Fast way to obtain a random index from an array of weights in python

Question

I regularly find myself in the position of needing a random index to an array or a list, where the probabilities of indices are not uniformly distributed, but according to c

Answer 1

Cumulative summing and bisect

In any generic case, it seems advisable to calculate the cumulative sum of weights, and use bisect from the bisect module to find a random point in the resulting sorted array

def weighted_choice(weights):
    cs = numpy.cumsum(weights)
    return bisect.bisect(cs, numpy.random.random() * cs[-1])

if speed is a concern. A more detailed analysis is given below.

Note: If the array is not flat, numpy.unravel_index can be used to transform a flat index into a shaped index, as seen in https://stackoverflow.com/a/19760118/1274613

Experimental Analysis

There are four more or less obvious solutions using numpy builtin functions. Comparing all of them using timeit gives the following result:

import timeit

weighted_choice_functions = [
"""import numpy
wc = lambda weights: numpy.random.choice(
    range(len(weights)),
    p=weights/weights.sum())
""",
"""import numpy
# Adapted from https://stackoverflow.com/a/19760118/1274613
def wc(weights):
    cs = numpy.cumsum(weights)
    return cs.searchsorted(numpy.random.random() * cs[-1], 'right')
""",
"""import numpy, bisect
# Using bisect mentioned in https://stackoverflow.com/a/13052108/1274613
def wc(weights):
    cs = numpy.cumsum(weights)
    return bisect.bisect(cs, numpy.random.random() * cs[-1])
""",
"""import numpy
wc = lambda weights: numpy.random.multinomial(
    1,
    weights/weights.sum()).argmax()
"""]

for setup in weighted_choice_functions:
    for ps in ["numpy.ones(40)",
               "numpy.arange(10)",
               "numpy.arange(200)",
               "numpy.arange(199,-1,-1)",
               "numpy.arange(4000)"]:
        timeit.timeit("wc(%s)"%ps, setup=setup)
    print()

The resulting output is

178.45797914802097
161.72161589498864
223.53492237901082
224.80936180002755
1901.6298267539823

15.197789980040397
19.985687876993325
20.795070077001583
20.919113760988694
41.6509403079981

14.240949985047337
17.335801470966544
19.433710905024782
19.52205040602712
35.60536142199999

26.6195822560112
20.501282756973524
31.271995796996634
27.20013752405066
243.09768892999273

This means that numpy.random.choice is surprisingly very slow, and even the dedicated numpy searchsorted method is slower than the type-naive bisect variant. (These results were obtained using Python 3.3.5 with numpy 1.8.1, so things may be different for other versions.) The function based on numpy.random.multinomial is less efficient for large weights than the methods based on cumulative summing. Presumably the fact that argmax has to iterate over the whole array and run comparisons each step plays a significant role, as can be seen as well from the four second difference between an increasing and a decreasing weight list.