def testing_function(name):
"""Function to test the models with arguments."""
# getting the customized configurations from the command-line arguments.
args = KGEArgParser().get_args(['-exp', 'True', '-mn', name])
# 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.save_model = False
config.debug = True
config.ent_hidden_size = 10
config.rel_hidden_size = 10
config.channels = 2
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()
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",
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,
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, config)
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 test_hyperparamter_loader(model_name):
knowledge_graph = KnowledgeGraph(dataset="freebase15k")
knowledge_graph.prepare_data()
# getting the customized configurations from the command-line arguments.
args = KGETuneArgParser().get_args([])
hyperparams = HyperparamterLoader(args).load_hyperparameter("freebase15k", model_name)
assert hyperparams["optimizer"] is not None
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 main():
args = KGEArgParser().get_args(sys.argv[1:])
knowledge_graph = KnowledgeGraph(dataset=args.dataset_name, custom_dataset_path=args.dataset_path)
knowledge_graph.prepare_data()
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.train_model()
def testing_function_with_args(name,
l1_flag,
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)
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
relations = trainer.infer_rels(1, 20, topk=5)
assert len(relations) == 5
trainer.exit_interactive_mode()
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,
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)
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()
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.config_local.test_num = 10
# perform the golden hyperparameter tuning.
bays_opt.optimize()
assert bays_opt.return_best() is not None
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)
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.__dict__), config
def test_return_empty_before_optimization(mocked_fmin):
"""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 = 'analogy'
bays_opt = BaysOptimizer(args=args)
bays_opt.config_local.test_num = 10
with pytest.raises(Exception) as e:
bays_opt.return_best()
assert mocked_fmin.called is False
assert e.value.args[0] == 'Cannot find golden setting. Has optimize() been called?'
def test_visualization(tmpdir):
result_path_dir = tmpdir.mkdir("result_path")
args = KGEArgParser().get_args([])
knowledge_graph = KnowledgeGraph(dataset="Freebase15k")
knowledge_graph.prepare_data()
config_def, model_def = Importer().import_model_config("analogy")
config = config_def(args=args)
config.epochs = 5
config.test_step = 1
config.test_num = 1
config.disp_result = True
config.save_model = False
config.debug = True
config.patience = -1
config.plot_embedding = True
config.plot_training_result = True
config.plot_testing_result = True
config.path_figures = result_path_dir
config.path_result = result_path_dir
trainer = Trainer(model_def(**config.__dict__), config)
trainer.build_model()
trainer.train_model()
files = [f for f in listdir(result_path_dir)]
assert any(map(lambda f: "_entity_plot" in f, files))
assert any(map(lambda f: "_rel_plot" in f, files))
assert any(map(lambda f: "_ent_n_rel_plot" in f, files))
assert any(map(lambda f: "_training_loss_plot_" in f, files))
assert any(map(lambda f: "_testing_hits_plot" in f, files))
assert any(map(lambda f: "_testing_latex_table_" in f, files))
assert any(map(lambda f: "_testing_table_" in f, files))
assert any(map(lambda f: "_testing_rank_plot_" in f, files))
assert any(map(lambda f: "_testing_hits_plot_" in f, files))
def main():
model_name = "transe"
dataset_name = "Freebase15k"
# 1. Tune the hyper-parameters for the selected model and dataset.
# p.s. this is using training and validation set.
args = KGETuneArgParser().get_args(
['-mn', model_name, '-ds', dataset_name])
# initializing bayesian optimizer and prepare data.
bays_opt = BaysOptimizer(args=args)
# perform the golden hyperparameter tuning.
bays_opt.optimize()
best = bays_opt.return_best()
# 2. Evaluate final model using the found best hyperparameters on testing set.
args = KGEArgParser().get_args(['-mn', model_name, '-ds', dataset_name])
# 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)
# Update the config params with the golden hyperparameter
for k, v in best.items():
config.__dict__[k] = v
model = model_def(**config.__dict__)
# Create, Compile and Train the model.
trainer = Trainer(model, config)
trainer.build_model()
trainer.train_model()
