640 lines
24 KiB
Python
640 lines
24 KiB
Python
"""
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================
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Metadata Routing
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================
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.. currentmodule:: sklearn
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This document shows how you can use the :ref:`metadata routing mechanism
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<metadata_routing>` in scikit-learn to route metadata through meta-estimators
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to the estimators consuming them. To better understand the rest of the
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document, we need to introduce two concepts: routers and consumers. A router is
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an object, in most cases a meta-estimator, which forwards given data and
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metadata to other objects and estimators. A consumer, on the other hand, is an
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object which accepts and uses a certain given metadata. For instance, an
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estimator taking into account ``sample_weight`` in its :term:`fit` method is a
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consumer of ``sample_weight``. It is possible for an object to be both a router
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and a consumer. For instance, a meta-estimator may take into account
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``sample_weight`` in certain calculations, but it may also route it to the
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underlying estimator.
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First a few imports and some random data for the rest of the script.
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"""
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# %%
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import warnings
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from pprint import pprint
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import numpy as np
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from sklearn import set_config
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from sklearn.base import (
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BaseEstimator,
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ClassifierMixin,
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MetaEstimatorMixin,
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RegressorMixin,
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TransformerMixin,
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clone,
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)
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from sklearn.linear_model import LinearRegression
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from sklearn.utils import metadata_routing
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from sklearn.utils.metadata_routing import (
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MetadataRouter,
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MethodMapping,
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get_routing_for_object,
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process_routing,
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)
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from sklearn.utils.validation import check_is_fitted
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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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# %%
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# This feature is only available if explicitly enabled:
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set_config(enable_metadata_routing=True)
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# %%
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# This utility function is a dummy to check if a metadata is passed.
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def check_metadata(obj, **kwargs):
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for key, value in kwargs.items():
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if value is not None:
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print(
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f"Received {key} of length = {len(value)} in {obj.__class__.__name__}."
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)
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else:
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print(f"{key} is None in {obj.__class__.__name__}.")
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# %%
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# A utility function to nicely print the routing information of an object
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def print_routing(obj):
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pprint(obj.get_metadata_routing()._serialize())
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# %%
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# Estimators
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# ----------
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# Here we demonstrate how an estimator can expose the required API to support
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# metadata routing as a consumer. Imagine a simple classifier accepting
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# ``sample_weight`` as a metadata on its ``fit`` and ``groups`` in its
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# ``predict`` method:
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class ExampleClassifier(ClassifierMixin, BaseEstimator):
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def fit(self, X, y, sample_weight=None):
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check_metadata(self, sample_weight=sample_weight)
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# all classifiers need to expose a classes_ attribute once they're fit.
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self.classes_ = np.array([0, 1])
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return self
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def predict(self, X, groups=None):
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check_metadata(self, groups=groups)
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# return a constant value of 1, not a very smart classifier!
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return np.ones(len(X))
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# %%
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# The above estimator now has all it needs to consume metadata. This is
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# accomplished by some magic done in :class:`~base.BaseEstimator`. There are
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# now three methods exposed by the above class: ``set_fit_request``,
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# ``set_predict_request``, and ``get_metadata_routing``. There is also a
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# ``set_score_request`` for ``sample_weight`` which is present since
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# :class:`~base.ClassifierMixin` implements a ``score`` method accepting
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# ``sample_weight``. The same applies to regressors which inherit from
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# :class:`~base.RegressorMixin`.
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#
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# By default, no metadata is requested, which we can see as:
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print_routing(ExampleClassifier())
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# %%
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# The above output means that ``sample_weight`` and ``groups`` are not
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# requested, but if a router is given those metadata, it should raise an error,
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# since the user has not explicitly set whether they are required or not. The
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# same is true for ``sample_weight`` in the ``score`` method, which is
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# inherited from :class:`~base.ClassifierMixin`. In order to explicitly set
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# request values for those metadata, we can use these methods:
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est = (
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ExampleClassifier()
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.set_fit_request(sample_weight=False)
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.set_predict_request(groups=True)
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.set_score_request(sample_weight=False)
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)
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print_routing(est)
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# %%
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# .. note ::
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# Please note that as long as the above estimator is not used in another
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# meta-estimator, the user does not need to set any requests for the
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# metadata and the set values are ignored, since a consumer does not
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# validate or route given metadata. A simple usage of the above estimator
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# would work as expected.
