pyriemann.classification.MDM¶

class pyriemann.classification.MDM(metric='riemann', n_jobs=1)¶

Classification by Minimum Distance to Mean.

For each of the given classes $k = 1, \ldots, K$, a centroid $\mathbf{M}^k$ is estimated according to the chosen metric.

Then, for each new matrix $\mathbf{X}$, the class is affected according to the nearest centroid [1]:

\[\hat{k} = \arg \min_{k} d (\mathbf{X}, \mathbf{M}^k)\]

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. Typical usecase is to pass “logeuclid” metric for the “mean” in order to boost the computional speed, and “riemann” for the “distance” in order to keep the good sensitivity for the classification.
n_jobsint, default=1: The number of jobs to use for the computation. This works by computing each of the class centroid 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.

Attributes:

classes_ndarray, shape (n_classes,): Labels for each class.
covmeans_ndarray, shape (n_classes, n_channels, n_channels): Centroids for each class.

See also

Kmeans
FgMDM
KNearestNeighbor

References

[1]

Multiclass Brain-Computer Interface Classification by Riemannian Geometry A. Barachant, S. Bonnet, M. Congedo, and C. Jutten. IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, p. 920-928, 2012.

[2]

Riemannian geometry applied to BCI classification A. Barachant, S. Bonnet, M. Congedo and C. Jutten. 9th International Conference Latent Variable Analysis and Signal Separation (LVA/ICA 2010), LNCS vol. 6365, 2010, p. 629-636.

__init__(metric='riemann', n_jobs=1)¶: Init.

fit(X, y, sample_weight=None)¶

Fit (estimates) the centroids.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_channels): Set of SPD/HPD matrices.
yndarray, shape (n_matrices,): Labels for each matrix.
sample_weightNone | ndarray, shape (n_matrices,), default=None: Weights for each matrix. If None, it uses equal weights.

Returns:

selfMDM instance: The MDM instance.

fit_predict(X, y, sample_weight=None)¶: Fit and predict in one function.

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/HPD matrices.

Returns:

predndarray of int, shape (n_matrices,): Predictions for each matrix according to the nearest centroid.

predict_proba(X)¶

Predict proba using softmax of negative squared distances.

Parameters:

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

Returns:

probndarray, shape (n_matrices, n_classes): Probabilities for each class.

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

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

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)¶

Get the distance to each centroid.

Parameters:

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

Returns:

distndarray, shape (n_matrices, n_classes): The distance to each centroid according to the metric.

Examples using `pyriemann.classification.MDM`¶

Compare covariance and kernel estimators

Classification accuracy vs class distinctiveness vs class separability

Classifier comparison

Motor imagery classification

Frequency band selection on the manifold for motor imagery classification

Augmented Covariance Matrix

Multiclass MEG ERP Decoding

Offline SSVEP-based BCI Multiclass Prediction

Visualization of SSVEP-based BCI Classification in Tangent Space

Motor imagery classification by transfer learning

Comparison of pipelines for transfer learning

pyriemann.classification.MDM¶

Examples using pyriemann.classification.MDM¶

Examples using `pyriemann.classification.MDM`¶