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ReStructuredText
330 lines
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ReStructuredText
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.. currentmodule:: sklearn
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.. TODO: update doc/conftest.py once document is updated and examples run.
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.. _metadata_routing:
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Metadata Routing
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================
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.. note::
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The Metadata Routing API is experimental, and is not yet implemented for all
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estimators. Please refer to the :ref:`list of supported and unsupported
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models <metadata_routing_models>` for more information. It may change without
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the usual deprecation cycle. By default this feature is not enabled. You can
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enable it by setting the ``enable_metadata_routing`` flag to
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``True``::
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>>> import sklearn
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>>> sklearn.set_config(enable_metadata_routing=True)
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Note that the methods and requirements introduced in this document are only
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relevant if you want to pass :term:`metadata` (e.g. ``sample_weight``) to a method.
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If you're only passing ``X`` and ``y`` and no other parameter / metadata to
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methods such as :term:`fit`, :term:`transform`, etc., then you don't need to set
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anything.
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This guide demonstrates how :term:`metadata` can be routed and passed between objects in
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scikit-learn. If you are developing a scikit-learn compatible estimator or
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meta-estimator, you can check our related developer guide:
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:ref:`sphx_glr_auto_examples_miscellaneous_plot_metadata_routing.py`.
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Metadata is data that an estimator, scorer, or CV splitter takes into account if the
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user explicitly passes it as a parameter. For instance, :class:`~cluster.KMeans` accepts
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`sample_weight` in its `fit()` method and considers it to calculate its centroids.
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`classes` are consumed by some classifiers and `groups` are used in some splitters, but
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any data that is passed into an object's methods apart from X and y can be considered as
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metadata. Prior to scikit-learn version 1.3, there was no single API for passing
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metadata like that if these objects were used in conjunction with other objects, e.g. a
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scorer accepting `sample_weight` inside a :class:`~model_selection.GridSearchCV`.
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With the Metadata Routing API, we can transfer metadata to estimators, scorers, and CV
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splitters using :term:`meta-estimators` (such as :class:`~pipeline.Pipeline` or
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:class:`~model_selection.GridSearchCV`) or functions such as
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:func:`~model_selection.cross_validate` which route data to other objects. In order to
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pass metadata to a method like ``fit`` or ``score``, the object consuming the metadata,
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must *request* it. This is done via `set_{method}_request()` methods, where `{method}`
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is substituted by the name of the method that requests the metadata. For instance,
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estimators that use the metadata in their `fit()` method would use `set_fit_request()`,
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and scorers would use `set_score_request()`. These methods allow us to specify which
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metadata to request, for instance `set_fit_request(sample_weight=True)`.
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For grouped splitters such as :class:`~model_selection.GroupKFold`, a
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``groups`` parameter is requested by default. This is best demonstrated by the
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following examples.
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Usage Examples
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**************
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Here we present a few examples to show some common use-cases. Our goal is to pass
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`sample_weight` and `groups` through :func:`~model_selection.cross_validate`, which
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routes the metadata to :class:`~linear_model.LogisticRegressionCV` and to a custom scorer
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made with :func:`~metrics.make_scorer`, both of which *can* use the metadata in their
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methods. In these examples we want to individually set whether to use the metadata
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within the different :term:`consumers <consumer>`.
