def main():
args = KGEArgParser().get_args(sys.argv[1:])
if Path(args.dataset_path).exists():
kdl = KnowledgeDataLoader(data_dir=args.dataset_path,
negative_sampling=args.sampling)
kg = kdl.get_knowledge_graph()
print('Successfully loaded {} triples from {}.'.format(
len(kdl.triples), kdl.data_dir))
else:
print('Unable to find dataset from path:', args.dataset_path)
print(
'Default loading Freebase15k dataset with default hyperparameters...'
)
kg = KnowledgeGraph()
kg.prepare_data()
kg.dump()
# TODO: Not sure why new dataset isn't cached on subsequent hits...
args.dataset_path = './data/' + kg.dataset_name
args.dataset_name = kg.dataset_name
# Add new model configurations to run.
models = [TransE(transe_config(args=args))]
for model in models:
print('---- Training Model: {} ----'.format(model.model_name))
trainer = Trainer(model=model, debug=args.debug)
trainer.build_model()
trainer.train_model()
tf.reset_default_graph()
def test_userdefined_dataset():
custom_dataset_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "resource/custom_dataset")
knowledge_graph = KnowledgeGraph(dataset="userdefineddataset",
negative_sample="uniform",
custom_dataset_path=custom_dataset_path)
knowledge_graph.prepare_data()
knowledge_graph.dump()
knowledge_graph.read_cache_data('triplets_train')
knowledge_graph.read_cache_data('triplets_test')
knowledge_graph.read_cache_data('triplets_valid')
knowledge_graph.read_cache_data('hr_t')
knowledge_graph.read_cache_data('tr_h')
knowledge_graph.read_cache_data('idx2entity')
knowledge_graph.read_cache_data('idx2relation')
knowledge_graph.read_cache_data('entity2idx')
knowledge_graph.read_cache_data('relation2idx')
knowledge_graph.dataset.read_metadata()
knowledge_graph.dataset.dump()
assert knowledge_graph.kg_meta.tot_train_triples == 1
assert knowledge_graph.kg_meta.tot_test_triples == 1
assert knowledge_graph.kg_meta.tot_valid_triples == 1
assert knowledge_graph.kg_meta.tot_entity == 6
assert knowledge_graph.kg_meta.tot_relation == 3
def test_fb15k_meta():
"""Function to test the the knowledge graph parse for Freebase and basic operations."""
knowledge_graph = KnowledgeGraph(dataset="freebase15k")
knowledge_graph.force_prepare_data()
knowledge_graph.dump()
assert knowledge_graph.is_cache_exists()
knowledge_graph.prepare_data()
knowledge_graph.dataset.read_metadata()
knowledge_graph.dataset.dump()
def test_fb15k_manipulate():
"""Function to test the the knowledge graph parse for Freebase and basic operations."""
knowledge_graph = KnowledgeGraph(dataset="freebase15k")
knowledge_graph.force_prepare_data()
knowledge_graph.dump()
knowledge_graph.read_cache_data('triplets_train')
knowledge_graph.read_cache_data('triplets_test')
knowledge_graph.read_cache_data('triplets_valid')
knowledge_graph.read_cache_data('hr_t')
knowledge_graph.read_cache_data('tr_h')
knowledge_graph.read_cache_data('idx2entity')
knowledge_graph.read_cache_data('idx2relation')
knowledge_graph.read_cache_data('entity2idx')
knowledge_graph.read_cache_data('relation2idx')
def test_dl50a():
"""Function to test the the knowledge graph parse for Deep learning knowledge base."""
knowledge_graph = KnowledgeGraph(dataset="deeplearning50a", negative_sample="uniform")
knowledge_graph.force_prepare_data()
knowledge_graph.dump()
def test_fb15k():
"""Function to test the the knowledge graph parse for Freebase."""
knowledge_graph = KnowledgeGraph(dataset="freebase15k", negative_sample="uniform")
knowledge_graph.force_prepare_data()
knowledge_graph.dump()
def test_yago():
"""Function to test the the knowledge graph parse for Yago Dataset."""
knowledge_graph = KnowledgeGraph(dataset="yago3_10", negative_sample="uniform")
knowledge_graph.force_prepare_data()
knowledge_graph.dump()
def test_wn18rr():
"""Function to test the the knowledge graph parse for Wordnet Dataset."""
knowledge_graph = KnowledgeGraph(dataset="wordnet18_rr", negative_sample="uniform")
knowledge_graph.force_prepare_data()
knowledge_graph.dump()
def test_benchmarks(dataset_name):
"""Function to test the the knowledge graph parse for Freebase."""
print("testing downloading from online sources of benchmarks and KG controller's handling.")
knowledge_graph = KnowledgeGraph(dataset=dataset_name)
knowledge_graph.force_prepare_data()
knowledge_graph.dump()