mirror of
https://github.com/wassname/optuna-dashboard.git
synced 2026-09-23 13:30:25 +08:00
212 lines
6.9 KiB
Python
212 lines
6.9 KiB
Python
import threading
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from typing import Generator
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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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from playwright.sync_api import Browser
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from playwright.sync_api import sync_playwright
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import pytest
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@pytest.fixture(scope="session")
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def port() -> int:
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return 8081
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@pytest.fixture(scope="session")
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def dummy_storage() -> 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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# Single-objective study
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study = optuna.create_study(study_name="single", 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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study = optuna.create_study(study_name="single-trial", storage=storage, sampler=sampler)
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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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study = optuna.create_study(
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study_name="single-1-param", 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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study = optuna.create_study(
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study_name="single-dynamic", 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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study = optuna.create_study(study_name="single-inf", 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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study = optuna.create_study(
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study_name="multi-objective",
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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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study = optuna.create_study(
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study_name="multi-dynamic",
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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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study = optuna.create_study(
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study_name="single-pruned-without-report", storage=storage, sampler=sampler
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)
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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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study = optuna.create_study(study_name="single-inf-report", 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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study = optuna.create_study(study_name="Issue 410", 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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optuna.create_study(study_name="single-no-trials", storage=storage, sampler=sampler)
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# No trials multi-objective study
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optuna.create_study(
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study_name="multi-no-trials",
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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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return storage
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@pytest.fixture(scope="session")
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def server(
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request: pytest.FixtureRequest, dummy_storage: optuna.storages.InMemoryStorage, port: int
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) -> None:
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app = wsgi(dummy_storage)
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httpd = make_server("127.0.0.1", 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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@pytest.fixture(scope="module")
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def browser() -> Generator[Browser, None, None]:
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with sync_playwright() as playwright:
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browser = playwright.chromium.launch()
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yield browser
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browser.close()
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