import threading from typing import Generator from wsgiref.simple_server import make_server import optuna from optuna_dashboard import wsgi from playwright.sync_api import Browser from playwright.sync_api import sync_playwright import pytest @pytest.fixture(scope="session") def port() -> int: return 8081 @pytest.fixture(scope="session") def dummy_storage() -> optuna.storages.InMemoryStorage: storage = optuna.storages.InMemoryStorage() sampler = optuna.samplers.RandomSampler(seed=0) # Single-objective study study = optuna.create_study(study_name="single", storage=storage, sampler=sampler) def objective_single(trial: optuna.Trial) -> float: x1 = trial.suggest_float("x1", 0, 10) x2 = trial.suggest_float("x2", 0, 10) return (x1 - 2) ** 2 + (x2 - 5) ** 2 study.optimize(objective_single, n_trials=50) # A single objective study with a single trial # Refs: https://github.com/optuna/optuna-dashboard/issues/401 study = optuna.create_study(study_name="single-trial", storage=storage, sampler=sampler) study.optimize(objective_single, n_trials=1) # Single-objective study with 1 parameter study = optuna.create_study( study_name="single-1-param", storage=storage, direction="maximize", sampler=sampler ) def objective_single_with_1param(trial: optuna.Trial) -> float: x1 = trial.suggest_float("x1", 0, 10) return -((x1 - 2) ** 2) study.optimize(objective_single_with_1param, n_trials=50) # Single-objective study with dynamic search space study = optuna.create_study( study_name="single-dynamic", storage=storage, direction="maximize", sampler=sampler ) def objective_single_dynamic(trial: optuna.Trial) -> float: category = trial.suggest_categorical("category", ["foo", "bar"]) if category == "foo": return (trial.suggest_float("x1", 0, 10) - 2) ** 2 else: return -((trial.suggest_float("x2", -10, 0) + 5) ** 2) study.optimize(objective_single_dynamic, n_trials=50) # Single objective study with 'inf', '-inf', or 'nan' value study = optuna.create_study(study_name="single-inf", storage=storage, sampler=sampler) def objective_single_inf(trial: optuna.Trial) -> float: x = trial.suggest_float("x", -10, 10) if trial.number % 3 == 0: return float("inf") elif trial.number % 3 == 1: return float("-inf") else: return x**2 study.optimize(objective_single_inf, n_trials=50) # Multi-objective study study = optuna.create_study( study_name="multi-objective", storage=storage, directions=["minimize", "minimize"], sampler=sampler, ) def objective_multi(trial: optuna.Trial) -> tuple[float, float]: x = trial.suggest_float("x", 0, 5) y = trial.suggest_float("y", 0, 3) v0 = 4 * x**2 + 4 * y**2 v1 = (x - 5) ** 2 + (y - 5) ** 2 return v0, v1 study.optimize(objective_multi, n_trials=50) # Multi-objective study with dynamic search space study = optuna.create_study( study_name="multi-dynamic", storage=storage, directions=["minimize", "minimize"], sampler=sampler, ) def objective_multi_dynamic(trial: optuna.Trial) -> tuple[float, float]: category = trial.suggest_categorical("category", ["foo", "bar"]) if category == "foo": x = trial.suggest_float("x1", 0, 5) y = trial.suggest_float("y1", 0, 3) v0 = 4 * x**2 + 4 * y**2 v1 = (x - 5) ** 2 + (y - 5) ** 2 return v0, v1 else: x = trial.suggest_float("x2", 0, 5) y = trial.suggest_float("y2", 0, 3) v0 = 2 * x**2 + 2 * y**2 v1 = (x - 2) ** 2 + (y - 3) ** 2 return v0, v1 study.optimize(objective_multi_dynamic, n_trials=50) # Pruning with no intermediate values study = optuna.create_study( study_name="single-pruned-without-report", storage=storage, sampler=sampler ) def objective_prune_without_report(trial: optuna.Trial) -> float: x = trial.suggest_float("x", -15, 30) y = trial.suggest_float("y", -15, 30) v = x**2 + y**2 if v > 100: raise optuna.TrialPruned() return v study.optimize(objective_prune_without_report, n_trials=100) # Single objective pruned after reported 'inf', '-inf', or 'nan' study = optuna.create_study(study_name="single-inf-report", storage=storage, sampler=sampler) def objective_single_inf_report(trial: optuna.Trial) -> float: x = trial.suggest_float("x", -10, 10) if trial.number % 3 == 0: trial.report(float("inf"), 1) elif trial.number % 3 == 1: trial.report(float("-inf"), 1) else: trial.report(float("nan"), 1) if x > 0: raise optuna.TrialPruned() else: return x**2 study.optimize(objective_single_inf_report, n_trials=50) # Issue 410 study = optuna.create_study(study_name="Issue 410", storage=storage, sampler=sampler) def objective_issue_410(trial: optuna.Trial) -> float: trial.suggest_categorical("resample_rate", ["50ms"]) trial.suggest_categorical("channels", ["all"]) trial.suggest_categorical("window_size", [256]) if trial.number > 15: raise Exception("Unexpected error") trial.suggest_categorical("cbow", [True]) trial.suggest_categorical("model", ["m1"]) trial.set_user_attr("epochs", 0) trial.set_user_attr("deterministic", True) if trial.number > 10: raise Exception("unexpeccted error") trial.set_user_attr("folder", "/path/to/folder") trial.set_user_attr("resample_type", "foo") trial.set_user_attr("run_id", "0001") return 1.0 study.optimize(objective_issue_410, n_trials=20, catch=(Exception,)) # No trials single-objective study optuna.create_study(study_name="single-no-trials", storage=storage, sampler=sampler) # No trials multi-objective study optuna.create_study( study_name="multi-no-trials", storage=storage, directions=["minimize", "maximize"], sampler=sampler, ) return storage @pytest.fixture(scope="session") def server( request: pytest.FixtureRequest, dummy_storage: optuna.storages.InMemoryStorage, port: int ) -> None: app = wsgi(dummy_storage) httpd = make_server("127.0.0.1", port, app) thread = threading.Thread(target=httpd.serve_forever) thread.start() def stop_server() -> None: httpd.shutdown() httpd.server_close() thread.join() request.addfinalizer(stop_server) @pytest.fixture(scope="module") def browser() -> Generator[Browser, None, None]: with sync_playwright() as playwright: browser = playwright.chromium.launch() yield browser browser.close()