8b3f79f3079834f00c7ec62d5decd6bc026c1bc8,python/ray/tune/examples/dragonfly_example.py,,,#,33

Before Change

    args, _ = parser.parse_known_args()
    ray.init()

    tune_kwargs = {
        "num_samples": 10 if args.smoke_test else 50,
        "config": {
            "iterations": 100,
            "LiNO3_vol": tune.uniform(0, 7),
            "Li2SO4_vol": tune.uniform(0, 7),
            "NaClO4_vol": tune.uniform(0, 7)
        },
    }

    // Optional: Pass the parameter space yourself
    // space = [{
    //     "name": "LiNO3_vol",
    //     "type": "float",
    //     "min": 0,
    //     "max": 7
    // }, {
    //     "name": "Li2SO4_vol",
    //     "type": "float",
    //     "min": 0,
    //     "max": 7
    // }, {
    //     "name": "NaClO4_vol",
    //     "type": "float",
    //     "min": 0,
    //     "max": 7
    // }]

    df_search = DragonflySearch(
        optimizer="bandit",
        domain="euclidean",
        // space=space,  // If you want to set the space manually
    )
    df_search = ConcurrencyLimiter(df_search, max_concurrent=4)

    scheduler = AsyncHyperBandScheduler()
    tune.run(
        objective,
        metric="objective",
        mode="max",
        name="dragonfly_search",
        search_alg=df_search,
        scheduler=scheduler,
        **tune_kwargs)

After Change

    )
    df_search = ConcurrencyLimiter(df_search, max_concurrent=4)

    scheduler = AsyncHyperBandScheduler()
    analysis = tune.run(
        objective,
        metric="objective",
        mode="max",
        name="dragonfly_search",
        search_alg=df_search,
        scheduler=scheduler,
        num_samples=10 if args.smoke_test else 50,
        config={
            "iterations": 100,
            "LiNO3_vol": tune.uniform(0, 7),
            "Li2SO4_vol": tune.uniform(0, 7),
            "NaClO4_vol": tune.uniform(0, 7)
        },
    )

    print("Best hyperparameters found were: ", analysis.best_config)

Instances