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est = ExampleClassifier()
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est.fit(X, y, sample_weight=my_weights)
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est.predict(X[:3, :], groups=my_groups)
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# %%
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# Now let's have a meta-estimator, which doesn't do much other than routing the
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# metadata.
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class MetaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
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def __init__(self, estimator):
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self.estimator = estimator
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def get_metadata_routing(self):
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# This method defines the routing for this meta-estimator.
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# In order to do so, a `MetadataRouter` instance is created, and the
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# right routing is added to it. More explanations follow.
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router = MetadataRouter(owner=self.__class__.__name__).add(
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estimator=self.estimator, method_mapping="one-to-one"
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)
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return router
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def fit(self, X, y, **fit_params):
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# meta-estimators are responsible for validating the given metadata.
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# `get_routing_for_object` is a safe way to construct a
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# `MetadataRouter` or a `MetadataRequest` from the given object.
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request_router = get_routing_for_object(self)
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request_router.validate_metadata(params=fit_params, method="fit")
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# we can use provided utility methods to map the given metadata to what
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# is required by the underlying estimator. Here `method` refers to the
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# parent's method, i.e. `fit` in this example.
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routed_params = request_router.route_params(params=fit_params, caller="fit")
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# the output has a key for each object's method which is used here,
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# i.e. parent's `fit` method, containing the metadata which should be
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# routed to them, based on the information provided in
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# `get_metadata_routing`.
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self.estimator_ = clone(self.estimator).fit(X, y, **routed_params.estimator.fit)
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self.classes_ = self.estimator_.classes_
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return self
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def predict(self, X, **predict_params):
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check_is_fitted(self)
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# same as in `fit`, we validate the given metadata
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request_router = get_routing_for_object(self)
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request_router.validate_metadata(params=predict_params, method="predict")
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# and then prepare the input to the underlying `predict` method.
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routed_params = request_router.route_params(
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params=predict_params, caller="predict"
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)
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return self.estimator_.predict(X, **routed_params.estimator.predict)
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# %%
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# Let's break down different parts of the above code.
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#
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# First, the :meth:`~utils.metadata_routing.get_routing_for_object` takes an
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# estimator (``self``) and returns a
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# :class:`~utils.metadata_routing.MetadataRouter` or a
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# :class:`~utils.metadata_routing.MetadataRequest` based on the output of the
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# estimator's ``get_metadata_routing`` method.
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#
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# Then in each method, we use the ``route_params`` method to construct a
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# dictionary of the form ``{"object_name": {"method_name": {"metadata":
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# value}}}`` to pass to the underlying estimator's method. The ``object_name``
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# (``estimator`` in the above ``routed_params.estimator.fit`` example) is the
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# same as the one added in the ``get_metadata_routing``. ``validate_metadata``
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# makes sure all given metadata are requested to avoid silent bugs. Now, we
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# illustrate the different behaviors and notably the type of errors raised:
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est = MetaClassifier(estimator=ExampleClassifier().set_fit_request(sample_weight=True))
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est.fit(X, y, sample_weight=my_weights)
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# %%
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# Note that the above example checks that ``sample_weight`` is correctly passed
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# to ``ExampleClassifier``, or else it would print that ``sample_weight`` is
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# ``None``:
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est.fit(X, y)
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# %%
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# If we pass an unknown metadata, an error is raised:
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try:
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est.fit(X, y, test=my_weights)
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except TypeError as e:
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print(e)
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# %%
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# And if we pass a metadata which is not explicitly requested:
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try:
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est.fit(X, y, sample_weight=my_weights).predict(X, groups=my_groups)
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except ValueError as e:
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print(e)
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# %%
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# Also, if we explicitly set it as not requested, but it is provided:
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est = MetaClassifier(
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estimator=ExampleClassifier()
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.set_fit_request(sample_weight=True)
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.set_predict_request(groups=False)
