.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/miscellaneous/plot_metadata_routing.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_miscellaneous_plot_metadata_routing.py: ================ Metadata Routing ================ .. currentmodule:: sklearn This document shows how you can use the :ref:`metadata routing mechanism ` in scikit-learn to route metadata through meta-estimators to the estimators consuming them. To better understand the rest of the document, we need to introduce two concepts: routers and consumers. A router is an object, in most cases a meta-estimator, which forwards given data and metadata to other objects and estimators. A consumer, on the other hand, is an object which accepts and uses a certain given metadata. For instance, an estimator taking into account ``sample_weight`` in its :term:`fit` method is a consumer of ``sample_weight``. It is possible for an object to be both a router and a consumer. For instance, a meta-estimator may take into account ``sample_weight`` in certain calculations, but it may also route it to the underlying estimator. First a few imports and some random data for the rest of the script. .. GENERATED FROM PYTHON SOURCE LINES 24-57 .. code-block:: Python import warnings from pprint import pprint import numpy as np from sklearn import set_config from sklearn.base import ( BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, TransformerMixin, clone, ) from sklearn.linear_model import LinearRegression from sklearn.utils import metadata_routing from sklearn.utils.metadata_routing import ( MetadataRouter, MethodMapping, get_routing_for_object, process_routing, ) from sklearn.utils.validation import check_is_fitted 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) .. GENERATED FROM PYTHON SOURCE LINES 58-59 This feature is only available if explicitly enabled: .. GENERATED FROM PYTHON SOURCE LINES 59-61 .. code-block:: Python set_config(enable_metadata_routing=True) .. GENERATED FROM PYTHON SOURCE LINES 62-63 This utility function is a dummy to check if a metadata is passed. .. GENERATED FROM PYTHON SOURCE LINES 63-75 .. code-block:: Python def check_metadata(obj, **kwargs): for key, value in kwargs.items(): if value is not None: print( f"Received {key} of length = {len(value)} in {obj.__class__.__name__}." ) else: print(f"{key} is None in {obj.__class__.__name__}.") .. GENERATED FROM PYTHON SOURCE LINES 76-77 A utility function to nicely print the routing information of an object .. GENERATED FROM PYTHON SOURCE LINES 77-81 .. code-block:: Python def print_routing(obj): pprint(obj.get_metadata_routing()._serialize()) .. GENERATED FROM PYTHON SOURCE LINES 82-88 Estimators ---------- Here we demonstrate how an estimator can expose the required API to support metadata routing as a consumer. Imagine a simple classifier accepting ``sample_weight`` as a metadata on its ``fit`` and ``groups`` in its ``predict`` method: .. GENERATED FROM PYTHON SOURCE LINES 88-103 .. code-block:: Python class ExampleClassifier(ClassifierMixin, BaseEstimator): def fit(self, X, y, sample_weight=None): check_metadata(self, sample_weight=sample_weight) # all classifiers need to expose a classes_ attribute once they're fit. self.classes_ = np.array([0, 1]) return self def predict(self, X, groups=None): check_metadata(self, groups=groups) # return a constant value of 1, not a very smart classifier! return np.ones(len(X)) .. GENERATED FROM PYTHON SOURCE LINES 104-114 The above estimator now has all it needs to consume metadata. This is accomplished by some magic done in :class:`~base.BaseEstimator`. There are now three methods exposed by the above class: ``set_fit_request``, ``set_predict_request``, and ``get_metadata_routing``. There is also a ``set_score_request`` for ``sample_weight`` which is present since :class:`~base.ClassifierMixin` implements a ``score`` method accepting ``sample_weight``. The same applies to regressors which inherit from :class:`~base.RegressorMixin`. By default, no metadata is requested, which we can see as: .. GENERATED FROM PYTHON SOURCE LINES 114-117 .. code-block:: Python print_routing(ExampleClassifier()) .. rst-class:: sphx-glr-script-out .. code-block:: none {'fit': {'sample_weight': None}, 'predict': {'groups': None}, 'score': {'sample_weight': None}} .. GENERATED FROM PYTHON