A weighted version of random.choice
Since Python 3.6 there is a method choices
from the random
module.
In [1]: import random
In [2]: random.choices(
...: population=[['a','b'], ['b','a'], ['c','b']],
...: weights=[0.2, 0.2, 0.6],
...: k=10
...: )
Out[2]:
[['c', 'b'],
['c', 'b'],
['b', 'a'],
['c', 'b'],
['c', 'b'],
['b', 'a'],
['c', 'b'],
['b', 'a'],
['c', 'b'],
['c', 'b']]
Note that random.choices
will sample with replacement, per the docs:
Return a
k
sized list of elements chosen from the population with replacement.
Note for completeness of answer:
When a sampling unit is drawn from a finite population and is returned to that population, after its characteristic(s) have been recorded, before the next unit is drawn, the sampling is said to be "with replacement". It basically means each element may be chosen more than once.
If you need to sample without replacement, then as @ronan-paixão's brilliant answer states, you can use numpy.choice
, whose replace
argument controls such behaviour.
Since version 1.7.0, NumPy has a choice
function that supports probability distributions.
from numpy.random import choice
draw = choice(list_of_candidates, number_of_items_to_pick,
p=probability_distribution)
Note that probability_distribution
is a sequence in the same order of list_of_candidates
. You can also use the keyword replace=False
to change the behavior so that drawn items are not replaced.
def weighted_choice(choices):
total = sum(w for c, w in choices)
r = random.uniform(0, total)
upto = 0
for c, w in choices:
if upto + w >= r:
return c
upto += w
assert False, "Shouldn't get here"