Is it possible to add suggest_discrete_int?

[hayata-yamamoto]

Hello, Developers,
Is it possible to add suggest_discrete_int method to trial?

Motivation

We want to make a grid somehow so that parameter search becomes more efficient and less time-consuming.

Description

Optuna already has a discrete_uniform method on optuna.Trial. It's quite useful, but some of the models require integer values as their arguments. For instance, XGBoost. Since n_estimator and max_depth must be an integer, it raises an error when I pass float values to these.

Therefore, trial.suggest_int or trial.suggest_categorical are used so that I input the values I want. If optuna has a feature of throwing discrete integer, this operation could vanish.

Alternatives (optional)

From my perspectives, there are two options,

1. To cast discrete_uniform output as an integer
2. To use suggest_categorical like suggest_categorical('hoge', np.arange(0, 10, 2))

Additional context (optional)

[toshihikoyanase]

Hi @hayata-yamamoto,

Thank you for your request!
We have already received similar requests like #510 and are discussing the design of new APIs there. (The last comment was about four month ago, though.) I think the (b) API of this comment is corresponding to your request.

(b) [MODIFIED] Trial.suggest_int(name: str, low: int, high: int, q: Optional[int]) (
suggest_int(name, low, high, q=10) is equivalent to Trial.suggest_discret_int(name, low, high, q=10)

So, I think we can merge this issue into #510. If it's OK, please add your comments there and close this issue.

[hayata-yamamoto]

@toshihikoyanase
Nice information! I'm sorry for bothering you!

I'll close this issue