pyriemann.classification.MeanField¶
- class pyriemann.classification.MeanField(power_list=[-1, 0, 1], method_label='sum_means', metric='riemann', n_jobs=1)¶
Classification by Minimum Distance to Mean Field.
Classification by Minimum Distance to Mean Field [1], defining several power means for each class.
- Parameters:
- power_listlist of float, default=[-1,0,+1]
Exponents of power means.
- method_label{“sum_means”, “inf_means”}, default=”sum_means”
Method to combine labels:
sum_means: it assigns the matrix to the class whom the sum of distances to means of the field is the lowest;
inf_means: it assigns the matrix to the class of the nearest mean of the field.
- metricstring, default=”riemann”
Metric used for distance estimation during prediction. For the list of supported metrics, see
pyriemann.utils.distance.distance()
.
- Attributes:
- classes_ndarray, shape (n_classes,)
Labels for each class.
- covmeans_dict of
n_powers
dicts ofn_classes
ndarrays of shape (n_channels, n_channels) Centroids for each power and each class.
See also
Notes
Added in version 0.3.
References
[1]The Riemannian Minimum Distance to Means Field Classifier M Congedo, PLC Rodrigues, C Jutten. BCI 2019 - 8th International Brain-Computer Interface Conference, Sep 2019, Graz, Austria.
- __init__(power_list=[-1, 0, 1], method_label='sum_means', 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:
- selfMeanField instance
The MeanField instance.
- fit_predict(**kwargs)¶
Warning
DEPRECATED: fit_predict() is deprecated and will be removed in 0.10.0; please use fit().predict().
- fit_transform(X, y, sample_weight=None)¶
Fit and transform in a single function.
- 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:
- distndarray, shape (n_matrices, n_classes)
Distance to each means field according to the metric.
- 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 means field.
- 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.
- 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 = '$UNCHANGED$') MeanField ¶
Request metadata passed to the
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 tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.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 infit
.
- 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 = '$UNCHANGED$') MeanField ¶
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.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 inscore
.
- Returns:
- selfobject
The updated object.
- transform(X)¶
Get the distance to each means field.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD/HPD matrices.
- Returns:
- distndarray, shape (n_matrices, n_classes)
Distance to each means field according to the metric.