def test_deprecated_methods():
tf.keras.backend.clear_session()
train_data = np.array([1, 2])
train_targets = np.array([[1, 0], [0, 1]])
graph = example_graph_1(feature_size=2)
_, keras_model, gen, train_gen = create_GAT_model(graph)
ensemble = Ensemble(keras_model, n_estimators=1, n_predictions=1)
bagging = BaggingEnsemble(keras_model, n_estimators=1, n_predictions=1)
models = [ensemble, bagging]
for model in models:
model.compile(optimizer=Adam(), loss=binary_crossentropy)
# check that each of the generator methods gives a warning, and also seems to behave like the
# non-deprecated method
with pytest.warns(DeprecationWarning, match="'fit_generator' .* 'fit'"):
ens_history = ensemble.fit_generator(train_gen, epochs=2, verbose=0)
assert len(ens_history) == 1
assert len(ens_history[0].history["loss"]) == 2
with pytest.warns(DeprecationWarning, match="'fit_generator' .* 'fit'"):
bag_history = bagging.fit_generator(
gen, train_data, train_targets, epochs=2, verbose=0
)
assert len(bag_history) == 1
assert len(bag_history[0].history["loss"]) == 2
for model in models:
with pytest.warns(
DeprecationWarning, match="'evaluate_generator' .* 'evaluate'"
):
eval_result = model.evaluate_generator(train_gen, verbose=0)
np.testing.assert_array_equal(eval_result, model.evaluate(train_gen, verbose=0))
with pytest.warns(DeprecationWarning, match="'predict_generator' .* 'predict'"):
pred_result = model.predict_generator(train_gen, verbose=0)
np.testing.assert_array_equal(pred_result, model.predict(train_gen, verbose=0))
def test_BaggingEnsemble_fit_generator():
tf.keras.backend.clear_session()
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_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 = BaggingEnsemble(keras_model, n_estimators=2, n_predictions=1)
ens.compile(optimizer=Adam(),
loss=categorical_crossentropy,
weighted_metrics=["acc"])
ens.fit_generator(
generator=generator,
train_data=train_data,
train_targets=train_targets,
epochs=1,
validation_data=train_gen,
verbose=0,
shuffle=False,
)
# This is a BaggingEnsemble so the generator in the below call is of the wrong type.
with pytest.raises(ValueError):
ens.fit_generator(
train_gen,
train_data=train_data,
train_targets=train_targets,
epochs=10,
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_data=train_gen,
verbose=0,
shuffle=False,
)
with pytest.raises(ValueError):
ens.fit_generator(
generator=generator,
train_data=None,
train_targets=None,
epochs=10,
validation_data=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_data=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_data=None,
verbose=0,
shuffle=False,
bag_size=10, # larger than the number of training points
)