def test_Ensemble_fit_generator():
tf.keras.backend.clear_session()
graph = example_graph_1(feature_size=10)
# 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]
train_gen = gnn_model[3]
ens = Ensemble(keras_model, n_estimators=2, n_predictions=1)
ens.compile(optimizer=Adam(),
loss=categorical_crossentropy,
weighted_metrics=["acc"])
ens.fit_generator(train_gen, epochs=1, verbose=0, shuffle=False)
with pytest.raises(ValueError):
ens.fit_generator(
generator=generator, # wrong type
epochs=10,
validation_data=train_gen,
verbose=0,
shuffle=False,
)
def test_fit_generator():
train_data = np.array([1, 2])
train_targets = np.array([[1, 0], [0, 1]])
graph = example_graph_1(feature_size=10)
# base_model, keras_model, generator, train_gen
gnn_models = [
create_graphSAGE_model(graph),
create_HinSAGE_model(graph),
create_graphSAGE_model(graph, link_prediction=True),
create_HinSAGE_model(graph, link_prediction=True),
create_GCN_model(graph),
create_GAT_model(graph),
]
for gnn_model in gnn_models:
keras_model = gnn_model[1]
generator = gnn_model[2]
train_gen = gnn_model[3]
ens = Ensemble(keras_model, n_estimators=2, n_predictions=1)
ens.compile(optimizer=Adam(),
loss=categorical_crossentropy,
weighted_metrics=["acc"])
# Specifying train_data and train_targets, implies the use of bagging so train_gen would
# be of the wrong type for this call to fit_generator.
with pytest.raises(ValueError):
ens.fit_generator(
train_gen,
train_data=train_data,
train_targets=train_targets,
epochs=10,
validation_generator=train_gen,
verbose=0,
shuffle=False,
)
with pytest.raises(ValueError):
ens.fit_generator(
generator=generator,
train_data=train_data,
train_targets=None, # Should not be None
epochs=10,
validation_generator=train_gen,
verbose=0,
shuffle=False,
)
with pytest.raises(ValueError):
ens.fit_generator(
generator=generator,
train_data=None,
train_targets=None,
epochs=10,
validation_generator=None,
verbose=0,
shuffle=False,
)
with pytest.raises(ValueError):
ens.fit_generator(
generator=generator,
train_data=train_data,
train_targets=train_targets,
epochs=10,
validation_generator=None,
verbose=0,
shuffle=False,
bag_size=
-1, # should be positive integer smaller than or equal to len(train_data) or None
)
with pytest.raises(ValueError):
ens.fit_generator(
generator=generator,
train_data=train_data,
train_targets=train_targets,
epochs=10,
validation_generator=None,
verbose=0,
shuffle=False,
bag_size=10, # larger than the number of training points
)