Hi! My question is probably basic, but I can't find any documentation about it. I simply want to pass a function as an argument in a Kedro node. For instance:

import numpy as np
def softmax(values):
    exp_values = np.exp(values)
    softmax_values = exp_values / np.sum(exp_values )

    return softmax_values


def compute_function(func, values):
    return func(values)


compute_function(softmax, [1, 2, 3, 4, 5])

But I want it in a node:

node(
  func='compute_function',
  inputs=[softmax, 'result_from_other_node'], # This will not work!
  outputs='result',
  name='name'
)

Thanks!

Where do you get this

func

from? Why can't you simply use

softmax(value)

?

softmax

and

compute_function

are defined in

nodes.py

, and imported in

pipeline.py

.

node(
  func='compute_function',
  inputs=[softmax, 'result_from_other_node'], 
  outputs='result',
  name='name'
)

My question is, how is this different from this? Why do you need another indirection function?

node(
  func=softmax,
  inputs=[ 'result_from_other_node'],
  outputs='result',
  name='name'
)

Maybe the example I gave simplified my problem too much

inputs

and

ouptuts

expected datasets or parameters. If you have a higher level function, you should look for

functools

or something like a decorator. Whatever it is, that should be done prior setting up the node and you pass that function directiont to

func

Thanks! I'll look at functools, I feel like this is what I need!

func=update_wrapper(
        wrapper=partial(compute_accuracies_nn_weight, weight_func=modified_softmax, alpha=[1, 2, 5, 10, 15]),
        wrapped=compute_accuracies_nn_weight
    )

This should move to

node.py

most likely. There's nothing wrong to do that in

pipeline.py

, purely for organising code only

Thanks for the tip!