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Richard Liaw
68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
"""This test checks that HyperOpt 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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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.hyperopt import HyperOptSearch
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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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assert type(config["activation"]) == str, \
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"Config is incorrect: {}".format(type(config["activation"]))
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for i in range(config["iterations"]):
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reporter(
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timesteps_total=i,
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mean_loss=(config["height"] - 14)**2 - abs(config["width"] - 3))
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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 hyperopt import hp
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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(configure_logging=False)
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space = {
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"width": hp.uniform("width", 0, 20),
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"height": hp.uniform("height", -100, 100),
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"activation": hp.choice("activation", ["relu", "tanh"])
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}
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current_best_params = [
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{
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"width": 1,
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"height": 2,
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"activation": 0 # Activation will be relu
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},
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{
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"width": 4,
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"height": 2,
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"activation": 1 # Activation will be tanh
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}
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]
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config = {
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"num_samples": 10 if args.smoke_test else 1000,
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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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algo = HyperOptSearch(
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space,
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metric="mean_loss",
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mode="min",
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points_to_evaluate=current_best_params)
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scheduler = AsyncHyperBandScheduler(metric="mean_loss", mode="min")
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run(easy_objective, search_alg=algo, scheduler=scheduler, **config)
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