"""This test checks that Skopt is functional.
It also checks that it is usable with a separate scheduler.
"""
import time
import ray
from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.suggest.skopt import SkOptSearch
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()
# The config will be automatically converted to SkOpt's search space
tune_kwargs = {
"num_samples": 10 if args.smoke_test else 50,
"config": {
"steps": 100,
"width": tune.uniform(0, 20),
"height": tune.uniform(-100, 100),
"activation": tune.choice(["relu", "tanh"])
}
}
# Optional: Pass the parameter space yourself
# space = {
# "width": (0, 20),
# "height": (-100, 100),
# "activation": ["relu", "tanh"]
# }
previously_run_params = [[10, 0, "relu"], [15, -20, "tanh"]]
known_rewards = [-189, -1144]
algo = SkOptSearch(
# parameter_names=space.keys(), # If you want to set the space
# parameter_ranges=space.values(), # If you want to set the space
metric="mean_loss",
mode="min",
points_to_evaluate=previously_run_params,
evaluated_rewards=known_rewards)
scheduler = AsyncHyperBandScheduler(metric="mean_loss", mode="min")
tune.run(
easy_objective,
name="skopt_exp_with_warmstart",
search_alg=algo,
scheduler=scheduler,
**tune_kwargs)