class BayesianOptimizer:
def __init__(self, space, max_trials, seed, **kwargs):
self.primary = PrimaryAlgo(space, {'BayesianOptimizer': kwargs})
self.primary.algorithm.random_state = seed
self.max_trials = max_trials
self.trial_count = 0
@property
def space(self):
return self.primary.space
def is_completed(self):
return self.trial_count >= self.max_trials
def get_params(self, seed=None):
if seed is None:
seed = random.randint(0, 100000)
self.primary.algorithm._init_optimizer()
optimizer = self.primary.algorithm.optimizer
optimizer.rng.seed(seed)
# Giving the same seed could be problematic since optimizer.rng and
# optimizer.base_estimator.rng would be synchronized and sample the same values.
optimizer.base_estimator_.random_state = optimizer.rng.randint(
0, 100000)
params = unflatten(
dict(zip(self.space.keys(),
self.primary.suggest()[0])))
logger.debug('Sampling:\n{}'.format(pprint.pformat(params)))
return params
def observe(self, params, objective):
params = flatten(params)
params = [[params[param_name] for param_name in self.space.keys()]]
results = [dict(objective=objective)]
self.primary.observe(params, results)
class TPEOptimizer:
def __init__(self, space, max_trials, seed, **kwargs):
self.primary = PrimaryAlgo(space, {'TPEOptimizer': kwargs})
self.primary.algorithm.random_state = seed
self.max_trials = max_trials
self.trial_count = 0
@property
def space(self):
return self.primary.space
def is_completed(self):
return self.trial_count >= self.max_trials
def get_params(self, seed):
if seed is None:
seed = random.randint(0, 100000)
self.primary.algorithm.study.sampler.rng.seed(seed)
self.primary.algorithm.study.sampler.random_sampler.rng.seed(seed)
params = unflatten(
dict(zip(self.space.keys(),
self.primary.suggest()[0])))
logger.debug('Sampling:\n{}'.format(pprint.pformat(params)))
return params
def observe(self, params, objective):
params = flatten(params)
params = [[params[param_name] for param_name in self.space.keys()]]
results = [dict(objective=objective)]
self.primary.observe(params, results)