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

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.PredictionEntropyScorer

How to use SKADA

How to use SKADA

Using GridSearchCV with skada

Using GridSearchCV with skada