.. 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 to the estimators, scorers, and CV splitters consuming them. To better understand the following document, we need to introduce two concepts: routers and consumers. A router is an object which forwards some given data and metadata to other objects. In most cases, a router is a :term:`meta-estimator`, i.e. an estimator which takes another estimator as a parameter. A function such as :func:`sklearn.model_selection.cross_validate` which takes an estimator as a parameter and forwards data and metadata, is also a router. A consumer, on the other hand, is an object which accepts and uses some 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 31-64 .. 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 65-66 Metadata routing is only available if explicitly enabled: .. GENERATED FROM PYTHON SOURCE LINES 66-69 .. code-block:: Python set_config(enable_metadata_routing=True) .. GENERATED FROM PYTHON SOURCE LINES 70-71 This utility function is a dummy to check if a metadata is passed: .. GENERATED FROM PYTHON SOURCE LINES 71-81 .. 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 82-83 A utility function to nicely print the routing information of an object: .. GENERATED FROM PYTHON SOURCE LINES 83-87 .. code-block:: Python def print_routing(obj): pprint(obj.get_metadata_routing()._serialize()) .. GENERATED FROM PYTHON SOURCE LINES 88-94 Consuming Estimator ------------------- 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 94-109 .. 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 110-120 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 120-123 .. 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 124-130 The above output means that ``sample_weight`` and ``groups`` are not requested by `ExampleClassifier`, and 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 130-139 .. 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 140-146 .. note :: Please note that as long as the above estimator is not used in a 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 146-151 .. 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 152-157 Routing Meta-Estimator ---------------------- Now, we show how to design a meta-estimator to be a router. As a simplified example, here is a meta-estimator, which doesn't do much other than routing the metadata. .. GENERATED FROM PYTHON SOURCE LINES 157-211 .. 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 # routing is added to it. More explanations follow below. router = MetadataRouter(owner=self.__class__.__name__).add( estimator=self.estimator, method_mapping=MethodMapping() .add(caller="fit", callee="fit") .add(caller="predict", callee="predict") .add(caller="score", callee="score"), ) return router def fit(self, X, y, **fit_params): # `get_routing_for_object` returns a copy of the `MetadataRouter` # constructed by the above `get_metadata_routing` method, that is # internally called. request_router = get_routing_for_object(self) # Meta-estimators are responsible for validating the given metadata. # `method` refers to the parent's method, i.e. `fit` in this example. request_router.validate_metadata(params=fit_params, method="fit") # `MetadataRouter.route_params` maps the given metadata to the metadata # required by the underlying estimator based on the routing information # defined by the MetadataRouter. The output of type `Bunch` has a key # for each consuming object and those hold keys for their consuming # methods, which then contain key for the metadata which should be # routed to them. routed_params = request_router.route_params(params=fit_params, caller="fit") # A sub-estimator is fitted and its classes are attributed to the # meta-estimator. 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) # As in `fit`, we get a copy of the object's MetadataRouter, request_router = get_routing_for_object(self) # then we validate the given metadata, 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 212-229 Let's break down different parts of the above code. First, the :meth:`~utils.metadata_routing.get_routing_for_object` takes our meta-estimator (``self``) and returns a :class:`~utils.metadata_routing.MetadataRouter` or, a :class:`~utils.metadata_routing.MetadataRequest` if the object is a consumer, 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. Next, we illustrate the different behaviors and notably the type of errors raised. .. GENERATED FROM PYTHON SOURCE LINES 229-235 .. code-block:: Python meta_est = MetaClassifier( estimator=ExampleClassifier().set_fit_request(sample_weight=True) ) meta_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())
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.. GENERATED FROM PYTHON SOURCE LINES 236-240 Note that the above example is calling our utility function `check_metadata()` via the `ExampleClassifier`. It checks that ``sample_weight`` is correctly passed to it. If it is not, like in the following example, it would print that ``sample_weight`` is ``None``: .. GENERATED FROM PYTHON SOURCE LINES 240-243 .. code-block:: Python meta_est.fit(X, y) .. rst-class:: sphx-glr-script-out .. code-block:: none sample_weight is None in ExampleClassifier. .. raw:: html 
MetaClassifier(estimator=ExampleClassifier())
