def test_graphsage_apply_1():
gs = GraphSAGE(
layer_sizes=[2, 2, 2],
n_samples=[2, 2, 2],
bias=False,
input_dim=2,
multiplicity=1,
normalize=None,
kernel_initializer="ones",
)
inp = [keras.Input(shape=(i, 2)) for i in [1, 2, 4, 8]]
out = gs(inp)
model = keras.Model(inputs=inp, outputs=out)
x = [
np.array([[[1, 1]]]),
np.array([[[2, 2], [2, 2]]]),
np.array([[[3, 3], [3, 3], [3, 3], [3, 3]]]),
np.array([[[4, 4], [4, 4], [4, 4], [4, 4], [5, 5], [5, 5], [5, 5],
[5, 5]]]),
]
expected = np.array([[16, 25]])
actual = model.predict(x)
assert expected == pytest.approx(actual)
# Use the node model:
xinp, xout = gs.build()
model2 = keras.Model(inputs=xinp, outputs=xout)
assert pytest.approx(expected) == model2.predict(x)
def gs_nai_model(num_samples, generator, targets, optimizer, bias, dropout, normalize):
layer_sizes = [50] * len(num_samples)
graphsage = GraphSAGE(
layer_sizes=layer_sizes,
generator=generator,
bias=bias,
dropout=dropout,
normalize=normalize,
)
# Build the model and expose input and output sockets of graphsage, for node pair inputs:
x_inp, x_out = graphsage.build()
pred = tf.keras.layers.Dense(units=targets.shape[1], activation="softmax")(x_out)
model = tf.keras.Model(inputs=x_inp, outputs=pred)
model.compile(optimizer=optimizer, loss=tf.keras.losses.categorical_crossentropy)
return model
def unsup_gs_model(num_samples, generator, optimizer, bias, dropout, normalize):
layer_sizes = [50] * len(num_samples)
graphsage = GraphSAGE(
layer_sizes=layer_sizes,
generator=generator,
bias=bias,
dropout=dropout,
normalize=normalize,
)
# Build the model and expose input and output sockets of graphsage, for node pair inputs:
x_inp, x_out = graphsage.build()
prediction = link_classification(
output_dim=1, output_act="sigmoid", edge_embedding_method="ip"
)(x_out)
model = tf.keras.Model(inputs=x_inp, outputs=prediction)
model.compile(optimizer=optimizer, loss=tf.keras.losses.binary_crossentropy)
return model
