I want to divide a sparse matrix's rows by scalars given in an array.
For example, I have a csr_matrix C :
C = [[2,4,6], [5,10,15]]
D = [2,5]
I want the result of C after division to be :
result = [[1, 2, 3], [1, 2, 3]]
I have tried this using the method that we use for numpy arrays:
result = C / D[:,None]
But this seems really slow. How to do this efficiently in sparse matrices?
Approach #1
Here's a sparse matrix solution using manual replication with indexing -
from scipy.sparse import csr_matrix
r,c = C.nonzero()
rD_sp = csr_matrix(((1.0/D)[r], (r,c)), shape=(C.shape))
out = C.multiply(rD_sp)
The output is a sparse matrix as well as opposed to the output from C / D[:,None] that creates a full matrix. As such, the proposed approach saves on memory.
Possible performance boost with replication using np.repeat instead of indexing -
val = np.repeat(1.0/D, C.getnnz(axis=1))
rD_sp = csr_matrix((val, (r,c)), shape=(C.shape))
Approach #2
Another approach could involve data method of the sparse matrix that gives us a flattened view into the sparse matrix for in-place results and also avoid the use of nonzero, like so -
val = np.repeat(D, C.getnnz(axis=1))
C.data /= val