intelligence.zenith_tune.tuners.function_runtime

FunctionRuntimeTuner Objects

class FunctionRuntimeTuner(GeneralTuner)

A tuner for runtime optimization.

__init__

def __init__(output_dir: str = "outputs",
             study_name: Optional[str] = None,
             db_path: Optional[str] = None,
             sampler: Optional[BaseSampler] = None,
             pruner: Optional[BasePruner] = None,
             auto_pruners: Optional[List[AutoPrunerBase]] = None,
             maximize: bool = False) -> None

Initialize the FunctionRuntimeTuner.

Arguments:

• output_dir str - The directory to store the study results. Defaults to "outputs".
• study_name str - The name of the study. Defaults to None.
• db_path Optional[str] - The path to the database file. Defaults to None.
• sampler Optional[BaseSampler] - The sampler to use. Defaults to None.
• pruner Optional[BasePruner] - The pruner to use. Defaults to None.
• auto_pruners Optional[List[AutoPrunerBase]] - List of auto pruners to monitor during execution. Defaults to None.
• maximize bool - Whether to maximize the objective function. Defaults to False.

optimize

def optimize(
    func: Callable[..., Any],
    n_trials: int,
    default_params: Optional[Dict[str, Any]] = None
) -> Tuple[float, Dict[str, Any]]

Optimize the given objective function using Optuna.

Arguments:

• func Callable[..., Any] - The objective function to optimize.
• n_trials int - The number of trials to run.
• default_params Optional[Dict[str, Any]] - Default parameters to use for the optimization.

Returns:

Tuple[float, Dict[str, Any]]: The best value and parameters found during optimization.