TimeSeriesSVC

Support Vector Classifier, for time series kernels.

Quickstart

from sktime.classification.kernel_based import TimeSeriesSVC

estimator = TimeSeriesSVC(kernel=None, kernel_params=None, kernel_mtype=None, C=1, 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)

Parameters(14)

kernelpairwise panel transformer or callable, optional, default see below

pairwise panel transformer inheriting from BasePairwiseTransformerPanel, or callable, must be of signature (X: Panel, X2: Panel) -> np.ndarray output must be mxn array if X is Panel of m Series, X``2 of n Series if ``distance_mtype is not set, must be able to take X, X2 which are pd_multiindex and numpy3D mtype default = mean Euclidean kernel, same as AggrDist(RBF()), where AggrDist is from sktime and RBF from sklearn

kernel_paramsdict, optional. default = None.

dictionary for distance parameters, in case that distance is a callable

kernel_mtypestr, or list of str optional. default = None.

mtype that kernel expects for X and X2, if a callable only set this if kernel is not BasePairwiseTransformerPanel descendant

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_samples / (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_samples, n_classes) as all other classifiers, or the original one-vs-one (‘ovo’) decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one (‘ovo’) is always used as multi-class strategy. 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.

Examples

>>> from sktime.classification.kernel_based import TimeSeriesSVC
>>> from sklearn.gaussian_process.kernels import RBF
>>> from sktime.dists_kernels import AggrDist
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (return_X_y = True, split = "train")
>>> X_test, y_test = load_unit_test (return_X_y = True, split = "test")
>>> 
>>> mean_gaussian_tskernel = AggrDist (RBF ())
>>> classifier = TimeSeriesSVC (kernel = mean_gaussian_tskernel)
>>> classifier. fit (X_train, y_train) TimeSeriesSVC(
... )
>>> y_pred = classifier. predict (X_test)