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https://github.com/wassname/optuna-dashboard.git
synced 2026-09-09 11:28:14 +08:00
Add generate_assets script for testing @optuna/react
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porink0424
Generated
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@@ -44,6 +44,7 @@
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"version": "0.1.0"
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},
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"../tslib/react": {
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"name": "@optuna/react",
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"version": "0.0.1",
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"license": "MIT",
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"dependencies": {
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@@ -67,16 +68,20 @@
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"@storybook/react": "^8.0.4",
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"@storybook/react-vite": "^8.0.4",
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"@storybook/test": "^8.0.4",
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"@testing-library/react": "^14.2.2",
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"@types/plotly.js-dist-min": "^2.3.4",
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"@types/react": "^18.2.55",
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"@types/react-dom": "^18.2.19",
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"@vitejs/plugin-react-swc": "^3.5.0",
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"jsdom": "^24.0.0",
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"storybook": "^8.0.4",
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"typescript": "^5.2.2",
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"vite": "^5.1.0"
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"vite": "^5.1.0",
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"vitest": "^1.4.0"
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}
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},
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"../tslib/storage": {
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"name": "@optuna/storage",
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"version": "0.0.1",
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"license": "MIT",
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"dependencies": {
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@@ -0,0 +1,311 @@
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import logging
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import os.path
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from typing import Tuple
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import shutil
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import math
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import optuna
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from optuna.distributions import CategoricalDistribution
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from optuna.distributions import FloatDistribution
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from optuna.storages import BaseStorage
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from optuna.storages import JournalFileStorage
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from optuna.storages import JournalStorage
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optuna.logging.set_verbosity(logging.CRITICAL)
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BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "asset")
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def remove_assets() -> None:
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if os.path.exists(BASE_DIR):
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shutil.rmtree(BASE_DIR)
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os.mkdir(BASE_DIR)
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def create_optuna_storage(storage: BaseStorage) -> optuna.storages.InMemoryStorage:
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# Single-objective study
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study = optuna.create_study(
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study_name="single-objective", storage=storage, sampler=optuna.samplers.RandomSampler()
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)
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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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x3 = trial.suggest_categorical("x3", ["foo", "bar"])
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return (x1 - 2) ** 2 + (x2 - 5) ** 2
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study.optimize(objective_single, n_trials=100)
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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-objective-dynamic", storage=storage, direction="maximize"
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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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study = optuna.create_study(
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study_name="check-rank-plot", storage=storage, sampler=optuna.samplers.RandomSampler()
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)
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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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x3 = trial.suggest_float("x3", 0, 10)
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x4 = trial.suggest_float("x4", 0, 10)
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x5 = trial.suggest_float("x5", 0, 10)
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x6 = trial.suggest_float("x6", 0, 10)
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return (x1 - 2) ** 2 + (x2 - 5) ** 2
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study.optimize(objective_single, n_trials=1000)
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# Single-objective study
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study = optuna.create_study(study_name="single-objective-user-attrs", storage=storage)
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def objective_single_user_attr(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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if x1 < 5:
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trial.set_user_attr("X", "foo")
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else:
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trial.set_user_attr("X", "bar")
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trial.set_user_attr("Y", x1 + x2)
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return (x1 - 2) ** 2 + (x2 - 5) ** 2
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study.optimize(objective_single_user_attr, n_trials=100)
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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)
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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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# Single objective pruned after reported 'inf', '-inf', or 'nan'
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study = optuna.create_study(study_name="single-inf-report", storage=storage)
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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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# Single objective with reported nan value
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study = optuna.create_study(study_name="single-nan-report", storage=storage)
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def objective_single_nan_report(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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trial.report(0.5, step=0)
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trial.report(math.nan, step=1)
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return (x1 - 2) ** 2 + (x2 - 5) ** 2
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study.optimize(objective_single_nan_report, n_trials=100)
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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-objective-1-param", storage=storage, direction="maximize"
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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 1 parameter
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study = optuna.create_study(study_name="long-parameter-names", storage=storage)
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def objective_long_parameter_names(trial: optuna.Trial) -> float:
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x1 = trial.suggest_float(
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"x1_long_parameter_names_long_long_long_long_long_long_long_long_long_long", 0, 10
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)
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x2 = trial.suggest_float(
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"x2_long_parameter_names_long_long_long_long_long_long_long_long_long_long", 0, 10
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)
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return (x1 - 2) ** 2 + (x2 - 5) ** 2
