def update_search_space(self, search_space):
"""
Get search space
Parameters
----------
search_space : dict
Search space
"""
logger.info('Update search space %s', search_space)
self.searchspace_json = search_space
self.space = json2space(self.searchspace_json)
self.random_state = np.random.RandomState()
self.population = []
is_rand = dict()
for item in self.space:
is_rand[item] = True
for i in range(self.population_size):
hyper_parameters = json2parameter(
self.searchspace_json, is_rand, self.random_state)
hyper_parameters = split_index(hyper_parameters)
checkpoint_dir = os.path.join(self.all_checkpoint_dir, str(i))
hyper_parameters['load_checkpoint_dir'] = os.path.join(checkpoint_dir, str(self.epoch))
hyper_parameters['save_checkpoint_dir'] = os.path.join(checkpoint_dir, str(self.epoch))
self.population.append(TrialInfo(checkpoint_dir=checkpoint_dir, hyper_parameters=hyper_parameters))
def _random_generate_individual(self):
is_rand = dict()
for item in self.space:
is_rand[item] = True
config = json2parameter(self.searchspace_json, is_rand,
self.random_state)
self.population.append(Individual(config=config))
def _generate_individual(self, parameter_id):
"""
This function will generate the config for a trial.
If at the first generation, randomly generates individuals to satisfy self.population_size.
Otherwise, random choose a pair of individuals and compare their fitnesses.
The worst of the pair will be removed. Copy the best of the pair and mutate it to generate a new individual.
Parameters
----------
parameter_id : int
Returns
-------
dict
A group of candidate parameters that evolution tuner generated.
"""
pos = -1
for i in range(len(self.population)):
if self.population[i].result is None:
pos = i
break
if pos != -1:
indiv = copy.deepcopy(self.population[pos])
self.population.pop(pos)
else:
random.shuffle(self.population)
# avoid only 1 individual has result
if len(
self.population
) > 1 and self.population[0].result < self.population[1].result:
self.population[0] = self.population[1]
# mutation on the worse individual
space = json2space(self.searchspace_json,
self.population[0].config)
is_rand = dict()
mutation_pos = space[random.randint(0, len(space) - 1)]
for i in range(len(self.space)):
is_rand[self.space[i]] = (self.space[i] == mutation_pos)
config = json2parameter(self.searchspace_json, is_rand,
self.random_state,
self.population[0].config)
if len(self.population) > 1:
self.population.pop(1)
indiv = Individual(config=config)
# remove "_index" from config and save params-id
self.running_trials[parameter_id] = indiv
config = split_index(indiv.config)
return config
def generate_parameters(self, parameter_id, **kwargs):
"""
This function will returns a dict of trial (hyper-)parameters, as a serializable object.
Parameters
----------
parameter_id : int
Returns
-------
dict
A group of candaidte parameters that evolution tuner generated.
"""
if not self.population:
raise RuntimeError('The population is empty')
pos = -1
for i in range(len(self.population)):
if self.population[i].result is None:
pos = i
break
if pos != -1:
indiv = copy.deepcopy(self.population[pos])
self.population.pop(pos)
total_config = indiv.config
else:
random.shuffle(self.population)
if self.population[0].result < self.population[1].result:
self.population[0] = self.population[1]
# mutation
space = json2space(self.searchspace_json,
self.population[0].config)
is_rand = dict()
mutation_pos = space[random.randint(0, len(space) - 1)]
for i in range(len(self.space)):
is_rand[self.space[i]] = (self.space[i] == mutation_pos)
config = json2parameter(self.searchspace_json, is_rand,
self.random_state,
self.population[0].config)
self.population.pop(1)
# remove "_index" from config and save params-id
total_config = config
self.total_data[parameter_id] = total_config
config = split_index(total_config)
return config
def update_search_space(self, search_space):
"""
Update search space.
Search_space contains the information that user pre-defined.
Parameters
----------
search_space : dict
"""
self.searchspace_json = search_space
self.space = json2space(self.searchspace_json)
self.random_state = np.random.RandomState()
self.population = []
is_rand = dict()
for item in self.space:
is_rand[item] = True
for _ in range(self.population_size):
config = json2parameter(self.searchspace_json, is_rand,
self.random_state)
self.population.append(Individual(config=config))
