SignatureTransformer

class SignatureTransformer(augmentation_list=('basepoint', 'addtime'), window_name='dyadic', window_depth=3, window_length=None, window_step=None, rescaling=None, sig_tfm='signature', depth=4, backend='esig')[source]

Transformation class from the signature method.

Follows the methodology laid out in the paper: “A Generalised Signature Method for Multivariate Time Series”

Parameters:

augmentation_list: list or tuple of strings,

possible strings are ['leadlag', 'ir', 'addtime', 'cumsum', 'basepoint'] Augmentations to apply to the data before computing the signature. The order of the augmentations is the order in which they are applied. default: (‘basepoint’, ‘addtime’)

window_name: str, one of ``[‘global’, ‘sliding’, ‘expanding’, ‘dyadic’]``

default: ‘dyadic’ Type of the window to use for the signature transform.

window_depth: int, default=3

The depth of the dyadic window. Ignored unless window_name is 'dyadic'.

window_length: None (default) or int

The length of the sliding/expanding window. (Active Ignored unless window_name is one of ['sliding, 'expanding'].

window_step: None (default) or int

The step of the sliding/expanding window. Ignored unless window_name is one of ['sliding, 'expanding'].

rescaling: None (default) or str, “pre” or “post”,

None: No rescaling is applied.
“pre”: rescale the path last signature term should be roughly O(1)
“post”: Rescales the output signature by multiplying the depth-d term by d!. Aim is that every term becomes ~O(1).

sig_tfm: str, one of ``[‘signature’, ‘logsignature’]``. default: ``’signature’``

The type of signature transform to use, plain or logarithmic.

depth: int, default=4

Signature truncation depth.

backend: str, one of: ``’esig’`` (default), or ``’iisignature’``.

The backend to use for signature computation.

Attributes:

signature_method: sklearn.Pipeline, A sklearn pipeline object that contains: all the steps to extract the signature features.

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 transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`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.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`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.
`setup_feature_pipeline`()	Set up the signature method as an sklearn pipeline.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.