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]
# anyways
c, target = shuffle(c, target, random_state=SEED)
if with_preprocessor:
# if preprocessor, we build a 2D array of quadruplets of indices
return Dataset(c, target, X, c[:, 0])
else:
# if not, we build a 3D array of quadruplets of samples
return Dataset(X[c], target, None, X[c[:, 0]])
quadruplets_learners = [(LSML(), build_quadruplets)]
ids_quadruplets_learners = list(
map(lambda x: x.__class__.__name__,
[learner for (learner, _) in quadruplets_learners]))
triplets_learners = [(SCML(), build_triplets)]
ids_triplets_learners = list(
map(lambda x: x.__class__.__name__,
[learner for (learner, _) in triplets_learners]))
pairs_learners = [
(ITML(max_iter=2), build_pairs), # max_iter=2 to be faster
(MMC(max_iter=2), build_pairs), # max_iter=2 to be faster
(SDML(prior='identity', balance_param=1e-5), build_pairs)
]
ids_pairs_learners = list(
map(lambda x: x.__class__.__name__,
[learner for (learner, _) in pairs_learners]))
classifiers = [(Covariance(), build_classification),
(LFDA(), build_classification), (LMNN(), build_classification),