def main(self):
timer = util.DelayTimer(max_minutes=None, period=SERVICE_PERIOD)
chkpoint_counter = 0
num_evals = 0
logger.info(f"Generating {self.num_workers} initial points...")
XX = self.optimizer.ask_initial(n_points=self.num_workers)
self.evaluator.add_eval_batch(XX)
# MAIN LOOP
for elapsed_str in timer:
logger.info(f"Elapsed time: {elapsed_str}")
results = list(self.evaluator.get_finished_evals())
num_evals += len(results)
chkpoint_counter += len(results)
if EXIT_FLAG or num_evals >= self.max_evals:
break
if results:
logger.info(
f"Refitting model with batch of {len(results)} evals")
self.optimizer.tell(results)
logger.info(
f"Drawing {len(results)} points with strategy {self.optimizer.strategy}")
# ! 'ask' is written as a generator because asking for a large batch is
# ! slow. We get better performance when ask is batched. The RF is
# ! constantly re-fitting during the call to ask. So it becomes slow
# ! when there are a large number of workers.
for batch in self.optimizer.ask(n_points=len(results)):
self.evaluator.add_eval_batch(batch)
if chkpoint_counter >= CHECKPOINT_INTERVAL:
self.evaluator.dump_evals()
chkpoint_counter = 0
logger.info('Hyperopt driver finishing')
self.evaluator.dump_evals()
def main(self):
timer = util.DelayTimer(max_minutes=None, period=SERVICE_PERIOD)
chkpoint_counter = 0
num_evals = 0
logger.info(f"Generating {self.num_workers} initial points...")
XX = self.optimizer.ask_initial(n_points=self.num_workers)
self.evaluator.add_eval_batch(XX)
# MAIN LOOP
for elapsed_str in timer:
logger.info(f"Elapsed time: {elapsed_str}")
results = list(self.evaluator.get_finished_evals())
num_evals += len(results)
chkpoint_counter += len(results)
if EXIT_FLAG or num_evals >= self.args.max_evals:
break
if results:
logger.info(
f"Refitting model with batch of {len(results)} evals")
self.optimizer.tell(results)
logger.info(
f"Drawing {len(results)} points with strategy {self.optimizer.strategy}"
)
for batch in self.optimizer.ask(n_points=len(results)):
self.evaluator.add_eval_batch(batch)
if chkpoint_counter >= CHECKPOINT_INTERVAL:
self.evaluator.dump_evals()
chkpoint_counter = 0
logger.info('Hyperopt driver finishing')
self.evaluator.dump_evals()
def main(self):
# opt = GAOptimizer(cfg)
# evaluator = evaluate.create_evaluator(cfg)
logger.info(f"Starting new run")
timer = util.DelayTimer(max_minutes=None, period=SERVICE_PERIOD)
timer = iter(timer)
elapsed_str = next(timer)
logger.info("Hyperopt GA driver starting")
logger.info(f"Elapsed time: {elapsed_str}")
if self.optimizer.pop is None:
logger.info("Generating initial population")
logger.info(f"{self.optimizer.INIT_POP_SIZE} individuals")
self.optimizer.pop = self.optimizer.toolbox.population(
n=self.optimizer.INIT_POP_SIZE)
individuals = self.optimizer.pop
self.evaluate_fitnesses(
individuals, self.optimizer, self.evaluator, self.args.eval_timeout_minutes)
self.optimizer.record_generation(num_evals=len(self.optimizer.pop))
with open('ga_logbook.log', 'w') as fp:
fp.write(str(self.optimizer.logbook))
print("best:", self.optimizer.halloffame[0])
while self.optimizer.current_gen < self.optimizer.NGEN:
self.optimizer.current_gen += 1
logger.info(
f"Generation {self.optimizer.current_gen} out of {self.optimizer.NGEN}")
logger.info(f"Elapsed time: {elapsed_str}")
# Select the next generation individuals
offspring = self.optimizer.toolbox.select(
self.optimizer.pop, len(self.optimizer.pop))
# Clone the selected individuals
offspring = list(map(self.optimizer.toolbox.clone, offspring))
# Apply crossover and mutation on the offspring
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if random.random() < self.optimizer.CXPB:
self.optimizer.toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
for mutant in offspring:
if random.random() < self.optimizer.MUTPB:
self.optimizer.toolbox.mutate(mutant)
del mutant.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
logger.info(f"Evaluating {len(invalid_ind)} invalid individuals")
self.evaluate_fitnesses(invalid_ind, self.optimizer, self.evaluator,
self.args.eval_timeout_minutes)
# The population is entirely replaced by the offspring
self.optimizer.pop[:] = offspring
self.optimizer.record_generation(num_evals=len(invalid_ind))
with open('ga_logbook.log', 'w') as fp:
fp.write(str(self.optimizer.logbook))
print("best:", self.optimizer.halloffame[0])