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import numpy as np
from numpy.testing import assert_array_almost_equal, assert_
from scipy.sparse import csr_matrix, csc_matrix, lil_matrix
import pytest
def test_csc_getrow():
N = 10
np.random.seed(0)
X = np.random.random((N, N))
X[X > 0.7] = 0
Xcsc = csc_matrix(X)
for i in range(N):
arr_row = X[i:i + 1, :]
csc_row = Xcsc.getrow(i)
assert_array_almost_equal(arr_row, csc_row.toarray())
assert_(type(csc_row) is csr_matrix)
def test_csc_getcol():
N = 10
np.random.seed(0)
X = np.random.random((N, N))
X[X > 0.7] = 0
Xcsc = csc_matrix(X)
for i in range(N):
arr_col = X[:, i:i + 1]
csc_col = Xcsc.getcol(i)
assert_array_almost_equal(arr_col, csc_col.toarray())
assert_(type(csc_col) is csc_matrix)
@pytest.mark.parametrize("matrix_input, axis, expected_shape",
[(csc_matrix([[1, 0],
[0, 0],
[0, 2]]),
0, (0, 2)),
(csc_matrix([[1, 0],
[0, 0],
[0, 2]]),
1, (3, 0)),
(csc_matrix([[1, 0],
[0, 0],
[0, 2]]),
'both', (0, 0)),
(csc_matrix([[0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 2, 3, 0, 1]]),
0, (0, 6))])
def test_csc_empty_slices(matrix_input, axis, expected_shape):
# see gh-11127 for related discussion
slice_1 = matrix_input.toarray().shape[0] - 1
slice_2 = slice_1
slice_3 = slice_2 - 1
if axis == 0:
actual_shape_1 = matrix_input[slice_1:slice_2, :].toarray().shape
actual_shape_2 = matrix_input[slice_1:slice_3, :].toarray().shape
elif axis == 1:
actual_shape_1 = matrix_input[:, slice_1:slice_2].toarray().shape
actual_shape_2 = matrix_input[:, slice_1:slice_3].toarray().shape
elif axis == 'both':
actual_shape_1 = matrix_input[slice_1:slice_2, slice_1:slice_2].toarray().shape
actual_shape_2 = matrix_input[slice_1:slice_3, slice_1:slice_3].toarray().shape
assert actual_shape_1 == expected_shape
assert actual_shape_1 == actual_shape_2
@pytest.mark.parametrize('ax', (-2, -1, 0, 1, None))
def test_argmax_overflow(ax):
# See gh-13646: Windows integer overflow for large sparse matrices.
dim = (100000, 100000)
A = lil_matrix(dim)
A[-2, -2] = 42
A[-3, -3] = 0.1234
A = csc_matrix(A)
idx = A.argmax(axis=ax)
if ax is None:
# idx is a single flattened index
# that we need to convert to a 2d index pair;
# can't do this with np.unravel_index because
# the dimensions are too large
ii = idx % dim[0]
jj = idx // dim[0]
else:
# idx is an array of size of A.shape[ax];
# check the max index to make sure no overflows
# we encountered
assert np.count_nonzero(idx) == A.nnz
ii, jj = np.max(idx), np.argmax(idx)
assert A[ii, jj] == A[-2, -2]
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