mlos_bench.optimizers.one_shot_optimizer module

No-op optimizer for mlos_bench that proposes a single configuration.

class mlos_bench.optimizers.one_shot_optimizer.OneShotOptimizer(tunables: TunableGroups, config: dict, global_config: dict | None = None, service: Service | None = None)

Bases: MockOptimizer

No-op optimizer that proposes a single configuration and returns.

Explicit configs (partial or full) are possible using configuration files.

Attributes:

config_space

Get the tunable parameters of the optimizer as a ConfigurationSpace.

current_iteration

The current number of iterations (suggestions) registered.

max_suggestions

The maximum number of iterations (suggestions) to run.

name

The name of the optimizer.

seed

The random seed for the optimizer.

start_with_defaults

Return True if the optimizer should start with the default values.

supports_preload

Return True if the optimizer supports pre-loading the data from previous experiments.

targets

A dictionary of {target: direction} of optimization targets.

tunable_params

Get the tunable parameters of the optimizer as TunableGroups.

Methods

bulk_register(configs, scores[, status])	Pre-load the optimizer with the bulk data from previous experiments.
get_best_observation()	Get the best observation so far.
not_converged()	Return True if not converged, False otherwise.
register(tunables, status[, score])	Register the observation for the given configuration.
suggest()	Always produce the same (initial) suggestion.

suggest() → TunableGroups

Always produce the same (initial) suggestion.

property supports_preload: bool

Return True if the optimizer supports pre-loading the data from previous experiments.