mirror of
https://github.com/wassname/optuna-dashboard.git
synced 2026-09-07 17:10:07 +08:00
258 lines
8.6 KiB
Python
258 lines
8.6 KiB
Python
import socket
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import threading
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from wsgiref.simple_server import make_server
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import optuna
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from optuna_dashboard import wsgi
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import pytest
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study_names = [
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"single",
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"single-trial",
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"single-1-param",
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"single-dynamic",
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"single-inf",
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"multi-objective",
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"multi-dynamic",
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"single-pruned-without-report",
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"single-inf-report",
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"issue-410",
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"single-no-trials",
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"multi-no-trials",
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]
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def make_dummy_storage(study_name: str) -> optuna.storages.InMemoryStorage:
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storage = optuna.storages.InMemoryStorage()
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sampler = optuna.samplers.RandomSampler(seed=0)
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# Sinble objective study
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if study_name == "single":
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study = optuna.create_study(study_name=study_name, storage=storage, sampler=sampler)
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def objective_single(trial: optuna.Trial) -> float:
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x1 = trial.suggest_float("x1", 0, 10)
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x2 = trial.suggest_float("x2", 0, 10)
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return (x1 - 2) ** 2 + (x2 - 5) ** 2
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study.optimize(objective_single, n_trials=50)
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# A single objective study with a single trial
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# Refs: https://github.com/optuna/optuna-dashboard/issues/401
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elif study_name == "single-trial":
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study = optuna.create_study(study_name=study_name, storage=storage, sampler=sampler)
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def objective_single(trial: optuna.Trial) -> float:
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x1 = trial.suggest_float("x1", 0, 10)
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x2 = trial.suggest_float("x2", 0, 10)
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return (x1 - 2) ** 2 + (x2 - 5) ** 2
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study.optimize(objective_single, n_trials=1)
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# Single-objective study with 1 parameter
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elif study_name == "single-1-param":
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study = optuna.create_study(
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study_name=study_name, storage=storage, direction="maximize", sampler=sampler
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)
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def objective_single_with_1param(trial: optuna.Trial) -> float:
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x1 = trial.suggest_float("x1", 0, 10)
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return -((x1 - 2) ** 2)
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study.optimize(objective_single_with_1param, n_trials=50)
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# Single-objective study with dynamic search space
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elif study_name == "single-dynamic":
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study = optuna.create_study(
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study_name=study_name, storage=storage, direction="maximize", sampler=sampler
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)
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def objective_single_dynamic(trial: optuna.Trial) -> float:
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category = trial.suggest_categorical("category", ["foo", "bar"])
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if category == "foo":
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return (trial.suggest_float("x1", 0, 10) - 2) ** 2
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else:
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return -((trial.suggest_float("x2", -10, 0) + 5) ** 2)
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study.optimize(objective_single_dynamic, n_trials=50)
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# Single objective study with 'inf', '-inf', or 'nan' value
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elif study_name == "single-inf":
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study = optuna.create_study(study_name=study_name, storage=storage, sampler=sampler)
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def objective_single_inf(trial: optuna.Trial) -> float:
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x = trial.suggest_float("x", -10, 10)
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if trial.number % 3 == 0:
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return float("inf")
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elif trial.number % 3 == 1:
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return float("-inf")
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else:
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return x**2
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study.optimize(objective_single_inf, n_trials=50)
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# Multi-objective study
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elif study_name == "multi-objective":
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study = optuna.create_study(
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study_name=study_name,
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storage=storage,
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directions=["minimize", "minimize"],
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sampler=sampler,
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)
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def objective_multi(trial: optuna.Trial) -> tuple[float, float]:
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x = trial.suggest_float("x", 0, 5)
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y = trial.suggest_float("y", 0, 3)
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v0 = 4 * x**2 + 4 * y**2
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v1 = (x - 5) ** 2 + (y - 5) ** 2
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return v0, v1
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study.optimize(objective_multi, n_trials=50)
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# Multi-objective study with dynamic search space
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elif study_name == "multi-dynamic":
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study = optuna.create_study(
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study_name=study_name,
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storage=storage,
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directions=["minimize", "minimize"],
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sampler=sampler,
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)
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def objective_multi_dynamic(trial: optuna.Trial) -> tuple[float, float]:
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category = trial.suggest_categorical("category", ["foo", "bar"])
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if category == "foo":
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x = trial.suggest_float("x1", 0, 5)
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y = trial.suggest_float("y1", 0, 3)
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v0 = 4 * x**2 + 4 * y**2
