def test_flow_batch_size(barbell, num_powers):
batch_size = 2
generator = AdjacencyPowerGenerator(barbell, num_powers=num_powers)
for x, y in generator.flow(batch_size=batch_size).take(1):
assert x[0].shape == (batch_size, )
assert y.shape == (batch_size, 1, len(barbell.nodes()))
assert x[1].shape == (batch_size, num_powers, len(barbell.nodes()))
def test_bad_init(barbell):
with pytest.raises(TypeError):
generator = AdjacencyPowerGenerator(None, num_powers=5)
with pytest.raises(ValueError, match="num_powers: expected.*found -1"):
generator = AdjacencyPowerGenerator(barbell, num_powers=-1)
with pytest.raises(TypeError, match="num_powers: expected.*found float"):
generator = AdjacencyPowerGenerator(barbell, num_powers=1.0)
def test_partial_powers(barbell, num_powers):
Aadj = normalize_adj(barbell.to_adjacency_matrix(),
symmetric=False).todense()
actual_powers = [Aadj]
for _ in range(num_powers - 1):
actual_powers.append(actual_powers[-1].dot(Aadj))
generator = AdjacencyPowerGenerator(barbell, num_powers=num_powers)
dataset = generator.flow(batch_size=1)
for i, (x, y) in enumerate(dataset.take(barbell.number_of_nodes())):
partial_powers = x[1].numpy()
for j in range(num_powers):
print(i, j)
assert np.allclose(partial_powers[0, j, :], actual_powers[j][i, :])
def test_bad_flow(barbell):
generator = AdjacencyPowerGenerator(barbell, num_powers=5)
with pytest.raises(ValueError, match="batch_size: expected.*found 0"):
generator.flow(0, num_parallel_calls=1)
with pytest.raises(TypeError, match="batch_size: expected.*found float"):
generator.flow(1.0, num_parallel_calls=1)
with pytest.raises(ValueError,
match="num_parallel_calls: expected.*found 0"):
generator.flow(1, num_parallel_calls=0)
with pytest.raises(TypeError,
match="num_parallel_calls: expected.*found float"):
generator.flow(1, num_parallel_calls=1.01)
def test_init(barbell):
generator = AdjacencyPowerGenerator(barbell, num_powers=5)
num_nodes = len(barbell.nodes())
assert generator.num_powers == 5
assert generator.Aadj_T.shape == (num_nodes, num_nodes)
assert generator.transition_matrix_T.shape == (num_nodes, num_nodes)
def test_partial_powers(barbell, num_powers, weighted):
raw_adj = barbell.to_adjacency_matrix(weighted=weighted)
Aadj = normalize_adj(raw_adj, symmetric=False).todense()
actual_powers = [Aadj]
for _ in range(num_powers - 1):
actual_powers.append(actual_powers[-1].dot(Aadj))
generator = AdjacencyPowerGenerator(barbell,
num_powers=num_powers,
weighted=weighted)
dataset = generator.flow(batch_size=1)
for i, (x, y) in enumerate(dataset.take(barbell.number_of_nodes())):
partial_powers = x[1].numpy()
for j in range(num_powers):
print(i, j)
np.testing.assert_allclose(partial_powers[:, j, :],
actual_powers[j][i, :],
rtol=1e-5,
atol=1e-8)
def test_flow(barbell):
generator = AdjacencyPowerGenerator(barbell, num_powers=5)
dataset = generator.flow(batch_size=1)
assert tf.data.experimental.cardinality(dataset).numpy() == -1