def test_create_same_size_parms():
ps = ParameterSet.create_from_parm_and_value_sizes(3, 4)
p1 = Parameter.create_with_unnamed_values("1", 4)
p2 = Parameter.create_with_unnamed_values("2", 4)
p3 = Parameter.create_with_unnamed_values("3", 4)
plist = [p1, p2, p3]
ps_expected = ParameterSet(plist)
assert all(
pactual.name == pexpected.name
for pactual, pexpected in zip(ps.parameters, ps_expected.parameters))
assert all(
len(pactual) == len(pexpected)
for pactual, pexpected in zip(ps.parameters, ps_expected.parameters))
def test_ipo_generate_5():
ps = ParameterSet.create_from_parm_and_value_sizes(5, 2)
gen = IpoGenerator(ps, 4)
cs = gen.generate_covering_array()
cs_expected = np.array([
[1, 1, 1, 1, 1],
[1, 1, 1, 2, 2],
[1, 1, 2, 1, 2],
[1, 1, 2, 2, 1],
[1, 2, 1, 1, 2],
[1, 2, 1, 2, 1],
[1, 2, 2, 1, 1],
[1, 2, 2, 2, 2],
[2, 1, 1, 1, 2],
[2, 1, 1, 2, 1],
[2, 1, 2, 1, 1],
[2, 1, 2, 2, 2],
[2, 2, 1, 1, 1],
[2, 2, 1, 2, 2],
[2, 2, 2, 1, 2],
[2, 2, 2, 2, 1],
],
dtype=DEFAULT_NDARRAY_TYPE)
assert np.array_equal(cs, cs_expected)
def test_pop(parm_set_5, parm_1, parm_2, parm_3, parm_4, parm_5):
popped_parm = parm_set_5.pop()
assert popped_parm == parm_5
expected_plist = [parm_1, parm_2, parm_3, parm_4]
ps_expected = ParameterSet(expected_plist)
assert parm_set_5.parameters == ps_expected.parameters
def test_do_horizontal_growth_2():
ps = ParameterSet.create_from_value_sizes([2, 2, 5])
g = IpoGenerator(ps, 2)
g.first_parameters()
actual = g.do_horizontal_growth(2)
assert len(actual) == 12
ie1 = InteractionElement({1: 1, 2: 5})
ie2 = InteractionElement({1: 2, 2: 5})
ie3 = InteractionElement({0: 1, 2: 5})
ie4 = InteractionElement({0: 2, 2: 1})
ie5 = InteractionElement({0: 2, 2: 2})
ie6 = InteractionElement({1: 1, 2: 2})
ie7 = InteractionElement({0: 2, 2: 5})
ie8 = InteractionElement({1: 2, 2: 1})
ie9 = InteractionElement({0: 1, 2: 3})
ie10 = InteractionElement({1: 2, 2: 3})
ie11 = InteractionElement({0: 1, 2: 4})
ie12 = InteractionElement({1: 1, 2: 4})
assert ie1 in actual
assert ie2 in actual
assert ie3 in actual
assert ie4 in actual
assert ie5 in actual
assert ie6 in actual
assert ie7 in actual
assert ie8 in actual
assert ie9 in actual
assert ie10 in actual
assert ie11 in actual
assert ie12 in actual
def test_generate_configurations_web_example():
ps = ParameterSet.create_from_value_sizes([3, 8, 2, 2])
gen = RecursiveGenerator(ps, 2)
cs = gen.generate_covering_array()
cs_expected = np.array([
[1, 1, 1, 1],
[1, 2, 2, 2],
[1, 3, 0, 0],
[2, 1, 2, 0],
[2, 2, 0, 1],
[2, 3, 1, 2],
[3, 1, 0, 2],
[3, 2, 1, 0],
[3, 3, 2, 1],
[1, 4, 1, 1],
[2, 4, 2, 2],
[3, 4, 0, 0],
[1, 5, 1, 1],
[2, 5, 2, 2],
[3, 5, 0, 0],
[1, 6, 1, 1],
[2, 6, 2, 2],
[3, 6, 0, 0],
[1, 7, 1, 1],
[2, 7, 2, 2],
[3, 7, 0, 0],
[1, 8, 1, 1],
[2, 8, 2, 2],
[3, 8, 0, 0],
],
dtype=DEFAULT_NDARRAY_TYPE)
assert np.array_equal(cs, cs_expected)
def test_configset_getitem():
"""
Verify that a parameter set and a covering array with
no "don't care" values present is correctly converted
to a data frame with the correct column headings and
value names.
