FunctionTransformer

class FunctionTransformer(func=None, inverse_func=None, *, check_inverse=True, kw_args=None, inv_kw_args=None, X_type=None)[source]

Constructs a transformer from an arbitrary callable.

A FunctionTransformer forwards its y (and optionally X) arguments to a user-defined function (or callable object) and returns the result of this function. This is useful for stateless transformations such as taking the log of frequencies, doing custom scaling, etc.

Note: If a lambda function is used as the func, then the resulting transformer will not be pickleable.

Parameters:

funccallable (X: X_type, **kwargs) -> X_type, default=identity (return X): The callable to use for the transformation. This will be passed the same arguments as transform, with args and kwargs forwarded. If func is None, then func will be the identity function.
inverse_funccallable (X: X_type, **kwargs) -> X_type, default=identity: The callable to use for the inverse transformation. This will be passed the same arguments as inverse transform, with args and kwargs forwarded. If inverse_func is None, then inverse_func will be the identity function.
check_inversebool, default=True: Whether to check that or func followed by inverse_func leads to the original inputs. It can be used for a sanity check, raising a warning when the condition is not fulfilled.
kw_argsdict, default=None: Dictionary of additional keyword arguments to pass to func.
inv_kw_argsdict, default=None: Dictionary of additional keyword arguments to pass to inverse_func.
X_typestr, one of “pd.DataFrame, pd.Series, np.ndarray”, or list thereof: default = [“pd.DataFrame”, “pd.Series”, “np.ndarray”] list of types that func is assumed to allow for X (see signature above) if X passed to transform/inverse_transform is not on the list,

it will be converted to the first list element before passed to funcs

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> import numpy as np
>>> from sktime.transformations.func_transform import FunctionTransformer
>>> transformer = FunctionTransformer(np.log1p, np.expm1)
>>> X = np.array([[0, 1], [2, 3]])
>>> transformer.fit_transform(X)
array([[0.        , 0.69314718],
       [1.09861229, 1.38629436]])

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.