wehub-resource-sync
108 lines
2.9 KiB
Python
108 lines
2.9 KiB
Python
"""This example demonstrates the usage of conditional search spaces with Tune.
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It also checks that it is usable with a separate scheduler.
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Requires the HyperOpt library to be installed (`pip install hyperopt`).
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For an example of using a Tune search space, see
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:doc:`/tune/examples/hyperopt_example`.
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"""
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import time
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from hyperopt import hp
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import ray
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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.search import ConcurrencyLimiter
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from ray.tune.search.hyperopt import HyperOptSearch
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def f_unpack_dict(dct: dict) -> dict:
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"""Unpacks all sub-dictionaries in given dictionary recursively.
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There should be no duplicated keys across all nested
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subdictionaries, or some instances will be lost without warning
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Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt
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Args:
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dct: dictionary to unpack
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Returns:
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dict: unpacked dictionary
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"""
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res = {}
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for k, v in dct.items():
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if isinstance(v, dict):
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res = {**res, **f_unpack_dict(v)}
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else:
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res[k] = v
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return res
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def evaluation_fn(step, width, height, mult=1):
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return (0.1 + width * step / 100) ** (-1) + height * 0.1 * mult
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def easy_objective(config_in):
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# Hyperparameters
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config = f_unpack_dict(config_in)
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width, height, mult = config["width"], config["height"], config.get("mult", 1)
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print(config)
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for step in range(config["steps"]):
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# Iterative training function - can be any arbitrary training procedure
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intermediate_score = evaluation_fn(step, width, height, mult)
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# Feed the score back back to Tune.
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tune.report({"iterations": step, "mean_loss": intermediate_score})
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time.sleep(0.1)
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config_space = {
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"activation": hp.choice(
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"activation",
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[
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{"activation": "relu", "mult": hp.uniform("mult", 1, 2)},
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{"activation": "tanh"},
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],
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),
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"width": hp.uniform("width", 0, 20),
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"height": hp.uniform("heright", -100, 100),
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"steps": 100,
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}
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def run_hyperopt_tune(config_dict=config_space, smoke_test=False):
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algo = HyperOptSearch(space=config_dict, metric="mean_loss", mode="min")
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algo = ConcurrencyLimiter(algo, max_concurrent=4)
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scheduler = AsyncHyperBandScheduler()
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tuner = tune.Tuner(
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easy_objective,
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tune_config=tune.TuneConfig(
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metric="mean_loss",
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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 smoke_test else 100,
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),
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)
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results = tuner.fit()
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print("Best hyperparameters found were: ", results.get_best_result().config)
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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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)
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args, _ = parser.parse_known_args()
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ray.init(configure_logging=False)
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run_hyperopt_tune(smoke_test=args.smoke_test)
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