Given the code snippet:
B = A @ M - T
where A
is a CSR scipy sparse matrix, M
and T
are two numpy arrays.
Question: During the matrix operations, does numpy treat A
as a dense matrix, or M
and 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.
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Answer
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'>