"""
p1 = Parameter("Colour", [RED, GREEN, BLUE])
p2 = Parameter("Pet", [BIRD, CAT, DOG])
p3 = Parameter("Speed", [FAST, MEDIUM, SLOW])
p4 = Parameter("Music", [SEVENTIES, EIGHTIES, TWENTIES])
parameter_set = ParameterSet([p1, p2, p3, p4])
# Covering array from...
# generator = RecursiveGenerator(parameter_set, 2)
# covering_array = generator.generate_covering_array()
covering_array = np.array([[1, 1, 1, 1], [1, 2, 2, 2], [1, 3, 3, 3],
[2, 1, 2, 3], [2, 2, 3, 1], [2, 3, 1, 2],
[3, 1, 3, 2], [3, 2, 1, 3], [3, 3, 2, 1]])
configurations = ConfigurationSet(parameter_set=parameter_set,
covering_array=covering_array)
expected = pd.Series({
"Colour": GREEN,
"Pet": BIRD,
"Speed": MEDIUM,
"Music": TWENTIES,
})
actual = configurations[3]
assert pd.Series.equals(actual, expected)
def test_set_item(parm_set_5, parm_1, parm_2, parm_3, parm_5, parm_6):
parm_set_5[3] = parm_6
expected_plist = [parm_1, parm_2, parm_3, parm_6, parm_5]
ps_expected = ParameterSet(expected_plist)
assert parm_set_5.parameters == ps_expected.parameters
def test_equals():
"""
Verify that a parameter set and a covering array that
contains a "don't care" value is correctly converted
to a data frame, where the "don't care" value becomes
Pandas' pd.NA value.
"""
p1 = Parameter("Colour", [RED, GREEN])
p2 = Parameter("Pet", [BIRD, CAT, DOG, FISH])
p3 = Parameter("Speed", [FAST, SLOW])
p4 = Parameter("Music", [EIGHTIES, TWENTIES])
parameter_set = ParameterSet([p1, p2, p3, p4])
# Covering array from...
# generator = RecursiveGenerator(parameter_set, 2)
# covering_array = generator.generate_covering_array()
covering_array = np.array([[1, 1, 1, 1], [1, 2, 2, 1], [2, 1, 2, 1],
[2, 2, 1, 0], [2, 2, 2, 2], [1, 1, 1, 2],
[1, 3, 1, 1], [2, 3, 2, 2], [1, 4, 1, 1],
[2, 4, 2, 2]])
# configs1 = ConfigurationSet(parameter_set, covering_array)
# configs2 = ConfigurationSet(parameter_set, covering_array)
configs1 = ConfigurationSet(parameter_set=parameter_set,
covering_array=covering_array)
configs2 = ConfigurationSet(parameter_set=parameter_set,
covering_array=covering_array)
assert configs1 is not configs2
assert configs1 == configs2
def test_dataframe_to_covering_array():
p1 = Parameter("Colour", [RED, GREEN])
p2 = Parameter("Pet", [BIRD, CAT, DOG, FISH])
p3 = Parameter("Speed", [FAST, SLOW])
p4 = Parameter("Music", [EIGHTIES, TWENTIES])
parameter_set = ParameterSet([p1, p2, p3, p4])
dataframe = pd.DataFrame([
[RED, BIRD, FAST, EIGHTIES],
[RED, CAT, SLOW, EIGHTIES],
[GREEN, BIRD, SLOW, EIGHTIES],
[GREEN, CAT, FAST, pd.NA],
[GREEN, CAT, SLOW, TWENTIES],
[RED, BIRD, FAST, TWENTIES],
[RED, DOG, FAST, EIGHTIES],
[GREEN, DOG, SLOW, TWENTIES],
[RED, FISH, FAST, EIGHTIES],
[GREEN, FISH, SLOW, TWENTIES],
],
columns=["Colour", "Pet", "Speed", "Music"])
