class RandomSampling(BaseStrategy):
"""Random sampling strategy.
We generate and evaluate a fixed number of points using all resources.
The optimization problem must implement OptimizationProblem and max_evals
must be a positive integer.
:param max_evals: Evaluation budget
:type max_evals: int
:param opt_prob: Optimization problem
:type opt_prob: OptimizationProblem
"""
def __init__(self, max_evals, opt_prob):
check_opt_prob(opt_prob)
if not isinstance(max_evals, int) and max_evals > 0:
raise ValueError("max_evals must be an integer >= exp_des.num_pts")
self.opt_prob = opt_prob
self.max_evals = max_evals
self.retry = RetryStrategy()
for _ in range(max_evals): # Generate the random points
x = np.random.uniform(low=opt_prob.lb, high=opt_prob.ub)
proposal = self.propose_eval(x)
self.retry.rput(proposal)
def propose_action(self):
"""Propose an action based on outstanding points."""
if not self.retry.empty(): # Propose next point
return self.retry.get()
elif self.retry.num_eval_outstanding == 0: # Budget exhausted
return self.propose_terminate()
