Python:用于调用较低级函数的可选参数 [英] Python : optional arguments for function calling lower level functions

问题描述

我有一个功能

def weights(vector, loss_function, clipping, max_iterations=100, tolerance=1e-5)

需要调用一个较低级别的损失函数，该函数可以是其中的任何一个，并且要在参数中传递矢量和裁剪:

which needs to call a lower level loss function which can be any of these with the vector and clipping passed in argument :

huber_loss(vector, clipping=2.38) cauchy_loss(vector, clipping=3.27) bisquare_loss(vector, clipping=1.04)

huber_loss(vector, clipping=2.38) cauchy_loss(vector, clipping=3.27) bisquare_loss(vector, clipping=1.04)

每个损失函数都有一个特殊的适当默认剪裁值，因此我们可以称它们为huber_loss(vector)或huber_loss(vector，2).

Each loss function has a special proper default clipping value so we can call them either huber_loss(vector) or huber_loss(vector,2) for example.

我希望在weights()中使裁剪参数为可选参数，而不在权重级别提供默认值，因为这将为所有损失函数提供相同的默认值，这是错误的.

I want to make the clipping parameter optional in weights() without giving a default value at weights' level because this would give the same default to all loss functions and that's wrong.

如何使裁剪参数的权重为可选，以便如果我们不给出值，则使用特定损失函数的默认值? (我知道我们可以设置默认Clipping = None并在损失函数中进行测试，如果Clipping = None，然后设置Clipping = 2.38等.但是我认为有一种更优雅的方法可以做到这一点.)

How to make the clipping parameter optional in weights so that if we don't give a value it uses the default value of the specific loss function ? (I know we can set default clipping=None and test in the loss function if clipping=None then set clipping = 2.38 etc.. but I think there's a much more elegant way to do it).

我试图通过这种方式解决问题:

I tried to solve the problem that way :

weights(vector, loss_function, max_iterations=100, tolerance=1e-5, *clipping)

但是如果我们想给裁剪指定一个特定的值而不指定max_iterations和公差，那是行不通的.

but if we want to give a specific value to clipping without specifying max_iterations and tolerance it doesn't work.

有什么主意如何以Pythonic优雅的方式解决这个问题?

Any idea how to solve this in a pythonic and elegant way ?

推荐答案

def weights(vector, loss_function, clipping=None, 
            max_iterations=100, tolerance=1e-5)

kwargs = {}
if clipping:
    kwargs['clipping'] = clipping

huber_loss(vector, **kwargs)

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