covering_array = ConfigurationSet.dataframe_to_covering_array(
dataframe, parameter_set)
expected = np.array(
[[1, 1, 1, 1], [1, 2, 2, 1], [2, 1, 2, 1], [2, 2, 1, 0], [2, 2, 2, 2],
[1, 1, 1, 2], [1, 3, 1, 1], [2, 3, 2, 2], [1, 4, 1, 1], [2, 4, 2, 2]],
dtype=DEFAULT_NDARRAY_TYPE)
assert np.array_equal(covering_array, expected)
def test_dict_round_trip(parmset_ae, parm_a, parm_b, parm_c, parm_e):
parmset_ae.set_adjacent(parm_a, parm_c, False)
parmset_ae.set_adjacent(parm_b, parm_e, False)
parmset_dict = parmset_ae.to_dict()
ps_new = ParameterSet.from_dict(parmset_dict)
assert ps_new == parmset_ae
def test_pop_index(parm_set_5, parm_1, parm_2, parm_3, parm_4, parm_5):
pop_index = 2
popped_parm = parm_set_5.pop(pop_index)
assert popped_parm == parm_3
ps_expected = ParameterSet([parm_1, parm_2, parm_4, parm_5])
assert parm_set_5.parameters == ps_expected.parameters
def test_generate_configurations7():
ps = ParameterSet.create_from_parm_and_value_sizes(4, 6)
gen = RecursiveGenerator(ps, 2)
cs = gen.generate_covering_array()
cs_expected = np.array([
[1, 1, 1, 1],
[2, 2, 2, 2],
[3, 3, 3, 3],
[4, 4, 4, 4],
[5, 5, 5, 5],
[6, 6, 6, 6],
[3, 4, 5, 6],
[4, 5, 6, 0],
[5, 6, 0, 1],
[6, 0, 1, 2],
[0, 1, 2, 3],
[1, 2, 3, 4],
[2, 3, 4, 5],
[6, 1, 3, 5],
[0, 2, 4, 6],
[1, 3, 5, 0],
[2, 4, 6, 1],
[3, 5, 0, 2],
[4, 6, 1, 3],
[5, 0, 2, 4],
[2, 5, 1, 4],
[3, 6, 2, 5],
[4, 0, 3, 6],
[5, 1, 4, 0],
[6, 2, 5, 1],
[0, 3, 6, 2],
[1, 4, 0, 3],
[5, 2, 6, 3],
[6, 3, 0, 4],
[0, 4, 1, 5],
[1, 5, 2, 6],
[2, 6, 3, 0],
[3, 0, 4, 1],
[4, 1, 5, 2],
[1, 6, 4, 2],
[2, 0, 5, 3],
[3, 1, 6, 4],
[4, 2, 0, 5],
[5, 3, 1, 6],
[6, 4, 2, 0],
[0, 5, 3, 1],
[4, 3, 2, 1],
[5, 4, 3, 2],
[6, 5, 4, 3],
[0, 6, 5, 4],
[1, 0, 6, 5],
[2, 1, 0, 6],
[3, 2, 1, 0],
],
dtype=DEFAULT_NDARRAY_TYPE)
assert np.array_equal(cs, cs_expected)
def test_is_zero_3():
cs = np.array([[1, 2, 3, 4]], dtype=DEFAULT_NDARRAY_TYPE)
pkw = InteractionElement({1: 2, 3: 5})
ps = ParameterSet.create_from_parm_and_value_sizes(13, 4)
g = IpoGenerator(ps, 2)
assert not g.is_zero(cs[0], pkw)
def test_create_different_size_parms():
p1 = Parameter.create_with_unnamed_values("1", 4)
p2 = Parameter.create_with_unnamed_values("2", 5)
p3 = Parameter.create_with_unnamed_values("3", 3)
plist = [p1, p2, p3]
ps = ParameterSet(plist)
assert ps.parameters == plist
def test_init_two_parms():
p1 = Parameter.create_with_unnamed_values("P1", 2)
p2 = Parameter.create_with_unnamed_values("P2", 3)
plist = [p1, p2]
ps = ParameterSet(plist)
assert len(ps.parameters) == 2
assert ps.parameters == plist
assert ps.parameters is not plist # copy, not reference
def test_get_test_value_recur_1():
ps = ParameterSet.create_from_parm_and_value_sizes(5, 2)
g = IpoGenerator(ps, 3)
cs = np.array([
[1, 1, 1, 1],
[1, 1, 2, 0],
[1, 2, 1, 0],
