"""This test checks that Dragonfly is functional.
It also checks that it is usable with a separate scheduler.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import time
import ray
from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.suggest.dragonfly import DragonflySearch
def objective(config):
for i in range(config["iterations"]):
vol1 = config["point"][0] # LiNO3
vol2 = config["point"][1] # Li2SO4
vol3 = config["point"][2] # NaClO4
vol4 = 10 - (vol1 + vol2 + vol3) # Water
# Synthetic functions
conductivity = vol1 + 0.1 * (vol2 + vol3)**2 + 2.3 * vol4 * (vol1**1.5)
# Add Gaussian noise to simulate experimental noise
conductivity += np.random.normal() * 0.01
tune.report(timesteps_total=i, objective=conductivity)
time.sleep(0.02)
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 50,
"config": {
"iterations": 100,
"LiNO3_vol": tune.uniform(0, 7),
"Li2SO4_vol": tune.uniform(0, 7),
"NaClO4_vol": tune.uniform(0, 7)
},
}
# Optional: Pass the parameter space yourself
# space = [{
# "name": "LiNO3_vol",
# "type": "float",
# "min": 0,
# "max": 7
# }, {
# "name": "Li2SO4_vol",
# "type": "float",
# "min": 0,
# "max": 7
# }, {
# "name": "NaClO4_vol",
# "type": "float",
# "min": 0,
# "max": 7
# }]
df_search = DragonflySearch(
optimizer="bandit",
domain="euclidean",
# space=space, # If you want to set the space manually
metric="objective",
mode="max")
scheduler = AsyncHyperBandScheduler(metric="objective", mode="max")
tune.run(
objective,
name="dragonfly_search",
search_alg=df_search,
scheduler=scheduler,
**tune_kwargs)