def test_RGCN_apply_dense_directed():
G, features = create_graph_features(is_directed=True)
As = get_As(G)
As = [np.expand_dims(A.todense(), 0) for A in As]
generator = RelationalFullBatchNodeGenerator(G, sparse=False)
rgcnModel = RGCN([2],
generator,
num_bases=10,
activations=["relu"],
dropout=0.5)
x_in, x_out = rgcnModel.in_out_tensors()
model = keras.Model(inputs=x_in, outputs=x_out)
# Check fit method
out_indices = np.array([[0, 1]], dtype="int32")
preds_1 = model.predict([features[None, :, :], out_indices] + As)
assert preds_1.shape == (1, 2, 2)
# Check fit method
preds_2 = model.predict(generator.flow(["a", "b"]))
assert preds_2.shape == (1, 2, 2)
assert preds_1 == pytest.approx(preds_2)
def test_RGCN_apply_sparse_directed():
G, features = create_graph_features(is_directed=True)
As = get_As(G)
As = [A.tocoo() for A in As]
A_indices = [
np.expand_dims(np.hstack((A.row[:, None], A.col[:, None])), 0) for A in As
]
A_values = [np.expand_dims(A.data, 0) for A in As]
generator = RelationalFullBatchNodeGenerator(G, sparse=True)
rgcnModel = RGCN([2], generator, num_bases=10, activations=["relu"], dropout=0.5)
x_in, x_out = rgcnModel.build()
model = keras.Model(inputs=x_in, outputs=x_out)
# Check fit method
out_indices = np.array([[0, 1]], dtype="int32")
preds_1 = model.predict([features[None, :, :], out_indices] + A_indices + A_values)
assert preds_1.shape == (1, 2, 2)
# Check fit_generator method
preds_2 = model.predict_generator(generator.flow(["a", "b"]))
assert preds_2.shape == (1, 2, 2)
assert preds_1 == pytest.approx(preds_2)
def generator_flow(self, G, node_ids, node_targets, sparse=False):
generator = RelationalFullBatchNodeGenerator(G, sparse=sparse)
n_nodes = len(G.nodes())
gen = generator.flow(node_ids, node_targets)
if sparse:
[X, tind, *As], y = gen[0]
As_indices = As[:self.num_relationships]
As_values = As[self.num_relationships:]
As_sparse = [
sps.coo_matrix(
(A_val[0], (A_ind[0, :, 0], A_ind[0, :, 1])),
shape=(n_nodes, n_nodes),
) for A_ind, A_val in zip(As_indices, As_values)
]
As_dense = [A.toarray() for A in As_sparse]
else:
[X, tind, *As], y = gen[0]
As_dense = As
assert np.allclose(X,
gen.features) # X should be equal to gen.features
assert tind.shape[1] == len(node_ids)
if node_targets is not None:
assert np.allclose(y, node_targets)
return As_dense, tind, y
def test_generator_flow_targets_as_list(self):
generator = RelationalFullBatchNodeGenerator(self.G)
node_ids = list(self.G.nodes())[:3]
node_targets = [1] * len(node_ids)
gen = generator.flow(node_ids, node_targets)
inputs, y = gen[0]
assert y.shape == (1, 3)
assert np.sum(y) == 3
def test_generator_flow_targets_not_iterator(self):
generator = RelationalFullBatchNodeGenerator(self.G)
node_ids = list(self.G.nodes())[:3]
node_targets = 1
with pytest.raises(TypeError):
generator.flow(node_ids, node_targets)
