def testConvertHyperOpt(self):
from ray.tune.suggest.hyperopt import HyperOptSearch
from hyperopt import hp
# Grid search not supported, should raise ValueError
with self.assertRaises(ValueError):
HyperOptSearch.convert_search_space({
"grid": tune.grid_search([0, 1])
})
config = {
"a": tune.sample.Categorical([2, 3, 4]).uniform(),
"b": {
"x": tune.sample.Integer(-15, -10),
"y": 4,
"z": tune.sample.Float(1e-4, 1e-2).loguniform()
}
}
converted_config = HyperOptSearch.convert_search_space(config)
hyperopt_config = {
"a": hp.choice("a", [2, 3, 4]),
"b": {
"x": hp.uniformint("x", -15, -10),
"y": 4,
"z": hp.loguniform("z", np.log(1e-4), np.log(1e-2))
}
}
searcher1 = HyperOptSearch(
space=converted_config,
random_state_seed=1234,
metric="a",
mode="max")
searcher2 = HyperOptSearch(
space=hyperopt_config,
random_state_seed=1234,
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(-15, -10)))
self.assertEqual(config1["b"]["y"], 4)
self.assertLess(1e-4, config1["b"]["z"])
self.assertLess(config1["b"]["z"], 1e-2)
searcher = HyperOptSearch(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": hp.uniform("b", 8, 9)}
searcher = HyperOptSearch(space=mixed_config, metric="a", mode="max")
config = searcher.suggest("0")
self.assertTrue(5 <= config["a"] <= 6)
self.assertTrue(8 <= config["b"] <= 9)
def testConvertHyperOpt(self):
from ray.tune.suggest.hyperopt import HyperOptSearch
from hyperopt import hp
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 = HyperOptSearch.convert_search_space(config)
hyperopt_config = {
"a": hp.choice("a", [2, 3, 4]),
"b": {
"x": hp.randint("x", 5),
"y": 4,
"z": hp.loguniform("z", np.log(1e-4), np.log(1e-2))
}
}
searcher1 = HyperOptSearch(space=converted_config,
random_state_seed=1234)
searcher2 = HyperOptSearch(space=hyperopt_config,
random_state_seed=1234)
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 = HyperOptSearch(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]
