pyriemann.preprocessing.Whitening¶

class pyriemann.preprocessing.Whitening(metric='euclid', dim_red=None, verbose=False)¶

Whitening, and optional unsupervised dimension reduction.

Implementation of the whitening, and an optional unsupervised dimension reduction, with SPD matrices as inputs.

Parameters:

metricstr, default=”euclid”

Metric for the estimation of mean matrix used for whitening and dimension reduction. For the list of supported metrics, see pyriemann.utils.mean.mean_covariance().

dim_redNone | dict, default=None

If None :: no dimension reduction during whitening.
If {'n_components': val} :: dimension reduction defining the number of components; val must be an integer superior to 1.
If {'expl_var': val} :: dimension reduction selecting the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by val. val must be a float in (0,1], typically 0.99.
If {'max_cond': val} :: dimension reduction selecting the number of components such that the condition number of the mean matrix is lower than val. This threshold has a physiological interpretation, because it can be viewed as the ratio between the power of the strongest component (usually, eye-blink source) and the power of the lowest component you don’t want to keep (acquisition sensor noise). val must be a float strictly superior to 1, typically 100.

verbosebool, default=False

Verbose flag.

Attributes:

n_components_int: If fit, the number of components after dimension reduction.
filters_ndarray, shape (n_channels, n_components_): If fit, the spatial filters to whiten SPD matrices.
inv_filters_ndarray, shape (n_components_, n_channels): If fit, the spatial filters to unwhiten SPD matrices.

Notes

Added in version 0.2.7.

__init__(metric='euclid', dim_red=None, verbose=False)¶: Init.

fit(X, y=None, sample_weight=None)¶

Train whitening spatial filters.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels): Set of SPD matrices.
yNone: Ignored as unsupervised.
sample_weightNone | ndarray, shape (n_matrices,), default=None: Weight of each matrix, to compute the weighted mean matrix used for whitening and dimension reduction. If None, it uses equal weights.

Returns:

selfWhitening instance: The Whitening instance.

fit_transform(X, y=None, sample_weight=None)¶

Fit and transform in a single function.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels): Set of SPD matrices.
yNone: Ignored as unsupervised.
sample_weightNone | ndarray, shape (n_matrices,), default=None: Weight of each matrix, to compute the weighted mean matrix used for whitening and dimension reduction. If None, it uses equal weights.

Returns:

X_newndarray, shape (n_matrices, n_components, n_components): Set of whitened, and optionally reduced, SPD matrices.

get_metadata_routing()¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

inverse_transform(X)¶

Apply inverse whitening spatial filters.

Parameters:

Xndarray, shape (n_matrices, n_components, n_components): Set of whitened, and optionally reduced, SPD matrices.

Returns:

X_newndarray, shape (n_matrices, n_channels, n_channels): Set of unwhitened, and optionally unreduced, SPD matrices.

partial_fit(X, y=None, *, sample_weight=None, alpha=None)¶

Partially fit whitening spatial filters.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels): Set of SPD matrices.
yNone: Ignored as unsupervised.
sample_weightNone | ndarray, shape (n_matrices,), default=None: Weights for each matrix. If None, it uses equal weights.
alphafloat | None, default=None: Update rate in [0, 1] for the mean: 0 for no update, 1 for full update. If None, alpha is defined as n_matrices divided by the number of matrices that have been already used for fit.

Returns:

selfWhitening instance: The Whitening instance.

Notes

Added in version 0.7.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → Whitening¶

Request metadata passed to the fit 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 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.

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_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

set_output(*, transform=None)¶

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance: Estimator instance.

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_partial_fit_request(*, alpha: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → Whitening¶

Request metadata passed to the partial_fit 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 partial_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 partial_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.

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:

alphastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for alpha parameter in partial_fit.
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in partial_fit.

Returns:

selfobject: The updated object.

transform(X)¶

Apply whitening spatial filters.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels): Set of SPD matrices.

Returns:

X_newndarray, shape (n_matrices, n_components, n_components): Set of whitened, and optionally reduced, SPD matrices.