SOURCE LINES 118-124 The above output means that ``sample_weight`` and ``groups`` are not requested, but if a router is given those metadata, it should raise an error, since the user has not explicitly set whether they are required or not. The same is true for ``sample_weight`` in the ``score`` method, which is inherited from :class:`~base.ClassifierMixin`. In order to explicitly set request values for those metadata, we can use these methods: .. GENERATED FROM PYTHON SOURCE LINES 124-133 .. code-block:: Python est = ( ExampleClassifier() .set_fit_request(sample_weight=False) .set_predict_request(groups=True) .set_score_request(sample_weight=False) ) print_routing(est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'fit': {'sample_weight': False}, 'predict': {'groups': True}, 'score': {'sample_weight': False}} .. GENERATED FROM PYTHON SOURCE LINES 134-140 .. note :: Please note that as long as the above estimator is not used in another meta-estimator, the user does not need to set any requests for the metadata and the set values are ignored, since a consumer does not validate or route given metadata. A simple usage of the above estimator would work as expected. .. GENERATED FROM PYTHON SOURCE LINES 140-145 .. code-block:: Python est = ExampleClassifier() est.fit(X, y, sample_weight=my_weights) est.predict(X[:3, :], groups=my_groups) .. rst-class:: sphx-glr-script-out .. code-block:: none Received sample_weight of length = 100 in ExampleClassifier. Received groups of length = 100 in ExampleClassifier. array([1., 1., 1.]) .. GENERATED FROM PYTHON SOURCE LINES 146-148 Now let's have a meta-estimator, which doesn't do much other than routing the metadata. .. GENERATED FROM PYTHON SOURCE LINES 148-194 .. code-block:: Python class MetaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator): def __init__(self, estimator): self.estimator = estimator def get_metadata_routing(self): # This method defines the routing for this meta-estimator. # In order to do so, a `MetadataRouter` instance is created, and the # right routing is added to it. More explanations follow. router = MetadataRouter(owner=self.__class__.__name__).add( estimator=self.estimator, method_mapping="one-to-one" ) return router def fit(self, X, y, **fit_params): # meta-estimators are responsible for validating the given metadata. # `get_routing_for_object` is a safe way to construct a # `MetadataRouter` or a `MetadataRequest` from the given object. request_router = get_routing_for_object(self) request_router.validate_metadata(params=fit_params, method="fit") # we can use provided utility methods to map the given metadata to what # is required by the underlying estimator. Here `method` refers to the # parent's method, i.e. `fit` in this example. routed_params = request_router.route_params(params=fit_params, caller="fit") # the output has a key for each object's method which is used here, # i.e. parent's `fit` method, containing the metadata which should be # routed to them, based on the information provided in # `get_metadata_routing`. self.estimator_ = clone(self.estimator).fit(X, y, **routed_params.estimator.fit) self.classes_ = self.estimator_.classes_ return self def predict(self, X, **predict_params): check_is_fitted(self) # same as in `fit`, we validate the given metadata request_router = get_routing_for_object(self) request_router.validate_metadata(params=predict_params, method="predict") # and then prepare the input to the underlying `predict` method. routed_params = request_router.route_params( params=predict_params, caller="predict" ) return self.estimator_.predict(X, **routed_params.estimator.predict) .. GENERATED FROM PYTHON SOURCE LINES 195-210 Let's break down different parts of the above code. First, the :meth:`~utils.metadata_routing.get_routing_for_object` takes an estimator (``self``) and returns a :class:`~utils.metadata_routing.MetadataRouter` or a :class:`~utils.metadata_routing.MetadataRequest` based on the output of the estimator's ``get_metadata_routing`` method. Then in each method, we use the ``route_params`` method to construct a dictionary of the form ``{"object_name": {"method_name": {"metadata": value}}}`` to pass to the underlying estimator's method. The ``object_name`` (``estimator`` in the above ``routed_params.estimator.fit`` example) is the same as the one added in the ``get_metadata_routing``. ``validate_metadata`` makes sure all given metadata are requested to avoid silent bugs. Now, we illustrate the different behaviors and notably the type of errors raised: .. GENERATED FROM PYTHON SOURCE LINES 210-214 .. code-block:: Python est = MetaClassifier(estimator=ExampleClassifier().set_fit_request(sample_weight=True)) est.fit(X, y, sample_weight=my_weights) .. rst-class:: sphx-glr-script-out .. code-block:: none Received sample_weight of length = 100 in ExampleClassifier. .. raw:: html 
