pyriemann.transfer.MDWM¶
- class pyriemann.transfer.MDWM(domain_tradeoff, target_domain, metric='riemann', n_jobs=1)¶
Classification by Minimum Distance to Weighted Mean.
Classification by nearest centroid. For each of the given classes, a centroid is estimated, according to the chosen metric, as a weighted mean of SPD matrices from the source domain, combined with the class centroid of the target domain [1] [2]. For classification, a given new matrix is attibuted to the class whose centroid is the nearest according to the chosen metric.
- Parameters:
- domain_tradeofffloat
Coefficient in [0,1] controlling the transfer, ie the trade-off between source and target domains. At 0, there is no transfer, only matrices acquired from the source domain are used. At 1, this is a calibration-free system as no matrices are required from the source domain.
- target_domainstring
Name of the target domain in extended labels.
- 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.- 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.
See also
MDM
Notes
New in version 0.4.
References
[1]Transfer learning for SSVEP-based BCI using Riemannian similarities between users E. Kalunga, S. Chevallier and Q. Barthelemy, in 26th European Signal Processing Conference (EUSIPCO), pp. 1685-1689. IEEE, 2018.
[2]Minimizing Subject-dependent Calibration for BCI with Riemannian Transfer Learning S. Khazem, S. Chevallier, Q. Barthelemy, K. Haroun and C. Nous, 10th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 523-526. IEEE, 2021.
- Attributes:
- classes_ndarray, shape (n_classes,)
Labels for each class.
- covmeans_ndarray, shape (n_classes, n_channels, n_channels)
Centroids for each class.
- __init__(domain_tradeoff, target_domain, metric='riemann', n_jobs=1)¶
Init.
- fit(X, y_enc, sample_weight=None)¶
Fit (estimates) the centroids.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices from source and target domain.
- y_encndarray, shape (n_matrices,)
Extended labels for each matrix.
- sample_weightNone | ndarray, shape (n_matrices_source,), default=None
Weights for each matrix from the source domains. If None, it uses equal weights.
- Returns:
- selfMDWM instance
The MDWM instance.
- fit_predict(X, y)¶
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 matrices.
- Returns:
- predndarray of int, shape (n_matrices,)
Predictions for each matrix according to the closest centroid.
- predict_proba(X)¶
Predict proba using softmax of negative squared distances.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- Returns:
- probndarray, shape (n_matrices, n_classes)
Probabilities for each class.
- score(X, y_enc, sample_weight=None)¶
Return the mean accuracy on the given test data and labels.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- y_encndarray, shape (n_matrices,)
Extended labels for each matrix.
- Returns:
- scorefloat
Mean accuracy of clf.predict(X) wrt. y_enc.
- set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$', y_enc: bool | None | str = '$UNCHANGED$') MDWM ¶
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.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 infit
.- y_encstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
y_enc
parameter infit
.
- Returns:
- selfobject
The updated object.
- 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 = '$UNCHANGED$', y_enc: bool | None | str = '$UNCHANGED$') MDWM ¶
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
.- y_encstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
y_enc
parameter inscore
.
- 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_classes)
The distance to each centroid according to the metric.