Scipy sparse matrix become dense matrix after assignment

Question

alpha = csr_matrix((1000,1000),dtype=np.float32)
beta = csr_matrix((1,1000),dtype=np.float32)
alpha[0,:] = beta

After initiation, alpha and beta sh

Answer 1

When I copy your steps I get

In [131]: alpha[0,:]=beta
/usr/lib/python3/dist-packages/scipy/sparse/compressed.py:730: 
   SparseEfficiencyWarning: Changing the sparsity structure of a
   csr_matrix is expensive. lil_matrix is more efficient.
   SparseEfficiencyWarning)

So that's the first indicator that you are doing something that the developers consider unwise.

We could dig into the csr __setitem__ code, but my guess is that it is converting your beta to dense, and then doing the assignment. And isn't automatically doing the eliminate_zeros step (either during or after the assignment).

Normally why would people be doing a[...]=...? Usually it's to build the sparse matrix. Zeroing out non-zero values is possible, but not frequent enough to treat as a special case.

It's possible for a variety of reasons to have 0 values in a sparse matrix. You could even insert the 0s into alpha.data directly. That's why there are 'cleanup' methods like eliminate_zeros and prune. Even nonzero applies a !=0 mask

    # convert to COOrdinate format
    A = self.tocoo()
    nz_mask = A.data != 0
    return (A.row[nz_mask],A.col[nz_mask])

In normal sparse practice you build the data in coo or other format, and then convert to csr for calculations. Matrix multiplication is it's strong point. That constructs a new sparse matrix. Modification of a csr is possible, but not encouraged.

====================

alpha.__setitem__?? (in Ipython) shows

def __setitem__(self, index, x):
    # Process arrays from IndexMixin
    i, j = self._unpack_index(index)
    i, j = self._index_to_arrays(i, j)

    if isspmatrix(x):
        x = x.toarray()
    ....
    self._set_many(i, j, x.ravel())

So yes, it converts the RHS to a dense array before doing the assignment.