"""This example demonstrates the usage of Dragonfly with Ray Tune.
It also checks that it is usable with a separate scheduler.
Requires the Dragonfly library to be installed (`pip install dragonfly-opt`).
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import time
from ray import train, tune
from ray.tune.search import ConcurrencyLimiter
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.search.dragonfly import DragonflySearch
def objective(config):
for i in range(config["iterations"]):
vol1 = config["LiNO3_vol"] # LiNO3
vol2 = config["Li2SO4_vol"] # Li2SO4
vol3 = config["NaClO4_vol"] # 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
train.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()
# 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
)
df_search = ConcurrencyLimiter(df_search, max_concurrent=4)
scheduler = AsyncHyperBandScheduler()
tuner = tune.Tuner(
objective,
tune_config=tune.TuneConfig(
metric="objective",
mode="max",
search_alg=df_search,
scheduler=scheduler,
num_samples=10 if args.smoke_test else 50,
),
run_config=train.RunConfig(
name="dragonfly_search",
),
param_space={
"iterations": 100,
"LiNO3_vol": tune.uniform(0, 7),
"Li2SO4_vol": tune.uniform(0, 7),
"NaClO4_vol": tune.uniform(0, 7),
},
)
results = tuner.fit()
print("Best hyperparameters found were: ", results.get_best_result().config)