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study.optimize(objective_long_parameter_names, 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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)
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study.set_metric_names(["v0", "v1"])
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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", storage=storage, directions=["minimize", "minimize"]
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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(study_name="binh-korn-function-with-constraints", storage=storage)
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def objective_prune_with_no_trials(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_with_no_trials, n_trials=100)
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# With failed trials
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study = optuna.create_study(study_name="failed trials", storage=storage)
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def objective_sometimes_got_failed(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 ValueError("unexpected error")
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return v
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study.optimize(objective_sometimes_got_failed, n_trials=100, catch=(Exception,))
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# No trials single-objective study
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study = optuna.create_study(study_name="no trials single-objective study", storage=storage)
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study.set_user_attr("foo", "bar")
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# study with waiting trials
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study = optuna.create_study(study_name="waiting-trials", storage=storage)
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study.enqueue_trial({"x": 0, "y": 10})
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study.enqueue_trial({"x": 10, "y": 20})
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# Study with Running Trials
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study = optuna.create_study(
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study_name="running-trials", storage=storage, directions=["minimize", "maximize"]
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)
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study.set_metric_names(["auc", "val_loss"])
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study.enqueue_trial({"x": 10, "y": "Foo"})
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study.ask({"x": FloatDistribution(0, 10), "y": CategoricalDistribution(["Foo", "Bar"])})
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study.ask({"x": FloatDistribution(0, 10), "y": CategoricalDistribution(["Foo", "Bar"])})
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# Single-objective study with constraints
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def constraints(trial: optuna.Trial) -> list[float]:
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return trial.user_attrs["constraint"]
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study = optuna.create_study(
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study_name="A single objective constraint optimization study",
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storage=storage,
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sampler=optuna.samplers.TPESampler(constraints_func=constraints),
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)
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def objective_constraints(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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v0 = 4 * x**2 + 4 * y**2
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trial.set_user_attr("constraint", [1000 - v0, x - 10, y - 10])
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return v0
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study.optimize(objective_constraints, n_trials=100)
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# Study with Running Trials
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study = optuna.create_study(
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study_name="objective-form-widgets",
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storage=storage,
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directions=["minimize", "minimize", "minimize", "minimize"],
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)
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study.set_metric_names(
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["Slider Objective", "Good or Bad", "Text Input Objective", "Validation Loss"]
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)
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trial = study.ask(
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{"x": FloatDistribution(0, 10), "y": CategoricalDistribution(["Foo", "Bar"])}
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)
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trial.set_user_attr("val_loss", 0.2)
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study.ask({"x": FloatDistribution(0, 10), "y": CategoricalDistribution(["Foo", "Bar"])})
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trial.set_user_attr("val_loss", 0.5)
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# No trials multi-objective study
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optuna.create_study(
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study_name="no trials multi-objective study",
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storage=storage,
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directions=["minimize", "maximize"],
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)
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# Single-objective study with intermediate values
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study = optuna.create_study(study_name="intermediate-values", storage=storage)
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def objective_intermediate_values(trial: optuna.Trial) -> float:
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trial.report(trial.number, step=0)
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trial.report(trial.number + 1, step=1)
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return 0.0
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study.optimize(objective_intermediate_values, n_trials=10)
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trial = study.ask(
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{"x": FloatDistribution(0, 10), "y": CategoricalDistribution(["Foo", "Bar"])}
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) # To create a running trial
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trial.report(trial.number, step=0)
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trial.report(trial.number + 1, step=1)
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# Single-objective study with intermediate values and constraints
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def constraints(trial: optuna.Trial) -> list[float]:
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return trial.user_attrs["constraint"]
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study = optuna.create_study(
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study_name="intermediate-values-constraints",
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storage=storage,
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sampler=optuna.samplers.NSGAIISampler(constraints_func=constraints),
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)
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def objective_intermediate_values_constraints(trial: optuna.Trial) -> float:
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trial.set_user_attr("constraint", [trial.number % 2])
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trial.report(trial.number, step=0)
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trial.report(trial.number + 1, step=1)
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return 0.0
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study.optimize(objective_intermediate_values_constraints, n_trials=10)
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trial = study.ask(
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{"x": FloatDistribution(0, 10), "y": CategoricalDistribution(["Foo", "Bar"])}
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) # To create a running trial
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trial.report(trial.number, step=0)
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trial.report(trial.number + 1, step=1)
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def main() -> None:
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remove_assets()
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storage = JournalStorage(JournalFileStorage(os.path.join(BASE_DIR, "journal.log")))
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create_optuna_storage(storage)
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if __name__ == "__main__":
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main()
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