SignatureKernel

class SignatureKernel(kernel=None, level=2, degree=1, theta=1, normalize=False, lowrank=False, rankbound=inf)[source]

Time series signature kernel, including high-order and low-rank variants.

Implements the signature kernel of Kiraly et al, see [1] and [2], including higher-order and low-rank approximation variants described therein.

Parameters:

kernelsktime pairwise (tabular) transformer, callable, or None: inner (tabular) kernel used in the signature sequence kernel if callable: function (2D np.ndarray x 2D np.ndarray) -> 2D np.ndarray pairwise kernel function, matrix sizes (n, d) x (m, d) -> (n x m) optional, default = None = Euclidean (linear) kernel with scale parameter 1
levelint, optional, default = 2: an integer >= 1, representing the level of truncation of the sequential kernel
degreeint, optional, default = 1: an integer >= 1, representing the order of approximation of sequential kernel can be set only if lowrank = False, otherwise ignored (always = 1)
thetafloat, optional, default=1.0: a positive scaling factor for the levels, i-th level is scaled by theta^i
normalizebool, optional, default = False: whether the output kernel matrix is normalized if True, sums and cumsums are divided by prod(K.shape)
lowrankbool, optional, default = False: whether to use low rank approximation in computing the kernel
rankboundint, optional, default = infinity: a hard threshold for the rank of the level matrices used only if lowrank = True

Attributes:

is_fitted: Whether fit has been called.

References

[1]

F. Kiraly, H. Oberhauser. 2016. “Kernels for sequentially ordered data.”, arXiv: 1601.08169.

[2]

F. Kiraly, H. Oberhauser. 2019. “Kernels for sequentially ordered data.”, Journal of Machine Learning Research.

Methods

`__call__`(X[, X2])	Compute distance/kernel matrix, call shorthand.
`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, X2])	Fit method for interface compatibility (no logic inside).
`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.
`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.
`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.
`transform`(X[, X2])	Compute distance/kernel matrix.
`transform_diag`(X)	Compute diagonal of distance/kernel matrix.