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The examples in this section require the following imports and data::
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>>> import numpy as np
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>>> from sklearn.metrics import make_scorer, accuracy_score
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>>> from sklearn.linear_model import LogisticRegressionCV, LogisticRegression
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>>> from sklearn.model_selection import cross_validate, GridSearchCV, GroupKFold
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>>> from sklearn.feature_selection import SelectKBest
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>>> from sklearn.pipeline import make_pipeline
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>>> n_samples, n_features = 100, 4
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>>> rng = np.random.RandomState(42)
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>>> X = rng.rand(n_samples, n_features)
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>>> y = rng.randint(0, 2, size=n_samples)
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>>> my_groups = rng.randint(0, 10, size=n_samples)
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>>> my_weights = rng.rand(n_samples)
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>>> my_other_weights = rng.rand(n_samples)
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Weighted scoring and fitting
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----------------------------
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The splitter used internally in :class:`~linear_model.LogisticRegressionCV`,
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:class:`~model_selection.GroupKFold`, requests ``groups`` by default. However, we need
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to explicitly request `sample_weight` for it and for our custom scorer by specifying
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`sample_weight=True` in :class:`~linear_model.LogisticRegressionCV`s `set_fit_request()`
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method and in :func:`~metrics.make_scorer`s `set_score_request()` method. Both
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:term:`consumers <consumer>` know how to use ``sample_weight`` in their `fit()` or
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`score()` methods. We can then pass the metadata in
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:func:`~model_selection.cross_validate` which will route it to any active consumers::
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>>> weighted_acc = make_scorer(accuracy_score).set_score_request(sample_weight=True)
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>>> lr = LogisticRegressionCV(
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... cv=GroupKFold(),
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... scoring=weighted_acc
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... ).set_fit_request(sample_weight=True)
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>>> cv_results = cross_validate(
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... lr,
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... X,
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... y,
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... params={"sample_weight": my_weights, "groups": my_groups},
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... cv=GroupKFold(),
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... scoring=weighted_acc,
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... )
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Note that in this example, :func:`~model_selection.cross_validate` routes ``my_weights``
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to both the scorer and :class:`~linear_model.LogisticRegressionCV`.
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If we would pass `sample_weight` in the params of
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:func:`~model_selection.cross_validate`, but not set any object to request it,
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`UnsetMetadataPassedError` would be raised, hinting to us that we need to explicitly set
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where to route it. The same applies if ``params={"sample_weights": my_weights, ...}``
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were passed (note the typo, i.e. ``weights`` instead of ``weight``), since
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``sample_weights`` was not requested by any of its underlying objects.
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Weighted scoring and unweighted fitting
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---------------------------------------
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When passing metadata such as ``sample_weight`` into a :term:`router`
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(:term:`meta-estimators` or routing function), all ``sample_weight`` :term:`consumers
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<consumer>` require weights to be either explicitly requested or explicitly not
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requested (i.e. ``True`` or ``False``). Thus, to perform an unweighted fit, we need to
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configure :class:`~linear_model.LogisticRegressionCV` to not request sample weights, so
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that :func:`~model_selection.cross_validate` does not pass the weights along::
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>>> weighted_acc = make_scorer(accuracy_score).set_score_request(sample_weight=True)
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>>> lr = LogisticRegressionCV(
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... cv=GroupKFold(), scoring=weighted_acc,
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... ).set_fit_request(sample_weight=False)
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>>> cv_results = cross_validate(
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... lr,
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... X,
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... y,
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... cv=GroupKFold(),
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... params={"sample_weight": my_weights, "groups": my_groups},
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... scoring=weighted_acc,
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... )
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If :meth:`linear_model.LogisticRegressionCV.set_fit_request` had not been called,
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:func:`~model_selection.cross_validate` would raise an error because ``sample_weight``
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is passed but :class:`~linear_model.LogisticRegressionCV` would not be explicitly
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configured to recognize the weights.
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Unweighted feature selection
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----------------------------
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Routing metadata is only possible if the object's method knows how to use the metadata,
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which in most cases means they have it as an explicit parameter. Only then we can set
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request values for metadata using `set_fit_request(sample_weight=True)`, for instance.
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This makes the object a :term:`consumer <consumer>`.