MetaClassifier(estimator=ExampleClassifier())
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 215-218 Note that the above example checks that ``sample_weight`` is correctly passed to ``ExampleClassifier``, or else it would print that ``sample_weight`` is ``None``: .. GENERATED FROM PYTHON SOURCE LINES 218-221 .. code-block:: Python est.fit(X, y) .. rst-class:: sphx-glr-script-out .. code-block:: none sample_weight is None in ExampleClassifier. .. raw:: html 
MetaClassifier(estimator=ExampleClassifier())
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 222-223 If we pass an unknown metadata, an error is raised: .. GENERATED FROM PYTHON SOURCE LINES 223-228 .. code-block:: Python try: est.fit(X, y, test=my_weights) except TypeError as e: print(e) .. rst-class:: sphx-glr-script-out .. code-block:: none MetaClassifier.fit got unexpected argument(s) {'test'}, which are not requested metadata in any object. .. GENERATED FROM PYTHON SOURCE LINES 229-230 And if we pass a metadata which is not explicitly requested: .. GENERATED FROM PYTHON SOURCE LINES 230-235 .. code-block:: Python try: est.fit(X, y, sample_weight=my_weights).predict(X, groups=my_groups) except ValueError as e: print(e) .. rst-class:: sphx-glr-script-out .. code-block:: none Received sample_weight of length = 100 in ExampleClassifier. [groups] are passed but are not explicitly set as requested or not for ExampleClassifier.predict .. GENERATED FROM PYTHON SOURCE LINES 236-237 Also, if we explicitly set it as not requested, but it is provided: .. GENERATED FROM PYTHON SOURCE LINES 237-247 .. code-block:: Python est = MetaClassifier( estimator=ExampleClassifier() .set_fit_request(sample_weight=True) .set_predict_request(groups=False) ) try: est.fit(X, y, sample_weight=my_weights).predict(X[:3, :], groups=my_groups) except TypeError as e: print(e) .. rst-class:: sphx-glr-script-out .. code-block:: none Received sample_weight of length = 100 in ExampleClassifier. MetaClassifier.predict got unexpected argument(s) {'groups'}, which are not requested metadata in any object. .. GENERATED FROM PYTHON SOURCE LINES 248-258 Another concept to introduce is **aliased metadata**. This is when an estimator requests a metadata with a different name than the default value. For instance, in a setting where there are two estimators in a pipeline, one could request ``sample_weight1`` and the other ``sample_weight2``. Note that this doesn't change what the estimator expects, it only tells the meta-estimator how to map the provided metadata to what's required. Here's an example, where we pass ``aliased_sample_weight`` to the meta-estimator, but the meta-estimator understands that ``aliased_sample_weight`` is an alias for ``sample_weight``, and passes it as ``sample_weight`` to the underlying estimator: .. GENERATED FROM PYTHON SOURCE LINES 258-263 .. code-block:: Python est = MetaClassifier( estimator=ExampleClassifier().set_fit_request(sample_weight="aliased_sample_weight") ) est.fit(X, y, aliased_sample_weight=my_weights) .. rst-class:: sphx-glr-script-out .. code-block:: none Received sample_weight of length = 100 in ExampleClassifier. .. raw:: html 
MetaClassifier(estimator=ExampleClassifier())
