def test_large_output_iter(self):
scml = SCML(max_iter=1, output_iter=2)
triplets = np.array([[[0, 1], [2, 1], [0, 0]]])
msg = ("The value of output_iter must be equal or smaller than"
" max_iter.")
with pytest.raises(ValueError) as raised_error:
scml.fit(triplets)
assert msg == raised_error.value.args[0]
def test_int_inputs(self, name):
value = 1.0
d = {name: value}
scml = SCML(**d)
triplets = np.array([[[0, 1], [2, 1], [0, 0]]])
msg = ("%s should be an integer, instead it is of type"
" %s" % (name, type(value)))
with pytest.raises(ValueError) as raised_error:
scml.fit(triplets)
assert msg == raised_error.value.args[0]
def test_raise_big_number_of_features():
triplets, _, _, X = build_triplets(with_preprocessor=False)
triplets = triplets[:3, :, :]
estimator = SCML(n_basis=320)
set_random_state(estimator)
with pytest.raises(ValueError) as exc_info:
estimator.fit(triplets)
assert exc_info.value.args[0] == \
"Number of features (4) is greater than the number of triplets(3)." \
"\nConsider using dimensionality reduction or using another basis " \
"generation scheme."
def test_dimension_reduction_msg(self):
scml = SCML(n_basis=2)
triplets = np.array([[[0, 1], [2, 1], [0, 0]], [[2, 1], [0, 1], [2,
0]],
[[0, 0], [2, 0], [0, 1]], [[2, 0], [0, 0], [2,
1]]])
msg = ("The number of bases with nonzero weight is less than the "
"number of features of the input, in consequence the "
"learned transformation reduces the dimension to 1.")
with pytest.warns(UserWarning) as raised_warning:
scml.fit(triplets)
assert msg == raised_warning[0].message.args[0]