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Unlike :class:`~linear_model.LogisticRegressionCV`,
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:class:`~feature_selection.SelectKBest` can't consume weights and therefore no request
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value for ``sample_weight`` on its instance is set and ``sample_weight`` is not routed
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to it::
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>>> weighted_acc = make_scorer(accuracy_score).set_score_request(sample_weight=True)
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>>> lr = LogisticRegressionCV(
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... cv=GroupKFold(), scoring=weighted_acc,
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... ).set_fit_request(sample_weight=True)
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>>> sel = SelectKBest(k=2)
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>>> pipe = make_pipeline(sel, lr)
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>>> cv_results = cross_validate(
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... pipe,
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... X,
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... y,
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... cv=GroupKFold(),
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... params={"sample_weight": my_weights, "groups": my_groups},
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... scoring=weighted_acc,
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... )
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Different scoring and fitting weights
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-------------------------------------
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Despite :func:`~metrics.make_scorer` and
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:class:`~linear_model.LogisticRegressionCV` both expecting the key
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``sample_weight``, we can use aliases to pass different weights to different
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consumers. In this example, we pass ``scoring_weight`` to the scorer, and
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``fitting_weight`` to :class:`~linear_model.LogisticRegressionCV`::
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>>> weighted_acc = make_scorer(accuracy_score).set_score_request(
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... sample_weight="scoring_weight"
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... )
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>>> lr = LogisticRegressionCV(
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... cv=GroupKFold(), scoring=weighted_acc,
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... ).set_fit_request(sample_weight="fitting_weight")
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>>> cv_results = cross_validate(
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... lr,
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... X,
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... y,
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... cv=GroupKFold(),
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... params={
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... "scoring_weight": my_weights,
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... "fitting_weight": my_other_weights,
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... "groups": my_groups,
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... },
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... scoring=weighted_acc,
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... )
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API Interface
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*************
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A :term:`consumer` is an object (estimator, meta-estimator, scorer, splitter) which
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accepts and uses some :term:`metadata` in at least one of its methods (for instance
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``fit``, ``predict``, ``inverse_transform``, ``transform``, ``score``, ``split``).
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Meta-estimators which only forward the metadata to other objects (child estimators,
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scorers, or splitters) and don't use the metadata themselves are not consumers.
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(Meta-)Estimators which route metadata to other objects are :term:`routers <router>`.
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A(n) (meta-)estimator can be a :term:`consumer` and a :term:`router` at the same time.
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(Meta-)Estimators and splitters expose a `set_{method}_request` method for each method
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which accepts at least one metadata. For instance, if an estimator supports
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``sample_weight`` in ``fit`` and ``score``, it exposes
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``estimator.set_fit_request(sample_weight=value)`` and
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``estimator.set_score_request(sample_weight=value)``. Here ``value`` can be:
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- ``True``: method requests a ``sample_weight``. This means if the metadata is provided,
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it will be used, otherwise no error is raised.
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- ``False``: method does not request a ``sample_weight``.
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- ``None``: router will raise an error if ``sample_weight`` is passed. This is in almost
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all cases the default value when an object is instantiated and ensures the user sets
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the metadata requests explicitly when a metadata is passed. The only exception are
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``Group*Fold`` splitters.
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- ``"param_name"``: alias for ``sample_weight`` if we want to pass different weights to
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different consumers. If aliasing is used the meta-estimator should not forward
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``"param_name"`` to the consumer, but ``sample_weight`` instead, because the consumer
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will expect a param called ``sample_weight``. This means the mapping between the
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metadata required by the object, e.g. ``sample_weight`` and the variable name provided
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by the user, e.g. ``my_weights`` is done at the router level, and not by the consuming
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object itself.
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Metadata are requested in the same way for scorers using ``set_score_request``.
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If a metadata, e.g. ``sample_weight``, is passed by the user, the metadata request for
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all objects which potentially can consume ``sample_weight`` should be set by the user,
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otherwise an error is raised by the router object. For example, the following code
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raises an error, since it hasn't been explicitly specified whether ``sample_weight``
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should be passed to the estimator's scorer or not::
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>>> param_grid = {"C": [0.1, 1]}
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>>> lr = LogisticRegression().set_fit_request(sample_weight=True)
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>>> try:
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... GridSearchCV(
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... estimator=lr, param_grid=param_grid
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... ).fit(X, y, sample_weight=my_weights)
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... except ValueError as e:
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... print(e)
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[sample_weight] are passed but are not explicitly set as requested or not
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requested for LogisticRegression.score, which is used within GridSearchCV.fit.
