def tunning_function(name):
"""Function to test the tuning of the models."""
# getting the customized configurations from the command-line arguments.
args = KGETuneArgParser().get_args([])
# initializing bayesian optimizer and prepare data.
args.debug = True
bays_opt = BaysOptimizer(args=args)
# perform the golden hyperparameter tuning.
bays_opt.optimize()
def tune(self):
"""Fuction to tune the hyper-parameters for the model
using training and validation set."""
# getting the customized configurations from the command-line arguments.
args = KGETuneArgParser().get_args([])
args.model = self.model
args.dataset_name = self.dataset
args.debug = self.debug
# initializing bayesian optimizer and prepare data.
bays_opt = BaysOptimizer(args=args)
# perform the golden hyperparameter tuning.
bays_opt.optimize()
self.best = bays_opt.return_best()
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()