mirror of
https://github.com/wassname/ray.git
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Camille Couturier
320cba313f
* Explicitely set scheduler in run() * Better formatting/indentation (after running format.sh) * Remove accidental paste in parameters definitions. * format
121 lines
3.2 KiB
Python
121 lines
3.2 KiB
Python
"""This test checks that AxSearch is functional.
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It also checks that it is usable with a separate scheduler.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import ray
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from ray.tune import run
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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, reporter):
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import time
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time.sleep(0.2)
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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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reporter(
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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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from ax.service.ax_client import AxClient
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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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ray.init()
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config = {
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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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},
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"stop": {
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"timesteps_total": 100
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}
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}
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parameters = [
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{
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"name": "x1",
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"type": "range",
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"bounds": [0.0, 1.0],
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"value_type": "float", # Optional, defaults to "bounds".
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"log_scale": False, # Optional, defaults to False.
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},
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{
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"name": "x2",
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"type": "range",
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"bounds": [0.0, 1.0],
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},
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{
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"name": "x3",
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"type": "range",
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"bounds": [0.0, 1.0],
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},
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{
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"name": "x4",
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"type": "range",
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"bounds": [0.0, 1.0],
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},
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{
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"name": "x5",
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"type": "range",
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"bounds": [0.0, 1.0],
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},
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{
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"name": "x6",
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"type": "range",
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"bounds": [0.0, 1.0],
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},
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]
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client = AxClient(enforce_sequential_optimization=False)
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client.create_experiment(
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parameters=parameters,
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objective_name="hartmann6",
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minimize=True, # Optional, defaults to False.
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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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algo = AxSearch(client, max_concurrent=4)
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scheduler = AsyncHyperBandScheduler(metric="hartmann6", mode="max")
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run(easy_objective,
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name="ax",
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search_alg=algo,
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scheduler=scheduler,
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**config)
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