class TPESampler(Sampler):
def __init__(self, optimize_mode='minimize'):
self.tpe_tuner = HyperoptTuner('tpe', optimize_mode)
self.cur_sample = None
self.index = None
self.total_parameters = {}
def update_sample_space(self, sample_space):
search_space = {}
for i, each in enumerate(sample_space):
search_space[str(i)] = {'_type': 'choice', '_value': each}
self.tpe_tuner.update_search_space(search_space)
def generate_samples(self, model_id):
self.cur_sample = self.tpe_tuner.generate_parameters(model_id)
self.total_parameters[model_id] = self.cur_sample
self.index = 0
def receive_result(self, model_id, result):
self.tpe_tuner.receive_trial_result(model_id,
self.total_parameters[model_id],
result)
def choice(self, candidates, mutator, model, index):
chosen = self.cur_sample[str(self.index)]
self.index += 1
return chosen
class TPESampler(Sampler):
def __init__(self, optimize_mode='minimize'):
# Move import here to eliminate some warning messages about dill.
from nni.algorithms.hpo.hyperopt_tuner import HyperoptTuner
self.tpe_tuner = HyperoptTuner('tpe', optimize_mode)
self.cur_sample: Optional[dict] = None
self.index: Optional[int] = None
self.total_parameters = {}
def update_sample_space(self, sample_space):
search_space = {}
for i, each in enumerate(sample_space):
search_space[str(i)] = {'_type': 'choice', '_value': each}
self.tpe_tuner.update_search_space(search_space)
def generate_samples(self, model_id):
self.cur_sample = self.tpe_tuner.generate_parameters(model_id)
self.total_parameters[model_id] = self.cur_sample
self.index = 0
def receive_result(self, model_id, result):
self.tpe_tuner.receive_trial_result(model_id,
self.total_parameters[model_id],
result)
def choice(self, candidates, mutator, model, index):
assert isinstance(self.index, int) and isinstance(
self.cur_sample, dict)
chosen = self.cur_sample[str(self.index)]
self.index += 1
return chosen