Issues with CuPy and the Sparse Implementation

[crumleyc]

Issues with CuPy and the Sparse Implementation

[crumleyc]

pydata/sparse#198

[crumleyc]

I have tried other unsuccessful attempts:

v = cp.random.random(176) #(176,)
indices = np.argpartition(v,-20)[-20:] #(20,)
col = cp.ones(20) #(20,)

cupyx.scipy.sparse.coo_matrix((v[indices],(indices,col)))

This results in an error saying that all parameters needs to be 1D arrays, even though they are all 1D.

v = cp.random.random(176) #(176,)
indices = np.argpartition(v,-20)[-20:] #(20,)
min = v[indices].min()
v[v < min] = 0
cupyx.scipy.sparse.coo_matrix(v)

I think this method only works when v is already a sparse matrix.

[crumleyc]

I noticed in dask's distributed repository, they had some unit testing where they implemented the cupyx.scipy.sparse.coo_matrix here. I got that to work.

[crumleyc]

u_new = da.dot(S,_V_.result()).compute() is causing an error:

TypeError: 'coo_matrix' object is not subscriptable

Where V.result() returns a dask cupy sparse array.

Looks like it is being looked into cupy/cupy#3178.