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[tune] Add algorithms for search space conversion (#10621)
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Kai Fricke
@@ -258,6 +258,113 @@ class SearchSpaceTest(unittest.TestCase):
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trial = analysis.trials[0]
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self.assertLess(trial.config["b"]["z"], 1e-2)
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def testConvertBOHB(self):
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from ray.tune.suggest.bohb import TuneBOHB
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import ConfigSpace
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config = {
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"a": tune.sample.Categorical([2, 3, 4]).uniform(),
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"b": {
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"x": tune.sample.Integer(0, 5).quantized(2),
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"y": 4,
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"z": tune.sample.Float(1e-4, 1e-2).loguniform()
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}
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}
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converted_config = TuneBOHB.convert_search_space(config)
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bohb_config = ConfigSpace.ConfigurationSpace()
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bohb_config.add_hyperparameters([
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ConfigSpace.CategoricalHyperparameter("a", [2, 3, 4]),
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ConfigSpace.UniformIntegerHyperparameter(
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"b/x", lower=0, upper=4, q=2),
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ConfigSpace.UniformFloatHyperparameter(
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"b/z", lower=1e-4, upper=1e-2, log=True)
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])
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converted_config.seed(1234)
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bohb_config.seed(1234)
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searcher1 = TuneBOHB(space=converted_config)
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searcher2 = TuneBOHB(space=bohb_config)
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config1 = searcher1.suggest("0")
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config2 = searcher2.suggest("0")
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self.assertEqual(config1, config2)
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self.assertIn(config1["a"], [2, 3, 4])
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self.assertIn(config1["b"]["x"], list(range(5)))
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self.assertLess(1e-4, config1["b"]["z"])
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self.assertLess(config1["b"]["z"], 1e-2)
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searcher = TuneBOHB(metric="a", mode="max")
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analysis = tune.run(
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_mock_objective, config=config, search_alg=searcher, num_samples=1)
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trial = analysis.trials[0]
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self.assertIn(trial.config["a"], [2, 3, 4])
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self.assertEqual(trial.config["b"]["y"], 4)
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def testConvertDragonfly(self):
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from ray.tune.suggest.dragonfly import DragonflySearch
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config = {
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"a": tune.sample.Categorical([2, 3, 4]).uniform(),
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"b": {
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"x": tune.sample.Integer(0, 5).quantized(2),
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"y": 4,
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"z": tune.sample.Float(1e-4, 1e-2).loguniform()
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}
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}
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with self.assertRaises(ValueError):
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converted_config = DragonflySearch.convert_search_space(config)
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config = {
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"a": 4,
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"b": {
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"z": tune.sample.Float(1e-4, 1e-2).loguniform()
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}
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}
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dragonfly_config = [{
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"name": "b/z",
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"type": "float",
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"min": 1e-4,
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"max": 1e-2
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}]
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converted_config = DragonflySearch.convert_search_space(config)
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np.random.seed(1234)
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searcher1 = DragonflySearch(
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optimizer="bandit",
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domain="euclidean",
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space=converted_config,
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metric="none")
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config1 = searcher1.suggest("0")
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np.random.seed(1234)
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searcher2 = DragonflySearch(
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optimizer="bandit",
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domain="euclidean",
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space=dragonfly_config,
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metric="none")
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config2 = searcher2.suggest("0")
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self.assertEqual(config1, config2)
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self.assertLess(config2["point"], 1e-2)
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searcher = DragonflySearch()
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invalid_config = {"a/b": tune.uniform(4.0, 8.0)}
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with self.assertRaises(ValueError):
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searcher.set_search_properties("none", "max", invalid_config)
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invalid_config = {"a": {"b/c": tune.uniform(4.0, 8.0)}}
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with self.assertRaises(ValueError):
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searcher.set_search_properties("none", "max", invalid_config)
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searcher = DragonflySearch(
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optimizer="bandit", domain="euclidean", metric="a", mode="max")
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analysis = tune.run(
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_mock_objective, config=config, search_alg=searcher, num_samples=1)
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trial = analysis.trials[0]
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self.assertLess(trial.config["point"], 1e-2)
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def testConvertHyperOpt(self):
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from ray.tune.suggest.hyperopt import HyperOptSearch
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from hyperopt import hp
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@@ -301,6 +408,48 @@ class SearchSpaceTest(unittest.TestCase):
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trial = analysis.trials[0]
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assert trial.config["a"] in [2, 3, 4]
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def testConvertNevergrad(self):
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from ray.tune.suggest.nevergrad import NevergradSearch
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import nevergrad as ng
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config = {
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"a": tune.sample.Categorical([2, 3, 4]).uniform(),
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"b": {
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"x": tune.sample.Integer(0, 5).quantized(2),
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"y": 4,
