How Do I Create Interacting Sparse Matrix?

Suppose I have two sparse matrix: from scipy.sparse import random from scipy import stats S0 = random(5000,100, density=0.01) S1 = random(5000,100,density=0.01) I want to create

Solution 1:

Here's a rewrite, working directly with the csrintptr. It save time by slicing the data and indices directly, rather than making a whole new 1 row csr matrix each row:

def test_iter2(A, B): 
    m,n1 = A.shape 
    n2 = B.shape[1] 
    Cshape = (m, n1*n2) 
    data = [] 
    col =  [] 
    row =  [] 
    for i in range(A.shape[0]): 
        slc1 = slice(A.indptr[i],A.indptr[i+1]) 
        data1 = A.data[slc1]; ind1 = A.indices[slc1] 
        slc2 = slice(B.indptr[i],B.indptr[i+1])  
        data2 = B.data[slc2]; ind2 = B.indices[slc2]  
        data.append(np.outer(data1, data2).ravel()) 
        col.append(((ind1*n2)[:,None]+ind2).ravel()) 
        row.append(np.full(len(data1)*len(data2), i)) 
    data = np.concatenate(data) 
    col = np.concatenate(col) 
    row = np.concatenate(row) 
    return sparse.coo_matrix((data,(row,col)),shape=Cshape) 

With a smaller test case, this saves quite a bit of time:

In [536]: S0=sparse.random(200,200, 0.01, format='csr')                                                   
In [537]: S1=sparse.random(200,200, 0.01, format='csr')                                                   
In [538]: timeit test_iter(S0,S1)                                                                         
42.8 ms ± 1.7 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [539]: timeit test_iter2(S0,S1)                                                                        
6.94 ms ± 27 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)