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.. GENERATED FROM PYTHON SOURCE LINES 244-245 If we pass an unknown metadata, an error is raised: .. GENERATED FROM PYTHON SOURCE LINES 245-250 .. code-block:: Python try: meta_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 routed to any object. .. GENERATED FROM PYTHON SOURCE LINES 251-252 And if we pass a metadata which is not explicitly requested: .. GENERATED FROM PYTHON SOURCE LINES 252-257 .. code-block:: Python try: meta_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 requested for ExampleClassifier.predict, which is used within MetaClassifier.predict. Call `ExampleClassifier.set_predict_request({metadata}=True/False)` for each metadata you want to request/ignore. .. GENERATED FROM PYTHON SOURCE LINES 258-259 Also, if we explicitly set it as not requested, but it is provided: .. GENERATED FROM PYTHON SOURCE LINES 259-269 .. code-block:: Python meta_est = MetaClassifier( estimator=ExampleClassifier() .set_fit_request(sample_weight=True) .set_predict_request(groups=False) ) try: meta_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 routed to any object. .. GENERATED FROM PYTHON SOURCE LINES 270-280 Another concept to introduce is **aliased metadata**. This is when an estimator requests a metadata with a different variable name than the default variable name. 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 is 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 280-285 .. code-block:: Python meta_est = MetaClassifier( estimator=ExampleClassifier().set_fit_request(sample_weight="aliased_sample_weight") ) meta_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())
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.. GENERATED FROM PYTHON SOURCE LINES 286-288 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 288-293 .. code-block:: Python try: meta_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 routed to any object. .. GENERATED FROM PYTHON SOURCE LINES 294-303 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 303-306 .. code-block:: Python print_routing(meta_est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'}, {'callee': 'predict', 'caller': 'predict'}, {'callee': 'score', 'caller': 'score'}], 'router': {'fit': {'sample_weight': 'aliased_sample_weight'}, 'predict': {'groups': None}, 'score': {'sample_weight': None}}}} .. GENERATED FROM PYTHON SOURCE LINES 307-315 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 order to understand how aliases work in meta-estimators, imagine our meta-estimator inside another one: .. GENERATED FROM PYTHON SOURCE LINES 315-320 .. code-block:: Python meta_meta_est = MetaClassifier(estimator=meta_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 321-335 In the above example, this is how the ``fit`` method of `meta_meta_est` will call their sub-estimator's ``fit`` methods:: # user feeds `my_weights` as `aliased_sample_weight` into `meta_meta_est`: meta_meta_est.fit(X, y, aliased_sample_weight=my_weights): ... # the first sub-estimator (`meta_est`) expects `aliased_sample_weight` self.estimator_.fit(X, y, aliased_sample_weight=aliased_sample_weight): ... # the second sub-estimator (`est`) expects `sample_weight` self.estimator_.fit(X, y, sample_weight=aliased_sample_weight): ... .. GENERATED FROM PYTHON SOURCE LINES 337-344 Consuming and routing Meta-Estimator ------------------------------------ For a slightly more complex example, consider a meta-estimator that routes metadata to an underlying estimator as before, but it also uses some metadata in its own methods. This meta-estimator is a consumer and a router at the same time. Implementing one is very similar to what we had before, but with a few tweaks. .. GENERATED FROM PYTHON SOURCE LINES 344-399 .. 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__) # defining metadata routing request values for usage in the meta-estimator .add_self_request(self) # defining metadata routing request values for usage in the sub-estimator .add( estimator=self.estimator, method_mapping=MethodMapping() .add(caller="fit", callee="fit") .add(caller="predict", callee="predict") .add(caller="score", callee="score"), ) ) return router # Since `sample_weight` is used and consumed here, it should be defined as # an explicit argument in the method's signature. All other metadata which # are only routed, will be passed as `**fit_params`: 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) # We add `sample_weight` to the `fit_params` dictionary. if sample_weight is not None: fit_params["sample_weight"] = sample_weight request_router = get_routing_for_object(self) request_router.validate_metadata(params=fit_params, method="fit") routed_params = request_router.route_params(params=fit_params, caller="fit") 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) # As in `fit`, we get a copy of the object's MetadataRouter, request_router = get_routing_for_object(self) # we validate the given metadata, 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 400-412 The key parts where the above meta-estimator differs from our previous meta-estimator is accepting ``sample_weight`` explicitly in ``fit`` and including it in ``fit_params``. Since ``sample_weight`` is an explicit argument, we can be sure that ``set_fit_request(sample_weight=...)