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sklearn.preprocessing._function_transformer.FunctionTransformer

Visibility: public Uploaded 13-08-2021 by Cameron Burke sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9 0 runs
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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

accept_sparse	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	default: false
func	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	default: null
inv_kw_args	Dictionary of additional keyword arguments to pass to inverse_func.	default: null
inverse_func	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	default: null
kw_args	Dictionary of additional keyword arguments to pass to func	default: null
pass_y	Indicate that transform should forward the y argument to the inner callable	default: false
validate	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	default: true

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sklearn.preprocessing._function_transformer.FunctionTransformer

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