Efficiently generating multiple instances of numpy.random.choice without replacement

Question

I\'m new to Python. While reading, please mention any other suggestions regarding ways to improve my Python code.

Question: How do I generate a 8x

Answer 1

How about shuffling, that is to say, permuting?

import random
import numpy
from timeit import Timer 

def B_rand_M(N):
    a = numpy.arange(1,9)
    M = numpy.zeros(shape = (8, N))
    for i in range (0, N):
        M[:, i] = numpy.random.permutation(a)
    return M

# your original implementation
def J_rand_M(N):
    M = numpy.zeros(shape = (8, N))
    for i in range (0, N):
        M[:, i] = numpy.random.choice(8, size = 8, replace = False) + 1 
    return M

some timings:

def compare(N):
    for f in (J_rand_M, B_rand_M):
        t = Timer(lambda: f(N)).timeit(6)
        print 'time for %s(%s): %.6f' % (f.__name__, N, t)

for i in range(6):
    print 'N = 10^%s' % i
    compare(10**i)
    print

gives

N = 10^0
time for J_rand_M(1): 0.001199
time for B_rand_M(1): 0.000080

N = 10^1
time for J_rand_M(10): 0.001112
time for B_rand_M(10): 0.000335

N = 10^2
time for J_rand_M(100): 0.011118
time for B_rand_M(100): 0.003022

N = 10^3
time for J_rand_M(1000): 0.110887
time for B_rand_M(1000): 0.030528

N = 10^4
time for J_rand_M(10000): 1.100540
time for B_rand_M(10000): 0.304696

N = 10^5
time for J_rand_M(100000): 11.151576
time for B_rand_M(100000): 3.049474

Answer 2

Create a random array of specified shape and then sort along the axis where you want to keep the limits, thus giving us a vectorized and very efficient solution. This would be based on this smart answer to MATLAB randomly permuting columns differently. Here's the implementation -

Sample run -

In [122]: N = 10

In [123]: np.argsort(np.random.rand(8,N),axis=0)+1
Out[123]: 
array([[7, 3, 5, 1, 1, 5, 2, 4, 1, 4],
       [8, 4, 3, 2, 2, 8, 5, 5, 6, 2],
       [1, 2, 4, 6, 5, 4, 4, 3, 4, 7],
       [5, 6, 2, 5, 8, 2, 7, 8, 5, 8],
       [2, 8, 6, 3, 4, 7, 1, 1, 2, 6],
       [6, 7, 7, 8, 6, 6, 3, 2, 7, 3],
       [4, 1, 1, 4, 3, 3, 8, 6, 8, 1],
       [3, 5, 8, 7, 7, 1, 6, 7, 3, 5]], dtype=int64)

Runtime tests -

In [124]: def sortbased_rand8(N):
     ...:     return np.argsort(np.random.rand(8,N),axis=0)+1
     ...: 
     ...: def rand_M(N):
     ...:     M = np.zeros(shape = (8, N))
     ...:     for i in range (0, N):
     ...:         M[:, i] = np.random.choice(8, size = 8, replace = False) + 1 
     ...:     return M
     ...: 

In [125]: N = 5000

In [126]: %timeit sortbased_rand8(N)
100 loops, best of 3: 1.95 ms per loop

In [127]: %timeit rand_M(N)
1 loops, best of 3: 233 ms per loop

Thus, awaits a 120x speedup!

Answer 3

Use below code for your array generation

import numpy as np
N=1e7 # THe value you want to have
np.random.randint(1,high=8,size=(8,N))

Hope this helps, it will surely not going to take that much time.

Answer 4

Just a comment on your runtime analysis of the problem - my intuition is that O(n) is the best possible runtime you can possibly obtain when generating O(n) truly random numbers.

Have you tried actually running your code with n = 10 million? Your assumption that the runtime will scale by 1000 when the input grows by a factor of 1000 may not be true in practice, as there is usually a constant term when executing any program (loading libraries, etc.), which may be significant depending on the problem.

That being said, it looks like the question linked by Eric Wright does a very thorough job and can easily be adapted to fit your question.