skada.metrics.ImportanceWeightedScorer
- class skada.metrics.ImportanceWeightedScorer(weight_estimator=None, scoring=None, greater_is_better=True)[source]
Score based on source data using sample weight.
See [17] for details.
- Parameters:
- weight_estimatorestimator object, optional
The estimator to use to estimate the densities of source and target observations. If None, a KernelDensity estimator is with default parameters used.
- scoringstr or callable, default=None
A string (see model evaluation documentation) or a scorer callable object / function with signature
scorer(estimator, X, y)
. If None, the provided estimator object's score method is used.- greater_is_betterbool, default=True
Whether scorer is a score function (default), meaning high is good, or a loss function, meaning low is good. In the latter case, the scorer object will sign-flip the outcome of the scorer.
- Attributes:
- weight_estimator_source_object
The estimator object fitted on the source data.
- weight_estimator_target_object
The estimator object fitted on the target data.
References
[17]Masashi Sugiyama et al. Covariate Shift Adaptation by Importance Weighted Cross Validation. Journal of Machine Learning Research, 2007.
- set_score_request(*, sample_domain: bool | None | str = '$UNCHANGED$') ImportanceWeightedScorer
Configure whether metadata should be requested to be passed to the
score
method.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config()
). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.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.
- Parameters:
- sample_domainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_domain
parameter inscore
.
- Returns:
- selfobject
The updated object.