If you have 2 CSR matrices (X and Y) of different shapes, you can simply use X!=Y and it will return True.
If CSR matrices are of the same shape, X!=Y returns a matrix of the same shape with boolean elements. You can then use numpy.where() to check whether they are the same or different.
Here is an example with 3 matrices A, B, and C of the shape 4x4:
import numpy as np
from scipy.sparse import csr_matrix
def comare_csr_matrix(x, y):
if np.sum(x != y) == 0:
return 'Same Matrices'
else:
return 'Different Matrices'
if __name__ == "__main__":
row = np.array([0, 0, 1, 1, 2, 3, 3])
col = np.array([0, 3, 0, 2, 1, 2, 3])
data = np.array([1 for i in range(len(col))])
data1 = np.array([2 for i in range(len(col))])
A = csr_matrix((data, (row, col)), shape=(4, 4))
B = csr_matrix((data, (row, col)), shape=(4, 4))
C = csr_matrix((data1, (row, col)), shape=(4, 4))
print(comare_csr_matrix(A, B))
print(comare_csr_matrix(B, C))