n_objectives = 2
directions = ["minimize"] * n_objectives
sampler_name = sys.argv[1]
# Sampler.
sampler_cls = getattr(
optuna.samplers,
sampler_name,
getattr(optuna.integration, sampler_name, None),
)
if sampler_cls is None:
raise ValueError("Unknown sampler: {}.".format(sampler_name))
sampler_kwargs = json.loads(sys.argv[2])
sampler = sampler_cls(**sampler_kwargs)
return optuna.create_study(
directions=directions,
sampler=sampler,
pruner=optuna.pruners.NopPruner(),
)
if __name__ == "__main__":
factory = OptunaSolverFactory(create_study)
runner = solver.SolverRunner(factory)
runner.run()
def create_solver(self, seed: int, problem: problem.ProblemSpec) -> solver.Solver:
return RandomSolver(seed, problem)
class RandomSolver(solver.Solver):
def __init__(self, seed: int, problem: problem.ProblemSpec):
self._rng = np.random.RandomState(seed)
self._problem = problem
def ask(self, idg: solver.TrialIdGenerator) -> solver.NextTrial:
params: List[Optional[float]] = []
for p in self._problem.params:
if p.distribution == problem.Distribution.UNIFORM:
params.append(self._rng.uniform(p.range.low, p.range.high))
else:
low = np.log(p.range.low)
high = np.log(p.range.high)
params.append(float(np.exp(self._rng.uniform(low, high))))
trial_id = idg.generate()
next_step = self._problem.last_step
return solver.NextTrial(trial_id, params, next_step)
def tell(self, trial: solver.EvaluatedTrial):
pass
if __name__ == "__main__":
runner = solver.SolverRunner(RandomSolverFactory())
runner.run()