def tell_examples(self, new_examples):
"""Special method that allows fast updating of the backend with new examples."""
data_points, losses = split_examples(new_examples, self.params)
self.result = self.optimizer.tell(data_points, losses)
current_point = self.optimizer.ask()
self.current_values = make_values(self.params, current_point)
def __init__(self, examples, params, base_estimator="gp", **options):
self.init_fallback_backend()
if not examples:
self.current_values = {}
return
data_points, losses = split_examples(examples, params)
dimensions = create_dimensions(params)
if isinstance(base_estimator, str):
base_estimator = py_str(base_estimator)
self.optimizer = Optimizer(dimensions, base_estimator, **options)
self.result = self.optimizer.tell(data_points, losses)
current_point = self.optimizer.ask()
self.current_values = make_values(params, current_point)
def __init__(self, examples, params, base_estimator="gp", **options):
self.init_fallback_backend()
if not examples:
self.current_values = {}
return
data_points, losses = split_examples(examples, params)
dimensions = [
create_dimension(name, func, *args)
for name, (func, args, kwargs) in sorted_items(params)
]
if isinstance(base_estimator, str):
base_estimator = py_str(base_estimator)
optimizer = Optimizer(dimensions, base_estimator, **options)
optimizer.tell(data_points, losses)
current_point = optimizer.ask()
self.current_values = make_values(params, current_point)