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v1 = (x - 5) ** 2 + (y - 5) ** 2
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return v0, v1
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else:
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x = trial.suggest_float("x2", 0, 5)
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y = trial.suggest_float("y2", 0, 3)
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v0 = 2 * x**2 + 2 * y**2
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v1 = (x - 2) ** 2 + (y - 3) ** 2
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return v0, v1
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study.optimize(objective_multi_dynamic, n_trials=50)
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# Pruning with no intermediate values
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elif study_name == "single-pruned-without-report":
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study = optuna.create_study(study_name=study_name, storage=storage, sampler=sampler)
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def objective_prune_without_report(trial: optuna.Trial) -> float:
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x = trial.suggest_float("x", -15, 30)
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y = trial.suggest_float("y", -15, 30)
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v = x**2 + y**2
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if v > 100:
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raise optuna.TrialPruned()
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return v
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study.optimize(objective_prune_without_report, n_trials=100)
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# Single objective pruned after reported 'inf', '-inf', or 'nan'
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elif study_name == "single-inf-report":
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study = optuna.create_study(study_name=study_name, storage=storage, sampler=sampler)
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def objective_single_inf_report(trial: optuna.Trial) -> float:
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x = trial.suggest_float("x", -10, 10)
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if trial.number % 3 == 0:
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trial.report(float("inf"), 1)
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elif trial.number % 3 == 1:
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trial.report(float("-inf"), 1)
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else:
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trial.report(float("nan"), 1)
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if x > 0:
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raise optuna.TrialPruned()
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else:
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return x**2
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study.optimize(objective_single_inf_report, n_trials=50)
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# Issue 410
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elif study_name == "issue-410":
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study = optuna.create_study(study_name=study_name, storage=storage, sampler=sampler)
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def objective_issue_410(trial: optuna.Trial) -> float:
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trial.suggest_categorical("resample_rate", ["50ms"])
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trial.suggest_categorical("channels", ["all"])
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trial.suggest_categorical("window_size", [256])
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if trial.number > 15:
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raise Exception("Unexpected error")
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trial.suggest_categorical("cbow", [True])
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trial.suggest_categorical("model", ["m1"])
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trial.set_user_attr("epochs", 0)
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trial.set_user_attr("deterministic", True)
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if trial.number > 10:
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raise Exception("unexpeccted error")
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trial.set_user_attr("folder", "/path/to/folder")
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trial.set_user_attr("resample_type", "foo")
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trial.set_user_attr("run_id", "0001")
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return 1.0
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study.optimize(objective_issue_410, n_trials=20, catch=(Exception,))
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# No trials single-objective study
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elif study_name == "single-no-trials":
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optuna.create_study(study_name=study_name, storage=storage, sampler=sampler)
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# No trials multi-objective study
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elif study_name == "multi-no-trials":
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optuna.create_study(
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study_name=study_name,
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storage=storage,
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directions=["minimize", "maximize"],
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sampler=sampler,
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)
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else:
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assert False, f"No study configuration of {study_name} in conftest.py"
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return storage
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def get_free_port() -> int:
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tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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tcp.bind(("", 0))
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_, port = tcp.getsockname()
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tcp.close()
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return port
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@pytest.fixture(scope="session", params=study_names)
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def storage(request: pytest.FixtureRequest) -> optuna.storages.InMemoryStorage:
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study_name = request.param
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storage = make_dummy_storage(study_name)
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return storage
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@pytest.fixture(scope="session")
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def server_url(request: pytest.FixtureRequest, storage: optuna.storages.InMemoryStorage) -> str:
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addr = "127.0.0.1"
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port = get_free_port()
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app = wsgi(storage)
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httpd = make_server(addr, port, app)
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thread = threading.Thread(target=httpd.serve_forever)
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thread.start()
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def stop_server() -> None:
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httpd.shutdown()
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httpd.server_close()
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thread.join()
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request.addfinalizer(stop_server)
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return f"http://{addr}:{port}/dashboard"
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@pytest.fixture(scope="session")
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def browser_context_args(browser_context_args: dict) -> dict:
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return {
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**browser_context_args,
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"viewport": {
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"width": 1000,
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"height": 3000,
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},
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}
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