Given the code snippet:
B = A @ M - T
A is a CSR scipy sparse matrix,
T are two numpy arrays.
Question: During the matrix operations, does numpy treat
A as a dense matrix, or
T as two sparse matrices?
I suspect that the latter case is true since the resulting matrix
B is not in the sparse format.
I also notices that this operation is much slower if I change the format of
A to dense, which sort of contradicts my guess.
Numpy doesn’t do sparse matrices. Scipy does the matrix multiplication (this means no multithreading, unlike numpy).
A is kept sparse but A @ M fills a dense array if M is a dense array.
>>> import numpy as np >>> from scipy import sparse >>> A = sparse.random(100, 10, density=0.1, format='csr') >>> B = np.random.rand(10, 10) >>> type(A@B) <class 'numpy.ndarray'> >>> type(B@A.T) <class 'numpy.ndarray'>
Note that some sparse operations still give matrixes, not arrays:
>>> N = sparse.random(100, 10, density=0.1, format='csr') >>> type(A@B - N) <class 'numpy.matrix'>