econml.grf.MultiOutputGRF
- class econml.grf.MultiOutputGRF(estimator)[source]
Bases:
BaseEstimator
Simple wrapper estimator that enables multiple outcome labels for grf estimators that only accept a single outcome.
Similar to MultiOutputRegressor.
Methods
__init__
(estimator)feature_importances
([max_depth, ...])fit
(X, T, y, *[, sample_weight])Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
oob_predict
(Xtrain)predict
(X[, interval, alpha])predict_and_var
(X)predict_projection_and_var
(X, projector)set_fit_request
(*[, T, sample_weight])Request metadata passed to the
fit
method.set_params
(**params)Set the parameters of this estimator.
set_predict_request
(*[, alpha, interval])Request metadata passed to the
predict
method.Attributes
feature_importances_
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
routing – A
MetadataRequest
encapsulating routing information.- Return type:
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- Parameters:
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
params – Parameter names mapped to their values.
- Return type:
- set_fit_request(*, T: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') MultiOutputGRF
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
T (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
T
parameter infit
.sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
sample_weight
parameter infit
.
- Returns:
self – The updated object.
- Return type:
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
**params (dict) – Estimator parameters.
- Returns:
self – Estimator instance.
- Return type:
estimator instance
- set_predict_request(*, alpha: bool | None | str = '$UNCHANGED$', interval: bool | None | str = '$UNCHANGED$') MultiOutputGRF
Request metadata passed to the
predict
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed topredict
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it topredict
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
alpha (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
alpha
parameter inpredict
.interval (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
interval
parameter inpredict
.
- Returns:
self – The updated object.
- Return type: