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scikit-learn 1.3.2
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# 1. Metadata Routing

Note

The Metadata Routing API is experimental, and is not implemented yet for many estimators. 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,
    ...     props={"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 `props={"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(),
    ...     props={"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(),
    ...     props={"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(),
    ...     props={
    ...         "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)