Element-wise maximum of two sparse matrices

Question

Is there an easy/build-in way to get the element-wise maximum of two (or ideally more) sparse matrices? I.e. a sparse equivalent of np.maximum.

Answer 1

No, there's no built-in way to do this in scipy.sparse. The easy solution is

np.maximum(X.A, Y.A)

but this is obviously going to be very memory-intensive when the matrices have large dimensions and it might crash your machine. A memory-efficient (but by no means fast) solution is

# convert to COO, if necessary
X = X.tocoo()
Y = Y.tocoo()

Xdict = dict(((i, j), v) for i, j, v in zip(X.row, X.col, X.data))
Ydict = dict(((i, j), v) for i, j, v in zip(Y.row, Y.col, Y.data))

keys = list(set(Xdict.iterkeys()).union(Ydict.iterkeys()))

XmaxY = [max(Xdict.get((i, j), 0), Ydict.get((i, j), 0)) for i, j in keys]
XmaxY = coo_matrix((XmaxY, zip(*keys)))

Note that this uses pure Python instead of vectorized idioms. You can try shaving some of the running time off by vectorizing parts of it.