[1, 2, 2, 0],
[2, 1, 1, 0],
[2, 1, 2, 0],
[2, 2, 1, 0],
[2, 2, 2, 0],
],
dtype=DEFAULT_NDARRAY_TYPE)
g.test_set = cs
orig_set = set()
orig_set.add(InteractionElement({0: 2, 2: 2, 3: 2}))
orig_set.add(InteractionElement({1: 2, 2: 1, 3: 2}))
orig_set.add(InteractionElement({0: 1, 2: 1, 3: 1}))
orig_set.add(InteractionElement({0: 2, 1: 1, 3: 1}))
orig_set.add(InteractionElement({0: 1, 1: 1, 3: 2}))
orig_set.add(InteractionElement({1: 2, 2: 2, 3: 2}))
orig_set.add(InteractionElement({0: 1, 1: 1, 3: 1}))
orig_set.add(InteractionElement({1: 2, 2: 2, 3: 1}))
orig_set.add(InteractionElement({1: 1, 2: 2, 3: 2}))
orig_set.add(InteractionElement({1: 1, 2: 2, 3: 1}))
orig_set.add(InteractionElement({0: 2, 2: 1, 3: 2}))
orig_set.add(InteractionElement({0: 2, 2: 1, 3: 1}))
orig_set.add(InteractionElement({0: 1, 2: 1, 3: 2}))
orig_set.add(InteractionElement({0: 2, 1: 1, 3: 2}))
orig_set.add(InteractionElement({0: 2, 2: 2, 3: 1}))
orig_set.add(InteractionElement({1: 2, 2: 1, 3: 1}))
orig_set.add(InteractionElement({0: 1, 2: 2, 3: 2}))
orig_set.add(InteractionElement({0: 2, 1: 2, 3: 2}))
orig_set.add(InteractionElement({1: 1, 2: 1, 3: 2}))
orig_set.add(InteractionElement({0: 1, 2: 2, 3: 1}))
orig_set.add(InteractionElement({0: 2, 1: 2, 3: 1}))
orig_set.add(InteractionElement({1: 1, 2: 1, 3: 1}))
orig_set.add(InteractionElement({0: 1, 1: 2, 3: 2}))
orig_set.add(InteractionElement({0: 1, 1: 2, 3: 1}))
config_num = 0
parm_num = 3
cover = 1
test_value_list = InteractionElement({0: 1, 3: 1})
actual = set()
g.get_test_value_recur(orig_set, config_num, parm_num, cover, actual,
test_value_list)
expected = set()
expected.add(InteractionElement({0: 1, 2: 1, 3: 1}))
expected.add(InteractionElement({0: 1, 1: 1, 3: 1}))
assert actual == expected
def test_insert(parm_set_5, parm_1, parm_2, parm_3, parm_4, parm_5, parm_6):
assert len(parm_set_5) == 5
parm_set_5.insert(3, parm_6)
expected_plist = [parm_1, parm_2, parm_3, parm_6, parm_4, parm_5]
assert len(parm_set_5) == 6
ps_expected = ParameterSet(expected_plist)
assert parm_set_5.parameters == ps_expected.parameters
def test_init_one_parm():
p = Parameter.create_with_unnamed_values('Z', 4)
plist = [
p,
]
ps = ParameterSet(plist)
assert ps.parameters == plist
assert ps.parameters is not plist # copy, not reference
def test_do_vertical_growth():
ps = ParameterSet.create_from_parm_and_value_sizes(5, 3)
g = IpoGenerator(ps, 2)
g.first_parameters()
s = g.do_horizontal_growth(2)
cs = g.do_vertical_growth(s, 2)
assert len(cs) == 1
cs_expected = np.array([[2, 2, 3]], dtype=DEFAULT_NDARRAY_TYPE)
assert np.array_equal(cs, cs_expected)
def test_init_three_parms():
p1 = Parameter.create_with_unnamed_values("P1", 4)
p2 = Parameter.create_with_unnamed_values("P2", 6)
p3 = Parameter.create_with_unnamed_values("P3", 2)
