SqrtTransformer

SqrtTransformer

class SqrtTransformer(offset='auto')[source]

Apply elementwise square root transformation to a time series.

Transformation performs the following operations element-wise:

• adds the constant offset (shift)

• applies the square root

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

Parameters:

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:

offsetint or float

User supplied offset value.

See also

BoxCoxTransformer

Applies Box-Cox power transformation. Can help normalize data and compress variance of the series.

LogTransformer

Transformer input data using natural log. Can help normalize data and compress variance of the series.

sktime.transformations.exponent.ExponentTransformer

Transform input data by raising it to an exponent. Can help compress variance of series if a fractional exponent is supplied.

Notes

For an input series Z the square root transformation is defined as \((Z + offset)^{0.5}\).

Examples

>>> from sktime.transformations.exponent import SqrtTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = SqrtTransformer()
>>> 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.