def test_predict_generator_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(generator=test_gen, predict_data=edge_ids_test) # We won't train the model instead use the initial random weights to test # the evaluate_generator method. test_predictions = ens.predict_generator(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_generator(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(generator=test_gen, predict_data=edge_ids_test) # We won't train the model instead use the initial random weights to test # the evaluate_generator method. test_predictions = ens.predict_generator(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_generator(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_generator(): 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(generator=test_gen, predict_data=test_data) # We won't train the model instead use the initial random weights to test # the evaluate_generator method. test_predictions = ens.predict_generator(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_generator(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(generator=test_gen, predict_data=test_data) # We won't train the model instead use the initial random weights to test # the evaluate_generator method. test_predictions = ens.predict_generator(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_generator(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]