Fastest Way to generate 1,000,000+ random numbers in python
I am currently writing an app in python that needs to generate large amount of random numbers, FAST. Currently I have a scheme going that uses numpy to generate all of the numbers in a giant batch (about ~500,000 at a time). While this seems to be faster than python's implementation. I still need it to go faster. Any ideas? I'm open to writing it in C and embedding it in the program or doing w/e it takes.
Constraints on the random numbers:
Any ideas? Would pre calculating these numbers and storing them in a file make this faster?
Thanks!
You can speed things up a bit from what mtrw posted above just by doing what you initially described (generating a bunch of random numbers and multiplying and dividing accordingly)...
Also, you probably already know this, but be sure to do the operations in-place (*=, /=, +=, etc) when working with large-ish numpy arrays. It makes a huge difference in memory usage with large arrays, and will give a considerable speed increase, too.
In [53]: def rand_row_doubles(row_limits, num):
....: ncols = len(row_limits)
....: x = np.random.random((num, ncols))
....: x *= row_limits
....: return x
....:
In [59]: %timeit rand_row_doubles(np.arange(7) + 1, 1000000)
10 loops, best of 3: 187 ms per loop
As compared to:
In [66]: %timeit ManyRandDoubles(np.arange(7) + 1, 1000000)
1 loops, best of 3: 222 ms per loop
It's not a huge difference, but if you're really worried about speed, it's something.
Just to show that it's correct:
In [68]: x.max(0)
Out[68]:
array([ 0.99999991, 1.99999971, 2.99999737, 3.99999569, 4.99999836,
5.99999114, 6.99999738])
In [69]: x.min(0)
Out[69]:
array([ 4.02099599e-07, 4.41729377e-07, 4.33480302e-08,
7.43497138e-06, 1.28446819e-05, 4.27614385e-07,
1.34106753e-05])
Likewise, for your "rows sum to one" part...
In [70]: def rand_rows_sum_to_one(nrows, ncols):
....: x = np.random.random((ncols, nrows))
....: y = x.sum(axis=0)
....: x /= y
....: return x.T
....:
In [71]: %timeit rand_rows_sum_to_one(1000000, 13)
1 loops, best of 3: 455 ms per loop
In [72]: x = rand_rows_sum_to_one(1000000, 13)
In [73]: x.sum(axis=1)
Out[73]: array([ 1., 1., 1., ..., 1., 1., 1.])
Honestly, even if you re-implement things in C, I'm not sure you'll be able to beat numpy by much on this one... I could be very wrong, though!
EDIT Created functions that return the full set of numbers, not just one row at a time. EDIT 2 Make the functions more pythonic (and faster), add solution for second question
For the first set of numbers, you might consider numpy.random.randint or numpy.random.uniform , which take low and high parameters. Generating an array of 7 x 1,000,000 numbers in a specified range seems to take < 0.7 second on my 2 GHz machine:
def LimitedRandInts(XLim, N):
rowlen = (1,N)
return [np.random.randint(low=0,high=lim,size=rowlen) for lim in XLim]
def LimitedRandDoubles(XLim, N):
rowlen = (1,N)
return [np.random.uniform(low=0,high=lim,size=rowlen) for lim in XLim]
>>> import numpy as np
>>> N = 1000000 #number of randoms in each range
>>> xLim = [x*500 for x in range(1,8)] #convenient limit generation
>>> fLim = [x/7.0 for x in range(1,8)]
>>> aa = LimitedRandInts(xLim, N)
>>> ff = LimitedRandDoubles(fLim, N)
This returns integers in [0,xLim-1] or floats in [0,fLim). The integer version took ~0.3 seconds, the double ~0.66, on my 2 GHz single-core machine.
For the second set, I used @Joe Kingston's suggestion.
def SumToOneRands(NumToSum, N):
aa = np.random.uniform(low=0,high=1.0,size=(NumToSum,N)) #13 rows by 1000000 columns, for instance
s = np.reciprocal(aa.sum(0))
aa *= s
return aa.T #get back to column major order, so aa[k] is the kth set of 13 numbers
>>> ll = SumToOneRands(13, N)
This takes ~1.6 seconds.
In all cases, result[k] gives you the kth set of data.
Try r = 1664525*r + 1013904223
from "an even quicker generator" in "Numerical Recipes in C" 2nd edition, Press et al., isbn 0521431085, p. 284.
np.random is certainly "more random"; see Linear congruential generator .
In python, use np.uint32 like this:
python -mtimeit -s '
import numpy as np
r = 1
r = np.array([r], np.uint32)[0] # 316 py -> 16 us np
# python longs can be arbitrarily long, so slow
' '
r = r*1664525 + 1013904223 # NR2 p. 284
'
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