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)
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try:
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est.fit(X, y, sample_weight=my_weights).predict(X[:3, :], groups=my_groups)
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except TypeError as e:
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print(e)
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# %%
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# Another concept to introduce is **aliased metadata**. This is when an estimator
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# requests a metadata with a different name than the default value. For
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# instance, in a setting where there are two estimators in a pipeline, one
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# could request ``sample_weight1`` and the other ``sample_weight2``. Note that
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# this doesn't change what the estimator expects, it only tells the
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# meta-estimator how to map the provided metadata to what's required. Here's an
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# example, where we pass ``aliased_sample_weight`` to the meta-estimator, but
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# the meta-estimator understands that ``aliased_sample_weight`` is an alias for
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# ``sample_weight``, and passes it as ``sample_weight`` to the underlying
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# estimator:
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est = MetaClassifier(
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estimator=ExampleClassifier().set_fit_request(sample_weight="aliased_sample_weight")
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)
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est.fit(X, y, aliased_sample_weight=my_weights)
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# %%
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# And passing ``sample_weight`` here will fail since it is requested with an
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# alias and ``sample_weight`` with that name is not requested:
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try:
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est.fit(X, y, sample_weight=my_weights)
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except TypeError as e:
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print(e)
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# %%
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# This leads us to the ``get_metadata_routing``. The way routing works in
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# scikit-learn is that consumers request what they need, and routers pass that
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# along. Additionally, a router exposes what it requires itself so that it can
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# be used inside another router, e.g. a pipeline inside a grid search object.
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# The output of the ``get_metadata_routing`` which is a dictionary
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# representation of a :class:`~utils.metadata_routing.MetadataRouter`, includes
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# the complete tree of requested metadata by all nested objects and their
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# corresponding method routings, i.e. which method of a sub-estimator is used
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# in which method of a meta-estimator:
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print_routing(est)
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# %%
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# As you can see, the only metadata requested for method ``fit`` is
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# ``"sample_weight"`` with ``"aliased_sample_weight"`` as the alias. The
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# ``~utils.metadata_routing.MetadataRouter`` class enables us to easily create
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# the routing object which would create the output we need for our
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# ``get_metadata_routing``. In the above implementation,
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# ``mapping="one-to-one"`` means there is a one to one mapping between
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# sub-estimator's methods and meta-estimator's ones, i.e. ``fit`` used in
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# ``fit`` and so on. In order to understand how aliases work in
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# meta-estimators, imagine our meta-estimator inside another one:
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meta_est = MetaClassifier(estimator=est).fit(X, y, aliased_sample_weight=my_weights)
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# %%
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# In the above example, this is how each ``fit`` method will call the
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# sub-estimator's ``fit``::
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#
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# meta_est.fit(X, y, aliased_sample_weight=my_weights):
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# ... # this estimator (est), expects aliased_sample_weight as seen above
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# self.estimator_.fit(X, y, aliased_sample_weight=aliased_sample_weight):
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# ... # now est passes aliased_sample_weight's value as sample_weight,
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# # which is expected by the sub-estimator
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# self.estimator_.fit(X, y, sample_weight=aliased_sample_weight)
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# ...
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# %%
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# Router and Consumer
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# -------------------
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# To show how a slightly more complex case would work, consider a case
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# where a meta-estimator uses some metadata, but it also routes them to an
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# underlying estimator. In this case, this meta-estimator is a consumer and a
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# router at the same time. This is how we can implement one, and it is very
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# similar to what we had before, with a few tweaks.
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class RouterConsumerClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
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def __init__(self, estimator):
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self.estimator = estimator
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def get_metadata_routing(self):
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router = (
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MetadataRouter(owner=self.__class__.__name__)
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.add_self_request(self)
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.add(estimator=self.estimator, method_mapping="one-to-one")
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)
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return router
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def fit(self, X, y, sample_weight, **fit_params):
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if self.estimator is None:
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raise ValueError("estimator cannot be None!")
