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 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 __init__(self, args=None):
"""store the information of database"""
if args.model.lower() in ["tucker", "tucker_v2", "conve", "convkb", "proje_pointwise"]:
raise Exception("Model %s has not been supported in tuning hyperparameters!" % args.model)
model_name = args.model.lower()
self.args = args
self.knowledge_graph = KnowledgeGraph(dataset=args.dataset_name, custom_dataset_path=args.dataset_path)
hyper_params = None
try:
self.model_obj = getattr(importlib.import_module(model_path + ".%s" % moduleMap[model_name]),
modelMap[model_name])
self.config_obj = getattr(importlib.import_module(config_path), configMap[model_name])
hyper_params = getattr(importlib.import_module(hyper_param_path), hypMap[model_name])()
except ModuleNotFoundError:
self._logger.error("%s not implemented! Select from: %s" % \
(model_name, ' '.join(map(str, modelMap.values()))))
from pykg2vec.config.config import KGEArgParser
kge_args = KGEArgParser().get_args([])
kge_args.dataset_name = args.dataset_name
kge_args.debug = self.args.debug
config = self.config_obj(kge_args)
model = self.model_obj(config)
self.trainer = Trainer(model)
self.search_space = hyper_params.search_space
self.max_evals = self.args.max_number_trials if not self.args.debug else 1
def test_generator_trane():
"""Function to test the generator for Translation distance based algorithm."""
knowledge_graph = KnowledgeGraph(dataset="freebase15k", negative_sample="uniform")
knowledge_graph.force_prepare_data()
args = KGEArgParser().get_args([])
start_time = timeit.default_timer()
config = TransEConfig(args)
gen = Generator(config=GeneratorConfig(data='train', algo='transe'), model_config=config)
print("----init time:", timeit.default_timer() - start_time)
for i in range(10):
start_time_batch = timeit.default_timer()
data = list(next(gen))
h = data[0]
r = data[1]
t = data[2]
# hr_t = data[3]
# tr_h = data[4]
print("----batch:", i, "----time:",timeit.default_timer() - start_time_batch)
print(h,r,t)
print("total time:", timeit.default_timer() - start_time)
gen.stop()
def test_generator_proje():
"""Function to test the generator for ProjE algorithm."""
knowledge_graph = KnowledgeGraph(dataset="freebase15k", negative_sample="uniform")
knowledge_graph.force_prepare_data()
args = KGEArgParser().get_args([])
config = ProjE_pointwiseConfig(args=args)
gen = iter(Generator(config=GeneratorConfig(data='train', algo='ProjE'), model_config=config))
for i in range(1000):
data = list(next(gen))
print("----batch:", i)
hr_hr = data[0]
hr_t = data[1]
tr_tr = data[2]
tr_h = data[3]
print("hr_hr:", hr_hr)
print("hr_t:", hr_t)
print("tr_tr:", tr_tr)
print("tr_h:", tr_h)
gen.stop()
def test_generator_pairwise():
"""Function to test the generator for pairwise based algorithm."""
knowledge_graph = KnowledgeGraph(dataset="freebase15k")
knowledge_graph.force_prepare_data()
config_def, model_def = Importer().import_model_config('transe')
generator = Generator(model_def(config_def(KGEArgParser().get_args([]))))
generator.start_one_epoch(10)
for i in range(10):
data = list(next(generator))
assert len(data) == 6
ph = data[0]
pr = data[1]
pt = data[2]
nh = data[3]
nr = data[4]
nt = data[5]
assert len(ph) == len(pr)
assert len(ph) == len(pt)
assert len(ph) == len(nh)
assert len(ph) == len(nr)
assert len(ph) == len(nt)
generator.stop()
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 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 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=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)
# Create, Compile and Train the model. While training, several evaluation will be performed.
trainer = Trainer(model=model)
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 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 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 test_generator_pointwise():
"""Function to test the generator for pointwise based algorithm."""
knowledge_graph = KnowledgeGraph(dataset="freebase15k")
knowledge_graph.force_prepare_data()
config_def, model_def = Importer().import_model_config("complex")
generator = Generator(model_def(config_def(KGEArgParser().get_args([]))))
generator.start_one_epoch(10)
for i in range(10):
data = list(next(generator))
assert len(data) == 4
h = data[0]
r = data[1]
t = data[2]
y = data[3]
assert len(h) == len(r)
assert len(h) == len(t)
assert set(y) == {1, -1}
generator.stop()
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 test_generator_proje():
"""Function to test the generator for projection based algorithm."""
knowledge_graph = KnowledgeGraph(dataset="freebase15k")
knowledge_graph.force_prepare_data()
config_def, model_def = Importer().import_model_config("proje_pointwise")
generator = Generator(model_def(config_def(KGEArgParser().get_args([]))))
generator.start_one_epoch(10)
for i in range(10):
data = list(next(generator))
assert len(data) == 5
h = data[0]
r = data[1]
t = data[2]
hr_t = data[3]
tr_h = data[4]
assert len(h) == len(r)
assert len(h) == len(t)
assert isinstance(hr_t, tf.SparseTensor)
assert isinstance(tr_h, tf.SparseTensor)
generator.stop()
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()