ScaledLogitTransformer

class ScaledLogitTransformer(lower_bound=None, upper_bound=None)[source]

Scaled logit transform or Log transform.

If both lower_bound and upper_bound are not None, a scaled logit transform is applied to the data. Otherwise, the transform applied is a log transform variation that ensures the resulting values from the inverse transform are bounded accordingly. The transform is applied to all scalar elements of the input array individually.

Combined with an sktime.forecasting.compose.TransformedTargetForecaster, it ensures that the forecast stays between the specified bounds (lower_bound, upper_bound).

Default is lower_bound = upper_bound = None, i.e., the identity transform.

The logarithm transform is obtained for lower_bound = 0, upper_bound = None.

Parameters:

lower_boundfloat, optional, default=None: lower bound of inverse transform function
upper_boundfloat, optional, default=None: upper bound of inverse transform function

Attributes:

is_fitted: Whether fit has been called.

Notes

The scaled logit transform is applied if both upper_bound and lower_bound are

not None:

\(log(\frac{x - a}{b - x})\), where a is the lower and b is the upper bound.

If upper_bound is None and lower_bound is not None the transform applied is

a log transform of the form:

\(log(x - a)\)

If lower_bound is None and upper_bound is not None the transform applied is

a log transform of the form:

\(- log(b - x)\)

References

[1]

Hyndsight - Forecasting within limits: https://robjhyndman.com/hyndsight/forecasting-within-limits/

[2]

Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed on January 24th 2022.

Examples

>>> import numpy as np
>>> from sktime.datasets import load_airline
>>> from sktime.transformations.scaledlogit import ScaledLogitTransformer
>>> from sktime.forecasting.trend import PolynomialTrendForecaster
>>> from sktime.forecasting.compose import TransformedTargetForecaster
>>> y = load_airline()
>>> fcaster = TransformedTargetForecaster([
...     ("scaled_logit", ScaledLogitTransformer(0, 650)),
...     ("poly", PolynomialTrendForecaster(degree=2))
... ])
>>> fcaster.fit(y)
TransformedTargetForecaster(...)
>>> y_pred = fcaster.predict(fh = np.arange(32))

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.