def _constraint_solver(parameter: p.Parameter, budget: int) -> p.Parameter:
"""Runs a suboptimization to solve the parameter constraints"""
parameter_without_constraint = parameter.copy()
parameter_without_constraint._constraint_checkers.clear()
opt = registry["OnePlusOne"](parameter_without_constraint,
num_workers=1,
budget=budget)
for _ in range(budget):
cand = opt.ask()
# Our objective function is minimum for the point the closest to
# the original candidate under the constraints.
penalty = sum(
utils._float_penalty(func(cand.value))
for func in parameter._constraint_checkers)
# TODO: this may not scale well with dimension
distance = np.tanh(
np.sum(cand.get_standardized_data(reference=parameter)**2))
# TODO: because of the return whenever constraints are satisfied, the first case never arises
loss = distance if penalty <= 0 else penalty + distance + 1.0
opt.tell(cand, loss)
if penalty <= 0: # constraints are satisfied
break
data = opt.recommend().get_standardized_data(
reference=parameter_without_constraint)
return parameter.spawn_child().set_standardized_data(data)
def _reset_copy(obj: p.Parameter) -> p.Parameter:
"""Copy a parameter and resets its random state to obtain variability"""
out = obj.copy()
out._set_random_state(None) # propagates None to sub-parameters
return out
def _reset_copy(obj: p.Parameter) -> p.Parameter:
"""Copy a parameter and resets its random state to obtain variability"""
out = obj.copy()
out.random_state = None
return out