在行和列中切片scipy.sparse.lil_matrix

Question

I would like to extract specific rows and columns from a scipy sparse matrix - probably lil_matrix will be the best choice here.

It works fine here:

from scipy import sparse
lilm=sparse.lil_matrix((10,10))
lilm[0:4,0:3]

This returns a 4x3 sparse matrix. I don't want a block from the matrix though, but rather single columns and rows. I'd expect this to work:

lilm[[1,2,3],[4,5,6]]

but it returns a 1x3 sparse matrix. This also doesn't work with numpy arrays, but there you can use numpy.ix_, as described in Slicing of a NumPy 2d array, or how do I extract an mxm submatrix from an nxn array (n>m)? .

How can one accomplish this behaviour with a lil_matrix ?

My question is partly answered in slicing sparse (scipy) matrix , but I couldn't get this to work for lil_matrix .

Answer 1

You will need to first extract the rows, then the columns:

>>> a = np.arange(100).reshape(10, 10)
>>> a
array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])

>>> lilm = scipy.sparse.lil_matrix(a)

>>> lilm[[1, 2, 3], :].toarray() # extract the rows first...
array([[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39]])

>>> lilm[[1, 2, 3], :][:, [4, 5, 6]].toarray() # ...then the columns
array([[14, 15, 16],
       [24, 25, 26],
       [34, 35, 36]])

You would of course remove the .toarray() from this last expression to get the return as a LIL sparse matrix.