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 264-266 And passing ``sample_weight`` here will fail since it is requested with an alias and ``sample_weight`` with that name is not requested: .. GENERATED FROM PYTHON SOURCE LINES 266-271 .. code-block:: Python try: est.fit(X, y, sample_weight=my_weights) except TypeError as e: print(e) .. rst-class:: sphx-glr-script-out .. code-block:: none MetaClassifier.fit got unexpected argument(s) {'sample_weight'}, which are not requested metadata in any object. .. GENERATED FROM PYTHON SOURCE LINES 272-281 This leads us to the ``get_metadata_routing``. The way routing works in scikit-learn is that consumers request what they need, and routers pass that along. Additionally, a router exposes what it requires itself so that it can be used inside another router, e.g. a pipeline inside a grid search object. The output of the ``get_metadata_routing`` which is a dictionary representation of a :class:`~utils.metadata_routing.MetadataRouter`, includes the complete tree of requested metadata by all nested objects and their corresponding method routings, i.e. which method of a sub-estimator is used in which method of a meta-estimator: .. GENERATED FROM PYTHON SOURCE LINES 281-284 .. code-block:: Python print_routing(est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'}, {'callee': 'partial_fit', 'caller': 'partial_fit'}, {'callee': 'predict', 'caller': 'predict'}, {'callee': 'predict_proba', 'caller': 'predict_proba'}, {'callee': 'predict_log_proba', 'caller': 'predict_log_proba'}, {'callee': 'decision_function', 'caller': 'decision_function'}, {'callee': 'score', 'caller': 'score'}, {'callee': 'split', 'caller': 'split'}, {'callee': 'transform', 'caller': 'transform'}, {'callee': 'inverse_transform', 'caller': 'inverse_transform'}, {'callee': 'fit_transform', 'caller': 'fit_transform'}, {'callee': 'fit_predict', 'caller': 'fit_predict'}], 'router': {'fit': {'sample_weight': 'aliased_sample_weight'}, 'predict': {'groups': None}, 'score': {'sample_weight': None}}}} .. GENERATED FROM PYTHON SOURCE LINES 285-294 As you can see, the only metadata requested for method ``fit`` is ``"sample_weight"`` with ``"aliased_sample_weight"`` as the alias. The ``~utils.metadata_routing.MetadataRouter`` class enables us to easily create the routing object which would create the output we need for our ``get_metadata_routing``. In the above implementation, ``mapping="one-to-one"`` means there is a one to one mapping between sub-estimator's methods and meta-estimator's ones, i.e. ``fit`` used in ``fit`` and so on. In order to understand how aliases work in meta-estimators, imagine our meta-estimator inside another one: .. GENERATED FROM PYTHON SOURCE LINES 294-297 .. code-block:: Python meta_est = MetaClassifier(estimator=est).fit(X, y, aliased_sample_weight=my_weights) .. rst-class:: sphx-glr-script-out .. code-block:: none Received sample_weight of length = 100 in ExampleClassifier. .. GENERATED FROM PYTHON SOURCE LINES 298-308 In the above example, this is how each ``fit`` method will call the sub-estimator's ``fit``:: meta_est.fit(X, y, aliased_sample_weight=my_weights): ... # this estimator (est), expects aliased_sample_weight as seen above self.estimator_.fit(X, y, aliased_sample_weight=aliased_sample_weight): ... # now est passes aliased_sample_weight's value as sample_weight, # which is expected by the sub-estimator self.estimator_.fit(X, y, sample_weight=aliased_sample_weight) ... .. GENERATED FROM PYTHON SOURCE LINES 310-317 Router and Consumer ------------------- To show how a slightly more complex case would work, consider a case where a meta-estimator uses some metadata, but it also routes them to an underlying estimator. In this case, this meta-estimator is a consumer and a router at the same time. This is how we can implement one, and it is very similar to what we had before, with a few tweaks. .. GENERATED FROM PYTHON SOURCE LINES 317-360 .. code-block:: Python class RouterConsumerClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator): def __init__(self, estimator): self.estimator = estimator 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 def fit(self, X, y, sample_weight, **fit_params): if self.estimator is None: raise ValueError("estimator cannot be None!") check_metadata(self, sample_weight=sample_weight) if sample_weight is not None: fit_params["sample_weight"] = sample_weight # meta-estimators are responsible for validating the given metadata request_router = get_routing_for_object(self) request_router.validate_metadata(params=fit_params, method="fit") # we can use provided utility methods to map the given metadata to what # is required by the underlying estimator params = request_router.route_params(params=fit_params, caller="fit") self.estimator_ = clone(self.estimator).fit(X, y, **params.estimator.fit) self.classes_ = self.estimator_.classes_ return self