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@richardliaw @hershg @ecederstrand @couturierc @adizim
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"""This test checks that AxSearch 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 ray
from ray.tune import run
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, reporter):
import time
time.sleep(0.2)
for i in range(config["iterations"]):
x = np.array([config.get("x{}".format(i + 1)) for i in range(6)])
reporter(
timesteps_total=i,
hartmann6=hartmann6(x),
l2norm=np.sqrt((x**2).sum()))
time.sleep(0.02)
if __name__ == "__main__":
import argparse
from ax.service.ax_client import AxClient
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init()
config = {
"num_samples": 10 if args.smoke_test else 50,
"config": {
"iterations": 100,
},
"stop": {
"timesteps_total": 100
}
}
parameters = [
{
"name": "x1",
"type": "range",
"bounds": [0.0, 1.0],
"value_type": "float", # Optional, defaults to "bounds".
"log_scale": False, # Optional, defaults to False.
},
{
"name": "x2",
"type": "range",
"bounds": [0.0, 1.0],
},
{
"name": "x3",
"type": "range",
"bounds": [0.0, 1.0],
},
{
"name": "x4",
"type": "range",
"bounds": [0.0, 1.0],
},
{
"name": "x5",
"type": "range",
"bounds": [0.0, 1.0],
},
{
"name": "x6",
"type": "range",
"bounds": [0.0, 1.0],
},
]
client = AxClient(enforce_sequential_optimization=False)
client.create_experiment(
parameters=parameters,
objective_name="hartmann6",
minimize=True, # Optional, defaults to False.
parameter_constraints=["x1 + x2 <= 2.0"], # Optional.
outcome_constraints=["l2norm <= 1.25"], # Optional.
)
algo = AxSearch(client, max_concurrent=4)
scheduler = AsyncHyperBandScheduler(metric="hartmann6", mode="max")
run(easy_objective,
name="ax",
search_alg=algo,
scheduler=scheduler,
**config)
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