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"""Test of min-max 1D features of sparse array classes"""
import pytest
import numpy as np
from numpy.testing import assert_equal, assert_array_equal
from scipy.sparse import coo_array, csr_array, csc_array, bsr_array
from scipy.sparse import coo_matrix, csr_matrix, csc_matrix, bsr_matrix
from scipy.sparse._sputils import isscalarlike
def toarray(a):
if isinstance(a, np.ndarray) or isscalarlike(a):
return a
return a.toarray()
formats_for_minmax = [bsr_array, coo_array, csc_array, csr_array]
formats_for_minmax_supporting_1d = [coo_array, csr_array]
@pytest.mark.parametrize("spcreator", formats_for_minmax_supporting_1d)
class Test_MinMaxMixin1D:
def test_minmax(self, spcreator):
D = np.arange(5)
X = spcreator(D)
assert_equal(X.min(), 0)
assert_equal(X.max(), 4)
assert_equal((-X).min(), -4)
assert_equal((-X).max(), 0)
def test_minmax_axis(self, spcreator):
D = np.arange(50)
X = spcreator(D)
for axis in [0, -1]:
assert_array_equal(
toarray(X.max(axis=axis)), D.max(axis=axis, keepdims=True)
)
assert_array_equal(
toarray(X.min(axis=axis)), D.min(axis=axis, keepdims=True)
)
for axis in [-2, 1]:
with pytest.raises(ValueError, match="axis out of range"):
X.min(axis=axis)
with pytest.raises(ValueError, match="axis out of range"):
X.max(axis=axis)
def test_numpy_minmax(self, spcreator):
dat = np.array([0, 1, 2])
datsp = spcreator(dat)
assert_array_equal(np.min(datsp), np.min(dat))
assert_array_equal(np.max(datsp), np.max(dat))
def test_argmax(self, spcreator):
D1 = np.array([-1, 5, 2, 3])
D2 = np.array([0, 0, -1, -2])
D3 = np.array([-1, -2, -3, -4])
D4 = np.array([1, 2, 3, 4])
D5 = np.array([1, 2, 0, 0])
for D in [D1, D2, D3, D4, D5]:
mat = spcreator(D)
assert_equal(mat.argmax(), np.argmax(D))
assert_equal(mat.argmin(), np.argmin(D))
assert_equal(mat.argmax(axis=0), np.argmax(D, axis=0))
assert_equal(mat.argmin(axis=0), np.argmin(D, axis=0))
D6 = np.empty((0,))
for axis in [None, 0]:
mat = spcreator(D6)
with pytest.raises(ValueError, match="to an empty matrix"):
mat.argmin(axis=axis)
with pytest.raises(ValueError, match="to an empty matrix"):
mat.argmax(axis=axis)
@pytest.mark.parametrize("spcreator", formats_for_minmax)
class Test_ShapeMinMax2DWithAxis:
def test_minmax(self, spcreator):
dat = np.array([[-1, 5, 0, 3], [0, 0, -1, -2], [0, 0, 1, 2]])
datsp = spcreator(dat)
for (spminmax, npminmax) in [
(datsp.min, np.min),
(datsp.max, np.max),
(datsp.nanmin, np.nanmin),
(datsp.nanmax, np.nanmax),
]:
for ax, result_shape in [(0, (4,)), (1, (3,))]:
assert_equal(toarray(spminmax(axis=ax)), npminmax(dat, axis=ax))
assert_equal(spminmax(axis=ax).shape, result_shape)
assert spminmax(axis=ax).format == "coo"
for spminmax in [datsp.argmin, datsp.argmax]:
for ax in [0, 1]:
assert isinstance(spminmax(axis=ax), np.ndarray)
# verify spmatrix behavior
spmat_form = {
'coo': coo_matrix,
'csr': csr_matrix,
'csc': csc_matrix,
'bsr': bsr_matrix,
}
datspm = spmat_form[datsp.format](dat)
for spm, npm in [
(datspm.min, np.min),
(datspm.max, np.max),
(datspm.nanmin, np.nanmin),
(datspm.nanmax, np.nanmax),
]:
for ax, result_shape in [(0, (1, 4)), (1, (3, 1))]:
assert_equal(toarray(spm(axis=ax)), npm(dat, axis=ax, keepdims=True))
assert_equal(spm(axis=ax).shape, result_shape)
assert spm(axis=ax).format == "coo"
for spminmax in [datspm.argmin, datspm.argmax]:
for ax in [0, 1]:
assert isinstance(spminmax(axis=ax), np.ndarray)
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