pyriemann.classification.SVC¶
- class pyriemann.classification.SVC(*, metric='riemann', kernel_fct=None, Cref=None, C=1.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)¶
Classification by support-vector machine.
Support-vector machine (SVM) classification with precomputed Riemannian kernel matrix according to different metrics as described in [1].
- Parameters:
- metric{“riemann”, “euclid”, “logeuclid”}, default=”riemann”
Metric for kernel matrix computation.
- CrefNone | callable | ndarray, shape (n_channels, n_channels)
Reference point for kernel matrix computation. If None, the mean of the training data according to the metric is used. If callable, the function is called on the training data to calculate Cref.
- kernel_fctNone | “precomputed” | callable
If None or “precomputed”, the kernel matrix for datasets X and Y is estimated according to pyriemann.utils.kernel(X, Y, Cref, metric). If callable, the callable is passed as the kernel parameter to sklearn.svm.SVC() [2]. The callable has to be of the form kernel(X, Y, Cref, metric).
- Cfloat, default=1.0
Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty.
- shrinkingbool, default=True
Whether to use the shrinking heuristic.
- probabilitybool, default=False
Whether to enable probability estimates. This must be enabled prior to calling fit, will slow down that method as it internally uses 5-fold cross-validation, and predict_proba may be inconsistent with predict.
- tolfloat, default=1e-3
Tolerance for stopping criterion.
- cache_sizefloat, default=200
Specify the size of the kernel cache (in MB).
- class_weightdict or ‘balanced’, default=None
Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_matrices / (n_classes * np.bincount(y))
.- verbosebool, default=False
Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.
- max_iterint, default=-1
Hard limit on iterations within solver, or -1 for no limit.
- decision_function_shape{‘ovo’, ‘ovr’}, default=’ovr’
Whether to return a one-vs-rest (‘ovr’) decision function of shape (n_matrices, n_classes) as all other classifiers, or the original one-vs-one (‘ovo’) decision function of libsvm which has shape (n_matrices, n_classes * (n_classes - 1) / 2). However, note that internally, one-vs-one (‘ovo’) is always used as a multi-class strategy to train models; an ovr matrix is only constructed from the ovo matrix. The parameter is ignored for binary classification.
- break_tiesbool, default=False
If true,
decision_function_shape='ovr'
, and number of classes > 2, predict will break ties according to the confidence values of decision_function; otherwise the first class among the tied classes is returned. Please note that breaking ties comes at a relatively high computational cost compared to a simple predict.- random_stateint, RandomState instance or None, default=None
Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls.
Notes
New in version 0.3.
References
[1]Classification of covariance matrices using a Riemannian-based kernel for BCI applications A. Barachant, S. Bonnet, M. Congedo and C. Jutten. Neurocomputing, Elsevier, 2013, 112, pp.172-178.
- __init__(*, metric='riemann', kernel_fct=None, Cref=None, C=1.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)¶
Init.
- property coef_¶
Weights assigned to the features when kernel=”linear”.
- Returns:
- ndarray of shape (n_features, n_classes)
- decision_function(X)¶
Evaluate the decision function for the samples in X.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
The input samples.
- Returns:
- Xndarray of shape (n_samples, n_classes * (n_classes-1) / 2)
Returns the decision function of the sample for each class in the model. If decision_function_shape=’ovr’, the shape is (n_samples, n_classes).
Notes
If decision_function_shape=’ovo’, the function values are proportional to the distance of the samples X to the separating hyperplane. If the exact distances are required, divide the function values by the norm of the weight vector (
coef_
). See also this question for further details. If decision_function_shape=’ovr’, the decision function is a monotonic transformation of ovo decision function.
- fit(X, y, sample_weight=None)¶
Fit.
- 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. Rescale C per matrix. Higher weights force the classifier to put more emphasis on these matrices. If None, it uses equal weights.
- Returns:
- selfSVC instance
The SVC instance.
- 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.
- property n_support_¶
Number of support vectors for each class.
- predict(X)¶
Perform classification on samples in X.
For an one-class model, +1 or -1 is returned.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples_test, n_samples_train)
For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).
- Returns:
- y_predndarray of shape (n_samples,)
Class labels for samples in X.
- predict_log_proba(X)¶
Compute log probabilities of possible outcomes for samples in X.
The model need to have probability information computed at training time: fit with attribute probability set to True.
- Parameters:
- Xarray-like of shape (n_samples, n_features) or (n_samples_test, n_samples_train)
For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).
- Returns:
- Tndarray of shape (n_samples, n_classes)
Returns the log-probabilities of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.
Notes
The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets.
- predict_proba(X)¶
Compute probabilities of possible outcomes for samples in X.
The model needs to have probability information computed at training time: fit with attribute probability set to True.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).
- Returns:
- Tndarray of shape (n_samples, n_classes)
Returns the probability of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.
Notes
The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets.
- property probA_¶
Parameter learned in Platt scaling when probability=True.
- Returns:
- ndarray of shape (n_classes * (n_classes - 1) / 2)
- property probB_¶
Parameter learned in Platt scaling when probability=True.
- Returns:
- ndarray of shape (n_classes * (n_classes - 1) / 2)
- 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 = '$UNCHANGED$') SVC ¶
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
.
- Returns:
- selfobject
The updated object.
- 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 = '$UNCHANGED$') SVC ¶
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
.
- Returns:
- selfobject
The updated object.