def test_triplet_diffs_toy(self):
expected_n_basis = 10
model = SCML_Supervised(n_basis=expected_n_basis)
X = np.array([[0, 0], [1, 1], [2, 2], [3, 3]])
triplets = np.array([[0, 1, 2], [0, 1, 3], [1, 0, 2], [1, 0, 3],
[2, 3, 1], [2, 3, 0], [3, 2, 1], [3, 2, 0]])
basis, n_basis = model._generate_bases_dist_diff(triplets, X)
# All points are along the same line, so the only possible basis will be
# the vector along that line normalized.
expected_basis = np.ones((expected_n_basis, 2)) / np.sqrt(2)
assert n_basis == expected_n_basis
np.testing.assert_allclose(basis, expected_basis)
def test_triplet_diffs(self, n_samples, n_features, n_classes):
X, y = make_classification(n_samples=n_samples, n_classes=n_classes,
n_features=n_features, n_informative=n_features,
n_redundant=0, n_repeated=0)
X = StandardScaler().fit_transform(X)
model = SCML_Supervised()
constraints = Constraints(y)
triplets = constraints.generate_knntriplets(X, model.k_genuine,
model.k_impostor)
basis, n_basis = model._generate_bases_dist_diff(triplets, X)
expected_n_basis = n_features * 80
assert n_basis == expected_n_basis
assert basis.shape == (expected_n_basis, n_features)
def test_triplet_diffs(self, n_samples, n_features, n_classes):
"""
Test that the correct value of n_basis is being generated with
different triplet constraints.
"""
X, y = make_classification(n_samples=n_samples, n_classes=n_classes,
n_features=n_features, n_informative=n_features,
n_redundant=0, n_repeated=0)
X = StandardScaler().fit_transform(X)
model = SCML_Supervised(n_basis=None) # Explicit n_basis=None
constraints = Constraints(y)
triplets = constraints.generate_knntriplets(X, model.k_genuine,
model.k_impostor)
msg = "As no value for `n_basis` was selected, "
with pytest.warns(UserWarning) as raised_warning:
basis, n_basis = model._generate_bases_dist_diff(triplets, X)
assert msg in str(raised_warning[0].message)
expected_n_basis = n_features * 80
assert n_basis == expected_n_basis
assert basis.shape == (expected_n_basis, n_features)