def test_generate_network_pairs_min_cosine(random_matrix, generate_network_f,
pairs_min_cosine):
"""
All cosine scores in outputted array should be strictly higher than
pairs_min_cosine. Values lower than pairs_min_cosine and negative cosine
scores should be filtered out.
"""
mzs, matrix = random_matrix
matrix = matrix.copy()
matrix[0, 1] = matrix[1, 0] = pairs_min_cosine + 0.1
matrix[0, 2] = matrix[2, 0] = pairs_min_cosine - 0.1
interactions = generate_network_f(matrix, mzs, pairs_min_cosine, 0)
seen1, seen2 = False, False
for source, target, delta, cosine in interactions:
assert cosine > pairs_min_cosine
assert matrix[source, target] == cosine
if (source == 0 and target == 1) or (source == 1 and target == 0):
seen1 = True
if (source == 0 and target == 2) or (source == 2 and target == 0):
seen2 = True
if matrix[0, 1] >= 0:
assert seen1
assert not seen2
def test_generate_network_empty(generate_network_f):
"""An empty matrix should not throw an error but return an empty array.
"""
matrix = np.empty((0, 0), dtype=np.float32)
interactions = generate_network_f(matrix, [], 0.65, 10)
assert interactions.size == 0
def test_generate_network_all_zero(random_matrix, generate_network_f):
"""
If all filtering parameters are set to zero, we should get all possibles
interactions excluding self loops.
"""
mzs, matrix = random_matrix
max_size = np.count_nonzero(np.triu(matrix)) - matrix.shape[0]
interactions = generate_network_f(matrix, mzs, 0, 0)
assert interactions.shape[0] == max_size
def test_generate_network_high_pairs_min_cosine(random_matrix,
generate_network_f,
pairs_min_cosine, top_k):
"""
If pairs_min_cosine is higher than 1, we should get an empty array.
"""
mzs, matrix = random_matrix
interactions = generate_network_f(matrix, mzs, pairs_min_cosine, top_k)
assert interactions.size == 0
def test_generate_network_high_top_k(random_matrix, generate_network_f):
"""
If top_k is high and pairs_min_cosine is zero, we should get all
possibles interactions excluding self loops.
"""
mzs, matrix = random_matrix
max_size = np.count_nonzero(np.triu(matrix)) - matrix.shape[0]
top_k = matrix.shape[0]
interactions = generate_network_f(matrix, mzs, 0, top_k)
assert interactions.shape[0] == max_size
def test_generate_network_self_loop(random_matrix, generate_network_f,
pairs_min_cosine, top_k):
"""
Output array should not include self-loops
"""
mzs, matrix = random_matrix
interactions = generate_network_f(matrix, mzs, pairs_min_cosine, top_k)
count = len(
[source for source, target, _, _ in interactions if source == target])
assert count == 0
def test_generate_network_list_larger_than_matrix(random_matrix,
generate_network_f):
"""
If list is larger than matrix, only part of the list will be used but
no error should be thrown.
"""
mzs, matrix = random_matrix
mzs = mzs + [1200.14225, 258.4475]
max_index = len(mzs)
interactions = generate_network_f(matrix, mzs, 0, 10)
assert max_index - 1 not in interactions['Source']
assert max_index - 1 not in interactions['Target']
assert max_index - 2 not in interactions['Source']
assert max_index - 2 not in interactions['Target']
def test_generate_network_matrix_larger_than_list(random_matrix,
generate_network_f):
"""
If matrix is larger than list, only part of the matrix will be used but
no error should be thrown.
"""
mzs, matrix = random_matrix
mzs = mzs[:-2]
max_index = matrix.shape[0]
interactions = generate_network_f(matrix, mzs, 0, 10)
assert max_index - 1 not in interactions['Source']
assert max_index - 1 not in interactions['Target']
assert max_index - 2 not in interactions['Source']
assert max_index - 2 not in interactions['Target']