MrSQM

class MrSQM(strat='RS', features_per_rep=500, selection_per_rep=2000, nsax=1, nsfa=0, custom_config=None, random_state=None, sfa_norm=True)[source]

MrSQM = Multiple Representations Sequence Miner.

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

MrSQM is an efficient time series classifier utilizing symbolic representations of time series. MrSQM implements four different feature selection strategies = (R,S,RS,SR) that can quickly select subsequences from multiple symbolic representations of time series data.

Parameters:

stratstr, one of ‘R’,’S’,’SR’, or ‘RS’, default=”RS”: feature selection strategy. By default set to ‘RS’. R and S are single-stage filters while RS and SR are two-stage filters.
features_per_repint, default=500: (maximum) number of features selected per representation.
selection_per_repint, default=2000: (maximum) number of candidate features selected per representation. Only applied in two stages strategies (RS and SR), otherwise ignored.
nsaxint, default=1: number of representations produced by sax transformation.
nsfaint, default=0: number of representations produced by sfa transformation.
custom_configdict, default=None: customized parameters for the symbolic transformation.
random_stateint, default=None.: random seed for the classifier.
sfa_normbool, default=True.: whether to apply time series normalisation (standardisation).

Attributes:

is_fitted: Whether fit has been called.

References

[1]

Thach Le Nguyen and Georgiana Ifrim. “MrSQM: Fast Time Series Classification with Symbolic Representations and Efficient Sequence Mining” arXiv preprint arXiv:2109.01036 (2021).

[2]

Thach Le Nguyen and Georgiana Ifrim. “Fast Time Series Classification with Random Symbolic Subsequences”. AALTD 2022.

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.