pyriemann.classification.TSclassifier¶
- class pyriemann.classification.TSclassifier(metric='riemann', tsupdate=False, clf=LogisticRegression())¶
Classification in the tangent space.
Project data in the tangent space and apply a classifier on the projected data. This is a simple helper to pipeline the tangent space projection and a classifier. Default classifier is LogisticRegression
- Parameters
- metricstring | dict, default=’riemann’
The type of metric used for reference matrix estimation (see mean_covariance for the list of supported metric) and for tangent space map (see tangent_space for the list of supported metric). The metric could be a dict with two keys, mean and map in order to pass different metrics for the reference matrix estimation and the tangent space mapping.
- tsupdatebool, default=False
Activate tangent space update for covariate shift correction between training and test, as described in [2]. This is not compatible with online implementation. Performance are better when the number of matrices for prediction is higher.
- clfsklearn classifier, default=LogisticRegression()
The classifier to apply in the tangent space.
See also
TangentSpace
Notes
New in version 0.2.4.
- Attributes
- classes_ndarray, shape (n_classes,)
Labels for each class.
- __init__(metric='riemann', tsupdate=False, clf=LogisticRegression())¶
Init.
- fit(X, y, sample_weight=None)¶
Fit TSclassifier.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD 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
- selfTSclassifier instance
The TSclassifier instance.
- 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)¶
Get the probability.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- Returns
- predndarray of ifloat, shape (n_matrices, n_classes)
Predictions 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)wrt. y.
- 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.