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]