pyriemann.clustering.Kmeans

class pyriemann.clustering.Kmeans(n_clusters=2, max_iter=100, metric='riemann', random_state=None, init='random', n_init=10, n_jobs=1, tol=0.0001)

Clustering by k-means with SPD matrices as inputs.

Find clusters that minimize the sum of squared distance to their centroids. This is a direct implementation of the k-means algorithm with a Riemannian metric.

Parameters:

n_clusterint, default=2

Number of clusters.

max_iterint, default=100

The maximum number of iteration to reach convergence.

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.

random_stateinteger or np.RandomState, optional

The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.

init‘random’ or ndarray, shape (n_clusters, n_channels, n_channels), default=’random’

Method for initialization of centers. ‘random’: choose k observations (rows) at random from data for the initial centroids. If an ndarray is passed, it should be of shape (n_clusters, n_channels, n_channels) and gives the initial centers.

n_initint, default=10

Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.

n_jobsint, default=1

The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.

tolfloat, default=1e-4

The stopping criterion to stop convergence, representing the minimum amount of change in labels between two iterations.

See also

Kmeans

MDM

Notes

New in version 0.2.2.

Attributes:

mdm_MDM instance.

MDM instance containing the centroids.

labels_

Labels of each point.

inertia_float

Sum of distances of samples to their closest cluster center.

__init__(n_clusters=2, max_iter=100, metric='riemann', random_state=None, init='random', n_init=10, n_jobs=1, tol=0.0001)

Init.

centroids()

Helper for fast access to the centroid.

Returns:

centroidslist of SPD matrices, len (n_cluster)

Return a list containing the centroid of each cluster.

fit(X, y=None)

Fit (estimates) the clusters.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

yndarray, shape (n_matrices,) | None, default=None

Not used, here for compatibility with sklearn API.

Returns:

selfKmeans instance

The Kmeans instance.

fit_predict(X, y=None)

Perform clustering on X and returns cluster labels.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input data.

yIgnored

Not used, present for API consistency by convention.

Returns:

labelsndarray of shape (n_samples,), dtype=np.int64

Cluster labels.

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.

predict(X)

Get the predictions.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

Returns:

predndarray of int, shape (n_matrices,)

Prediction for each matrix according to the closest centroid.

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_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → Kmeans

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.

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 in score.

Returns:

selfobject

The updated object.

transform(X)

Get the distance to each centroid.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels)

Set of SPD matrices.

Returns:

distndarray, shape (n_matrices, n_cluster)

The distance to each centroid according to the metric.