SAXlegacy

class SAXlegacy(word_length=8, alphabet_size=4, window_size=12, remove_repeat_words=False, save_words=False, return_pandas_data_series=True)[source]

Symbolic Aggregate approXimation (SAX) transformer.

as described in Jessica Lin, Eamonn Keogh, Li Wei and Stefano Lonardi, “Experiencing SAX: a novel symbolic representation of time series” Data Mining and Knowledge Discovery, 15(2):107-144 Overview: for each series:

run a sliding window across the series for each window

shorten the series with PAA (Piecewise Approximate Aggregation) discretise the shortened series into fixed bins form a word from these discrete values

by default SAX produces a single word per series (window_size=0). SAX returns a pandas data frame where column 0 is the histogram (sparse pd.series) of each series.

Parameters:

word_length: int, length of word to shorten window to (using
PAA) (default 8)
alphabet_size: int, number of values to discretise each value
to (default to 4)
window_size: int, size of window for sliding. Input series
length for whole series transform (default to 12)
remove_repeat_words: boolean, whether to use numerosity reduction (
default False)
save_words: boolean, whether to use numerosity reduction (
default False)
return_pandas_data_series: boolean, default = True: set to true to return Pandas Series as a result of transform. setting to true reduces speed significantly but is required for automatic test.

Attributes:

words: history = []

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.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.