def run(self):
run_trial = create_remote_fn(self.experiment)
meta_spec = self.experiment.spec['meta']
ray.init(**meta_spec.get('resources', {}))
register_ray_serializer()
max_generation = meta_spec['max_generation']
pop_size = meta_spec['max_trial'] or calc_population_size(
self.experiment)
logger.info(
f'EvolutionarySearch max_generation: {max_generation}, population size: {pop_size}'
)
trial_data_dict = {}
config_hash = {} # config hash_str to trial_index
toolbox = self.init_deap()
population = toolbox.population(n=pop_size)
for gen in range(1, max_generation + 1):
logger.info(f'Running generation: {gen}/{max_generation}')
ray_id_to_config = {}
pending_ids = []
for individual in population:
config = dict(individual.items())
hash_str = util.to_json(config, indent=0)
if hash_str not in config_hash:
trial_index = self.experiment.info_space.tick(
'trial')['trial']
config_hash[hash_str] = config['trial_index'] = trial_index
ray_id = run_trial.remote(self.experiment, config)
ray_id_to_config[ray_id] = config
pending_ids.append(ray_id)
individual['trial_index'] = config_hash[hash_str]
trial_data_dict.update(
get_ray_results(pending_ids, ray_id_to_config))
for individual in population:
trial_index = individual.pop('trial_index')
trial_data = trial_data_dict.get(
trial_index, {'fitness': 0}) # if trial errored
individual.fitness.values = trial_data['fitness'],
preview = 'Fittest of population preview:'
for individual in tools.selBest(population, k=min(10, pop_size)):
preview += f'\nfitness: {individual.fitness.values[0]}, {individual}'
logger.info(preview)
# prepare offspring for next generation
if gen < max_generation:
population = toolbox.select(population, len(population))
# Vary the pool of individuals
population = algorithms.varAnd(population,
toolbox,
cxpb=0.5,
mutpb=0.5)
ray.shutdown()
return trial_data_dict
def run(self):
meta_spec = self.experiment.spec['meta']
ray.init(**meta_spec.get('resources', {}))
max_generation = meta_spec['max_generation']
pop_size = meta_spec['max_trial'] or calc_population_size(self.experiment)
logger.info(f'EvolutionarySearch max_generation: {max_generation}, population size: {pop_size}')
trial_data_dict = {}
config_hash = {} # config hash_str to trial_index
toolbox = self.init_deap()
population = toolbox.population(n=pop_size)
for gen in range(1, max_generation + 1):
logger.info(f'Running generation: {gen}/{max_generation}')
ray_id_to_config = {}
pending_ids = []
for individual in population:
config = dict(individual.items())
hash_str = util.to_json(config, indent=0)
if hash_str not in config_hash:
trial_index = self.experiment.info_space.tick('trial')['trial']
config_hash[hash_str] = config['trial_index'] = trial_index
ray_id = run_trial.remote(self.experiment, config)
ray_id_to_config[ray_id] = config
pending_ids.append(ray_id)
individual['trial_index'] = config_hash[hash_str]
trial_data_dict.update(get_ray_results(pending_ids, ray_id_to_config))
for individual in population:
trial_index = individual.pop('trial_index')
trial_data = trial_data_dict.get(trial_index, {'fitness': 0}) # if trial errored
individual.fitness.values = trial_data['fitness'],
preview = 'Fittest of population preview:'
for individual in tools.selBest(population, k=min(10, pop_size)):
preview += f'\nfitness: {individual.fitness.values[0]}, {individual}'
logger.info(preview)
# prepare offspring for next generation
if gen < max_generation:
population = toolbox.select(population, len(population))
# Vary the pool of individuals
population = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.5)
ray.worker.cleanup()
return trial_data_dict
def __str__(self):
return 'body: ' + util.to_json(util.get_class_attr(self))
def __str__(self):
return 'body: ' + util.to_json(util.get_class_attr(self))