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check_metadata(self, sample_weight=sample_weight)
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if sample_weight is not None:
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fit_params["sample_weight"] = sample_weight
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# meta-estimators are responsible for validating the given metadata
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request_router = get_routing_for_object(self)
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request_router.validate_metadata(params=fit_params, method="fit")
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# we can use provided utility methods to map the given metadata to what
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# is required by the underlying estimator
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params = request_router.route_params(params=fit_params, caller="fit")
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self.estimator_ = clone(self.estimator).fit(X, y, **params.estimator.fit)
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self.classes_ = self.estimator_.classes_
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return self
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def predict(self, X, **predict_params):
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check_is_fitted(self)
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# same as in ``fit``, we validate the given metadata
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request_router = get_routing_for_object(self)
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request_router.validate_metadata(params=predict_params, method="predict")
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# and then prepare the input to the underlying ``predict`` method.
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params = request_router.route_params(params=predict_params, caller="predict")
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return self.estimator_.predict(X, **params.estimator.predict)
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# %%
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# The key parts where the above estimator differs from our previous
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# meta-estimator is accepting ``sample_weight`` explicitly in ``fit`` and
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# including it in ``fit_params``. Making ``sample_weight`` an explicit argument
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# makes sure ``set_fit_request(sample_weight=...)`` is present for this class.
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# In a sense, this means the estimator is both a consumer, as well as a router
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# of ``sample_weight``.
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#
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# In ``get_metadata_routing``, we add ``self`` to the routing using
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# ``add_self_request`` to indicate this estimator is consuming
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# ``sample_weight`` as well as being a router; which also adds a
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# ``$self_request`` key to the routing info as illustrated bellow. Now let's
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# look at some examples:
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# %%
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# - No metadata requested
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est = RouterConsumerClassifier(estimator=ExampleClassifier())
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print_routing(est)
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# %%
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# - ``sample_weight`` requested by underlying estimator
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est = RouterConsumerClassifier(
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estimator=ExampleClassifier().set_fit_request(sample_weight=True)
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)
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print_routing(est)
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# %%
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# - ``sample_weight`` requested by meta-estimator
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est = RouterConsumerClassifier(estimator=ExampleClassifier()).set_fit_request(
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sample_weight=True
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)
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print_routing(est)
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# %%
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# Note the difference in the requested metadata representations above.
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#
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# - We can also alias the metadata to pass different values to them:
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est = RouterConsumerClassifier(
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estimator=ExampleClassifier().set_fit_request(sample_weight="clf_sample_weight"),
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).set_fit_request(sample_weight="meta_clf_sample_weight")
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print_routing(est)
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# %%
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# However, ``fit`` of the meta-estimator only needs the alias for the
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# sub-estimator, since it doesn't validate and route its own required metadata:
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est.fit(X, y, sample_weight=my_weights, clf_sample_weight=my_other_weights)
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# %%
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# - Alias only on the sub-estimator. This is useful if we don't want the
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# meta-estimator to use the metadata, and we only want the metadata to be used
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# by the sub-estimator.
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est = RouterConsumerClassifier(
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estimator=ExampleClassifier().set_fit_request(sample_weight="aliased_sample_weight")
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).set_fit_request(sample_weight=True)
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print_routing(est)
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# %%
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# Simple Pipeline
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# ---------------
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# A slightly more complicated use-case is a meta-estimator which does something
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# similar to the :class:`~pipeline.Pipeline`. Here is a meta-estimator, which
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# accepts a transformer and a classifier, and applies the transformer before
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# running the classifier.
