LogTransformer

LogTransformer

class LogTransformer(offset=0, scale=1)[source]

Natural logarithm transformation.

The Natural logarithm transformation can be used to make the data more normally distributed and stabilize its variance.

Transforms each data point x to log(scale *(x+offset))

Parameters:

offsetfloat , default = 0

Additive constant applied to all the data.

scalefloat , default = 1

Multiplicative scaling constant applied to all the data.

Attributes:

is_fitted

Whether fit has been called.

See also

BoxCoxTransformer

Applies Box-Cox power transformation. 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.

sktime.transformations.exponent.SqrtTransformer

Transform input data by taking its square root. Can help compress variance of input series.

Notes

The log transformation is applied as \(ln(y)\).

Examples

>>> from sktime.transformations.boxcox import LogTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = LogTransformer()
>>> y_hat = 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.