69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
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
|
|
|
|
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.
|
|
|
|
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.
|
|
|
|
"""
|
|
def __init__(self, func=None, validate=True,
|
|
accept_sparse=False, pass_y=False):
|
|
self.func = func
|
|
self.validate = validate
|
|
self.accept_sparse = accept_sparse
|
|
self.pass_y = pass_y
|
|
|
|
def fit(self, X, y=None):
|
|
if self.validate:
|
|
check_array(X, self.accept_sparse)
|
|
return self
|
|
|
|
def transform(self, X, y=None):
|
|
if self.validate:
|
|
X = check_array(X, self.accept_sparse)
|
|
func = self.func if self.func is not None else _identity
|
|
|
|
|
|
return func(X, *((y,) if self.pass_y else ()))
|