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class SimplePipeline(ClassifierMixin, BaseEstimator):
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_required_parameters = ["estimator"]
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def __init__(self, transformer, classifier):
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self.transformer = transformer
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self.classifier = classifier
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def get_metadata_routing(self):
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router = (
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MetadataRouter(owner=self.__class__.__name__)
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.add(
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transformer=self.transformer,
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method_mapping=MethodMapping()
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.add(callee="fit", caller="fit")
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.add(callee="transform", caller="fit")
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.add(callee="transform", caller="predict"),
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)
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.add(classifier=self.classifier, method_mapping="one-to-one")
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)
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return router
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def fit(self, X, y, **fit_params):
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params = process_routing(self, "fit", fit_params)
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self.transformer_ = clone(self.transformer).fit(X, y, **params.transformer.fit)
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X_transformed = self.transformer_.transform(X, **params.transformer.transform)
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self.classifier_ = clone(self.classifier).fit(
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X_transformed, y, **params.classifier.fit
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)
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return self
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def predict(self, X, **predict_params):
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params = process_routing(self, "predict", predict_params)
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X_transformed = self.transformer_.transform(X, **params.transformer.transform)
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return self.classifier_.predict(X_transformed, **params.classifier.predict)
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# %%
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# Note the usage of :class:`~utils.metadata_routing.MethodMapping` to declare
|
|
# which methods of the child estimator (callee) are used in which methods of
|
|
# the meta estimator (caller). As you can see, we use the transformer's
|
|
# ``transform`` and ``fit`` methods in ``fit``, and its ``transform`` method in
|
|
# ``predict``, and that's what you see implemented in the routing structure of
|
|
# the pipeline class.
|
|
#
|
|
# Another difference in the above example with the previous ones is the usage
|
|
# of :func:`~utils.metadata_routing.process_routing`, which processes the input
|
|
# parameters, does the required validation, and returns the `params` which we
|
|
# had created in previous examples. This reduces the boilerplate code a
|
|
# developer needs to write in each meta-estimator's method. Developers are
|
|
# strongly recommended to use this function unless there is a good reason
|
|
# against it.
|
|
#
|
|
# In order to test the above pipeline, let's add an example transformer.
|
|
|
|
|
|
class ExampleTransformer(TransformerMixin, BaseEstimator):
|
|
def fit(self, X, y, sample_weight=None):
|
|
check_metadata(self, sample_weight=sample_weight)
|
|
return self
|
|
|
|
def transform(self, X, groups=None):
|
|
check_metadata(self, groups=groups)
|
|
return X
|
|
|
|
def fit_transform(self, X, y, sample_weight=None, groups=None):
|
|
return self.fit(X, y, sample_weight).transform(X, groups)
|
|
|
|
|
|
# %%
|
|
# Note that in the above example, we have implemented ``fit_transform`` which
|
|
# calls ``fit`` and ``transform`` with the appropriate metadata. This is only
|
|
# required if ``transform`` accepts metadata, since the default ``fit_transform``
|
|
# implementation in :class:`~base.TransformerMixin` doesn't pass metadata to
|
|
# ``transform``.
|
|
#
|
|
# Now we can test our pipeline, and see if metadata is correctly passed around.
|
|
# This example uses our simple pipeline, and our transformer, and our
|
|
# consumer+router estimator which uses our simple classifier.
|
|
|
|
est = SimplePipeline(
|
|
transformer=ExampleTransformer()
|
|
# we transformer's fit to receive sample_weight
|
|
.set_fit_request(sample_weight=True)
|
|
# we want transformer's transform to receive groups
|
|
.set_transform_request(groups=True),
|
|
classifier=RouterConsumerClassifier(
|
|
estimator=ExampleClassifier()
|
|
# we want this sub-estimator to receive sample_weight in fit
|
|
.set_fit_request(sample_weight=True)
|
|
# but not groups in predict
|
|
.set_predict_request(groups=False),
|
|
).set_fit_request(
|
|
# and we want the meta-estimator to receive sample_weight as well
|
|
sample_weight=True
|
|
),
|
|
)
|
|
est.fit(X, y, sample_weight=my_weights, groups=my_groups).predict(
|
|
X[:3], groups=my_groups
|
|
)
