pyriemann.clustering.PotatoField

class pyriemann.clustering.PotatoField(n_potatoes=1, p_threshold=0.01, z_threshold=3, metric='riemann', n_iter_max=10, pos_label=1, neg_label=0)

Artifact detection with the Riemannian Potato Field.

The Riemannian Potato Field [1] is a clustering method used to detect artifact in multichannel signals. Processing SPD/HPD matrices, the algorithm combines several potatoes of low dimension, each one being designed to capture specific artifact typically affecting specific subsets of channels and/or specific frequency bands.

Parameters:
n_potatoesint, default=1

Number of potatoes in the field.

p_thresholdfloat, default=0.01

Threshold on probability to being clean, in (0, 1), combining probabilities of potatoes using Fisher’s method.

z_thresholdfloat, default=3

Threshold on z-score of distance to reject artifacts. It is the number of standard deviations from the mean of distances to the centroid.

metricstring | dict, default=”riemann”

Metric used for mean estimation (for the list of supported metrics, see pyriemann.utils.mean.mean_covariance()) and for distance estimation (see pyriemann.utils.distance.distance()). The metric can be a dict with two keys, “mean” and “distance” in order to pass different metrics.

n_iter_maxint, default=10

The maximum number of iteration to reach convergence.

pos_labelint, default=1

The positive label corresponding to clean data.

neg_labelint, default=0

The negative label corresponding to artifact data.

See also

Potato

Notes

Added in version 0.3.

References

[1]

The Riemannian Potato Field: A Tool for Online Signal Quality Index of EEG Q. Barthélemy, L. Mayaud, D. Ojeda, and M. Congedo. IEEE Transactions on Neural Systems and Rehabilitation Engineering, IEEE Institute of Electrical and Electronics Engineers, 2019, 27 (2), pp.244-255

__init__(n_potatoes=1, p_threshold=0.01, z_threshold=3, metric='riemann', n_iter_max=10, pos_label=1, neg_label=0)

Init.

fit(X, y=None, sample_weight=None)

Fit the potato field.

Fit the potato field from SPD/HPD matrices, with iterative outlier removal to obtain reliable potatoes.

Parameters:
Xlist of n_potatoes ndarrays of shape (n_matrices, n_channels, n_channels) with same n_matrices but potentially different n_channels

List of sets of SPD/HPD matrices, each corresponding to a different subset of channels and/or filtering with a specific frequency band.

yNone | ndarray, shape (n_matrices,), default=None

Labels corresponding to each matrix: positive (resp. negative) label corresponds to a clean (resp. artifact) matrix. If None, all matrices are considered as clean.

sample_weightNone | ndarray, shape (n_matrices,), default=None

Weights for each matrix. If None, it uses equal weights.

Returns:
selfPotatoField instance

The PotatoField instance.

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

Fit and transform in a single function.

Parameters:
Xlist of n_potatoes ndarrays of shape (n_matrices, n_channels, n_channels) with same n_matrices but potentially different n_channels

List of sets of SPD/HPD matrices, each corresponding to a different subset of channels and/or filtering with a specific frequency band.

yNone | ndarray, shape (n_matrices,), default=None

Labels corresponding to each matrix: positive (resp. negative) label corresponds to a clean (resp. artifact) matrix. If None, all matrices are considered as clean.

sample_weightNone | ndarray, shape (n_matrices,), default=None

Weights for each matrix. If None, it uses equal weights.

Returns:
zndarray, shape (n_matrices, n_potatoes)

Standardized log-distances to the centroids.

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.

partial_fit(X, y=None, *, sample_weight=None, alpha=0.1)

Partially fit the potato field.

This partial fit can be used to update dynamic or semi-dymanic online potatoes with clean matrices.

Parameters:
Xlist of n_potatoes ndarrays of shape (n_matrices, n_channels, n_channels) with same n_matrices but potentially different n_channels

List of sets of SPD/HPD matrices, each corresponding to a different subset of channels and/or filtering with a specific frequency band.

yNone | ndarray, shape (n_matrices,), default=None

Labels corresponding to each matrix: positive (resp. negative) label corresponds to a clean (resp. artifact) matrix. If None, all matrices are considered as clean.

sample_weightNone | ndarray, shape (n_matrices,), default=None

Weights for each matrix. If None, it uses equal weights.

alphafloat, default=0.1

Update rate in [0, 1] for the centroid, and mean and standard deviation of log-distances: 0 for no update, 1 for full update.

Returns:
selfPotatoField instance

The PotatoField instance.

predict(X)

Predict artifact from data.

Parameters:
Xlist of n_potatoes ndarrays of shape (n_matrices, n_channels, n_channels) with same n_matrices but potentially different n_channels

List of sets of SPD/HPD matrices, each corresponding to a different subset of channels and/or filtering with a specific frequency band.

Returns:
predndarray of bool, shape (n_matrices,)

The artifact detection: True if the matrix is clean, and False if the matrix contains an artifact.

predict_proba(X)

Predict probability obtained combining probabilities of potatoes.

Predict probability obtained combining probabilities of potatoes using Fisher’s method. A threshold of 0.01 can be used.

Parameters:
Xlist of n_potatoes ndarrays of shape (n_matrices, n_channels, n_channels) with same n_matrices but potentially different n_channels

List of sets of SPD/HPD matrices, each corresponding to a different subset of channels and/or filtering with a specific frequency band.

Returns:
probandarray, shape (n_matrices,)

Matrix is considered as normal/clean for high value of proba. It is considered as abnormal/artifacted for low value of proba.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels)

Test set of SPD matrices.

yndarray, shape (n_matrices,)

True labels for each matrix.

sample_weightNone | ndarray, shape (n_matrices,), default=None

Weights for each matrix.

Returns:
scorefloat

Mean accuracy of clf.predict(X) wrt. y.

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

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 = '$UNCHANGED$') → PotatoField

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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → PotatoField

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

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.

transform(X)

Return the standardized log-distances to the centroids.

Return the standardized log-distances to the centroids, ie geometric z-scores of distances.

Parameters:
Xlist of n_potatoes ndarrays of shape (n_matrices, n_channels, n_channels) with same n_matrices but potentially different n_channels

List of sets of SPD/HPD matrices, each corresponding to a different subset of channels and/or filtering with a specific frequency band.

Returns:
zndarray, shape (n_matrices, n_potatoes)

Standardized log-distances to the centroids.

Examples using pyriemann.clustering.PotatoField

Online Artifact Detection with Riemannian Potato Field

Online Artifact Detection with Riemannian Potato Field