pyriemann.regression.KNearestNeighborRegressor

class pyriemann.regression.KNearestNeighborRegressor(n_neighbors=5, metric='riemann')

Regression by k-nearest-neighbors.

Regression by k-nearest neighbors (k-NN). For each matrix of the test set, the pairwise distance to each matrix of the training set is estimated. The value is calculated according to the softmax average w.r.t. distance of the k-nearest neighbors.

Note

DISCLAIMER: this is an unpublished algorithm.

Parameters:
n_neighborsint, default=5

Number of neighbors.

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.

Attributes:
values_ndarray, shape (n_matrices,)

Training target values.

covmeans_ndarray, shape (n_matrices, n_channels, n_channels)

Training matrices.

Notes

Added in version 0.3.

__init__(n_neighbors=5, metric='riemann')

Init.

fit(X, y, sample_weight=None)

Fit (store the training data).

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

Set of SPD/HPD matrices.

yndarray, shape (n_matrices,)

Target values for each matrix.

sample_weightNone

Not used, here for compatibility with sklearn API.

Returns:
selfKNearestNeighborRegressor instance

The KNearestNeighborRegressor 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 matrices.

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

Labels for each matrix.

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

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

Returns:
distndarray, shape (n_matrices, n_centroids)

Distance to each 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.

predict(X)

Get the predictions.

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

Set of SPD/HPD matrices.

Returns:
predndarray, shape (n_matrices,)

Predictions for each matrix according to the closest neighbors.

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)

Return the coefficient of determination of the prediction.

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

Test set of SPD/HPD matrices.

yndarray, shape (n_matrices,)

True values for each matrix.

Returns:
scorefloat

R2 of self.predict(X) wrt. y.

Notes

Added in version 0.4.

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

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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 = '$UNCHANGED$') → KNearestNeighborRegressor

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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)

Distance to each centroid according to the metric.