"""This example demonstrates the usage of HEBO with Ray Tune.
It also checks that it is usable with a separate scheduler.
Requires the HEBO library to be installed (`pip install 'HEBO>=0.2.0'`).
"""
import time
from ray import air, tune
from ray.air import session
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.search.hebo import HEBOSearch
def evaluation_fn(step, width, height):
time.sleep(0.1)
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.
session.report({"iterations": step, "mean_loss": intermediate_score})
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()
# The config will be automatically converted to HEBO's DesignSpace
# Optional: Pass the parameter space yourself
#
# from hebo.design_space.design_space import DesignSpace
# space_cfg = [
# {
# "name": "width",
# "type": "num",
# "lb": 0,
# "ub": 20
# },
# {
# "name": "height",
# "type": "num",
# "lb": -100,
# "ub": 100
# },
# {
# "name": "activation",
# "type": "cat",
# "categories": ["relu", "tanh"]
# },
# ]
# space = DesignSpace().parse(space_cfg)
previously_run_params = [
{"width": 10, "height": 0, "activation": "relu"}, # Activation will be relu
{"width": 15, "height": -20, "activation": "tanh"}, # Activation will be tanh
]
known_rewards = [-189, -1144]
# maximum number of concurrent trials
max_concurrent = 8
algo = HEBOSearch(
# space = space, # If you want to set the space
points_to_evaluate=previously_run_params,
evaluated_rewards=known_rewards,
random_state_seed=123, # for reproducibility
max_concurrent=max_concurrent,
)
scheduler = AsyncHyperBandScheduler()
tuner = tune.Tuner(
easy_objective,
tune_config=tune.TuneConfig(
metric="mean_loss",
mode="min",
search_alg=algo,
scheduler=scheduler,
num_samples=10 if args.smoke_test else 50,
),
run_config=air.RunConfig(
name="hebo_exp_with_warmstart",
),
param_space={
"steps": 100,
"width": tune.uniform(0, 20),
"height": tune.uniform(-100, 100),
"activation": tune.choice(["relu", "tanh"]),
},
)
results = tuner.fit()
print("Best hyperparameters found were: ", results.get_best_result().config)