FunctionTransformer

Constructs a transformer from an arbitrary callable.

Quickstart

from sktime.transformations.func_transform import FunctionTransformer

estimator = FunctionTransformer(func=None, inverse_func=None, *, check_inverse=True, kw_args=None, inv_kw_args=None, X_type=None)

Parameters(6)

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

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]])