RandomIntervalFeatureExtractor

RandomIntervalFeatureExtractor

class RandomIntervalFeatureExtractor(n_intervals='sqrt', min_length=None, max_length=None, features=None, random_state=None)[source]

Random interval feature extractor transform.

Transformer that segments time-series into random intervals and subsequently extracts series-to-primitives features from each interval.

n_intervals: str{‘sqrt’, ‘log’, ‘random’}, int or float, optional ( default=’sqrt’)

Number of random intervals to generate, where m is length of time series: - If “log”, log of m is used. - If “sqrt”, sqrt of m is used. - If “random”, random number of intervals is generated. - If int, n_intervals intervals are generated. - If float, int(n_intervals * m) is used with n_intervals giving the fraction of intervals of the time series length.

For all arguments relative to the length of the time series, the generated number of intervals is always at least 1.

features: list of functions, optional (default=None)

Applies each function to random intervals to extract features. If None, the mean is extracted.

random_state:int, RandomState instance, optional (default=None)

• If int, random_state is the seed used by the random number generator;

• If RandomState instance, random_state is the random number generator;

• If None, the random number generator is the RandomState instance used

by np.random.

Attributes:

is_fitted

Whether fit has been called.

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 skbase object.
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.