def set_basic_conf(self):
from optuna.samplers import TPESampler
space = [
ot_param.suggest_uniform("width", 0, 20),
ot_param.suggest_uniform("height", -100, 100)
]
def cost(space, reporter):
reporter(loss=(space["height"] - 14)**2 - abs(space["width"] - 3))
search_alg = OptunaSearch(
space, sampler=TPESampler(seed=10), metric="loss", mode="min")
return search_alg, cost
def _get_optuna_params(self):
from ray.tune.suggest.optuna import param
config_space = []
for param_name, space in self.param_distributions.items():
prior = "uniform"
param_name = str(param_name)
if isinstance(space,
tuple) and len(space) >= 2 and len(space) <= 3:
try:
low = float(space[0])
high = float(space[1])
except Exception:
raise ValueError(
"low and high need to be of type float, "
f"are of type {type(low)} and {type(high)}") from None
if len(space) == 3:
prior = space[2]
if prior not in ["uniform", "log-uniform"]:
raise ValueError(
"prior needs to be either "
f"'uniform' or 'log-uniform', was {prior}")
if prior == "log-uniform":
config_space.append(
param.suggest_loguniform(param_name, low, high))
else:
config_space.append(
param.suggest_uniform(param_name, low, high))
elif isinstance(space, list):
config_space.append(
param.suggest_categorical(param_name, space))
else:
config_space.append(space)
return config_space
# Feed the score back back to Tune.
tune.report(iterations=step, mean_loss=intermediate_score)
time.sleep(0.1)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init(configure_logging=False)
space = [
param.suggest_uniform("width", 0, 20),
param.suggest_uniform("height", -100, 100),
# This is an ignored parameter.
param.suggest_categorical("activation", ["relu", "tanh"])
]
config = {
"num_samples": 10 if args.smoke_test else 100,
"config": {
"steps": 100,
}
}
algo = OptunaSearch(space, metric="mean_loss", mode="min")
scheduler = AsyncHyperBandScheduler(metric="mean_loss", mode="min")
tune.run(easy_objective, search_alg=algo, scheduler=scheduler, **config)
