184 lines
6.3 KiB
Python
184 lines
6.3 KiB
Python
import numpy as np
|
|
from numpy.testing import assert_array_almost_equal, assert_
|
|
from scipy.sparse import csr_matrix, csc_matrix, csr_array, csc_array, hstack
|
|
from scipy import sparse
|
|
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.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
|
|
|
|
|
|
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()
|
|
|
|
|
|
def test_csr_hstack_int64():
|
|
"""
|
|
Tests if hstack properly promotes to indices and indptr arrays to np.int64
|
|
when using np.int32 during concatenation would result in either array
|
|
overflowing.
|
|
"""
|
|
max_int32 = np.iinfo(np.int32).max
|
|
|
|
# First case: indices would overflow with int32
|
|
data = [1.0]
|
|
row = [0]
|
|
|
|
max_indices_1 = max_int32 - 1
|
|
max_indices_2 = 3
|
|
|
|
# Individual indices arrays are representable with int32
|
|
col_1 = [max_indices_1 - 1]
|
|
col_2 = [max_indices_2 - 1]
|
|
|
|
X_1 = csr_matrix((data, (row, col_1)))
|
|
X_2 = csr_matrix((data, (row, col_2)))
|
|
|
|
assert max(max_indices_1 - 1, max_indices_2 - 1) < max_int32
|
|
assert X_1.indices.dtype == X_1.indptr.dtype == np.int32
|
|
assert X_2.indices.dtype == X_2.indptr.dtype == np.int32
|
|
|
|
# ... but when concatenating their CSR matrices, the resulting indices
|
|
# array can't be represented with int32 and must be promoted to int64.
|
|
X_hs = hstack([X_1, X_2], format="csr")
|
|
|
|
assert X_hs.indices.max() == max_indices_1 + max_indices_2 - 1
|
|
assert max_indices_1 + max_indices_2 - 1 > max_int32
|
|
assert X_hs.indices.dtype == X_hs.indptr.dtype == np.int64
|
|
|
|
# Even if the matrices are empty, we must account for their size
|
|
# contribution so that we may safely set the final elements.
|
|
X_1_empty = csr_matrix(X_1.shape)
|
|
X_2_empty = csr_matrix(X_2.shape)
|
|
X_hs_empty = hstack([X_1_empty, X_2_empty], format="csr")
|
|
|
|
assert X_hs_empty.shape == X_hs.shape
|
|
assert X_hs_empty.indices.dtype == np.int64
|
|
|
|
# Should be just small enough to stay in int32 after stack. Note that
|
|
# we theoretically could support indices.max() == max_int32, but due to an
|
|
# edge-case in the underlying sparsetools code
|
|
# (namely the `coo_tocsr` routine),
|
|
# we require that max(X_hs_32.shape) < max_int32 as well.
|
|
# Hence we can only support max_int32 - 1.
|
|
col_3 = [max_int32 - max_indices_1 - 1]
|
|
X_3 = csr_matrix((data, (row, col_3)))
|
|
X_hs_32 = hstack([X_1, X_3], format="csr")
|
|
assert X_hs_32.indices.dtype == np.int32
|
|
assert X_hs_32.indices.max() == max_int32 - 1
|
|
|
|
@pytest.mark.parametrize("cls", [csr_matrix, csr_array, csc_matrix, csc_array])
|
|
def test_mixed_index_dtype_int_indexing(cls):
|
|
# https://github.com/scipy/scipy/issues/20182
|
|
rng = np.random.default_rng(0)
|
|
base_mtx = cls(sparse.random(50, 50, random_state=rng, density=0.1))
|
|
indptr_64bit = base_mtx.copy()
|
|
indices_64bit = base_mtx.copy()
|
|
indptr_64bit.indptr = base_mtx.indptr.astype(np.int64)
|
|
indices_64bit.indices = base_mtx.indices.astype(np.int64)
|
|
|
|
for mtx in [base_mtx, indptr_64bit, indices_64bit]:
|
|
np.testing.assert_array_equal(mtx[[1,2], :].toarray(), base_mtx[[1, 2], :].toarray())
|
|
np.testing.assert_array_equal(mtx[:, [1, 2]].toarray(), base_mtx[:, [1, 2]].toarray()) |