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from ..base import BaseEstimator, TransformerMixin
from ..utils import check_array
def _identity(X):
"""The identity function.
"""
return X
class FunctionTransformer(BaseEstimator, TransformerMixin):
"""Constructs a transformer from an arbitrary callable.
A FunctionTransformer forwards its X (and optionally y) arguments to a
user-defined function or function 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.
A FunctionTransformer will not do any checks on its function's output.
Note: If a lambda is used as the function, then the resulting
transformer will not be pickleable.
.. versionadded:: 0.17
Read more in the :ref:`User Guide <function_transformer>`.
Parameters
----------
func : callable, optional default=None
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_func : callable, optional default=None
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.
validate : bool, optional default=True
Indicate that the input X array should be checked before calling
func. If validate is false, there will be no input validation.
If it is true, then X will be converted to a 2-dimensional NumPy
array or sparse matrix. If this conversion is not possible or X
contains NaN or infinity, an exception is raised.
accept_sparse : boolean, optional
Indicate that func accepts a sparse matrix as input. If validate is
False, this has no effect. Otherwise, if accept_sparse is false,
sparse matrix inputs will cause an exception to be raised.
pass_y: bool, optional default=False
Indicate that transform should forward the y argument to the
inner callable.
kw_args : dict, optional
Dictionary of additional keyword arguments to pass to func.
inv_kw_args : dict, optional
Dictionary of additional keyword arguments to pass to inverse_func.
"""
def __init__(self, func=None, inverse_func=None, validate=True,
accept_sparse=False, pass_y=False,
kw_args=None, inv_kw_args=None):
self.func = func
self.inverse_func = inverse_func
self.validate = validate
self.accept_sparse = accept_sparse
self.pass_y = pass_y
self.kw_args = kw_args
self.inv_kw_args = inv_kw_args
def fit(self, X, y=None):
if self.validate:
check_array(X, self.accept_sparse)
return self
def transform(self, X, y=None):
return self._transform(X, y, self.func, self.kw_args)
def inverse_transform(self, X, y=None):
return self._transform(X, y, self.inverse_func, self.inv_kw_args)
def _transform(self, X, y=None, func=None, kw_args=None):
if self.validate:
X = check_array(X, self.accept_sparse)
if func is None:
func = _identity
return func(X, *((y,) if self.pass_y else ()),
**(kw_args if kw_args else {}))
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