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 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 (seepyriemann.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.
See also
MDM
Notes
New 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
- Attributes:
- covmean_ndarray, shape (n_channels, n_channels)
Centroid of potato.
- __init__(metric='riemann', threshold=3, n_iter_max=100, pos_label=1, neg_label=0)¶
Init.
- fit(X, y=None)¶
Fit the potato from SPD matrices.
Fit the potato from SPD matrices, with an iterative outlier removal to obtain a reliable potato.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD 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.
- Returns:
- selfPotato instance
The Potato instance.
- fit_transform(X, y=None, **fit_params)¶
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns:
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- 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, alpha=0.1)¶
Partially fit the potato from SPD matrices.
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 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.
- 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 matrices.
- Returns:
- predndarray of bool, shape (n_matrices,)
The artifact detection: True if the matrix is clean, and False if the matrix contain 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 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.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
Test samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns:
- scorefloat
Mean accuracy of
self.predict(X)
w.r.t. y.
- set_output(*, transform=None)¶
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”}, default=None
Configure output of transform and fit_transform.
“default”: Default output format of a transformer
“pandas”: DataFrame output
None: Transform configuration is unchanged
- 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 = '$UNCHANGED$') Potato ¶
Request metadata passed to the
partial_fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed topartial_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 topartial_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.New 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 inpartial_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
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.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.New 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 inscore
.
- 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 matrices.
- Returns:
- zndarray, shape (n_matrices,)
The standardized log-distance to the centroid.