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"z": tune.sample.Float(1e-4, 1e-2).loguniform()
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}
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}
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converted_config = NevergradSearch.convert_search_space(config)
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nevergrad_config = ng.p.Dict(
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a=ng.p.Choice([2, 3, 4]),
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b=ng.p.Dict(
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x=ng.p.Scalar(lower=0, upper=5).set_integer_casting(),
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z=ng.p.Log(lower=1e-4, upper=1e-2)))
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searcher1 = NevergradSearch(
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optimizer=ng.optimizers.OnePlusOne, space=converted_config)
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searcher2 = NevergradSearch(
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optimizer=ng.optimizers.OnePlusOne, space=nevergrad_config)
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np.random.seed(1234)
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config1 = searcher1.suggest("0")
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np.random.seed(1234)
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config2 = searcher2.suggest("0")
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self.assertEqual(config1, config2)
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self.assertIn(config1["a"], [2, 3, 4])
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self.assertIn(config1["b"]["x"], list(range(5)))
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self.assertLess(1e-4, config1["b"]["z"])
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self.assertLess(config1["b"]["z"], 1e-2)
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searcher = NevergradSearch(
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optimizer=ng.optimizers.OnePlusOne, metric="a", mode="max")
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analysis = tune.run(
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_mock_objective, config=config, search_alg=searcher, num_samples=1)
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trial = analysis.trials[0]
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assert trial.config["a"] in [2, 3, 4]
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def testConvertOptuna(self):
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from ray.tune.suggest.optuna import OptunaSearch, param
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from optuna.samplers import RandomSampler
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@@ -341,6 +490,90 @@ class SearchSpaceTest(unittest.TestCase):
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trial = analysis.trials[0]
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assert trial.config["a"] in [2, 3, 4]
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def testConvertSkOpt(self):
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from ray.tune.suggest.skopt import SkOptSearch
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config = {
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"a": tune.sample.Categorical([2, 3, 4]).uniform(),
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"b": {
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"x": tune.sample.Integer(0, 5).quantized(2),
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"y": 4,
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"z": tune.sample.Float(1e-4, 1e-2).loguniform()
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}
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}
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converted_config = SkOptSearch.convert_search_space(config)
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skopt_config = {"a": [2, 3, 4], "b/x": (0, 5), "b/z": (1e-4, 1e-2)}
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searcher1 = SkOptSearch(space=converted_config)
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searcher2 = SkOptSearch(space=skopt_config)
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np.random.seed(1234)
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config1 = searcher1.suggest("0")
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np.random.seed(1234)
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config2 = searcher2.suggest("0")
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self.assertEqual(config1, config2)
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self.assertIn(config1["a"], [2, 3, 4])
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self.assertIn(config1["b"]["x"], list(range(5)))
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self.assertLess(1e-4, config1["b"]["z"])
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self.assertLess(config1["b"]["z"], 1e-2)
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searcher = SkOptSearch(metric="a", mode="max")
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analysis = tune.run(
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_mock_objective, config=config, search_alg=searcher, num_samples=1)
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trial = analysis.trials[0]
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self.assertIn(trial.config["a"], [2, 3, 4])
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self.assertEqual(trial.config["b"]["y"], 4)
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def testConvertZOOpt(self):
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from ray.tune.suggest.zoopt import ZOOptSearch
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from zoopt import ValueType
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config = {
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"a": tune.sample.Categorical([2, 3, 4]).uniform(),
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"b": {
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"x": tune.sample.Integer(0, 5).quantized(2),
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"y": 4,
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"z": tune.sample.Float(1e-4, 1e-2).loguniform()
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}
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}
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# Does not support categorical variables
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with self.assertRaises(ValueError):
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converted_config = ZOOptSearch.convert_search_space(config)
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config = {
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"a": 2,
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"b": {
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"x": tune.sample.Integer(0, 5).uniform(),
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"y": 4,
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"z": tune.sample.Float(-3, 7).uniform().quantized(1e-4)
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}
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}
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converted_config = ZOOptSearch.convert_search_space(config)
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zoopt_config = {
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"b/x": (ValueType.DISCRETE, [0, 5], True),
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"b/z": (ValueType.CONTINUOUS, [-3, 7], 1e-4)
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}
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searcher1 = ZOOptSearch(dim_dict=converted_config, budget=5)
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searcher2 = ZOOptSearch(dim_dict=zoopt_config, budget=5)
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np.random.seed(1234)
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config1 = searcher1.suggest("0")
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np.random.seed(1234)
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config2 = searcher2.suggest("0")
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self.assertEqual(config1, config2)
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self.assertIn(config1["b"]["x"], list(range(5)))
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self.assertLess(-3, config1["b"]["z"])
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self.assertLess(config1["b"]["z"], 7)
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searcher = ZOOptSearch(budget=5, metric="a", mode="max")
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analysis = tune.run(
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_mock_objective, config=config, search_alg=searcher, num_samples=1)
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trial = analysis.trials[0]
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self.assertEqual(trial.config["b"]["y"], 4)
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if __name__ == "__main__":
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import pytest
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