def test_evaluate_generator_link_prediction():
edge_ids_test = np.array([[1, 2], [2, 3], [1, 3]])
edge_labels_test = np.array([1, 1, 0])
graph = example_graph_1(feature_size=4)
# base_model, keras_model, generator, train_gen
gnn_models = [
create_graphSAGE_model(graph, link_prediction=True),
create_HinSAGE_model(graph, link_prediction=True),
]
for gnn_model in gnn_models:
keras_model = gnn_model[1]
generator = gnn_model[2]
ens = Ensemble(keras_model, n_estimators=2, n_predictions=3)
ens.compile(optimizer=Adam(),
loss=binary_crossentropy,
weighted_metrics=["acc"])
# Check that passing invalid parameters is handled correctly. We will not check error handling for those
# parameters that Keras will be responsible for.
with pytest.raises(ValueError):
ens.evaluate_generator(
generator=generator,
test_data=edge_ids_test,
test_targets=edge_labels_test,
)
with pytest.raises(ValueError):
ens.evaluate_generator(
generator=generator,
test_data=edge_labels_test,
test_targets=None, # must give test_targets
)
with pytest.raises(ValueError):
ens.evaluate_generator(
generator=generator.flow(edge_ids_test, edge_labels_test),
test_data=edge_ids_test,
test_targets=edge_labels_test,
)
# We won't train the model instead use the initial random weights to test
# the evaluate_generator method.
test_metrics_mean, test_metrics_std = ens.evaluate_generator(
generator.flow(edge_ids_test, edge_labels_test))
assert len(test_metrics_mean) == len(test_metrics_std)
assert len(test_metrics_mean.shape) == 1
assert len(test_metrics_std.shape) == 1
def test_evaluate_generator():
test_data = np.array([3, 4, 5])
test_targets = np.array([[1, 0], [0, 1], [0, 1]])
graph = example_graph_1(feature_size=5)
# base_model, keras_model, generator, train_gen
gnn_models = [
create_graphSAGE_model(graph),
create_HinSAGE_model(graph),
create_GCN_model(graph),
create_GAT_model(graph),
]
for gnn_model in gnn_models:
keras_model = gnn_model[1]
generator = gnn_model[2]
ens = Ensemble(keras_model, n_estimators=2, n_predictions=1)
ens.compile(optimizer=Adam(),
loss=categorical_crossentropy,
weighted_metrics=["acc"])
# Check that passing invalid parameters is handled correctly. We will not check error handling for those
# parameters that Keras will be responsible for.
with pytest.raises(ValueError):
ens.evaluate_generator(generator=generator,
test_data=test_data,
test_targets=test_targets)
with pytest.raises(ValueError):
ens.evaluate_generator(
generator=generator,
test_data=test_data,
test_targets=None, # must give test_targets
)
with pytest.raises(ValueError):
ens.evaluate_generator(
generator=generator.flow(test_data, test_targets),
test_data=test_data,
test_targets=test_targets,
)
# We won't train the model instead use the initial random weights to test
# the evaluate_generator method.
test_metrics_mean, test_metrics_std = ens.evaluate_generator(
generator.flow(test_data, test_targets))
assert len(test_metrics_mean) == len(test_metrics_std)
assert len(test_metrics_mean.shape) == 1
assert len(test_metrics_std.shape) == 1