python - Set row of csr_matrix

Question

I have a sparse csr_matrix, and I want to change the values of a single row to different values. I can't find an easy and efficient implementation however. This is what it has to do:

A = csr_matrix([[0, 1, 0],
                [1, 0, 1],
                [0, 1, 0]])
new_row = np.array([-1, -1, -1])
print(set_row_csr(A, 2, new_row).todense())

>>> [[ 0,  1, 0],
     [ 1,  0, 1],
     [-1, -1, -1]]

This is my current implementation of set_row_csr:

def set_row_csr(A, row_idx, new_row):
    A[row_idx, :] = new_row
    return A

But this gives me a SparseEfficiencyWarning. Is there a way of getting this done without manual index juggling, or is this my only way out?

Answer 1

physicalattraction's answer is indeed significantly quicker. It's much faster than my solution, which was to just add a separate matrix with that single row set. Though the addition solution was faster than the slicing solution.

The take away for me is that the fastest way to set rows in a csr_matrix or columns in a csc_matrix is to modify the underlying data yourself.

def time_copy(A, num_tries = 10000):
    start = time.time()
    for i in range(num_tries):
        B = A.copy()
    end = time.time()
    return end - start

def test_method(func, A, row_idx, new_row, num_tries = 10000):
    start = time.time()
    for i in range(num_tries):
        func(A.copy(), row_idx, new_row)
    end = time.time()
    copy_time = time_copy(A, num_tries)
    print("Duration {}".format((end - start) - copy_time))

def set_row_csr_slice(A, row_idx, new_row):
    A[row_idx,:] = new_row

def set_row_csr_addition(A, row_idx, new_row):
    indptr = np.zeros(A.shape[1] + 1)
    indptr[row_idx +1:] = A.shape[1]
    indices = np.arange(A.shape[1])
    A += csr_matrix((new_row, indices, indptr), shape=A.shape)

>>> A = csr_matrix((np.ones(1000), (np.random.randint(0,1000,1000), np.random.randint(0, 1000, 1000))))
>>> test_method(set_row_csr_slice, A, 200, np.ones(A.shape[1]), num_tries = 10000)
Duration 4.938395977020264

>>> test_method(set_row_csr_addition, A, 200, np.ones(A.shape[1]), num_tries = 10000)
Duration 2.4161765575408936

>>> test_method(set_row_csr, A, 200, np.ones(A.shape[1]), num_tries = 10000)
Duration 0.8432261943817139

The slice solution also scales much worse with the size and sparsity of the matrix.

# Larger matrix, same fraction sparsity
>>> A = csr_matrix((np.ones(10000), (np.random.randint(0,10000,10000), np.random.randint(0, 10000, 10000))))
>>> test_method(set_row_csr_slice, A, 200, np.ones(A.shape[1]), num_tries = 10000)
Duration 18.335174798965454

>>> test_method(set_row_csr, A, 200, np.ones(A.shape[1]), num_tries = 10000)
Duration 1.1089558601379395

# Super sparse matrix
>>> A = csr_matrix((np.ones(100), (np.random.randint(0,10000,100), np.random.randint(0, 10000, 100))))
>>> test_method(set_row_csr_slice, A, 200, np.ones(A.shape[1]), num_tries = 10000)
Duration 13.371600151062012

>>> test_method(set_row_csr, A, 200, np.ones(A.shape[1]), num_tries = 10000)
Duration 1.0454308986663818