def main():
args = KGEArgParser().get_args(sys.argv[1:])
config_def, model_def = Importer().import_model_config(args.model_name.lower())
config = config_def(args)
model = model_def(**config.__dict__)
trainer = Trainer(model, config)
trainer.build_model()
trainer.infer_tails(1, 10, topk=5)
trainer.infer_heads(10, 20, topk=5)
trainer.infer_rels(1, 20, topk=5)
def test_inference_on_pretrained_model():
args = KGEArgParser().get_args([])
config_def, model_def = Importer().import_model_config("transe")
config = config_def(args)
config.load_from_data = os.path.join(os.path.dirname(__file__), "resource",
"pretrained", "TransE",
Trainer.TRAINED_MODEL_FILE_NAME)
model = model_def(**config.__dict__)
# Create the model and load the trained weights.
trainer = Trainer(model, config)
trainer.build_model()
#takes head, relation
tails = trainer.infer_tails(1, 10, topk=5)
assert len(tails) == 5
#takes relation, tail
heads = trainer.infer_heads(10, 20, topk=5)
assert len(heads) == 5
#takes head, tail
relations = trainer.infer_rels(1, 20, topk=5)
assert len(relations) == 5
def main():
# getting the customized configurations from the command-line arguments.
args = KGEArgParser().get_args(sys.argv[1:])
# Extracting the corresponding model config and definition from Importer().
config_def, model_def = Importer().import_model_config(
args.model_name.lower())
config = config_def(args)
model = model_def(**config.__dict__)
# Create the model and load the trained weights.
trainer = Trainer(model, config)
trainer.build_model()
trainer.infer_tails(1, 10, topk=5)
trainer.infer_heads(10, 20, topk=5)
trainer.infer_rels(1, 20, topk=5)
def testing_function_with_args(name,
distance_measure=None,
bilinear=None,
display=False):
"""Function to test the models with arguments."""
tf.reset_default_graph()
# getting the customized configurations from the command-line arguments.
args = KGEArgParser().get_args([])
# Preparing data and cache the data for later usage
knowledge_graph = KnowledgeGraph(dataset=args.dataset_name,
negative_sample=args.sampling)
knowledge_graph.prepare_data()
# Extracting the corresponding model config and definition from Importer().
config_def, model_def = Importer().import_model_config(name)
config = config_def(args=args)
config.epochs = 1
config.test_step = 1
config.test_num = 10
config.disp_result = display
config.save_model = True
model = model_def(config)
# Create, Compile and Train the model. While training, several evaluation will be performed.
trainer = Trainer(model=model, debug=True)
trainer.build_model()
trainer.train_model()
#can perform all the inference here after training the model
trainer.enter_interactive_mode()
#takes head, relation
trainer.infer_tails(1, 10, topk=5)
#takes relation, tail
trainer.infer_heads(10, 20, topk=5)
#takes head, tail
trainer.infer_rels(1, 20, topk=5)
trainer.exit_interactive_mode()
def main():
# getting the customized configurations from the command-line arguments.
args = KGEArgParser().get_args(sys.argv[1:])
# Preparing data and cache the data for later usage
knowledge_graph = KnowledgeGraph(dataset=args.dataset_name, negative_sample=args.sampling)
knowledge_graph.prepare_data()
sess_infer = tf.InteractiveSession()
# Extracting the corresponding model config and definition from Importer().
config_def, model_def = Importer().import_model_config(args.model_name.lower())
config = config_def(args=args)
model = model_def(config)
# Create, Compile and Train the model. While training, several evaluation will be performed.
trainer = Trainer(model=model, debug=args.debug)
trainer.build_model()
trainer.train_model()
#can perform all the inference here after training the model
#takes head, relation
trainer.infer_tails(1,10,sess_infer,topk=5)
#takes relation, tails
trainer.infer_heads(10,20,sess_infer,topk=5)
sess_infer.close()
def testing_function_with_args(name, l1_flag, display=False):
"""Function to test the models with arguments."""
# getting the customized configurations from the command-line arguments.
args = KGEArgParser().get_args([])
# Preparing data and cache the data for later usage
knowledge_graph = KnowledgeGraph(dataset=args.dataset_name)
knowledge_graph.prepare_data()
# Extracting the corresponding model config and definition from Importer().
config_def, model_def = Importer().import_model_config(name)
config = config_def(args)
config.epochs = 1
config.test_step = 1
config.test_num = 10
config.disp_result = display
config.save_model = True
config.l1_flag = l1_flag
config.debug = True
model = model_def(**config.__dict__)
# Create, Compile and Train the model. While training, several evaluation will be performed.
trainer = Trainer(model, config)
trainer.build_model()
trainer.train_model()
#can perform all the inference here after training the model
trainer.enter_interactive_mode()
#takes head, relation
tails = trainer.infer_tails(1, 10, topk=5)
assert len(tails) == 5
#takes relation, tail
heads = trainer.infer_heads(10, 20, topk=5)
assert len(heads) == 5
#takes head, tail
if not name in ["conve", "proje_pointwise", "tucker"]:
relations = trainer.infer_rels(1, 20, topk=5)
assert len(relations) == 5
trainer.exit_interactive_mode()