def predict(self, X, **predict_params): check_is_fitted(self) # same as in ``fit``, we validate the given metadata request_router = get_routing_for_object(self) request_router.validate_metadata(params=predict_params, method="predict") # and then prepare the input to the underlying ``predict`` method. params = request_router.route_params(params=predict_params, caller="predict") return self.estimator_.predict(X, **params.estimator.predict) .. GENERATED FROM PYTHON SOURCE LINES 361-373 The key parts where the above estimator differs from our previous meta-estimator is accepting ``sample_weight`` explicitly in ``fit`` and including it in ``fit_params``. Making ``sample_weight`` an explicit argument makes sure ``set_fit_request(sample_weight=...)`` is present for this class. In a sense, this means the estimator is both a consumer, as well as a router of ``sample_weight``. In ``get_metadata_routing``, we add ``self`` to the routing using ``add_self_request`` to indicate this estimator is consuming ``sample_weight`` as well as being a router; which also adds a ``$self_request`` key to the routing info as illustrated below. Now let's look at some examples: .. GENERATED FROM PYTHON SOURCE LINES 375-376 - No metadata requested .. GENERATED FROM PYTHON SOURCE LINES 376-380 .. code-block:: Python est = RouterConsumerClassifier(estimator=ExampleClassifier()) print_routing(est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'$self_request': {'fit': {'sample_weight': None}, 'score': {'sample_weight': None}}, 'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'}, {'callee': 'partial_fit', 'caller': 'partial_fit'}, {'callee': 'predict', 'caller': 'predict'}, {'callee': 'predict_proba', 'caller': 'predict_proba'}, {'callee': 'predict_log_proba', 'caller': 'predict_log_proba'}, {'callee': 'decision_function', 'caller': 'decision_function'}, {'callee': 'score', 'caller': 'score'}, {'callee': 'split', 'caller': 'split'}, {'callee': 'transform', 'caller': 'transform'}, {'callee': 'inverse_transform', 'caller': 'inverse_transform'}, {'callee': 'fit_transform', 'caller': 'fit_transform'}, {'callee': 'fit_predict', 'caller': 'fit_predict'}], 'router': {'fit': {'sample_weight': None}, 'predict': {'groups': None}, 'score': {'sample_weight': None}}}} .. GENERATED FROM PYTHON SOURCE LINES 381-382 - ``sample_weight`` requested by underlying estimator .. GENERATED FROM PYTHON SOURCE LINES 382-387 .. code-block:: Python est = RouterConsumerClassifier( estimator=ExampleClassifier().set_fit_request(sample_weight=True) ) print_routing(est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'$self_request': {'fit': {'sample_weight': None}, 'score': {'sample_weight': None}}, 'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'}, {'callee': 'partial_fit', 'caller': 'partial_fit'}, {'callee': 'predict', 'caller': 'predict'}, {'callee': 'predict_proba', 'caller': 'predict_proba'}, {'callee': 'predict_log_proba', 'caller': 'predict_log_proba'}, {'callee': 'decision_function', 'caller': 'decision_function'}, {'callee': 'score', 'caller': 'score'}, {'callee': 'split', 'caller': 'split'}, {'callee': 'transform', 'caller': 'transform'}, {'callee': 'inverse_transform', 'caller': 'inverse_transform'}, {'callee': 'fit_transform', 'caller': 'fit_transform'}, {'callee': 'fit_predict', 'caller': 'fit_predict'}], 'router': {'fit': {'sample_weight': True}, 'predict': {'groups': None}, 'score': {'sample_weight': None}}}} .. GENERATED FROM PYTHON SOURCE LINES 388-389 - ``sample_weight`` requested by meta-estimator .. GENERATED FROM PYTHON SOURCE LINES 389-394 .. code-block:: Python est = RouterConsumerClassifier(estimator=ExampleClassifier()).set_fit_request( sample_weight=True ) print_routing(est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'$self_request': {'fit': {'sample_weight': True}, 'score': {'sample_weight': None}}, 'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'}, {'callee': 'partial_fit', 'caller': 'partial_fit'}, {'callee': 'predict', 'caller': 'predict'}, {'callee': 'predict_proba', 'caller': 'predict_proba'}, {'callee': 'predict_log_proba', 'caller': 'predict_log_proba'}, {'callee': 'decision_function', 'caller': 'decision_function'}, {'callee': 'score', 'caller': 'score'}, {'callee': 'split', 'caller': 'split'}, {'callee': 'transform', 'caller': 'transform'}, {'callee': 