If I have a function that should have conditional behavior, what's the best way to do it?

Question

Suppose I have a Neural Network. I can perform a handful of operations in it, like so:

def one_iteration(my_op, data, ...):
    for i in range(...):
        ...
        my_op(data)
        ...

Imagine that for loop is doing a bit more than just being a for loop, and it's how I iterate over a collection of objects. Now, if pass as my_op a training function, I'm set. But, if I want to pass a evaluate function as my_op , now I want to keep my intermediate results to know how I'm doing. So I would do:

def one_iteration(my_op, data, ...):
    results = []
    for i in range(...):
        ...
        result = my_op(data)
        results.append(result)
    return results

And I would have a list/array with the results for me to evaluate how this network performed over that iteration. Now, suppose I want my_op to be validate , and for that, I need even more intermediate information. I could maybe do something like this:

def one_iteration(my_op, data, is_validation, ...):
    results = []
    if is_validation:
        more_results = []
    for i in range(...):
        ...
        result, other_result = my_op(data)
        results.append(result)
        if is_validation:
            more_results.append(other_result)
    return results

But, it starts getting all sorts of messy at this point. I know I could implement a separate train , evaluate and validate method, and within each one, I could do what's needed, but the problem I see is that each method will be repeating the way it iterates over the object (in this case, the for loop), and so, if I change how I iterate, I have to change it in 3 different places. Is there a design pattern I'm missing here?

Answer 1

So pass two methods: one that is the meta-operation, which accepts the operation:

def one_iteration(meta_op, my_op, data, ...):
    for i in range(...):
        ...
        meta_op(my_op, data)
        ...

And let meta_op decide how to handle the results.