8000 FEA Add metadata routing for SequentialFeatureSelector (#29260) · scikit-learn/scikit-learn@234260d · GitHub
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OmarManzooradam2392glemaitre
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FEA Add metadata routing for SequentialFeatureSelector (#29260)
Co-authored-by: Adam Li <adam2392@gmail.com> Co-authored-by: Guillaume Lemaitre <guillaume@probabl.ai>
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‎doc/metadata_routing.rst

Lines changed: 1 addition & 1 deletion
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@@ -285,6 +285,7 @@ Meta-estimators and functions supporting metadata routing:
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- :class:`sklearn.ensemble.BaggingClassifier`
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- :class:`sklearn.ensemble.BaggingRegressor`
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- :class:`sklearn.feature_selection.SelectFromModel`
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- :class:`sklearn.feature_selection.SequentialFeatureSelector`
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- :class:`sklearn.impute.IterativeImputer`
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- :class:`sklearn.linear_model.ElasticNetCV`
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- :class:`sklearn.linear_model.LarsCV`
@@ -324,4 +325,3 @@ Meta-estimators and tools not supporting metadata routing yet:
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- :class:`sklearn.ensemble.AdaBoostRegressor`
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- :class:`sklearn.feature_selection.RFE`
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- :class:`sklearn.feature_selection.RFECV`
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- :class:`sklearn.feature_selection.SequentialFeatureSelector`

‎doc/whats_new/v1.6.rst

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@@ -83,6 +83,11 @@ more details.
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params to the underlying regressor.
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:pr:`29136` by :user:`Omar Salman <OmarManzoor>`.
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- |Feature| :class:`feature_selection.SequentialFeatureSelector` now supports
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metadata routing in its `fit` method and passes the corresponding params to
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the :func:`model_selection.cross_val_score` function.
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:pr:`29260` by :user:`Omar Salman <OmarManzoor>`.
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- |Feature| :func:`model_selection.validation_curve` now supports metadata routing for
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the `fit` method of its estimator and for its underlying CV splitter and scorer.
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:pr:`29329` by :user:`Stefanie Senger <StefanieSenger>`.

