How to use timeit where each test requires random setup

Question

I have a function f(x) that takes as input a list x of 100 random floats between 0 and 1. Different lists will result in different running times of f.

I want to find out how long f takes to run on average, over a large number of different random lists. What's the best way to do this? Should I use timeit and if so is there a way I can do this without including the time it takes to generate each random list in each trial?

This is how I would do it without timeit (pseudocode):

for i = 1 to 10000:
    x = random list
    start = current time
    f(x)
    end = current time
    results.append(end - start)
return mean(results)

Answer 1

You can make a timer decorator:

Here is some example code:

from time import time


class Timer(object):
    def __init__(self, func):
        """
        Decorator that times a function
        @param func: Function being decorated
        @type func: callable
        """
        self.func = func

    def __call__(self, *args, **kwargs):
        start = time()
        self.func(*args, **kwargs)
        end = time()
        return end - start


@Timer
def cheese():
    for var in xrange(9999999):
        continue

for var in xrange(100):
    print cheese()

Working example, with fewer loops.

Answer 2

import timeit, random

def summer(myList):
    result = 0
    for num in myList:
        result += num
    return result

for i in range(10):
    x = [random.randint(0, 100) for i in range(100000)]
    print timeit.timeit("summer(x)", setup="from __main__ import x, summer", number = 100)

You can import the variable using from __main__ import x