MrSEQL

class MrSEQL(seql_mode='fs', symrep='sax', custom_config=None)[source]

MrSEQL = Multiple Representations Sequence Learning classification model.

Direct Interface to MrSEQLClassifier from mrseql. Note: mrseql itself is copyleft (GPL3). This interface is permissive license (BSD3).

MrSEQL is an efficient time series classifier utilizing symbolic representations of time series, using SAX and SFA features.

Parameters:

seql_modestr, either ‘clf’ or ‘fs’ (default).: In the ‘clf’ mode, Mr-SEQL is an ensemble of SEQL models, while in the ‘fs’ mode Mr-SEQL, trains a logistic regression model with features extracted by SEQL from symbolic representations of time series.
symrepstr, or list or tuple of string, strings being ‘sax’ or ‘sfa’.: default = “sax”, i.e., only SAX features, no SFA features. The symbolic representations to be used to transform the input time series.
custom_configdict, optional, default=None: Customized parameters for the symbolic transformation. If defined, symrep will be ignored. (no documentation of this parameter is provided in the original mrseql code)

Attributes:

is_fitted: Whether fit has been called.

References

[1]

Thach Le Nguyen, Severin Gsponer, Iulia Ilie, Martin O’Reilly, Georgiana Ifrim. “Interpretable Time Series Classification Using Linear Models and Multi-resolution Multi-domain Symbolic Representations”, Data Mining and Knowledge Discovery, 2019.

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.