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[tune] Add OptunaSearcher wrapper around Optuna samplers (#10044)

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Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
Co-authored-by: Kai Fricke <kai@anyscale.com>
This commit is contained in:
krfricke
2020-08-13 01:13:22 +02:00
committed by GitHub
parent 7a8b922841
commit 16486a8df3
7 changed files with 249 additions and 1 deletions
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python/ray/tune/tests/test_tune_restore.py
+18 -1
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@@ -19,6 +19,7 @@ from ray.tune.suggest.dragonfly import DragonflySearch
from ray.tune.suggest.bayesopt import BayesOptSearch
from ray.tune.suggest.skopt import SkOptSearch
from ray.tune.suggest.nevergrad import NevergradSearch
from ray.tune.suggest.optuna import OptunaSearch, param as ot_param
from ray.tune.suggest.sigopt import SigOptSearch
from ray.tune.suggest.zoopt import ZOOptSearch
from ray.tune.utils import validate_save_restore
@@ -292,7 +293,23 @@ class NevergradWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
return search_alg, cost
class DragonflyWarmSTartTest(AbstractWarmStartTest, unittest.TestCase):
class OptunaWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
def set_basic_conf(self):
from optuna.samplers import TPESampler
space = [
ot_param.suggest_uniform("width", 0, 20),
ot_param.suggest_uniform("height", -100, 100)
]
def cost(space, reporter):
reporter(loss=(space["height"] - 14)**2 - abs(space["width"] - 3))
search_alg = OptunaSearch(
space, sampler=TPESampler(seed=10), metric="loss", mode="min")
return search_alg, cost
class DragonflyWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
def set_basic_conf(self):
from dragonfly.opt.gp_bandit import EuclideanGPBandit
from dragonfly.exd.experiment_caller import EuclideanFunctionCaller