|
|
|
|
# %%
|
|
# Deprecation / Default Value Change
|
|
# ----------------------------------
|
|
# In this section we show how one should handle the case where a router becomes
|
|
# also a consumer, especially when it consumes the same metadata as its
|
|
# sub-estimator, or a consumer starts consuming a metadata which it wasn't in
|
|
# an older release. In this case, a warning should be raised for a while, to
|
|
# let users know the behavior is changed from previous versions.
|
|
|
|
|
|
class MetaRegressor(MetaEstimatorMixin, RegressorMixin, BaseEstimator):
|
|
def __init__(self, estimator):
|
|
self.estimator = estimator
|
|
|
|
def fit(self, X, y, **fit_params):
|
|
params = process_routing(self, "fit", fit_params)
|
|
self.estimator_ = clone(self.estimator).fit(X, y, **params.estimator.fit)
|
|
|
|
def get_metadata_routing(self):
|
|
router = MetadataRouter(owner=self.__class__.__name__).add(
|
|
estimator=self.estimator, method_mapping="one-to-one"
|
|
)
|
|
return router
|
|
|
|
|
|
# %%
|
|
# As explained above, this is now a valid usage:
|
|
|
|
reg = MetaRegressor(estimator=LinearRegression().set_fit_request(sample_weight=True))
|
|
reg.fit(X, y, sample_weight=my_weights)
|
|
|
|
|
|
# %%
|
|
# Now imagine we further develop ``MetaRegressor`` and it now also *consumes*
|
|
# ``sample_weight``:
|
|
|
|
|
|
class WeightedMetaRegressor(MetaEstimatorMixin, RegressorMixin, BaseEstimator):
|
|
__metadata_request__fit = {"sample_weight": metadata_routing.WARN}
|
|
|
|
def __init__(self, estimator):
|
|
self.estimator = estimator
|
|
|
|
def fit(self, X, y, sample_weight=None, **fit_params):
|
|
params = process_routing(self, "fit", fit_params, sample_weight=sample_weight)
|
|
check_metadata(self, sample_weight=sample_weight)
|
|
self.estimator_ = clone(self.estimator).fit(X, y, **params.estimator.fit)
|
|
|
|
def get_metadata_routing(self):
|
|
router = (
|
|
MetadataRouter(owner=self.__class__.__name__)
|
|
.add_self_request(self)
|
|
.add(estimator=self.estimator, method_mapping="one-to-one")
|
|
)
|
|
return router
|
|
|
|
|
|
# %%
|
|
# The above implementation is almost no different than ``MetaRegressor``, and
|
|
# because of the default request value defined in ``__metadata_request__fit``
|
|
# there is a warning raised.
|
|
|
|
with warnings.catch_warnings(record=True) as record:
|
|
WeightedMetaRegressor(
|
|
estimator=LinearRegression().set_fit_request(sample_weight=False)
|
|
).fit(X, y, sample_weight=my_weights)
|
|
for w in record:
|
|
print(w.message)
|
|
|
|
|
|
# %%
|
|
# When an estimator supports a metadata which wasn't supported before, the
|
|
# following pattern can be used to warn the users about it.
|
|
|
|
|
|
class ExampleRegressor(RegressorMixin, BaseEstimator):
|
|
__metadata_request__fit = {"sample_weight": metadata_routing.WARN}
|
|
|
|
def fit(self, X, y, sample_weight=None):
|
|
check_metadata(self, sample_weight=sample_weight)
|
|
return self
|
|
|
|
def predict(self, X):
|
|
return np.zeros(shape=(len(X)))
|
|
|
|
|
|
with warnings.catch_warnings(record=True) as record:
|
|
MetaRegressor(estimator=ExampleRegressor()).fit(X, y, sample_weight=my_weights)
|
|
for w in record:
|
|
print(w.message)
|
|
|
|
# %%
|
|
# Third Party Development and scikit-learn Dependency
|
|
# ---------------------------------------------------
|
|
#
|
|
# As seen above, information is communicated between classes using
|
|
# :class:`~utils.metadata_routing.MetadataRequest` and
|
|
# :class:`~utils.metadata_routing.MetadataRouter`. It is strongly not advised,
|
|
# but possible to vendor the tools related to metadata-routing if you strictly
|
|
# want to have a scikit-learn compatible estimator, without depending on the
|
|
# scikit-learn package. If the following conditions are met, you do NOT need to
|
|
# modify your code at all:
|
|
#
|
|
# - your estimator inherits from :class:`~base.BaseEstimator`
|
|
# - the parameters consumed by your estimator's methods, e.g. ``fit``, are
|
|
# explicitly defined in the method's signature, as opposed to being
|
|
# ``*args`` or ``*kwargs``.
|
|
# - you do not route any metadata to the underlying objects, i.e. you're not a
|
|
# *router*.
|