How to best row-scale a SciPy sparse matrix?

Question

I've got a SciPy sparse matrix A, let's say in CSR format, and a vector v of matching length.

What's the best way of row-scaling A with v, i.e., performing diag(v) * A?

Answer 1

The easy way is to let scipy handle the gory details, and simply do:

scipy.sparse.spdiags(v, 0, len(v), len(v)) * A

EDIT If (and only if) your matrix is stored in CSC format, you can do the operation in place as follows:

A_csc.data = A_csc.data * v[A_csc.indices]

I've done some timings, at it wildly depends on the sparsity of the matrix and its size, feel free to play with the following code:

from __future__ import division
import numpy as np
import scipy.sparse as sps
import timeit

A_csr = None
A_csc = None
v = None

def time_row_scaling(n, dens) :
    global A_csr, A_csc, v
    v = np.random.rand(n)
    A_csr = sps.rand(n, n, density=dens, format='csr')
    A_csc = A_csr.tocsc()
    def row_scale(A_csc, v) :
        A_csc.data = A_csc.data * v[A_csc.indices]
    row_scaled_1 = sps.spdiags(v, 0, n , n) * A_csr
    row_scaled_2 = sps.spdiags(v, 0, n , n) * A_csc
    row_scale(A_csc, v)
    if n < 1000 :
        np.testing.assert_almost_equal(row_scaled_1.toarray(),
                                       row_scaled_2.toarray())
        np.testing.assert_almost_equal(row_scaled_1.toarray(),
                                       A_csc.toarray())
    A_csc = A_csr.tocsc()
    t1 = timeit.timeit('sps.spdiags(v, 0, len(v) , len(v)) * A_csr',
                       'from __main__ import sps, v, A_csr',
                       number=1)
    t2 = timeit.timeit('sps.spdiags(v, 0, len(v), len(v)) * A_csc',
                       'from __main__ import sps, v, A_csc',
                       number=1)
    t3 = timeit.timeit('A_csc.data = A_csc.data * v[A_csc.indices]',
                       'from __main__ import A_csc, v',
                       number=1)
    print t1, t2, t3

>>> time_row_scaling(1000, 0.01)
0.00100659830939 0.00102425072673 0.000231944553347
>>> time_row_scaling(1000, 0.1)
0.0017328105481 0.00311339379218 0.00239826562947
>>> time_row_scaling(10000, 0.01)
0.0162369397769 0.0359325217874 0.0216837368279
>>> time_row_scaling(10000, 0.1)
0.167978350747 0.492032396702 0.209231639536

Summary seems to be, if it is CSR, or really big, go with the simple first method. If it is a smallish, very sparse matrix, then the in place method will be faster, although all times are small then.