plist = [p1, p2, p3]
ps = ParameterSet(plist)
assert len(ps.parameters) == 3
assert ps.parameters == plist
assert ps.parameters is not plist # copy, not reference
def test_append(parm_set_5, parm_1, parm_2, parm_3, parm_4, parm_5, parm_6):
assert len(parm_set_5) == 5
parm_set_5.append(parm_6)
assert len(parm_set_5) == 6
expected_plist = [parm_1, parm_2, parm_3, parm_4, parm_5, parm_6]
ps_expected = ParameterSet(expected_plist)
assert parm_set_5.parameters == ps_expected.parameters
def test_generate_configurations10():
ps = ParameterSet.create_from_parm_and_value_sizes(32, 2)
gen = RecursiveGenerator(ps, 2)
cs = gen.generate_covering_array()
cs_expected = np.array([
[
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1
],
[
2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2,
2, 1, 2, 2, 1, 2, 2, 1, 1, 1
],
[
1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1,
2, 2, 1, 2, 2, 1, 2, 1, 1, 1
],
[
2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2,
1, 2, 2, 1, 2, 2, 1, 0, 0, 0
],
[
2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1,
1, 2, 2, 2, 2, 2, 2, 1, 1, 1
],
[
2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2,
2, 1, 1, 1, 2, 2, 2, 0, 0, 0
],
[
1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2,
2, 2, 2, 2, 1, 1, 1, 1, 1, 1
],
[
1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 0
],
[
2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1,
1, 1, 1, 1, 1, 1, 1, 2, 2, 1
],
[
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 1, 2, 2
],
[
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 2, 1, 2
],
],
dtype=DEFAULT_NDARRAY_TYPE)
assert np.array_equal(cs, cs_expected)
def test_extend(parm_set_5, parm_1, parm_2, parm_3, parm_4, parm_5, parm_6,
parm_7):
assert len(parm_set_5) == 5
parm_set_5.extend([parm_6, parm_7])
assert len(parm_set_5) == 7
expected_plist = [parm_1, parm_2, parm_3, parm_4, parm_5, parm_6, parm_7]
ps_expected = ParameterSet(expected_plist)
assert parm_set_5.parameters == ps_expected.parameters
def test_from_dict(parmset_ae, parm_a, parm_b, parm_c, parm_d, parm_e):
parmset_dict = {
"uid":
"337f7234-85a1-45a0-be77-0934ec232f21",
"parameters":
[p.to_dict() for p in [parm_a, parm_b, parm_c, parm_d, parm_e]],
'adjacency': [[True, True, False, True, True],
[True, True, True, True, False],
[False, True, True, True, True],
[True, True, True, True, True],
[True, False, True, True, True]]
}
ps = ParameterSet.from_dict(parmset_dict)
expected_ps = ParameterSet([parm_a, parm_b, parm_c, parm_d, parm_e],
uid="337f7234-85a1-45a0-be77-0934ec232f21")
expected_ps.set_adjacent(parm_a, parm_c, False)
expected_ps.set_adjacent(parm_b, parm_e, False)
assert ps == expected_ps
def test_contains_dash_in_t_3():
cs1 = np.array([[4, 4, 4, 4], [3, 3, 3, 3]], dtype=DEFAULT_NDARRAY_TYPE)
