TimeSeriesSVC

class 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)[source]

Support Vector Classifier, for time series kernels.

An adapted version of the scikit-learn SVC for time series data.

Any sktime pairwise transformers are supported as kernels, including time series kernels and standard kernels on “flattened” time series.

Caveat: typically, SVC literature assumes kernels to be positive semi-definite. However, any pairwise transformer can be passed as kernel, including distances. This will still produce classification results, which may or may not be performant.

Parameters:

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.

Attributes:

is_fitted: Whether fit has been called.

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)

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X, y)	Fit time series classifier to training data.
`fit_predict`(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
`fit_predict_proba`(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts labels for sequences in X.
`predict_proba`(X)	Predicts labels probabilities for sequences in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y)	Scores predicted labels against ground truth labels on X.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.