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1. Metadata Routing#

Note

The Metadata Routing API is experimental, and is not implemented yet for many estimators. Please refer to the list of supported and unsupported models for more information. It may change without the usual deprecation cycle. By default this feature is not enabled. You can enable this feature by setting the enable_metadata_routing flag to True:

>>> import sklearn
>>> sklearn.set_config(enable_metadata_routing=True)

This guide demonstrates how metadata such as sample_weight can be routed and passed along to estimators, scorers, and CV splitters through meta-estimators such as Pipeline and GridSearchCV. In order to pass metadata to a method such as fit or score, the object consuming the metadata, must request it. For estimators and splitters, this is done via set_*_request methods, e.g. set_fit_request(...), and for scorers this is done via the set_score_request method. For grouped splitters such as GroupKFold, a groups parameter is requested by default. This is best demonstrated by the following examples.

If you are developing a scikit-learn compatible estimator or meta-estimator, you can check our related developer guide: Metadata Routing.

Note

Note that the methods and requirements introduced in this document are only relevant if you want to pass metadata (e.g. sample_weight) to a method. If you’re only passing X and y and no other parameter / metadata to methods such as fit, transform, etc, then you don’t need to set anything.

1.1. Usage Examples#

Here we present a few examples to show different common use-cases. The examples in this section require the following imports and data:

>>> import numpy as np
>>> from sklearn.metrics import make_scorer, accuracy_score
>>> from sklearn.linear_model import LogisticRegressionCV, LogisticRegression
>>> from sklearn.model_selection import cross_validate, GridSearchCV, GroupKFold
>>> from sklearn.feature_selection import SelectKBest
>>> from sklearn.pipeline import make_pipeline
>>> n_samples, n_features = 100, 4
>>> rng = np.random.RandomState(42)
>>> X = rng.rand(n_samples, n_features)
>>> y = rng.randint(0, 2, size=n_samples)
>>> my_groups = rng.randint(0, 10, size=n_samples)
>>> my_weights = rng.rand(n_samples)
>>> my_other_weights = rng.rand(n_samples)

1.1.1. Weighted scoring and fitting#

Here GroupKFold requests groups by default. However, we need to explicitly request weights for our scorer and the internal cross validation of LogisticRegressionCV. Both of these consumers know how to use metadata called sample_weight:

>>> weighted_acc = make_scorer(accuracy_score).set_score_request(
...     sample_weight=True
... )
>>> lr = LogisticRegressionCV(
...     cv=GroupKFold(), scoring=weighted_acc,
... ).set_fit_request(sample_weight=True)
>>> cv_results = cross_validate(
...     lr,
...     X,
...     y,
...     params={"sample_weight": my_weights, "groups": my_groups},
...     cv=GroupKFold(),
...     scoring=weighted_acc,
... )

Note that in this example, my_weights is passed to both the scorer and LogisticRegressionCV.

Error handling: if params={"sample_weigh": my_weights, ...} were passed (note the typo), cross_validate would raise an error, since sample_weigh was not requested by any of its underlying objects.

1.1.2. Weighted scoring and unweighted fitting#

When passing metadata such as sample_weight around, all sample_weight consumers require weights to be either explicitly requested or not requested (i.e. True or False) when used in another router such as a Pipeline or a *GridSearchCV. To perform an unweighted fit, we need to configure LogisticRegressionCV to not request sample weights, so that cross_validate does not pass the weights along:

>>> weighted_acc = make_scorer(accuracy_score).set_score_request(
...     sample_weight=True
... )
>>> lr = LogisticRegressionCV(
...     cv=GroupKFold(), scoring=weighted_acc,
... ).set_fit_request(sample_weight=False)
>>> cv_results = cross_validate(
...     lr,
...     X,
...     y,
...     cv=GroupKFold(),
...     params={"sample_weight": my_weights, "groups": my_groups},
...     scoring=weighted_acc,
... )

If linear_model.LogisticRegressionCV.set_fit_request has not been called, cross_validate will raise an error because sample_weight is passed in but LogisticRegressionCV would not be explicitly configured to recognize the weights.

1.1.3. Unweighted feature selection#

Setting request values for metadata are only required if the object, e.g. estimator, scorer, etc., is a consumer of that metadata Unlike LogisticRegressionCV, SelectKBest doesn’t consume weights and therefore no request value for sample_weight on its instance is set and sample_weight is not routed to it:

>>> weighted_acc = make_scorer(accuracy_score).set_score_request(
...     sample_weight=True
... )
>>> lr = LogisticRegressionCV(
...     cv=GroupKFold(), scoring=weighted_acc,
... ).set_fit_request(sample_weight=True)
>>> sel = SelectKBest(k=2)
>>> pipe = make_pipeline(sel, lr)
>>> cv_results = cross_validate(
...     pipe,
...     X,
...     y,
...     cv=GroupKFold(),
...     params={"sample_weight": my_weights, "groups": my_groups},
...     scoring=weighted_acc,
... )

1.1.4. Advanced: Different scoring and fitting weights#

Despite make_scorer and LogisticRegressionCV both expecting the key sample_weight, we can use aliases to pass different weights to different consumers. In this example, we pass scoring_weight to the scorer, and fitting_weight to LogisticRegressionCV:

>>> weighted_acc = make_scorer(accuracy_score).set_score_request(
...    sample_weight="scoring_weight"
... )
>>> lr = LogisticRegressionCV(
...     cv=GroupKFold(), scoring=weighted_acc,
... ).set_fit_request(sample_weight="fitting_weight")
>>> cv_results = cross_validate(
...     lr,
...     X,
...     y,
...     cv=GroupKFold(),
...     params={
...         "scoring_weight": my_weights,
...         "fitting_weight": my_other_weights,
...         "groups": my_groups,
...     },
...     scoring=weighted_acc,
... )

1.2. API Interface#

A consumer is an object (estimator, meta-estimator, scorer, splitter) which accepts and uses some metadata in at least one of its methods (fit, predict, inverse_transform, transform, score, split). Meta-estimators which only forward the metadata to other objects (the child estimator, scorers, or splitters) and don’t use the metadata themselves are not consumers. (Meta-)Estimators which route metadata to other objects are routers. A(n) (meta-)estimator can be a consumer and a router at the same time. (Meta-)Estimators and splitters expose a set_*_request method for each method which accepts at least one metadata. For instance, if an estimator supports sample_weight in fit and score, it exposes estimator.set_fit_request(sample_weight=value) and estimator.set_score_request(sample_weight=value). Here value can be:

  • True: method requests a sample_weight. This means if the metadata is provided, it will be used, otherwise no error is raised.

  • False: method does not request a sample_weight.

  • None: router will raise an error if sample_weight is passed. This is in almost all cases the default value when an object is instantiated and ensures the user sets the metadata requests explicitly when a metadata is passed. The only exception are Group*Fold splitters.

  • "param_name": if this estimator is used in a meta-estimator, the meta-estimator should forward "param_name" as sample_weight to this estimator. This means the mapping between the metadata required by the object, e.g. sample_weight and what is provided by the user, e.g. my_weights is done at the router level, and not by the object, e.g. estimator, itself.

Metadata are requested in the same way for scorers using set_score_request.

If a metadata, e.g. sample_weight, is passed by the user, the metadata request for all objects which potentially can consume sample_weight should be set by the user, otherwise an error is raised by the router object. For example, the following code raises an error, since it hasn’t been explicitly specified whether sample_weight should be passed to the estimator’s scorer or not:

>>> param_grid = {"C": [0.1, 1]}
>>> lr = LogisticRegression().set_fit_request(sample_weight=True)
>>> try:
...     GridSearchCV(
...         estimator=lr, param_grid=param_grid
...     ).fit(X, y, sample_weight=my_weights)
... except ValueError as e:
...     print(e)
[sample_weight] are passed but are not explicitly set as requested or not for
LogisticRegression.score

The issue can be fixed by explicitly setting the request value:

>>> lr = LogisticRegression().set_fit_request(
...     sample_weight=True
... ).set_score_request(sample_weight=False)

At the end we disable the configuration flag for metadata routing:

>>> sklearn.set_config(enable_metadata_routing=False)

1.3. Metadata Routing Support Status#

All consumers (i.e. simple estimators which only consume metadata and don’t route them) support metadata routing, meaning they can be used inside meta-estimators which support metadata routing. However, development of support for metadata routing for meta-estimators is in progress, and here is a list of meta-estimators and tools which support and don’t yet support metadata routing.

Meta-estimators and functions supporting metadata routing:

Meta-estimators and tools not supporting metadata routing yet:

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