def get_params(self, seed=None):
if seed is None:
seed = random.randint(0, 100000)
arguments = unflatten(
dict(zip(self.space.keys(),
self.space.sample(seed=seed)[0])))
logger.debug('Sampling:\n{}'.format(pprint.pformat(arguments)))
return arguments
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 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 get_params(self, seed=None):
params = self.linear_space.reverse(self.params[len(self.trials)])
arguments = unflatten(dict(zip(self.space.keys(), params)))
logger.debug('Sampling:\n{}'.format(pprint.pformat(arguments)))
return arguments
def update(config, arguments):
pairs = [argument.split("=") for argument in arguments]
kwargs = unflatten(dict((pair[0], eval(pair[1])) for pair in pairs))
return merge_configs(config, kwargs)
def merge(config, subconfig):
flattened_config = copy.deepcopy(flatten(config))
flattened_config.update(flatten(subconfig))
return unflatten(flattened_config)