Given a tuple items
, I get the powerset with itertools
:
items = tuple(('a', 'b', 'c'))
combos = powerset(items)
def powerset(iterable):
"powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
s = list(iterable)
return tuple(chain.from_iterable(combinations(s, r) for r in range(1, len(s)+1)))
My goal is to convert the powerset output combos
:
#combos =
(('a',),
('b',),
('c',),
('a', 'b'),
('a', 'c'),
('b', 'c'),
('a', 'b', 'c'))
into an np.array
where for each row, each item is placed in the column corresponding to the location of that item in the original tuple cols
.
Desired result:
#res =
[['a', '', ''],
['', 'b', ''],
['', '', 'c'],
['a', 'b', ''],
['a', '', 'c'],
['', 'b', 'c'],
['a', 'b', 'c']]
#shape = (7,3)
I was able to achieve this with the code below. However, is there a more pythonic, faster/memory efficient way to convert the output than my attempt where I loop through the array and place each individual item in the correct column in the result array?
Full code:
from itertools import combinations, chain
import numpy as np
def columnizePowerset(powerset, items):
combo_arr = np.array([list(x) for x in powerset], dtype=object) # Convert to np.array
res = np.empty([len(powerset), len(items)], dtype=str) # Initialize result array
for i in range(len(combo_arr)): # Loop through rows
for j in range(len(combo_arr[i])): # Loop through items in row
col = np.where(np.array(items) == combo_arr[i][j]) # Identify column for item
res[i][col] = combo_arr[i][j] # Place item in correct column
return res
def powerset(iterable):
"powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
s = list(iterable)
return tuple(chain.from_iterable(combinations(s, r) for r in range(1, len(s)+1)))
if __name__ == "__main__":
items = tuple(('a', 'b', 'c')) # Given
combos = powerset(items) # Perform powerset
res = columnizePowerset(combos, items) # Convert powerset to columnized array
print(res)
The order isn't exactly the same as powerset
yields, but the pattern reminded me of binary – you can get the same items by looking at the bits of increasing integers...
I'm not a NumPy wizard, but this seems reasonably numpy-er. (See post edit history for worse solutions.)
import numpy as np
def powerset_numpy(items):
n = len(items)
bit_indexes = np.arange(1, 2 ** n)
n_bits = bit_indexes.shape[0]
item_bits = np.repeat(np.arange(0, n), n_bits).reshape((n, -1))
item_values = np.repeat(items, n_bits).reshape((n, -1))
n_bits = (bit_indexes & (1 << item_bits)).astype(bool)
return np.where(n_bits, item_values, '').T
print(powerset_numpy(['a', 'b', 'c']))
prints out
[['a' '' '']
['' 'b' '']
['a' 'b' '']
['' '' 'c']
['a' '' 'c']
['' 'b' 'c']
['a' 'b' 'c']]