ExponentTransformer

class ExponentTransformer(power=0.5, offset='auto')[source]

Apply element-wise exponentiation transformation to a time series.

Transformation performs the following operations element-wise:

adds the constant offset (shift)
raises to the power provided (exponentiation)

Offset=”auto” computes offset as the smallest offset that ensure all elements are non-negative before exponentiation.

Parameters:

powerint or float, default=0.5: The power to raise the input timeseries to.
offset“auto”, int or float, default=”auto”: Offset to be added to the input timeseries prior to raising the timeseries to the given power. If “auto” the series is checked to determine if it contains negative values. If negative values are found then the offset will be equal to the absolute value of the most negative value. If not negative values are present the offset is set to zero. If an integer or float value is supplied it will be used as the offset.

Attributes:

powerint or float: User supplied power.
offsetint or float, or iterable.: User supplied offset value. Scalar or 1D iterable with as many values as X columns in transform.

Notes

For an input series Z the exponent transformation is defined as \((Z + offset)^{power}\).

Examples

>>> from sktime.transformations.exponent import ExponentTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = ExponentTransformer()
>>> y_transform = transformer.fit_transform(y)

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.