def test_predict_link_prediction():
tf.keras.backend.clear_session()
edge_ids_test = np.array([[1, 2], [2, 3], [1, 3]])
graph = example_graph_1(feature_size=2)
# 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=1)
ens.compile(
optimizer=Adam(), loss=binary_crossentropy, weighted_metrics=["acc"]
)
test_gen = generator.flow(edge_ids_test)
# 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.predict(generator=test_gen, predict_data=edge_ids_test)
# We won't train the model instead use the initial random weights to test
# the evaluate method.
test_predictions = ens.predict(test_gen, summarise=True)
print("test_predictions shape {}".format(test_predictions.shape))
assert len(test_predictions) == len(edge_ids_test)
assert test_predictions.shape[1] == 1
test_predictions = ens.predict(test_gen, summarise=False)
assert test_predictions.shape[0] == ens.n_estimators
assert test_predictions.shape[1] == ens.n_predictions
assert test_predictions.shape[2] == len(edge_ids_test)
assert test_predictions.shape[3] == 1
#
# Repeat for BaggingEnsemble
ens = BaggingEnsemble(keras_model, n_estimators=2, n_predictions=1)
ens.compile(
optimizer=Adam(), loss=binary_crossentropy, weighted_metrics=["acc"]
)
test_gen = generator.flow(edge_ids_test)
# 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.predict(generator=test_gen, predict_data=edge_ids_test)
# We won't train the model instead use the initial random weights to test
# the evaluate method.
test_predictions = ens.predict(test_gen, summarise=True)
print("test_predictions shape {}".format(test_predictions.shape))
assert len(test_predictions) == len(edge_ids_test)
assert test_predictions.shape[1] == 1
test_predictions = ens.predict(test_gen, summarise=False)
assert test_predictions.shape[0] == ens.n_estimators
assert test_predictions.shape[1] == ens.n_predictions
assert test_predictions.shape[2] == len(edge_ids_test)
assert test_predictions.shape[3] == 1
def test_predict():
tf.keras.backend.clear_session()
# test_data = np.array([[0, 0], [1, 1], [0.8, 0.8]])
test_data = np.array([4, 5, 6])
test_targets = np.array([[1, 0], [0, 1], [0, 1]])
graph = example_graph_1(feature_size=2)
# 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 i, gnn_model in enumerate(gnn_models):
keras_model = gnn_model[1]
generator = gnn_model[2]
ens = Ensemble(keras_model, n_estimators=2, n_predictions=2)
ens.compile(
optimizer=Adam(), loss=categorical_crossentropy, weighted_metrics=["acc"]
)
test_gen = generator.flow(test_data)
# 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.predict(generator=test_gen, predict_data=test_data)
# We won't train the model instead use the initial random weights to test
# the evaluate method.
test_predictions = ens.predict(test_gen, summarise=True)
print("test_predictions shape {}".format(test_predictions.shape))
if i > 1:
# GAT and GCN are full batch so the batch dimension is 1
assert len(test_predictions) == 1
assert test_predictions.shape[1] == test_targets.shape[0]
else:
assert len(test_predictions) == len(test_data)
assert test_predictions.shape[-1] == test_targets.shape[-1]
test_predictions = ens.predict(test_gen, summarise=False)
assert test_predictions.shape[0] == ens.n_estimators
assert test_predictions.shape[1] == ens.n_predictions
if i > 1:
assert test_predictions.shape[2] == 1
else:
assert test_predictions.shape[2] == len(test_data)
assert test_predictions.shape[-1] == test_targets.shape[-1]
#
# Repeat for BaggingEnsemble
ens = BaggingEnsemble(keras_model, n_estimators=2, n_predictions=2)
ens.compile(
optimizer=Adam(), loss=categorical_crossentropy, weighted_metrics=["acc"]
)
test_gen = generator.flow(test_data)
# 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.predict(generator=test_gen, predict_data=test_data)
# We won't train the model instead use the initial random weights to test
# the evaluate method.
test_predictions = ens.predict(test_gen, summarise=True)
print("test_predictions shape {}".format(test_predictions.shape))
if i > 1:
# GAT and GCN are full batch so the batch dimension is 1
assert len(test_predictions) == 1
assert test_predictions.shape[1] == test_targets.shape[0]
else:
assert len(test_predictions) == len(test_data)
assert test_predictions.shape[-1] == test_targets.shape[-1]
test_predictions = ens.predict(test_gen, summarise=False)
assert test_predictions.shape[0] == ens.n_estimators
assert test_predictions.shape[1] == ens.n_predictions
if i > 1:
assert test_predictions.shape[2] == 1
else:
assert test_predictions.shape[2] == len(test_data)
assert test_predictions.shape[-1] == test_targets.shape[-1]