def base_optimizer(black_box_function, mp_opt=False):
global max_cc
limit_obs, count, init_points = 30, 0, 6 if mp_opt else 5
if mp_opt:
search_space = [
Integer(1, 32), # Concurrency
Integer(1, 4), # Parallesism
Integer(1, 10), # Pipeline
]
else:
search_space = [
Integer(1, max_cc), # Concurrency
]
optimizer = BO(
dimensions=search_space,
base_estimator="GP", #[GP, RF, ET, GBRT],
acq_func="gp_hedge", # [LCB, EI, PI, gp_hedge]
acq_optimizer="auto", #[sampling, lbfgs, auto]
n_initial_points=init_points,
model_queue_size= limit_obs,
)
while True:
count += 1
if len(optimizer.yi) > limit_obs:
optimizer.yi = optimizer.yi[-limit_obs:]
optimizer.Xi = optimizer.Xi[-limit_obs:]
logger.info("Iteration {0} Starts ...".format(count))
t1 = time.time()
res = optimizer.run(func=black_box_function, n_iter=1)
t2 = time.time()
logger.info("Iteration {0} Ends, Took {3} Seconds. Best Params: {1} and Score: {2}.".format(
count, res.x, np.round(res.fun), np.round(t2-t1, 2)))
if optimizer.yi[-1] == -1:
logger.info("Optimizer Exits ...")
break
def base_optimizer(configurations, black_box_function, logger, verbose=True):
limit_obs, count = 25, 0
max_thread = configurations["thread_limit"]
iterations = configurations["bayes"]["num_of_exp"]
mp_opt = configurations["mp_opt"]
if mp_opt:
search_space = [
Integer(1, max_thread), # Concurrency
Integer(1, 10), # Parallesism
Integer(1, 10), # Pipeline
Integer(5, 20), # Chunk/Block Size in KB: power of 2
]
else:
search_space = [
Integer(1, max_thread), # Concurrency
]
params = []
optimizer = Optimizer(
dimensions=search_space,
base_estimator="GP", #[GP, RF, ET, GBRT],
acq_func="gp_hedge", # [LCB, EI, PI, gp_hedge]
acq_optimizer="auto", #[sampling, lbfgs, auto]
n_random_starts=configurations["bayes"]["initial_run"],
model_queue_size=limit_obs,
# acq_func_kwargs= {},
# acq_optimizer_kwargs={}
)
while True:
count += 1
if len(optimizer.yi) > limit_obs:
optimizer.yi = optimizer.yi[-limit_obs:]
optimizer.Xi = optimizer.Xi[-limit_obs:]
if verbose:
logger.info("Iteration {0} Starts ...".format(count))
t1 = time.time()
res = optimizer.run(func=black_box_function, n_iter=1)
t2 = time.time()
if verbose:
logger.info(
"Iteration {0} Ends, Took {3} Seconds. Best Params: {1} and Score: {2}."
.format(count, res.x, res.fun, np.round(t2 - t1, 2)))
last_value = optimizer.yi[-1]
if last_value == 10**10:
logger.info("Optimizer Exits ...")
break
cc = optimizer.Xi[-1][0]
if iterations < 1:
reset = False
if (last_value > 0) and (cc < max_thread):
max_thread = max(cc, 2)
reset = True
if (last_value < 0) and (cc == max_thread) and (
cc < configurations["thread_limit"]):
max_thread = min(cc + 5, configurations["thread_limit"])
reset = True
if reset:
search_space[0] = Integer(1, max_thread)
optimizer = Optimizer(
dimensions=search_space,
n_initial_points=configurations["bayes"]["initial_run"],
acq_optimizer="lbfgs",
model_queue_size=limit_obs)
if iterations == count:
logger.info("Best parameters: {0} and score: {1}".format(
res.x, res.fun))
params = res.x
break
return params
