def test_generate_knntriplets(k_genuine, k_impostor):
"""Checks edge and over the edge cases of knn triplet construction with not
enough neighbors"""
T_test = [[0, 1, 3], [0, 1, 4], [0, 1, 5], [0, 2, 3], [0, 2, 4], [0, 2, 5],
[1, 0, 3], [1, 0, 4], [1, 0, 5], [1, 2, 3], [1, 2, 4], [1, 2, 5],
[2, 0, 3], [2, 0, 4], [2, 0, 5], [2, 1, 3], [2, 1, 4], [2, 1, 5],
[3, 4, 0], [3, 4, 1], [3, 4, 2], [3, 5, 0], [3, 5, 1], [3, 5, 2],
[4, 3, 0], [4, 3, 1], [4, 3, 2], [4, 5, 0], [4, 5, 1], [4, 5, 2],
[5, 3, 0], [5, 3, 1], [5, 3, 2], [5, 4, 0], [5, 4, 1], [5, 4, 2]]
X = np.array([[0, 0], [2, 2], [4, 4], [8, 8], [16, 16], [32, 32], [33,
33]])
y = np.array([1, 1, 1, 2, 2, 2, -1])
msg1 = ("The class 1 has 3 elements, which is not sufficient to "
f"generate {k_genuine+1} genuine neighbors "
"as specified by k_genuine")
msg2 = ("The class 2 has 3 elements, which is not sufficient to "
f"generate {k_genuine+1} genuine neighbors "
"as specified by k_genuine")
msg3 = ("The class 1 has 3 elements of other classes, which is "
f"not sufficient to generate {k_impostor} impostor "
"neighbors as specified by k_impostor")
msg4 = ("The class 2 has 3 elements of other classes, which is "
f"not sufficient to generate {k_impostor} impostor "
"neighbors as specified by k_impostor")
msgs = [msg1, msg2, msg3, msg4]
with pytest.warns(UserWarning) as user_warning:
T = Constraints(y).generate_knntriplets(X, k_genuine, k_impostor)
assert any([[msg in str(warn.message) for msg in msgs]
for warn in user_warning])
assert np.array_equal(sorted(T.tolist()), T_test)
def test_unknown_labels_not_in_chunks(num_chunks, chunk_size):
"""Checks that unknown labels are not assigned to any chunk."""
labels = gen_labels_for_chunks(num_chunks, chunk_size)
constraints = Constraints(labels)
chunks = constraints.chunks(num_chunks=num_chunks, chunk_size=chunk_size,
random_state=SEED)
assert np.all(chunks[labels < 0] < 0)
def test_generate_knntriplets_under_edge(k_genuine, k_impostor, T_test):
"""Checks under the edge cases of knn triplet construction with enough
neighbors"""
X = np.array([[0, 0], [2, 2], [4, 4], [8, 8], [16, 16], [32, 32], [33, 33]])
y = np.array([1, 1, 1, 2, 2, 2, -1])
T = Constraints(y).generate_knntriplets(X, k_genuine, k_impostor)
assert np.array_equal(sorted(T.tolist()), T_test)
def test_exact_num_points_for_chunks(num_chunks, chunk_size):
"""Checks that the chunk generation works well with just enough points."""
labels = gen_labels_for_chunks(num_chunks, chunk_size)
constraints = Constraints(labels)
chunks = constraints.chunks(num_chunks=num_chunks,
chunk_size=chunk_size,
random_state=SEED)
chunk_no, size_each_chunk = np.unique(chunks, return_counts=True)
np.testing.assert_array_equal(size_each_chunk, chunk_size)
assert chunk_no.shape[0] == num_chunks
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_chunk_case_one_miss_point(num_chunks, chunk_size):
"""Checks that the chunk generation breaks when one point is missing."""
labels = gen_labels_for_chunks(num_chunks, chunk_size)
assert len(labels) >= 1
constraints = Constraints(labels[1:])
with pytest.raises(ValueError) as e:
constraints.chunks(num_chunks=num_chunks, chunk_size=chunk_size,
random_state=SEED)
expected_message = (('Not enough possible chunks of %d elements in each'
' class to form expected %d chunks - maximum number'
' of chunks is %d'
) % (chunk_size, num_chunks, num_chunks - 1))
assert str(e.value) == expected_message
def test_generate_knntriplets(k_genuine, k_impostor):
"""Checks edge and over the edge cases of knn triplet construction with not
enough neighbors"""
T_test = [[0, 1, 3], [0, 1, 4], [0, 1, 5], [0, 2, 3], [0, 2, 4], [0, 2, 5],
[1, 0, 3], [1, 0, 4], [1, 0, 5], [1, 2, 3], [1, 2, 4], [1, 2, 5],
[2, 0, 3], [2, 0, 4], [2, 0, 5], [2, 1, 3], [2, 1, 4], [2, 1, 5],
[3, 4, 0], [3, 4, 1], [3, 4, 2], [3, 5, 0], [3, 5, 1], [3, 5, 2],
[4, 3, 0], [4, 3, 1], [4, 3, 2], [4, 5, 0], [4, 5, 1], [4, 5, 2],
[5, 3, 0], [5, 3, 1], [5, 3, 2], [5, 4, 0], [5, 4, 1], [5, 4, 2]]
X = np.array([[0, 0], [2, 2], [4, 4], [8, 8], [16, 16], [32, 32], [33,
33]])
y = np.array([1, 1, 1, 2, 2, 2, -1])
T = Constraints(y).generate_knntriplets(X, k_genuine, k_impostor)
assert np.array_equal(sorted(T.tolist()), T_test)
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)
def test_generate_knntriplets_k_genuine():
"""Checks the correct error raised when k_genuine is too big """
X, y = shuffle(*make_blobs(random_state=SEED), random_state=SEED)
label, labels_count = np.unique(y, return_counts=True)
labels_count_min = np.min(labels_count)
idx_smallest_label, = np.where(labels_count == labels_count_min)
k_genuine = labels_count_min
warn_msgs = []
for idx in idx_smallest_label:
warn_msgs.append(
"The class {} has {} elements, which is not sufficient "
"to generate {} genuine neighbors as specified by "
"k_genuine. Will generate {} genuine neighbors instead."
"\n".format(label[idx], k_genuine, k_genuine + 1, k_genuine - 1))
with pytest.warns(UserWarning) as raised_warning:
Constraints(y).generate_knntriplets(X, k_genuine, 1)
for warn in raised_warning:
assert str(warn.message) in warn_msgs
def test_generate_knntriplets_k_impostor():
"""Checks the correct error raised when k_impostor is too big """
X, y = shuffle(*make_blobs(random_state=SEED), random_state=SEED)
length = len(y)
label, labels_count = np.unique(y, return_counts=True)
labels_count_max = np.max(labels_count)
idx_biggest_label, = np.where(labels_count == labels_count_max)
k_impostor = length - labels_count_max + 1
warn_msgs = []
for idx in idx_biggest_label:
warn_msgs.append(
"The class {} has {} elements of other classes, which is"
" not sufficient to generate {} impostor neighbors as "
"specified by k_impostor. Will generate {} impostor "
"neighbors instead.\n".format(label[idx], k_impostor - 1,
k_impostor, k_impostor - 1))
with pytest.warns(UserWarning) as raised_warning:
Constraints(y).generate_knntriplets(X, 1, k_impostor)
for warn in raised_warning:
assert str(warn.message) in warn_msgs