Brute force feature selection - sum items across list combinations

Question

I have x lists of parameters & a list containing a list of the x lists. A total score is calculated by summing each corresponding item in each list.

For example, I have 2 lists & a main list like so:

a = [1,2]
b = [2,1]
mainList = ['a','b']

I want all the unique combinations - this is given by:

for L in range(0, len(mainList)+1):
for subset in itertools.combinations(mainList, L):
    print(subset)

Output:

()
('a',)
('b',)
('a', 'b')

For each permutation, I wish to create a new list containing the overall score by summing the items of each included list.

So for example, in this case the desired outputs would be:

[0,0] 
[1,2] #i.e. just a
[2,1] #i.e. just b
[3,3] #i.e. a+b

This is where I'm stuck - I'm not sure how to get the desired output above.

I suspect I'm overcomplicating it & there's just a quick way to do it.

Answer 1

What you need is to somehow connect a & b strings with corresponding lists. I think the cleanest way is to have a dictionary containing lists' names and lists with their values.

Here is an example:

list_dict = {'a': a, 'b': b}

for L in range(0, len(mainList)+1):
    for subset in itertools.combinations(mainList, L):
        temp = [0, 0]
        for sub in subset:
            temp[0] += list_dict[sub][0]
            temp[1] += list_dict[sub][1]
        print(temp)