skada.GaussianReweightAdapter

class skada.GaussianReweightAdapter(reg='auto')[source]

Gaussian approximation re-weighting method.

See [1] for details.

Parameters:
reg'auto' or float, default="auto"

The regularization parameter of the covariance estimator. Possible values:

  • None: no shrinkage.

  • 'auto': automatic shrinkage using the Ledoit-Wolf lemma.

  • float between 0 and 1: fixed shrinkage parameter.

Attributes:
`mean_source_`array-like, shape (n_features,)

Mean of the source data.

`cov_source_`array-like, shape (n_features, n_features)

Mean of the source data.

`mean_target_`array-like, shape (n_features,)

Mean of the target data.

`cov_target_`array-like, shape (n_features, n_features)

Covariance of the target data.

References

[1]

Hidetoshi Shimodaira. Improving predictive inference under covariate shift by weighting the log-likelihood function. In Journal of Statistical Planning and Inference, 2000.

fit(X, y=None, *, sample_domain=None)[source]

Fit adaptation parameters.

Parameters:
Xarray-like, shape (n_samples, n_features)

The source data.

yarray-like, shape (n_samples,)

The source labels.

sample_domainarray-like, shape (n_samples,)

The domain labels (same as sample_domain).

Returns:
selfobject

Returns self.

set_fit_request(*, sample_domain: bool | None | str = '$UNCHANGED$') → GaussianReweightAdapter

Configure whether metadata should be requested to be passed to the fit 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 fit 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 fit.

  • 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 fit.

Returns:
selfobject

The updated object.

set_transform_request(*, allow_source: bool | None | str = '$UNCHANGED$', sample_domain: bool | None | str = '$UNCHANGED$') → GaussianReweightAdapter

Configure whether metadata should be requested to be passed to the transform 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 transform 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 transform.

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

Metadata routing for allow_source parameter in transform.

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

Metadata routing for sample_domain parameter in transform.

Returns:
selfobject

The updated object.

Examples using skada.GaussianReweightAdapter

How to use SKADA

How to use SKADA