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]