ARDRegression` #25697

jpangas · 2023-02-24T21:43:48Z

Reference Issues/PRs

What does this implement/fix? Explain your changes.

This PR deprecates the n_iter attribute in favour of max_iter in BayesianRidge and ARDRegression.

Any other comments?

Micky774

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!

Micky774 · 2023-02-28T19:10:43Z

sklearn/linear_model/_bayes.py

+        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,
+                )


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

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.

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

This is a better approach. I agree with your suggestion. I will make the necessary changes in both the code and tests. Thanks

Indeed, we don't want to allow setting both parameters at the same time.

sklearn/linear_model/_bayes.py

jpangas · 2023-03-01T18:51:24Z

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!
Thanks @Micky774 , for reviewing and giving me the suggestions. They will definitely help me complete this ASAP.

glemaitre

A couple of changes once @Micky774's comments are addressed.

sklearn/linear_model/_bayes.py

glemaitre · 2023-03-06T12:14:44Z

sklearn/linear_model/_bayes.py

@@ -32,9 +33,20 @@ class BayesianRidge(RegressorMixin, LinearModel):

    Parameters
    ----------
-    n_iter : int, default=300
+    n_iter : int


You can then move this parameter at the end of the parameter list.

sklearn/linear_model/_bayes.py

glemaitre · 2023-03-06T12:18:14Z

sklearn/linear_model/_bayes.py

+        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,
+                )


Indeed, we don't want to allow setting both parameters at the same time.

sklearn/linear_model/_bayes.py

Micky774 · 2023-03-06T20:07:05Z

Looks like you may have merged with an out-of-date version of main. Could you correct this and sync your branch with the latest main?

jpangas · 2023-03-06T20:11:53Z

Yes, I am working on that.

jpangas · 2023-03-06T21:09:44Z

Done @Micky774 , Does this need a changelog entry? I don't think so.

thomasjpfan · 2023-03-06T22:28:17Z

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

Only a couple of nitpicks. It looks good. We only miss an entry in the changelog as you previously mentioned.

sklearn/linear_model/_bayes.py

glemaitre · 2023-03-07T09:38:32Z

sklearn/linear_model/_bayes.py

@@ -226,8 +244,26 @@ def fit(self, X, y, sample_weight=None):
        self : object
            Returns the instance itself.
        """
+


Probably some black I assume as well.

sklearn/linear_model/tests/test_bayes.py

glemaitre

I just pushed the little nitpicks that I proposed and added the changelog entry. LGTM now.

@Micky774 do you want to merge this one.

jpangas · 2023-03-20T09:51:43Z

@glemaitre, I realized I have to deprecate ARDRegression too. submitting before EOD.

glemaitre

Oh Indeed. Completely forgot this one.

jpangas · 2023-03-20T11:25:32Z

sklearn/linear_model/_bayes.py

@@ -509,6 +553,11 @@ class ARDRegression(RegressorMixin, LinearModel):

        .. versionadded:: 1.0

+    n_iter_ : int


Have I included this attribute correctly? It previously wasn't there in ARDRegression but is required for estimators that have the max_iter attribute.

Let's put it under scores_ to be consistent with BayesRidge.

sklearn/linear_model/_bayes.py

jpangas · 2023-03-20T11:27:41Z

ping @glemaitre and @Micky774

glemaitre

Looks good. I think we can remove some duplicated code using a function.

glemaitre · 2023-03-20T14:51:33Z

sklearn/linear_model/_bayes.py

@@ -509,6 +553,11 @@ class ARDRegression(RegressorMixin, LinearModel):

        .. versionadded:: 1.0

+    n_iter_ : int


Let's put it under scores_ to be consistent with BayesRidge.

glemaitre · 2023-03-20T14:53:59Z

sklearn/linear_model/_bayes.py

+        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


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.

Among, _bayes.py or _base.py? Where would be the ideal file to include this function?

I'd say in _bayes.py just to keep it local

sklearn/linear_model/tests/test_bayes.py

glemaitre · 2023-03-20T14:56:06Z

doc/whats_new/v1.3.rst

+- |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.


We need to also mention that n_iter_ was added to ARDRegression.

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?

Would it be beneficial to include them as two entries? It's a bit clunky as a single entry

I think it could work if it was on it’s own. WDYT @glemaitre ?

We can make it two entries.

On it. I will push the change before EOD.

glemaitre

LGTM on my side once the entry is split for readibility.

Micky774

LGTM

Micky774 · 2023-04-02T13:02:33Z

Thank you @jpangas for the contribution!

jpangas · 2023-04-02T13:23:06Z

Thanks for the guidance and feedback too.

…learn#25697) Co-authored-by: Guillaume Lemaitre <[email protected]>

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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. 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jpangas changed the title ~~Deprecate n_iter in Bayesian Ridge and ARDRegression~~ MAINT Replace n_iter in Bayesian Ridge and ARDRegression Feb 24, 2023

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API Replace n_iter in Bayesian Ridge and ARDRegression (scikit-…

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…learn#25697) Co-authored-by: Guillaume Lemaitre <[email protected]>

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lucyleeow mentioned this pull request Feb 20, 2024

API Deprecate n_iter in favour of max_iter for TSNE #28471

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		@@ -509,6 +553,11 @@ class ARDRegression(RegressorMixin, LinearModel):

		.. versionadded:: 1.0

		n_iter_ : int

API Replace n_iter in Bayesian Ridge and ARDRegression #25697

API Replace n_iter in Bayesian Ridge and ARDRegression #25697

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jpangas commented Apr 2, 2023

API Replace `n_iter` in `Bayesian Ridge` and `ARDRegression` #25697

API Replace `n_iter` in `Bayesian Ridge` and `ARDRegression` #25697

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