Note
Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder
Metadata Routing#
This document shows how you can use the 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 meta-estimator,
i.e. an estimator which takes another estimator as a parameter. A function such
as 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 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.
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
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)
Metadata routing is only available if explicitly enabled:
set_config(enable_metadata_routing=True)
This utility function is a dummy to check if a metadata is passed:
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__}.")
A utility function to nicely print the routing information of an object:
def print_routing(obj):
pprint(obj.get_metadata_routing()._serialize())
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:
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))
The above estimator now has all it needs to consume metadata. This is
accomplished by some magic done in 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
ClassifierMixin implements a score method accepting
sample_weight. The same applies to regressors which inherit from
RegressorMixin.
By default, no metadata is requested, which we can see as:
print_routing(ExampleClassifier())
{'fit': {'sample_weight': None},
'predict': {'groups': None},
'score': {'sample_weight': None}}
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 ClassifierMixin. In order to
explicitly set request values for those metadata, we can use these methods:
est = (
ExampleClassifier()
.set_fit_request(sample_weight=False)
.set_predict_request(groups=True)
.set_score_request(sample_weight=False)
)
print_routing(est)
{'fit': {'sample_weight': False},
'predict': {'groups': True},
'score': {'sample_weight': False}}
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.
est = ExampleClassifier()
est.fit(X, y, sample_weight=my_weights)
est.predict(X[:3, :], groups=my_groups)
Received sample_weight of length = 100 in ExampleClassifier.
Received groups of length = 100 in ExampleClassifier.
array([1., 1., 1.])
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.
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)
Let’s break down different parts of the above code.
First, the get_routing_for_object takes our
meta-estimator (self) and returns a
MetadataRouter or, a
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.
meta_est = MetaClassifier(
estimator=ExampleClassifier().set_fit_request(sample_weight=True)
)
meta_est.fit(X, y, sample_weight=my_weights)
Received sample_weight of length = 100 in ExampleClassifier.
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:
meta_est.fit(X, y)
sample_weight is None in ExampleClassifier.
If we pass an unknown metadata, an error is raised:
try:
meta_est.fit(X, y, test=my_weights)
except TypeError as e:
print(e)
MetaClassifier.fit got unexpected argument(s) {'test'}, which are not routed to any object.
And if we pass a metadata which is not explicitly requested:
try:
meta_est.fit(X, y, sample_weight=my_weights).predict(X, groups=my_groups)
except ValueError as e:
print(e)
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.
Also, if we explicitly set it as not requested, but it is provided:
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)
Received sample_weight of length = 100 in ExampleClassifier.
MetaClassifier.predict got unexpected argument(s) {'groups'}, which are not routed to any object.
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:
meta_est = MetaClassifier(
estimator=ExampleClassifier().set_fit_request(sample_weight="aliased_sample_weight")
)
meta_est.fit(X, y, aliased_sample_weight=my_weights)
Received sample_weight of length = 100 in ExampleClassifier.
Passing sample_weight here will fail since it is requested with an
alias and sample_weight with that name is not requested:
try:
meta_est.fit(X, y, sample_weight=my_weights)
except TypeError as e:
print(e)
MetaClassifier.fit got unexpected argument(s) {'sample_weight'}, which are not routed to any object.
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 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:
print_routing(meta_est)
{'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}}}}
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:
meta_meta_est = MetaClassifier(estimator=meta_est).fit(
X, y, aliased_sample_weight=my_weights
)
Received sample_weight of length = 100 in ExampleClassifier.
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):
...
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.
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)
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
$self_request key to the routing info as illustrated below. Now let’s
look at some examples:
No metadata requested
meta_est = RouterConsumerClassifier(estimator=ExampleClassifier())
print_routing(meta_est)
{'$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}}}}
sample_weightrequested by sub-estimator
meta_est = RouterConsumerClassifier(
estimator=ExampleClassifier().set_fit_request(sample_weight=True)
)
print_routing(meta_est)
{'$self_request': {'fit': {'sample_weight': None},
'score': {'sample_weight': None}},
'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'},
{'callee': 'predict', 'caller': 'predict'},
{'callee': 'score', 'caller': 'score'}],
'router': {'fit': {'sample_weight': True},
'predict': {'groups': None},
'score': {'sample_weight': None}}}}
sample_weightrequested by meta-estimator
meta_est = RouterConsumerClassifier(estimator=ExampleClassifier()).set_fit_request(
sample_weight=True
)
print_routing(meta_est)
{'$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}}}}
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:
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)
{'$self_request': {'fit': {'sample_weight': 'meta_clf_sample_weight'},
'score': {'sample_weight': None}},
'estimator': {'mapping': [{'callee': 'fit', 'caller': 'fit'},
{'callee': 'predict', 'caller': 'predict'},
{'callee': 'score', 'caller': 'score'}],
'router': {'fit': {'sample_weight': 'clf_sample_weight'},
'predict': {'groups': None},
'score': {'sample_weight': None}}}}
However, fit of the meta-estimator only needs the alias for the
sub-estimator and addresses their own sample weight as sample_weight, since
it doesn’t validate and route its own required metadata:
meta_est.fit(X, y, sample_weight=my_weights, clf_sample_weight=my_other_weights)
Received sample_weight of length = 100 in RouterConsumerClassifier.
Received sample_weight of length = 100 in ExampleClassifier.
Alias only on the sub-estimator:
This is useful when we don’t want the meta-estimator to use the metadata, but the sub-estimator should.
meta_est = RouterConsumerClassifier(
estimator=ExampleClassifier().set_fit_request(sample_weight="aliased_sample_weight")
)
print_routing(meta_est)
{'$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}}}}
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.
Simple Pipeline#
A slightly more complicated use-case is a meta-estimator resembling a
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.
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
)
Note the usage of 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 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.
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 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.
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
)
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.])
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):
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
As explained above, this is a valid usage if my_weights aren’t supposed
to be passed as sample_weight to MetaRegressor:
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):
# 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
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.
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)
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.
When an estimator consumes a metadata which it didn’t consume 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)
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.
At the end we disable the configuration flag for metadata routing:
set_config(enable_metadata_routing=False)
Third Party Development and scikit-learn Dependency#
As seen above, information is communicated between classes using
MetadataRequest and
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
BaseEstimatorthe parameters consumed by your estimator’s methods, e.g.
fit, are explicitly defined in the method’s signature, as opposed to being*argsor*kwargs.your estimator does not route any metadata to the underlying objects, i.e. it’s not a router.
Total running time of the script: (0 minutes 0.045 seconds)
Related examples
Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples
