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https://github.com/wassname/ray.git
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Richard Liaw
87557a00fa
* start refactoring of search algorithms * format * needs tests * fix * suggestions * Fix PBT * lint * refactoring * hyperopt_working * dragonfly * hyperopt * change_half_of_algs * save * code-removed * remove_lots_of_unneccessary * changes * formatting * suggest * reset * rm * tests * search-change * exception * refactor-doc * search * py * moredocs * Update doc/source/tune-searchalg.rst * concurrency * max * tune * betterwarning * bohb * tests * test-change Co-authored-by: ujvl <misraujval@gmail.com>
83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
"""This test checks that Dragonfly 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 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.dragonfly import DragonflySearch
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def objective(config, reporter):
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import numpy as np
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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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vol1 = config["point"][0] # LiNO3
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vol2 = config["point"][1] # Li2SO4
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vol3 = config["point"][2] # NaClO4
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vol4 = 10 - (vol1 + vol2 + vol3) # Water
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# Synthetic functions
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conductivity = vol1 + 0.1 * (vol2 + vol3)**2 + 2.3 * vol4 * (vol1**1.5)
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# Add Gaussian noise to simulate experimental noise
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conductivity += np.random.normal() * 0.01
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reporter(timesteps_total=i, objective=conductivity)
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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 dragonfly.opt.gp_bandit import EuclideanGPBandit
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from dragonfly.exd.experiment_caller import EuclideanFunctionCaller
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from dragonfly import load_config
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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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domain_vars = [{
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"name": "LiNO3_vol",
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"type": "float",
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"min": 0,
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"max": 7
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}, {
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"name": "Li2SO4_vol",
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"type": "float",
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"min": 0,
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"max": 7
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}, {
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"name": "NaClO4_vol",
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"type": "float",
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"min": 0,
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"max": 7
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}]
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domain_config = load_config({"domain": domain_vars})
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func_caller = EuclideanFunctionCaller(
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None, domain_config.domain.list_of_domains[0])
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optimizer = EuclideanGPBandit(func_caller, ask_tell_mode=True)
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algo = DragonflySearch(optimizer, metric="objective", mode="max")
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scheduler = AsyncHyperBandScheduler(metric="objective", mode="max")
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run(objective,
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name="dragonfly_search",
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search_alg=algo,
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scheduler=scheduler,
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**config)
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