'inverse_transform', 'caller': 'inverse_transform'}, {'callee': 'fit_transform', 'caller': 'fit_transform'}, {'callee': 'fit_predict', 'caller': 'fit_predict'}], 'router': {'fit': {'sample_weight': None}, 'predict': {'groups': None}, 'score': {'sample_weight': None}}}} .. GENERATED FROM PYTHON SOURCE LINES 395-398 Note the difference in the requested metadata representations above. - We can also alias the metadata to pass different values to them: .. GENERATED FROM PYTHON SOURCE LINES 398-404 .. code-block:: Python est = RouterConsumerClassifier( estimator=ExampleClassifier().set_fit_request(sample_weight="clf_sample_weight"), ).set_fit_request(sample_weight="meta_clf_sample_weight") print_routing(est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'$self_request': {'fit': {'sample_weight': 'meta_clf_sample_weight'}, 'score': {'sample_weight': None}}, 'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'}, {'callee': 'partial_fit', 'caller': 'partial_fit'}, {'callee': 'predict', 'caller': 'predict'}, {'callee': 'predict_proba', 'caller': 'predict_proba'}, {'callee': 'predict_log_proba', 'caller': 'predict_log_proba'}, {'callee': 'decision_function', 'caller': 'decision_function'}, {'callee': 'score', 'caller': 'score'}, {'callee': 'split', 'caller': 'split'}, {'callee': 'transform', 'caller': 'transform'}, {'callee': 'inverse_transform', 'caller': 'inverse_transform'}, {'callee': 'fit_transform', 'caller': 'fit_transform'}, {'callee': 'fit_predict', 'caller': 'fit_predict'}], 'router': {'fit': {'sample_weight': 'clf_sample_weight'}, 'predict': {'groups': None}, 'score': {'sample_weight': None}}}} .. GENERATED FROM PYTHON SOURCE LINES 405-407 However, ``fit`` of the meta-estimator only needs the alias for the sub-estimator, since it doesn't validate and route its own required metadata: .. GENERATED FROM PYTHON SOURCE LINES 407-409 .. code-block:: Python est.fit(X, y, sample_weight=my_weights, clf_sample_weight=my_other_weights) .. rst-class:: sphx-glr-script-out .. code-block:: none Received sample_weight of length = 100 in RouterConsumerClassifier. Received sample_weight of length = 100 in ExampleClassifier. .. raw:: html 
RouterConsumerClassifier(estimator=ExampleClassifier())
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 410-413 - Alias only on the sub-estimator. This is useful if we don't want the meta-estimator to use the metadata, and we only want the metadata to be used by the sub-estimator. .. GENERATED FROM PYTHON SOURCE LINES 413-419 .. code-block:: Python est = RouterConsumerClassifier( estimator=ExampleClassifier().set_fit_request(sample_weight="aliased_sample_weight") ).set_fit_request(sample_weight=True) print_routing(est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'$self_request': {'fit': {'sample_weight': True}, 'score': {'sample_weight': None}}, 'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'}, {'callee': 'partial_fit', 'caller': 'partial_fit'}, {'callee': 'predict', 'caller': 'predict'}, {'callee': 'predict_proba', 'caller': 'predict_proba'}, {'callee': 'predict_log_proba', 'caller': 'predict_log_proba'}, {'callee': 'decision_function', 'caller': 'decision_function'}, {'callee': 'score', 'caller': 'score'}, {'callee': 'split', 'caller': 'split'}, {'callee': 'transform', 'caller': 'transform'}, {'callee': 'inverse_transform', 'caller': 'inverse_transform'}, {'callee': 'fit_transform', 'caller': 'fit_transform'}, {'callee': 'fit_predict', 'caller': 'fit_predict'}], 'router': {'fit': {'sample_weight': 'aliased_sample_weight'}, 'predict': {'groups': None}, 'score': {'sample_weight': None}}}} .. GENERATED FROM PYTHON SOURCE LINES 420-426 Simple Pipeline --------------- A slightly more complicated use-case is a meta-estimator which does something similar to the :class:`~pipeline.Pipeline`. Here is a meta-estimator, which accepts a transformer and a classifier, and applies the transformer before running the classifier. .. GENERATED FROM PYTHON SOURCE LINES 426-467 .. code-block:: Python class SimplePipeline(ClassifierMixin, BaseEstimator): _required_parameters = ["estimator"] def __init__(self, transformer, classifier): self.transformer = transformer self.classifier = classifier def get_metadata_routing(self): router = ( MetadataRouter(owner=self.__class__.