ax_exampleΒΆ

"""This test checks that AxSearch is functional.

It also checks that it is usable with a separate scheduler.
"""
import numpy as np
import time

import ray
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()

    ray.init()

    tune_kwargs = {
        "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
        }
    }
    algo = AxSearch(
        max_concurrent=4,
        metric="hartmann6",
        mode="min",
        parameter_constraints=["x1 + x2 <= 2.0"],  # Optional.
        outcome_constraints=["l2norm <= 1.25"],  # Optional.
    )
    scheduler = AsyncHyperBandScheduler(metric="hartmann6", mode="min")
    tune.run(
        easy_objective,
        name="ax",
        search_alg=algo,
        scheduler=scheduler,
        **tune_kwargs)