def test_GCN_apply_sparse():
G, features = create_graph_features()
adj = nx.to_numpy_array(G)[None, :, :]
n_nodes = features.shape[0]
nodes = G.nodes()
node_features = pd.DataFrame.from_dict(
{n: f
for n, f in zip(nodes, features)}, orient="index")
G = StellarGraph(G, node_features=node_features)
generator = FullBatchNodeGenerator(G, sparse=False, method="none")
gcnModel = GCN([2], ["relu"], generator=generator, dropout=0.5)
x_in, x_out = gcnModel.node_model()
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, adj])
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 test_gat_serialize(self):
G = example_graph_1(feature_size=self.F_in)
gen = FullBatchNodeGenerator(G, sparse=self.sparse, method=self.method)
gat = GAT(
layer_sizes=self.layer_sizes,
activations=self.activations,
attn_heads=self.attn_heads,
generator=gen,
bias=True,
normalize="l2",
)
x_in, x_out = gat.node_model()
model = keras.Model(inputs=x_in, outputs=x_out)
ng = gen.flow(G.nodes())
# Save model
model_json = model.to_json()
# Set all weights to one
model_weights = [np.ones_like(w) for w in model.get_weights()]
# Load model from json & set all weights
model2 = keras.models.model_from_json(
model_json, custom_objects={"GraphAttention": GraphAttention})
model2.set_weights(model_weights)
# Test deserialized model
actual = model2.predict_generator(ng)
expected = np.ones(
(G.number_of_nodes(),
self.layer_sizes[-1])) * (1.0 / G.number_of_nodes())
assert np.allclose(expected, actual[0])
def test_APPNP_apply_propagate_model_dense():
G, features = create_graph_features()
adj = nx.to_scipy_sparse_matrix(G)
features, adj = GCN_Aadj_feats_op(features, adj)
adj = adj.todense()[None, :, :]
n_nodes = features.shape[0]
nodes = G.nodes()
node_features = pd.DataFrame.from_dict(
{n: f
for n, f in zip(nodes, features)}, orient="index")
G = StellarGraph(G, node_features=node_features)
generator = FullBatchNodeGenerator(G, sparse=False, method="gcn")
appnpnModel = APPNP([2], ["relu"], generator=generator, dropout=0.5)
fully_connected_model = keras.Sequential()
fully_connected_model.add(Dense(2))
x_in, x_out = appnpnModel.propagate_model(fully_connected_model)
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, adj])
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 test_gat_node_model_no_norm(self):
G = example_graph_1(feature_size=self.F_in)
gen = FullBatchNodeGenerator(G, sparse=self.sparse, method=self.method)
gat = GAT(
layer_sizes=self.layer_sizes,
activations=self.activations,
attn_heads=self.attn_heads,
generator=gen,
bias=True,
normalize=None,
)
gat._layers[1].kernel_initializer = keras.initializers.get("ones")
gat._layers[1].attn_kernel_initializer = keras.initializers.get("ones")
gat._layers[3].kernel_initializer = keras.initializers.get("ones")
gat._layers[3].attn_kernel_initializer = keras.initializers.get("ones")
x_in, x_out = gat.node_model()
model = keras.Model(inputs=x_in, outputs=x_out)
ng = gen.flow(G.nodes())
actual = model.predict_generator(ng)
expected = np.ones((G.number_of_nodes(), self.layer_sizes[-1])) * (
self.F_in * self.layer_sizes[0] * self.attn_heads)
assert np.allclose(expected, actual[0])
def test_APPNP_apply_sparse():
G, features = create_graph_features()
adj = nx.to_scipy_sparse_matrix(G)
features, adj = GCN_Aadj_feats_op(features, adj)
adj = adj.tocoo()
A_indices = np.expand_dims(np.hstack((adj.row[:, None], adj.col[:, None])),
0)
A_values = np.expand_dims(adj.data, 0)
nodes = G.nodes()
node_features = pd.DataFrame.from_dict(
{n: f
for n, f in zip(nodes, features)}, orient="index")
G = StellarGraph(G, node_features=node_features)
generator = FullBatchNodeGenerator(G, sparse=True, method="gcn")
appnpnModel = APPNP([2], ["relu"], generator=generator, dropout=0.5)
x_in, x_out = appnpnModel.node_model()
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)
