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 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,
custom_dataset_path=args.dataset_path)
knowledge_graph.prepare_data()
# 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
trainer.enter_interactive_mode()
code.interact(local=locals())
trainer.exit_interactive_mode()
def testing_function_with_args(name,
distance_measure=None,
bilinear=None,
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,
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 = False
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()
tf.reset_default_graph()
def test(self):
"""Function to evaluate final model on testing set while training the model using
best hyper-paramters on merged training and validation set."""
args = KGEArgParser().get_args([])
args.model = self.model
args.dataset_name = self.dataset
args.debug = self.debug
# 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(
args.model_name.lower())
config = config_def(args=args)
# Update the config params with the golden hyperparameter
for k, v in self.best.items():
config.__dict__[k] = v
model = model_def(config)
if self.debug:
config.epochs = 1
# Create, Compile and Train the model. While training, several evaluation will be performed.
trainer = Trainer(model=model)
trainer.build_model()
trainer.train_model()
def testing_function(name,
distance_measure=None,
bilinear=None,
display=False):
"""Function to test the models."""
knowledge_graph = KnowledgeGraph(dataset="freebase15k",
negative_sample="uniform")
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()
config.epochs = 1
config.test_step = 1
config.test_num = 10
config.disp_result = display
config.save_model = False
if distance_measure is not None:
config.distance_measure = distance_measure
if bilinear is not None:
config.bilinear = bilinear
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()
tf.reset_default_graph()
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_kgpipeline():
"""Function to test the KGPipeline function."""
# Preparing data and cache the data for later usage
knowledge_graph = KnowledgeGraph(dataset="Freebase15k")
knowledge_graph.prepare_data()
kg_pipeline = KGPipeline(model="transe", dataset="Freebase15k", debug=True)
kg_pipeline.tune()
kg_pipeline.test()
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 testing_function_with_args(name,
l1_flag,
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
config.L1_flag = l1_flag
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
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
trainer.exit_interactive_mode()
def tunning_function(name):
"""Function to test the tuning of the models."""
knowledge_graph = KnowledgeGraph(dataset="freebase15k")
knowledge_graph.prepare_data()
# getting the customized configurations from the command-line arguments.
args = KGETuneArgParser().get_args([])
# initializing bayesian optimizer and prepare data.
args.debug = True
args.model = name
bays_opt = BaysOptimizer(args=args)
bays_opt.trainer.config.test_num = 10
# perform the golden hyperparameter tuning.
bays_opt.optimize()
def main():
# getting the customized configurations from the command-line arguments.
args = KGEArgParser().get_args(sys.argv[1:])
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(
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.load_model()
trainer.export_embeddings()
def get_model(result_path_dir, configured_epochs, patience, config_key):
args = KGEArgParser().get_args([])
knowledge_graph = KnowledgeGraph(dataset="Freebase15k")
knowledge_graph.prepare_data()
config_def, model_def = Importer().import_model_config(config_key)
config = config_def(args=args)
config.epochs = configured_epochs
config.test_step = 1
config.test_num = 1
config.disp_result = False
config.save_model = False
config.path_result = result_path_dir
config.debug = True
config.patience = patience
return model_def(config)
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,
custom_dataset_path=args.dataset_path)
knowledge_graph.prepare_data()
# 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)
# Create, Compile and Train the model. While training, several evaluation will be performed.
trainer = Trainer(model=model)
trainer.build_model()
trainer.train_model()
def experiment(model_name):
args = KGEArgParser().get_args([])
args.exp = True
args.dataset_name = "fb15k"
# Preparing data and cache the data for later usage
knowledge_graph = KnowledgeGraph(dataset=args.dataset_name,
negative_sample=args.sampling,
custom_dataset_path=args.dataset_path)
knowledge_graph.prepare_data()
# Extracting the corresponding model config and definition from Importer().
config_def, model_def = Importer().import_model_config(model_name)
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)
trainer.build_model()
trainer.train_model()
def run_pykg2vec():
# getting the customized configurations from the command-line arguments.
args = PyKG2VecArgParser().get_args(sys.argv[1:])
args.dataset_path = preprocess(args.triples_path, args.dataset_name)
# Preparing data and cache the data for later usage
knowledge_graph = KnowledgeGraph(dataset=args.dataset_name,
negative_sample=args.sampling,
custom_dataset_path=args.dataset_path)
knowledge_graph.prepare_data()
# 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()
def testing_function(name,
distance_measure=None,
bilinear=None,
display=False,
ent_hidden_size=None,
rel_hidden_size=None,
channels=None):
"""Function to test the models with arguments."""
# getting the customized configurations from the command-line arguments.
args = KGEArgParser().get_args(['-exp', 'True'])
# 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 = False
config.debug = True
if ent_hidden_size:
config.ent_hidden_size = ent_hidden_size
if rel_hidden_size:
config.rel_hidden_size = rel_hidden_size
if channels:
config.channels = channels
model = model_def(config)
# Create, Compile and Train the model. While training, several evaluation will be performed.
trainer = Trainer(model=model)
trainer.build_model()
trainer.train_model()
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()