RandomSamplesAugmenter

RandomSamplesAugmenter

class RandomSamplesAugmenter(n=1.0, without_replacement=True, random_state=42)[source]

Draw random samples from time series.

transform takes a time series \(X={x_1, x_2, ... , x_m}\) with \(m\) elements and returns \(X_t={x_i, x_{i+1}, ... , x_n}\), where \({x_i, x_{i+1}, ... , x_n}\) are \(n`=``n`\) random samples drawn from \(X\) (with or without_replacement).

Parameters:

n: int or float, optional (default = 1.0)

To specify an exact number of samples to draw, set n to an int value. Number of samples to draw. To specify the returned samples as a proportion of the given times series set n to a float value \(n \in [0, 1]\). By default, the same number of samples is returned as given by the input time series.

without_replacement: bool, optional (default = True)

Whether to draw without replacement. If True, every sample of the input times series X will appear at most once in Xt.

random_state: None or int or ``np.random.RandomState`` instance, optional

“If int or RandomState, use it for drawing the random variates. If None, rely on self.random_state. Default is None.” [1]

References and Footnotes

———-

[1]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.random_state.html

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.