pyriemann.clustering.Potato

class pyriemann.clustering.Potato(metric='riemann', threshold=3, n_iter_max=100, pos_label=1, neg_label=0)

Artifact detection with the Riemannian Potato.

The Riemannian Potato [1] is a clustering method used to detect artifact in multichannel signals. Processing SPD/HPD matrices, the algorithm iteratively estimates the centroid of clean matrices by rejecting every matrix that is too far from it.

Parameters:

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.

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.

n_iter_maxint, default=100

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.

Attributes:

covmean_ndarray, shape (n_channels, n_channels)

Centroid of potato.

See also

MDM

Notes

Added in version 0.2.3.

References

[1]

The Riemannian Potato: an automatic and adaptive artifact detection method for online experiments using Riemannian geometry A. Barachant, A Andreev, and M. Congedo. TOBI Workshop lV, Jan 2013, Sion, Switzerland. pp.19-20.

[2]

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__(metric='riemann', threshold=3, n_iter_max=100, pos_label=1, neg_label=0)

Init.

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

Fit the potato.

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

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD matrices.

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:

selfPotato instance

The Potato 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/HPD matrices.

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,)

Standardized log-distance to the centroid.

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.

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

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD matrices.

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:

selfPotato instance

The Potato instance.

predict(X)

Predict artifact from data.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD matrices.

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)

Return probability of belonging to the potato / being clean.

It is the probability to reject the null hypothesis “clean data”, computing the right-tailed probability from z-score.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD matrices.

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 = '$UNCHANGED$') → Potato

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 = '$UNCHANGED$') → Potato

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 = '$UNCHANGED$') → Potato

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-distance to the centroid.

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

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD/HPD matrices.

Returns:

zndarray, shape (n_matrices,)

Standardized log-distance to the centroid.

Examples using pyriemann.clustering.Potato

Online Artifact Detection with Riemannian Potato

Online Artifact Detection with Riemannian Potato

Online Artifact Detection with Riemannian Potato Field

Online Artifact Detection with Riemannian Potato Field

Mean and median comparison

Mean and median comparison