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https://github.com/wassname/ray.git
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Richard Liaw
80 lines
2.3 KiB
Python
80 lines
2.3 KiB
Python
"""This example demonstrates the usage of AxSearch with Ray Tune.
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It also checks that it is usable with a separate scheduler.
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"""
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import numpy as np
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import time
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from ray import tune
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from ray.tune.schedulers import AsyncHyperBandScheduler
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from ray.tune.suggest.ax import AxSearch
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def hartmann6(x):
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alpha = np.array([1.0, 1.2, 3.0, 3.2])
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A = np.array([
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[10, 3, 17, 3.5, 1.7, 8],
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[0.05, 10, 17, 0.1, 8, 14],
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[3, 3.5, 1.7, 10, 17, 8],
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[17, 8, 0.05, 10, 0.1, 14],
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])
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P = 10**(-4) * np.array([
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[1312, 1696, 5569, 124, 8283, 5886],
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[2329, 4135, 8307, 3736, 1004, 9991],
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[2348, 1451, 3522, 2883, 3047, 6650],
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[4047, 8828, 8732, 5743, 1091, 381],
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])
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y = 0.0
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for j, alpha_j in enumerate(alpha):
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t = 0
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for k in range(6):
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t += A[j, k] * ((x[k] - P[j, k])**2)
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y -= alpha_j * np.exp(-t)
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return y
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def easy_objective(config):
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for i in range(config["iterations"]):
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x = np.array([config.get("x{}".format(i + 1)) for i in range(6)])
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tune.report(
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timesteps_total=i,
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hartmann6=hartmann6(x),
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l2norm=np.sqrt((x**2).sum()))
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time.sleep(0.02)
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--smoke-test", action="store_true", help="Finish quickly for testing")
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args, _ = parser.parse_known_args()
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algo = AxSearch(
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max_concurrent=4,
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parameter_constraints=["x1 + x2 <= 2.0"], # Optional.
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outcome_constraints=["l2norm <= 1.25"], # Optional.
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)
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scheduler = AsyncHyperBandScheduler()
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analysis = tune.run(
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easy_objective,
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name="ax",
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metric="hartmann6", # provided in the 'easy_objective' function
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mode="min",
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search_alg=algo,
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scheduler=scheduler,
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num_samples=10 if args.smoke_test else 50,
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config={
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"iterations": 100,
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"x1": tune.uniform(0.0, 1.0),
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"x2": tune.uniform(0.0, 1.0),
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"x3": tune.uniform(0.0, 1.0),
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"x4": tune.uniform(0.0, 1.0),
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"x5": tune.uniform(0.0, 1.0),
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"x6": tune.uniform(0.0, 1.0),
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},
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stop={"timesteps_total": 100})
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print("Best hyperparameters found were: ", analysis.best_config)
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