cs2 = None
pkw = InteractionElement({0: 1, 1: 2})
piu = InteractionElement({3: 2})
ps = ParameterSet.create_from_parm_and_value_sizes(13, 4)
g = IpoGenerator(ps, 2)
g.test_set = cs1
assert g.contains_dash_in_t(cs2, pkw, piu) == -1
def test_do_horizontal_growth_1():
ps = ParameterSet.create_from_parm_and_value_sizes(13, 3)
g = IpoGenerator(ps, 2)
g.first_parameters()
actual = g.do_horizontal_growth(2)
assert len(g.test_set[0]) == 3
assert len(g.test_set) == 9
assert len(actual) == 2
ie1 = InteractionElement({1: 2, 2: 3})
ie2 = InteractionElement({0: 2, 2: 3})
assert ie1 in actual
assert ie2 in actual
def test_add_new_test():
cs = np.array([1, 2, 3, 4], dtype=DEFAULT_NDARRAY_TYPE)
pkw = InteractionElement({0: 2, 1: 3, 2: 4})
piu = InteractionElement({3: 5})
ps = ParameterSet.create_from_parm_and_value_sizes(13, 4)
g = IpoGenerator(ps, 2)
cs_new = g.add_new_test(cs, pkw, piu)
cs_expected = np.array([[1, 2, 3, 4], [2, 3, 4, 5]],
dtype=DEFAULT_NDARRAY_TYPE)
assert np.array_equal(cs_new, cs_expected)
def test_pairs_covered_in_2():
ps = ParameterSet.create_from_value_sizes([2, 2, 3, 2])
g = IpoGenerator(ps, 2)
cs = np.array([[1, 2, 3]], dtype=DEFAULT_NDARRAY_TYPE)
g.test_set = cs
ie1 = InteractionElement({2: 3, 3: 1})
ie2 = InteractionElement({0: 2, 3: 1})
ie3 = InteractionElement({1: 1, 3: 1})
s = {ie1, ie2, ie3}
s2 = g.pairs_covered_in(s, 0, 3, 1)
assert len(s2) == 1
assert ie1 in s2
def test_configset_iterate():
"""
Verify that a parameter set and a covering array with
no "don't care" values present is correctly converted
to a data frame with the correct column headings and
value names.
"""
p1 = Parameter("Colour", [RED, GREEN])
p2 = Parameter("Pet", [CAT, DOG])
p3 = Parameter("Speed", [FAST, SLOW])
parameter_set = ParameterSet([p1, p2, p3])
# Covering array from...
# generator = RecursiveGenerator(parameter_set, 2)
# covering_array = generator.generate_covering_array()
covering_array = np.array([[1, 1, 1], [1, 2, 2], [2, 1, 2], [2, 2, 1]])
configurations = ConfigurationSet(parameter_set=parameter_set,
covering_array=covering_array)
expected = [
pd.Series({
"Colour": RED,
"Pet": CAT,
"Speed": FAST,
}),
pd.Series({
"Colour": RED,
"Pet": DOG,
"Speed": SLOW,
}),
pd.Series({
"Colour": GREEN,
"Pet": CAT,
"Speed": SLOW,
}),
pd.Series({
"Colour": GREEN,
"Pet": DOG,
"Speed": FAST,
})
]
for index, actual in enumerate(configurations):
assert pd.Series.equals(actual, expected[index])
def test_first_parameters():
ps = ParameterSet.create_from_parm_and_value_sizes(13, 3)
g = IpoGenerator(ps, 2)
g.first_parameters()
cs_expected = np.array([
[1, 1],
[1, 2],
[1, 3],
[2, 1],
[2, 2],
[2, 3],
[3, 1],
[3, 2],
[3, 3],
],
dtype=DEFAULT_NDARRAY_TYPE)
assert np.array_equal(g.test_set, cs_expected)
