Given a matrix A, I want to apply different random shuffles for different row of A; for example,
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
becomes
array([[1, 3, 2],
[6, 5, 4],
[7, 9, 8]])
Of course we can loop through the matrix and make every row randomly shuffle; however iteration is slow and I am asking if there is more efficient way to do this.
Picked up this neat trick from Divakar which involves randn and argsort:
np.random.seed(0)
s = np.arange(16).reshape(4, 4)
np.take_along_axis(s, np.random.randn(*s.shape).argsort(axis=1), axis=1)
array([[ 1, 0, 3, 2],
[ 4, 6, 5, 7],
[11, 10, 8, 9],
[14, 12, 13, 15]])
For a 2D array, this can be simplified to
s[np.arange(len(s))[:,None], np.random.randn(*s.shape).argsort(axis=1)]
array([[ 1, 0, 3, 2],
[ 4, 6, 5, 7],
[11, 10, 8, 9],
[14, 12, 13, 15]])
You can also apply np.random.permutation over each row independently to return a new array.
np.apply_along_axis(np.random.permutation, axis=1, arr=s)
array([[ 3, 1, 0, 2],
[ 4, 6, 5, 7],
[ 8, 9, 10, 11],
[15, 14, 13, 12]])
Performance -
s = np.arange(10000 * 100).reshape(10000, 100)
%timeit s[np.arange(len(s))[:,None], np.random.randn(*s.shape).argsort(axis=1)]
%timeit np.apply_along_axis(np.random.permutation, 1, s)
84.6 ms ± 857 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
842 ms ± 8.06 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
I've noticed it depends on the dimensions of your data, make sure to test it out first.
Codewise you can use numpy's apply_along_axis as
np.apply_along_axis(np.random.shuffle, 1, matrix)
but it doesn't seem to be more efficient than iterating at least for a 3x3 matrix, for that method I get
> %%timeit
> np.apply_along_axis(np.random.shuffle, 1, test)
67 µs ± 1.8 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
while the iteration gives
> %%timeit
> for i in range(test.shape[0]):
> np.random.shuffle(test[i])
20.3 µs ± 284 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
apply_along_axis is essentially just iterate over the 'other' axes. No speed promises. It makes iteration prettier for 3d and larger; does nothing for 2d.
apply_along_axis. Another answer for columns is here and here and here