skada.metrics.PredictionEntropyScorer

class skada.metrics.PredictionEntropyScorer(greater_is_better=False, reduction='mean')[source]

Score based on the entropy of predictions on unsupervised dataset.

See [18] for details.

Parameters:
greater_is_betterbool, default=False

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.

reduction: str, default='mean'

Specifies the reduction to apply to the entropy values. Must be one of ['none', 'mean', 'sum']. If 'none', the entropy values for each sample are returned ([1]_ method). If 'mean', the mean of the entropy values is returned. If 'sum', the sum of the entropy values is returned.

Returns:
entropyfloat or ndarray of floats

If reduction is 'none', then ndarray of shape (n_samples,). Otherwise float.

References

[18]

Pietro Morerio et al. Minimal-Entropy correlation alignment for unsupervised deep domain adaptation. ICLR, 2018.

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

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.

Examples using skada.metrics.PredictionEntropyScorer

How to use SKADA

How to use SKADA

Using GridSearchCV with skada

Using GridSearchCV with skada