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.

References

[17]

Masashi Sugiyama et al. Covariate Shift Adaptation by Importance Weighted Cross Validation. Journal of Machine Learning Research, 2007.

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.

set_score_request(*, sample_domain: bool | None | str = '$UNCHANGED$') → ImportanceWeightedScorer

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • 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:
sample_domainstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_domain parameter in score.

Returns:
selfobject

The updated object.

Examples using skada.metrics.ImportanceWeightedScorer

Using GridSearchCV with skada

Using GridSearchCV with skada