def testConvertOptuna(self):
from ray.tune.suggest.optuna import OptunaSearch, param
from optuna.samplers import RandomSampler
# Grid search not supported, should raise ValueError
with self.assertRaises(ValueError):
OptunaSearch.convert_search_space({
"grid": tune.grid_search([0, 1])
})
config = {
"a": tune.sample.Categorical([2, 3, 4]).uniform(),
"b": {
"x": tune.sample.Integer(0, 5).quantized(2),
"y": 4,
"z": tune.sample.Float(1e-4, 1e-2).loguniform()
}
}
converted_config = OptunaSearch.convert_search_space(config)
optuna_config = [
param.suggest_categorical("a", [2, 3, 4]),
param.suggest_int("b/x", 0, 5, 2),
param.suggest_loguniform("b/z", 1e-4, 1e-2)
]
sampler1 = RandomSampler(seed=1234)
searcher1 = OptunaSearch(
space=converted_config, sampler=sampler1, metric="a", mode="max")
sampler2 = RandomSampler(seed=1234)
searcher2 = OptunaSearch(
space=optuna_config, sampler=sampler2, metric="a", mode="max")
config1 = searcher1.suggest("0")
config2 = searcher2.suggest("0")
self.assertEqual(config1, config2)
self.assertIn(config1["a"], [2, 3, 4])
self.assertIn(config1["b"]["x"], list(range(5)))
self.assertLess(1e-4, config1["b"]["z"])
self.assertLess(config1["b"]["z"], 1e-2)
searcher = OptunaSearch(metric="a", mode="max")
analysis = tune.run(
_mock_objective, config=config, search_alg=searcher, num_samples=1)
trial = analysis.trials[0]
assert trial.config["a"] in [2, 3, 4]
mixed_config = {
"a": tune.uniform(5, 6),
"b": tune.uniform(8, 9) # Cannot mix List and Dict
}
searcher = OptunaSearch(space=mixed_config, metric="a", mode="max")
config = searcher.suggest("0")
self.assertTrue(5 <= config["a"] <= 6)
self.assertTrue(8 <= config["b"] <= 9)
def testOptunaReportTooOften(self):
from ray.tune.suggest.optuna import OptunaSearch
from optuna.samplers import RandomSampler
searcher = OptunaSearch(
sampler=RandomSampler(seed=1234),
space=OptunaSearch.convert_search_space(self.config),
metric="metric",
mode="max",
)
searcher.suggest("trial_1")
searcher.on_trial_result("trial_1", {
"training_iteration": 1,
"metric": 1
})
searcher.on_trial_complete("trial_1", {
"training_iteration": 2,
"metric": 1
})
# Report after complete should not fail
searcher.on_trial_result("trial_1", {
"training_iteration": 3,
"metric": 1
})
searcher.on_trial_complete("trial_1", {
"training_iteration": 4,
"metric": 1
})
def set_basic_conf(self):
from optuna.samplers import TPESampler
space = OptunaSearch.convert_search_space({
"width": tune.uniform(0, 20),
"height": tune.uniform(-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
def testConvertOptuna(self):
from ray.tune.suggest.optuna import OptunaSearch, param
from optuna.samplers import RandomSampler
config = {
"a": tune.sample.Categorical([2, 3, 4]).uniform(),
"b": {
"x": tune.sample.Integer(0, 5).quantized(2),
"y": 4,
"z": tune.sample.Float(1e-4, 1e-2).loguniform()
}
}
converted_config = OptunaSearch.convert_search_space(config)
optuna_config = [
param.suggest_categorical("a", [2, 3, 4]),
param.suggest_int("b/x", 0, 5, 2),
param.suggest_loguniform("b/z", 1e-4, 1e-2)
]
sampler1 = RandomSampler(seed=1234)
searcher1 = OptunaSearch(space=converted_config,
sampler=sampler1,
base_config=config)
sampler2 = RandomSampler(seed=1234)
searcher2 = OptunaSearch(space=optuna_config,
sampler=sampler2,
base_config=config)
config1 = searcher1.suggest("0")
config2 = searcher2.suggest("0")
self.assertEqual(config1, config2)
self.assertIn(config1["a"], [2, 3, 4])
self.assertIn(config1["b"]["x"], list(range(5)))
self.assertEqual(config1["b"]["y"], 4)
self.assertLess(1e-4, config1["b"]["z"])
self.assertLess(config1["b"]["z"], 1e-2)
searcher = OptunaSearch(metric="a", mode="max")
analysis = tune.run(_mock_objective,
config=config,
search_alg=searcher,
num_samples=1)
trial = analysis.trials[0]
assert trial.config["a"] in [2, 3, 4]