`` is present for this method. The meta-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 ``$self_request`` key to the routing info as illustrated below. Now let's look at some examples: .. GENERATED FROM PYTHON SOURCE LINES 414-415 - No metadata requested .. GENERATED FROM PYTHON SOURCE LINES 415-419 .. code-block:: Python meta_est = RouterConsumerClassifier(estimator=ExampleClassifier()) print_routing(meta_est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'$self_request': {'fit': {'sample_weight': None}, 'score': {'sample_weight': None}}, 'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'}, {'callee': 'predict', 'caller': 'predict'}, {'callee': 'score', 'caller': 'score'}], 'router': {'fit': {'sample_weight': None}, 'predict': {'groups': None}, 'score': {'sample_weight': None}}}} .. GENERATED FROM PYTHON SOURCE LINES 420-421 - ``sample_weight`` requested by sub-estimator .. GENERATED FROM PYTHON SOURCE LINES 421-426 .. code-block:: Python meta_est = RouterConsumerClassifier( estimator=ExampleClassifier().set_fit_request(sample_weight=True) ) print_routing(meta_est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'$self_request': {'fit': {'sample_weight': None}, 'score': {'sample_weight': None}}, 'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'}, {'callee': 'predict', 'caller': 'predict'}, {'callee': 'score', 'caller': 'score'}], 'router': {'fit': {'sample_weight': True}, 'predict': {'groups': None}, 'score': {'sample_weight': None}}}} .. GENERATED FROM PYTHON SOURCE LINES 427-428 - ``sample_weight`` requested by meta-estimator .. GENERATED FROM PYTHON SOURCE LINES 428-433 .. code-block:: Python meta_est = RouterConsumerClassifier(estimator=ExampleClassifier()).set_fit_request( sample_weight=True ) print_routing(meta_est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'$self_request': {'fit': {'sample_weight': True}, 'score': {'sample_weight': None}}, 'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'}, {'callee': 'predict', 'caller': 'predict'}, {'callee': 'score', 'caller': 'score'}], 'router': {'fit': {'sample_weight': None}, 'predict': {'groups': None}, 'score': {'sample_weight': None}}}} .. GENERATED FROM PYTHON SOURCE LINES 434-438 Note the difference in the requested metadata representations above. - We can also alias the metadata to pass different values to the fit methods of the meta- and the sub-estimator: .. GENERATED FROM PYTHON SOURCE LINES 438-444 .. code-block:: Python meta_est = RouterConsumerClassifier( estimator=ExampleClassifier().set_fit_request(sample_weight="clf_sample_weight"), ).set_fit_request(sample_weight="meta_clf_sample_weight") print_routing(meta_est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'$self_request': {'fit': {'sample_weight': 'meta_clf_sample_weight'}, 'score': {'sample_weight': None}}, 'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'}, {'callee': 'predict', 'caller': 'predict'}, {'callee': 'score', 'caller': 'score'}], 'router': {'fit': {'sample_weight': 'clf_sample_weight'}, 'predict': {'groups': None}, 'score': {'sample_weight': None}}}} .. GENERATED FROM PYTHON SOURCE LINES 445-448 However, ``fit`` of the meta-estimator only needs the alias for the sub-estimator and addresses their own sample weight as `sample_weight`, since it doesn't validate and route its own required metadata: .. GENERATED FROM PYTHON SOURCE LINES 448-450 .. code-block:: Python meta_est.fit(X, y, sample_weight=my_weights, clf_sample_weight=my_other_weights) .. rst-class:: sphx-glr-script-out .. code-block:: none Received sample_weight of length = 100 in RouterConsumerClassifier. Received sample_weight of length = 100 in ExampleClassifier. .. raw:: html 
RouterConsumerClassifier(estimator=ExampleClassifier())
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.. GENERATED FROM PYTHON SOURCE LINES 451-455 - Alias only on the sub-estimator: This is useful when we don't want the meta-estimator to use the metadata, but the sub-estimator should. .. GENERATED FROM PYTHON SOURCE LINES 455-459 .. code-block:: Python meta_est = RouterConsumerClassifier( estimator=ExampleClassifier().set_fit_request(sample_weight="aliased_sample_weight") ) print_routing(meta_est) .. rst-class:: sphx-glr-script-out .. code-block:: none {'$self_request': {'fit': {'sample_weight': None}, 'score': {'sample_weight': None}}, 'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'}, {'callee': 'predict', 'caller': 'predict'}, {'callee': 'score', 'caller': 'score'}], 'router': {'fit': {'sample_weight': 'aliased_sample_weight'}, 'predict': {'groups': None}, 'score': {'sample_weight': None}}}} .. GENERATED FROM PYTHON SOURCE LINES 460-463 The meta-estimator cannot use `aliased_sample_weight`, because it expects it passed as `sample_weight`. This would apply even if `set_fit_request(sample_weight=True)` was set on it. .. GENERATED FROM PYTHON SOURCE LINES 465-474 Simple Pipeline --------------- A slightly more complicated use-case is a meta-estimator resembling a :class:`~pipeline.Pipeline`. Here is a meta-estimator, which accepts a transformer and a classifier. When calling its `fit` method, it applies the transformer's `fit` and `transform` before running the classifier on the transformed data. Upon `predict`, it applies the transformer's `transform` before predicting with the classifier's `predict` method on the transformed new data. .. GENERATED FROM PYTHON SOURCE LINES 474-531 .. code-block:: Python class SimplePipeline(ClassifierMixin, BaseEstimator): def __init__(self, transformer, classifier): self.transformer = transformer self.classifier = classifier def get_metadata_routing(self): router = ( MetadataRouter(owner=self.