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 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?'