"""This test checks that ZOOptSearch is functional.
It also checks that it is usable with a separate scheduler.
"""
import time
import ray
from ray import tune
from ray.tune.suggest.zoopt import ZOOptSearch
from ray.tune.schedulers import AsyncHyperBandScheduler
from zoopt import ValueType # noqa: F401
def evaluation_fn(step, width, height):
return (0.1 + width * step / 100)**(-1) + height * 0.1
def easy_objective(config):
# Hyperparameters
width, height = config["width"], config["height"]
for step in range(config["steps"]):
# Iterative training function - can be any arbitrary training procedure
intermediate_score = evaluation_fn(step, width, height)
# 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()
tune_kwargs = {
"num_samples": 10 if args.smoke_test else 1000,
"config": {
"steps": 10,
"height": tune.quniform(-10, 10, 1e-2),
"width": tune.randint(0, 10)
}
}
# Optional: Pass the parameter space yourself
# space = {
# # for continuous dimensions: (continuous, search_range, precision)
# "height": (ValueType.CONTINUOUS, [-10, 10], 1e-2),
# # for discrete dimensions: (discrete, search_range, has_order)
# "width": (ValueType.DISCRETE, [0, 10], True)
# }
zoopt_search = ZOOptSearch(
algo="Asracos", # only support ASRacos currently
budget=tune_kwargs["num_samples"],
# dim_dict=space, # If you want to set the space yourself
metric="mean_loss",
mode="min")
scheduler = AsyncHyperBandScheduler(metric="mean_loss", mode="min")
tune.run(
easy_objective,
search_alg=zoopt_search,
name="zoopt_search",
scheduler=scheduler,
**tune_kwargs)