__name__) .add( transformer=self.transformer, method_mapping=MethodMapping() .add(callee="fit", caller="fit") .add(callee="transform", caller="fit") .add(callee="transform", caller="predict"), ) .add(classifier=self.classifier, method_mapping="one-to-one") ) return router def fit(self, X, y, **fit_params): params = process_routing(self, "fit", **fit_params) self.transformer_ = clone(self.transformer).fit(X, y, **params.transformer.fit) X_transformed = self.transformer_.transform(X, **params.transformer.transform) self.classifier_ = clone(self.classifier).fit( X_transformed, y, **params.classifier.fit ) return self def predict(self, X, **predict_params): params = process_routing(self, "predict", **predict_params) X_transformed = self.transformer_.transform(X, **params.transformer.transform) return self.classifier_.predict(X_transformed, **params.classifier.predict) .. GENERATED FROM PYTHON SOURCE LINES 468-484 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. .. GENERATED FROM PYTHON SOURCE LINES 484-499 .. code-block:: Python 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) .. GENERATED FROM PYTHON SOURCE LINES 500-509 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. .. GENERATED FROM PYTHON SOURCE LINES 509-531 .. code-block:: Python 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 ) .. rst-class:: sphx-glr-script-out .. code-block:: none Received sample_weight of length = 100 in ExampleTransformer. Received groups of length = 100 in ExampleTransformer. Received sample_weight of length = 100 in RouterConsumerClassifier. Received sample_weight of length = 100 in ExampleClassifier. Received groups of length = 100 in ExampleTransformer. groups is None in ExampleClassifier. array([1., 1., 1.]) .. GENERATED FROM PYTHON SOURCE LINES 532-539 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. .. GENERATED FROM PYTHON SOURCE LINES 539-556 .. code-block:: Python 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 .. GENERATED FROM PYTHON SOURCE LINES 557-558 As explained above, this is now a valid usage: .. GENERATED FROM PYTHON SOURCE LINES 558-563 .. code-block:: Python reg = MetaRegressor(estimator=LinearRegression().set_fit_request(sample_weight=True)) reg.fit(X, y, sample_weight=my_weights) .. GENERATED FROM PYTHON SOURCE LINES 564-566 Now imagine we further develop ``MetaRegressor`` and it now also *consumes* ``sample_weight``: .. GENERATED FROM PYTHON SOURCE LINES 566-588 .. code-block:: Python 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", sample_weight=sample_weight, **fit_params) 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 .. GENERATED FROM PYTHON SOURCE LINES 589-592 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. .. GENERATED FROM PYTHON SOURCE LINES 592-601 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none Received sample_weight of length = 100 in WeightedMetaRegressor. Support for sample_weight has recently been added to this class. To maintain backward compatibility, it is ignored now. You can set the request value to False to silence this warning, or to True to consume and use the metadata. .. GENERATED FROM PYTHON SOURCE LINES 602-604 When an estimator supports a metadata which wasn't supported before, the following pattern can be used to warn the users about it. .. GENERATED FROM PYTHON SOURCE LINES 604-622 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none sample_weight is None in ExampleRegressor. Support for sample_weight has recently been added to this class. To maintain backward compatibility, it is ignored now. You can set the request value to False to silence this warning, or to True to consume and use the metadata. .. GENERATED FROM PYTHON SOURCE LINES 623-640 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*. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.046 seconds) .. _sphx_glr_download_auto_examples_miscellaneous_plot_metadata_routing.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.4.X?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_metadata_routing.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/miscellaneous/plot_metadata_routing.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_metadata_routing.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_metadata_routing.py ` .. include:: plot_metadata_routing.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_