How would I replace values less than 4 with 0 in this array without triggering a SparseEfficiencyWarning and without reducing its sparsity?
from scipy import sparse
x = sparse.csr_matrix(
[[0, 1, 2, 3, 4],
[1, 2, 3, 4, 5],
[0, 0, 0, 2, 5]])
x[x < 4] = 0
x.toarray() # verifies that this works
Note also that the sparsity between the initial version of x is 11 stored elements, which rises to 15 stored elements after doing the masking.
Manipulate the data array directly
from scipy import sparse
x = sparse.csr_matrix(
[[0, 1, 2, 3, 4],
[1, 2, 3, 4, 5],
[0, 0, 0, 2, 5]])
x.data[x.data < 4] = 0
>>> x.toarray()
array([[0, 0, 0, 0, 4],
[0, 0, 0, 4, 5],
[0, 0, 0, 0, 5]])
>>> x.data
array([0, 0, 0, 4, 0, 0, 0, 4, 5, 0, 5])
Note that the sparsity is unchanged and there are zero values unless you run x.eliminate_zeros().
x.eliminate_zeros()
>>> x.data
array([4, 4, 5, 5])
If for some reason you don't want to use a boolean mask & fancy indexing in numpy, you can loop over the array with numba:
import numba
@numba.jit(nopython=True)
def _set_array_less_than_to_zero(array, value):
for i in range(len(array)):
if array[i] < value:
array[i] = 0
This should also be faster than the numpy indexing by a fairly substantial degree.
array = np.arange(10)
_set_array_less_than_to_zero(array, 5)
>>> array
array([0, 0, 0, 0, 0, 5, 6, 7, 8, 9])
x.data[x.data < i] step?
x.data is copy-on-write or if the boolean x.data < i array can't be allocated. You could iterate over the array to change it instead of making a boolean mask and using indexing if it's x.data < i that's the problem, but its probably a copy on write issue, which you can only fix by changing other code that isn't here.