‎sklearn/feature_selection/_sequential.py

Lines changed: 61 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -10,18 +10,22 @@
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import numpy as np
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from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier
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from ..metrics import get_scorer_names
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from ..metrics import check_scoring, get_scorer_names
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from ..model_selection import check_cv, cross_val_score
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from ..utils._metadata_requests import (
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MetadataRouter,
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MethodMapping,
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_raise_for_params,
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_routing_enabled,
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process_routing,
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)
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from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
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from ..utils._tags import _safe_tags
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from ..utils.metadata_routing import _RoutingNotSupportedMixin
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from ..utils.validation import check_is_fitted
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from ._base import SelectorMixin
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class SequentialFeatureSelector(
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_RoutingNotSupportedMixin, SelectorMixin, MetaEstimatorMixin, BaseEstimator
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):
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class SequentialFeatureSelector(SelectorMixin, MetaEstimatorMixin, BaseEstimator):
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"""Transformer that performs Sequential Feature Selection.
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This Sequential Feature Selector adds (forward selection) or
@@ -191,7 +195,7 @@ def __init__(
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# SequentialFeatureSelector.estimator is not validated yet
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prefer_skip_nested_validation=False
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)
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def fit(self, X, y=None):
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def fit(self, X, y=None, **params):
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"""Learn the features to select from X.
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Parameters
@@ -204,11 +208,24 @@ def fit(self, X, y=None):
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Target values. This parameter may be ignored for
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unsupervised learning.
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**params : dict, default=None
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Parameters to be passed to the underlying `estimator`, `cv`
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and `scorer` objects.
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.. versionadded:: 1.6
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Only available if `enable_metadata_routing=True`,
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which can be set by using
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``sklearn.set_config(enable_metadata_routing=True)``.
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See :ref:`Metadata Routing User Guide <metadata_routing>` for
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more details.
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Returns
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-------
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self : object
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Returns the instance itself.
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"""
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_raise_for_params(params, self, "fit")
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tags = self._get_tags()
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X = self._validate_data(
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X,
@@ -251,9 +268,15 @@ def fit(self, X, y=None):
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old_score = -np.inf
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is_auto_select = self.tol is not None and self.n_features_to_select == "auto"
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# We only need to verify the routing here and not use the routed params
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# because internally the actual routing will also take place inside the
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# `cross_val_score` function.
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if _routing_enabled():
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process_routing(self, "fit", **params)
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for _ in range(n_iterations):
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new_feature_idx, new_score = self._get_best_new_feature_score(
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cloned_estimator, X, y, cv, current_mask
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cloned_estimator, X, y, cv, current_mask, **params
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)
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if is_auto_select and ((new_score - old_score) < self.tol):
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break
@@ -269,7 +292,7 @@ def fit(self, X, y=None):
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return self
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def _get_best_new_feature_score(self, estimator, X, y, cv, current_mask):
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def _get_best_new_feature_score(self, estimator, X, y, cv, current_mask, **params):
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# Return the best new feature and its score to add to the current_mask,
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# i.e. return the best new feature and its score to add (resp. remove)
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# when doing forward selection (resp. backward selection).
@@ -290,6 +313,7 @@ def _get_best_new_feature_score(self, estimator, X, y, cv, current_mask):
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cv=cv,
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scoring=self.scoring,
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n_jobs=self.n_jobs,
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params=params,
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).mean()
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new_feature_idx = max(scores, key=lambda feature_idx: scores[feature_idx])
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return new_feature_idx, scores[new_feature_idx]
@@ -302,3 +326,32 @@ def _more_tags(self):
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return {
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"allow_nan": _safe_tags(self.estimator, key="allow_nan"),
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}
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def get_metadata_routing(self):
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"""Get metadata routing of this object.
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Please check :ref:`User Guide <metadata_routing>` on how the routing
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mechanism works.
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.. versionadded:: 1.6
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Returns
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-------
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routing : MetadataRouter
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A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
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routing information.
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"""
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router = MetadataRouter(owner=self.__class__.__name__)
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router.add(
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estimator=self.estimator,
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method_mapping=MethodMapping().add(caller="fit", callee="fit"),
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)
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router.add(
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splitter=check_cv(self.cv, classifier=is_classifier(self.estimator)),
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method_mapping=MethodMapping().add(caller="fit", callee="split"),
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)
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router.add(
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scorer=check_scoring(self.estimator, scoring=self.scoring),
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method_mapping=MethodMapping().add(caller="fit", callee="score"),
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)
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return router

‎sklearn/feature_selection/tests/test_sequential.py

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Original file line numberDiff line numberDiff line change
@@ -321,3 +321,12 @@ def test_cv_generator_support():
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sfs = SequentialFeatureSelector(knc, n_features_to_select=5, cv=splits)
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sfs.fit(X, y)
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325+
326+
def test_fit_rejects_params_with_no_routing_enabled():
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X, y = make_classification(random_state=42)
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est = LinearRegression()
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sfs = SequentialFeatureSelector(estimator=est)
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with pytest.raises(ValueError, match="is only supported if"):
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sfs.fit(X, y, sample_weight=np.ones_like(y))

‎sklearn/tests/test_metaestimators_metadata_routing.py

Lines changed: 12 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -407,6 +407,18 @@ def enable_slep006():
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],
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"method_mapping": {"fit": ["fit", "score"]},
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},
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{
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"metaestimator": SequentialFeatureSelector,
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"estimator_name": "estimator",
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"estimator": "classifier",
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"X": X,
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"y": y,
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"estimator_routing_methods": ["fit"],
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"scorer_name": "scoring",
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"scorer_routing_methods": ["fit"],
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"cv_name": "cv",
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"cv_routing_methods": ["fit"],
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},
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]
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"""List containing all metaestimators to be tested and their settings
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@@ -450,7 +462,6 @@ def enable_slep006():
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AdaBoostRegressor(),
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RFE(ConsumingClassifier()),
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RFECV(ConsumingClassifier()),
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SequentialFeatureSelector(ConsumingClassifier()),
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]
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