autointent.configs.HPOConfig

class autointent.configs.HPOConfig(/, **data)

Bases: pydantic.BaseModel

Configuration for hyperparameter optimization using Optuna.

For more detailed information about the TPE sampler and its parameters, refer to Optuna’s documentation of TPESampler, study.optimize, RandomSampler.

Parameters:

data (Any)

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

sampler: autointent.custom_types.SamplerType = None

n_trials: int = None

timeout: float | None = None

n_jobs: int = None

n_startup_trials: int = None

consider_prior: bool = None

prior_weight: int = None

n_ei_candidates: int = None

constant_liar: bool = None