__class__.__name__) # We add the routing for the transformer. .add( transformer=self.transformer, method_mapping=MethodMapping() # The metadata is routed such that it retraces how # `SimplePipeline` internally calls the transformer's `fit` and # `transform` methods in its own methods (`fit` and `predict`). .add(caller="fit", callee="fit") .add(caller="fit", callee="transform") .add(caller="predict", callee="transform"), ) # We add the routing for the classifier. .add( classifier=self.classifier, method_mapping=MethodMapping() .add(caller="fit", callee="fit") .add(caller="predict", callee="predict"), ) ) return router def fit(self, X, y, **fit_params): routed_params = process_routing(self, "fit", **fit_params) self.transformer_ = clone(self.transformer).fit( X, y, **routed_params.transformer.fit ) X_transformed = self.transformer_.transform( X, **routed_params.transformer.transform ) self.classifier_ = clone(self.classifier).fit( X_transformed, y, **routed_params.classifier.fit ) return self def predict(self, X, **predict_params): routed_params = process_routing(self, "predict", **predict_params) X_transformed = self.transformer_.transform( X, **routed_params.transformer.transform ) return self.classifier_.predict( X_transformed, **routed_params.classifier.predict ) .. GENERATED FROM PYTHON SOURCE LINES 532-548 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, `SimplePipeline` uses 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 `routed_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 548-563 .. 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 564-573 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 `SimplePipeline`, our `ExampleTransformer`, and our `RouterConsumerClassifier` which uses our `ExampleClassifier`. .. GENERATED FROM PYTHON SOURCE LINES 573-594 .. code-block:: Python pipe = SimplePipeline( transformer=ExampleTransformer() # we set transformer's fit to receive sample_weight .set_fit_request(sample_weight=True) # we set 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), ) # and we want the meta-estimator to receive sample_weight as well .set_fit_request(sample_weight=True), ) pipe.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 595-602 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 602-620 .. code-block:: Python class MetaRegressor(MetaEstimatorMixin, RegressorMixin, BaseEstimator): def __init__(self, estimator): self.estimator = estimator def fit(self, X, y, **fit_params): routed_params = process_routing(self, "fit", **fit_params) self.estimator_ = clone(self.estimator).fit(X, y, **routed_params.estimator.fit) def get_metadata_routing(self): router = MetadataRouter(owner=self.__class__.__name__).add( estimator=self.estimator, method_mapping=MethodMapping().add(caller="fit", callee="fit"), ) return router .. GENERATED FROM PYTHON SOURCE LINES 621-623 As explained above, this is a valid usage if `my_weights` aren't supposed to be passed as `sample_weight` to `MetaRegressor`: .. GENERATED FROM PYTHON SOURCE LINES 623-628 .. 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 629-631 Now imagine we further develop ``MetaRegressor`` and it now also *consumes* ``sample_weight``: .. GENERATED FROM PYTHON SOURCE LINES 631-660 .. code-block:: Python class WeightedMetaRegressor(MetaEstimatorMixin, RegressorMixin, BaseEstimator): # show warning to remind user to explicitly set the value with # `.set_{method}_request(sample_weight={boolean})` __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): routed_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, **routed_params.estimator.fit) def get_metadata_routing(self): router = ( MetadataRouter(owner=self.__class__.__name__) .add_self_request(self) .add( estimator=self.estimator, method_mapping=MethodMapping().add(caller="fit", callee="fit"), ) ) return router .. GENERATED FROM PYTHON SOURCE LINES 661-664 The above implementation is almost the same as ``MetaRegressor``, and because of the default request value defined in ``__metadata_request__fit`` there is a warning raised when fitted. .. GENERATED FROM PYTHON SOURCE LINES 664-673 .. 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. Using `set_fit_request(sample_weight={True, False})` on this method of the class, 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 674-676 When an estimator consumes a metadata which it didn't consume before, the following pattern can be used to warn the users about it. .. GENERATED FROM PYTHON SOURCE LINES 676-694 .. 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. Using `set_fit_request(sample_weight={True, False})` on this method of the class, 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 695-696 At the end we disable the configuration flag for metadata routing: .. GENERATED FROM PYTHON SOURCE LINES 696-699 .. code-block:: Python set_config(enable_metadata_routing=False) .. GENERATED FROM PYTHON SOURCE LINES 700-717 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 all of 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``. - your estimator does not route any metadata to the underlying objects, i.e. it's not a *router*. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.042 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/main?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 `_