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Call `LogisticRegression.set_score_request({metadata}=True/False)` for each metadata
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you want to request/ignore.
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The issue can be fixed by explicitly setting the request value::
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>>> lr = LogisticRegression().set_fit_request(
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... sample_weight=True
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... ).set_score_request(sample_weight=False)
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At the end of the **Usage Examples** section, we disable the configuration flag for
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metadata routing::
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>>> sklearn.set_config(enable_metadata_routing=False)
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.. _metadata_routing_models:
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Metadata Routing Support Status
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*******************************
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All consumers (i.e. simple estimators which only consume metadata and don't
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route them) support metadata routing, meaning they can be used inside
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meta-estimators which support metadata routing. However, development of support
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for metadata routing for meta-estimators is in progress, and here is a list of
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meta-estimators and tools which support and don't yet support metadata routing.
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Meta-estimators and functions supporting metadata routing:
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- :class:`sklearn.calibration.CalibratedClassifierCV`
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- :class:`sklearn.compose.ColumnTransformer`
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- :class:`sklearn.covariance.GraphicalLassoCV`
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- :class:`sklearn.ensemble.VotingClassifier`
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- :class:`sklearn.ensemble.VotingRegressor`
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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.impute.IterativeImputer`
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- :class:`sklearn.linear_model.ElasticNetCV`
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- :class:`sklearn.linear_model.LarsCV`
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- :class:`sklearn.linear_model.LassoCV`
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- :class:`sklearn.linear_model.LassoLarsCV`
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- :class:`sklearn.linear_model.LogisticRegressionCV`
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- :class:`sklearn.linear_model.MultiTaskElasticNetCV`
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- :class:`sklearn.linear_model.MultiTaskLassoCV`
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- :class:`sklearn.linear_model.RANSACRegressor`
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- :class:`sklearn.linear_model.RidgeClassifierCV`
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- :class:`sklearn.linear_model.RidgeCV`
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- :class:`sklearn.model_selection.GridSearchCV`
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- :class:`sklearn.model_selection.HalvingGridSearchCV`
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- :class:`sklearn.model_selection.HalvingRandomSearchCV`
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- :class:`sklearn.model_selection.RandomizedSearchCV`
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- :func:`sklearn.model_selection.cross_validate`
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- :func:`sklearn.model_selection.cross_val_score`
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- :func:`sklearn.model_selection.cross_val_predict`
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- :class:`sklearn.multiclass.OneVsOneClassifier`
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- :class:`sklearn.multiclass.OneVsRestClassifier`
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- :class:`sklearn.multiclass.OutputCodeClassifier`
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- :class:`sklearn.multioutput.ClassifierChain`
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- :class:`sklearn.multioutput.MultiOutputClassifier`
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- :class:`sklearn.multioutput.MultiOutputRegressor`
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- :class:`sklearn.linear_model.OrthogonalMatchingPursuitCV`
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- :class:`sklearn.multioutput.RegressorChain`
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- :class:`sklearn.pipeline.FeatureUnion`
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- :class:`sklearn.pipeline.Pipeline`
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Meta-estimators and tools not supporting metadata routing yet:
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- :class:`sklearn.compose.TransformedTargetRegressor`
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- :class:`sklearn.ensemble.AdaBoostClassifier`
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- :class:`sklearn.ensemble.AdaBoostRegressor`
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- :class:`sklearn.ensemble.StackingClassifier`
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- :class:`sklearn.ensemble.StackingRegressor`
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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`
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- :class:`sklearn.impute.IterativeImputer`
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- :class:`sklearn.linear_model.RANSACRegressor`
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- :class:`sklearn.model_selection.learning_curve`
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- :class:`sklearn.model_selection.permutation_test_score`
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- :class:`sklearn.model_selection.validation_curve`
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- :class:`sklearn.semi_supervised.SelfTrainingClassifier`
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