skada.metrics.SoftNeighborhoodDensity

class skada.metrics.SoftNeighborhoodDensity(T=0.05, greater_is_better=True)[source]

Score based on the entropy of similarity between unsupervised dataset.

See [19] for details.

Parameters:
Tfloat

Temperature in the Eq. 2 in [1]_. Default is set to 0.05, the value proposed in the paper.

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

[19]

Kuniaki Saito et al. Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density. International Conference on Computer Vision, 2021.

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

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 (see sklearn.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 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.

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.