SignatureClassifier

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

Classification module using signature-based features.

This simply initialises the SignatureTransformer class which builds the feature extraction pipeline, then creates a new pipeline by appending a classifier after the feature extraction step.

The default parameters are set to best practice parameters found in: “A Generalised Signature Method for Multivariate TimeSeries” [1]

Note that the final classifier used on the UEA datasets involved tuning the hyper-parameters:

depth over [1, 2, 3, 4, 5, 6]
window_depth over [2, 3, 4]
RandomForestClassifier hyper-parameters.

as these were found to be the most dataset dependent hyper-parameters.

Thus, we recommend always tuning at least these parameters to any given dataset.

Parameters:

estimatorsklearn estimator, default=RandomForestClassifier: This should be any sklearn-type estimator. Defaults to RandomForestClassifier.
augmentation_list: list of tuple of strings, default=(“basepoint”, “addtime”): List of augmentations to be applied before the signature transform is applied.
window_name: str, default=”dyadic”: The name of the window transform to apply.
window_depth: int, default=3: The depth of the dyadic window. (Active only if window_name == 'dyadic'].
window_length: int, default=None: The length of the sliding/expanding window. (Active only if `window_name in [‘sliding, ‘expanding’].
window_step: int, default=None: The step of the sliding/expanding window. (Active only if `window_name in [‘sliding, ‘expanding’].
rescaling: str, default=None: The method of signature rescaling.
sig_tfm: str, default=”signature”: String to specify the type of signature transform. One of: [‘signature’, ‘logsignature’]).
depth: int, default=4: Signature truncation depth.
random_state: int, default=None: Random state initialisation.

Attributes:

signature_method: sklearn.Pipeline: An sklearn pipeline that performs the signature feature extraction step.
pipeline: sklearn.Pipeline: The classifier appended to the signature_method pipeline to make a classification pipeline.
n_classes_int: Number of classes. Extracted from the data.
classes_ndarray of shape (n_classes_): Holds the label for each class.

References

[1]

Morrill, James, et al. “A generalised signature method for multivariate time series feature extraction.” arXiv preprint arXiv:2006.00873 (2020). https://arxiv.org/pdf/2006.00873.pdf

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.