mlos_bench.optimizers.track_best_optimizer module¶
Mock optimizer for mlos_bench.
- class mlos_bench.optimizers.track_best_optimizer.TrackBestOptimizer(tunables: TunableGroups, config: dict, global_config: dict | None = None, service: Service | None = None)¶
Bases:
Optimizer
Base Optimizer class that keeps track of the best score and configuration.
- 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 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
()Generate the next suggestion.
- get_best_observation() Tuple[Dict[str, float], TunableGroups] | Tuple[None, None] ¶
Get the best observation so far.
- Returns:
- (value, tunables)Tuple[Dict[str, float], TunableGroups]
The best value and the corresponding configuration. (None, None) if no successful observation has been registered yet.
- register(tunables: TunableGroups, status: Status, score: Dict[str, int | float | str | None] | None = None) Dict[str, float] | None ¶
Register the observation for the given configuration.
- Parameters:
- tunablesTunableGroups
The configuration that has been benchmarked. Usually it’s the same config that the .suggest() method returned.
- statusStatus
Final status of the experiment (e.g., SUCCEEDED or FAILED).
- scoreOptional[Dict[str, TunableValue]]
A dict with the final benchmark results. None if the experiment was not successful.
- Returns:
- valueOptional[Dict[str, float]]
Benchmark scores extracted (and possibly transformed) from the dataframe that’s being MINIMIZED.