115 lines
3.4 KiB
Python
115 lines
3.4 KiB
Python
import numpy as np
|
|
from numpy.testing import assert_array_almost_equal, assert_
|
|
from scipy.sparse import csr_matrix
|
|
|
|
import pytest
|
|
|
|
|
|
def _check_csr_rowslice(i, sl, X, Xcsr):
|
|
np_slice = X[i, sl]
|
|
csr_slice = Xcsr[i, sl]
|
|
assert_array_almost_equal(np_slice, csr_slice.toarray()[0])
|
|
assert_(type(csr_slice) is csr_matrix)
|
|
|
|
|
|
def test_csr_rowslice():
|
|
N = 10
|
|
np.random.seed(0)
|
|
X = np.random.random((N, N))
|
|
X[X > 0.7] = 0
|
|
Xcsr = csr_matrix(X)
|
|
|
|
slices = [slice(None, None, None),
|
|
slice(None, None, -1),
|
|
slice(1, -2, 2),
|
|
slice(-2, 1, -2)]
|
|
|
|
for i in range(N):
|
|
for sl in slices:
|
|
_check_csr_rowslice(i, sl, X, Xcsr)
|
|
|
|
|
|
def test_csr_getrow():
|
|
N = 10
|
|
np.random.seed(0)
|
|
X = np.random.random((N, N))
|
|
X[X > 0.7] = 0
|
|
Xcsr = csr_matrix(X)
|
|
|
|
for i in range(N):
|
|
arr_row = X[i:i + 1, :]
|
|
csr_row = Xcsr.getrow(i)
|
|
|
|
assert_array_almost_equal(arr_row, csr_row.toarray())
|
|
assert_(type(csr_row) is csr_matrix)
|
|
|
|
|
|
def test_csr_getcol():
|
|
N = 10
|
|
np.random.seed(0)
|
|
X = np.random.random((N, N))
|
|
X[X > 0.7] = 0
|
|
Xcsr = csr_matrix(X)
|
|
|
|
for i in range(N):
|
|
arr_col = X[:, i:i + 1]
|
|
csr_col = Xcsr.getcol(i)
|
|
|
|
assert_array_almost_equal(arr_col, csr_col.toarray())
|
|
assert_(type(csr_col) is csr_matrix)
|
|
|
|
@pytest.mark.parametrize("matrix_input, axis, expected_shape",
|
|
[(csr_matrix([[1, 0, 0, 0],
|
|
[0, 0, 0, 0],
|
|
[0, 2, 3, 0]]),
|
|
0, (0, 4)),
|
|
(csr_matrix([[1, 0, 0, 0],
|
|
[0, 0, 0, 0],
|
|
[0, 2, 3, 0]]),
|
|
1, (3, 0)),
|
|
(csr_matrix([[1, 0, 0, 0],
|
|
[0, 0, 0, 0],
|
|
[0, 2, 3, 0]]),
|
|
'both', (0, 0)),
|
|
(csr_matrix([[0, 1, 0, 0, 0],
|
|
[0, 0, 0, 0, 0],
|
|
[0, 0, 2, 3, 0]]),
|
|
0, (0, 5))])
|
|
def test_csr_empty_slices(matrix_input, axis, expected_shape):
|
|
# see gh-11127 for related discussion
|
|
slice_1 = matrix_input.A.shape[0] - 1
|
|
slice_2 = slice_1
|
|
slice_3 = slice_2 - 1
|
|
|
|
if axis == 0:
|
|
actual_shape_1 = matrix_input[slice_1:slice_2, :].A.shape
|
|
actual_shape_2 = matrix_input[slice_1:slice_3, :].A.shape
|
|
elif axis == 1:
|
|
actual_shape_1 = matrix_input[:, slice_1:slice_2].A.shape
|
|
actual_shape_2 = matrix_input[:, slice_1:slice_3].A.shape
|
|
elif axis == 'both':
|
|
actual_shape_1 = matrix_input[slice_1:slice_2, slice_1:slice_2].A.shape
|
|
actual_shape_2 = matrix_input[slice_1:slice_3, slice_1:slice_3].A.shape
|
|
|
|
assert actual_shape_1 == expected_shape
|
|
assert actual_shape_1 == actual_shape_2
|
|
|
|
|
|
def test_csr_bool_indexing():
|
|
data = csr_matrix([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
|
|
list_indices1 = [False, True, False]
|
|
array_indices1 = np.array(list_indices1)
|
|
list_indices2 = [[False, True, False], [False, True, False], [False, True, False]]
|
|
array_indices2 = np.array(list_indices2)
|
|
list_indices3 = ([False, True, False], [False, True, False])
|
|
array_indices3 = (np.array(list_indices3[0]), np.array(list_indices3[1]))
|
|
slice_list1 = data[list_indices1].toarray()
|
|
slice_array1 = data[array_indices1].toarray()
|
|
slice_list2 = data[list_indices2]
|
|
slice_array2 = data[array_indices2]
|
|
slice_list3 = data[list_indices3]
|
|
slice_array3 = data[array_indices3]
|
|
assert (slice_list1 == slice_array1).all()
|
|
assert (slice_list2 == slice_array2).all()
|
|
assert (slice_list3 == slice_array3).all()
|
|
|