"""This test checks that SigOpt 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.sigopt import SigOptSearch
def evaluate(step, width, height):
return (0.1 + width * step / 100)**(-1) + height * 0.01
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 = evaluate(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
import os
assert "SIGOPT_KEY" in os.environ, \
"SigOpt API key must be stored as environment variable at SIGOPT_KEY"
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init()
space = [
{
"name": "width",
"type": "int",
"bounds": {
"min": 0,
"max": 20
},
},
{
"name": "height",
"type": "int",
"bounds": {
"min": -100,
"max": 100
},
},
]
config = {
"num_samples": 10 if args.smoke_test else 1000,
"config": {
"steps": 10
}
}
algo = SigOptSearch(
space,
name="SigOpt Example Experiment",
max_concurrent=1,
metric="mean_loss",
mode="min")
scheduler = AsyncHyperBandScheduler(metric="mean_loss", mode="min")
tune.run(
easy_objective,
name="my_exp",
search_alg=algo,
scheduler=scheduler,
**config)