cosine similarity on large sparse matrix with numpy

Question

The code below causes my system to run out of memory before it completes.

Can you suggest a more efficient means of computing the cosine similarity on a large matrix, such as the one below?

I would like to have the cosine similarity computed for each of the 65000 rows in my original matrix (mat) relative to all of the others so that the result is a 65000 x 65000 matrix where each element is the cosine similarity between two rows in the original matrix.

import numpy as np
from scipy import sparse
from sklearn.metrics.pairwise import cosine_similarity

mat = np.random.rand(65000, 10)

sparse_mat = sparse.csr_matrix(mat)

similarities = cosine_similarity(sparse_mat)

After running that last line I always run out of memory and the program either freezes or crashes with a MemoryError. This occurs whether I run on my 8 gb local RAM or on a 64 gb EC2 instance.

Answer 1

Same problem here. I've got a big, non-sparse matrix. It fits in memory just fine, but cosine_similarity crashes for whatever unknown reason, probably because they copy the matrix one time too many somewhere. So I made it compare small batches of rows "on the left" instead of the entire matrix:

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

def cosine_similarity_n_space(m1, m2, batch_size=100):
    assert m1.shape[1] == m2.shape[1]
    ret = np.ndarray((m1.shape[0], m2.shape[0]))
    for row_i in range(0, int(m1.shape[0] / batch_size) + 1):
        start = row_i * batch_size
        end = min([(row_i + 1) * batch_size, m1.shape[0]])
        if end <= start:
            break # cause I'm too lazy to elegantly handle edge cases
        rows = m1[start: end]
        sim = cosine_similarity(rows, m2) # rows is O(1) size
        ret[start: end] = sim
    return ret

No crashes for me; YMMV. Try different batch sizes to make it faster. I used to only compare 1 row at a time, and it took about 30X as long on my machine.

Stupid yet effective sanity check:

import random
while True:
    m = np.random.rand(random.randint(1, 100), random.randint(1, 100))
    n = np.random.rand(random.randint(1, 100), m.shape[1])
    assert np.allclose(cosine_similarity(m, n), cosine_similarity_n_space(m, n))

Answer 2

I would run it in chunks like this

from sklearn.metrics.pairwise import cosine_similarity

# Change chunk_size to control resource consumption and speed
# Higher chunk_size means more memory/RAM needed but also faster 
chunk_size = 500 
matrix_len = your_matrix.shape[0] # Not sparse numpy.ndarray

def similarity_cosine_by_chunk(start, end):
    if end > matrix_len:
        end = matrix_len
    return cosine_similarity(X=your_matrix[start:end], Y=your_matrix) # scikit-learn function

for chunk_start in xrange(0, matrix_len, chunk_size):
    cosine_similarity_chunk = similarity_cosine_by_chunk(chunk_start, chunk_start+chunk_size)
    # Handle cosine_similarity_chunk  ( Write it to file_timestamp and close the file )
    # Do not open the same file again or you may end up with out of memory after few chunks