"""This example demonstrates the usage of AxSearch with Ray Tune.
It also checks that it is usable with a separate scheduler.
"""
import numpy as np
import time
from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.suggest.ax import AxSearch
def hartmann6(x):
alpha = np.array([1.0, 1.2, 3.0, 3.2])
A = np.array([
[10, 3, 17, 3.5, 1.7, 8],
[0.05, 10, 17, 0.1, 8, 14],
[3, 3.5, 1.7, 10, 17, 8],
[17, 8, 0.05, 10, 0.1, 14],
])
P = 10**(-4) * np.array([
[1312, 1696, 5569, 124, 8283, 5886],
[2329, 4135, 8307, 3736, 1004, 9991],
[2348, 1451, 3522, 2883, 3047, 6650],
[4047, 8828, 8732, 5743, 1091, 381],
])
y = 0.0
for j, alpha_j in enumerate(alpha):
t = 0
for k in range(6):
t += A[j, k] * ((x[k] - P[j, k])**2)
y -= alpha_j * np.exp(-t)
return y
def easy_objective(config):
for i in range(config["iterations"]):
x = np.array([config.get("x{}".format(i + 1)) for i in range(6)])
tune.report(
timesteps_total=i,
hartmann6=hartmann6(x),
l2norm=np.sqrt((x**2).sum()))
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()
algo = AxSearch(
max_concurrent=4,
parameter_constraints=["x1 + x2 <= 2.0"], # Optional.
outcome_constraints=["l2norm <= 1.25"], # Optional.
)
scheduler = AsyncHyperBandScheduler()
analysis = tune.run(
easy_objective,
name="ax",
metric="hartmann6", # provided in the 'easy_objective' function
mode="min",
search_alg=algo,
scheduler=scheduler,
num_samples=10 if args.smoke_test else 50,
config={
"iterations": 100,
"x1": tune.uniform(0.0, 1.0),
"x2": tune.uniform(0.0, 1.0),
"x3": tune.uniform(0.0, 1.0),
"x4": tune.uniform(0.0, 1.0),
"x5": tune.uniform(0.0, 1.0),
"x6": tune.uniform(0.0, 1.0),
},
stop={"timesteps_total": 100})
print("Best hyperparameters found were: ", analysis.best_config)
