mlos_bench.optimizers.track_best_optimizer

Mock optimizer for mlos_bench.

Classes

TrackBestOptimizer

Base Optimizer class that keeps track of the best score and configuration.

Module Contents

class mlos_bench.optimizers.track_best_optimizer.TrackBestOptimizer(tunables: mlos_bench.tunables.tunable_groups.TunableGroups, config: dict, global_config: dict | None = None, service: mlos_bench.services.base_service.Service | None = None)[source]

Bases: mlos_bench.optimizers.base_optimizer.Optimizer

Base Optimizer class that keeps track of the best score and configuration.

Create a new optimizer for the given configuration space defined by the tunables.

Parameters:

  • tunables (TunableGroups) – The tunables to optimize.

  • config (dict) – Free-format key/value pairs of configuration parameters to pass to the optimizer.

  • global_config (Optional[dict])

  • service (Optional[Service])

get_best_observation() → Tuple[Dict[str, float], mlos_bench.tunables.tunable_groups.TunableGroups] | Tuple[None, None][source]

Get the best observation so far.

Returns:

(value, tunables) – The best value and the corresponding configuration. (None, None) if no successful observation has been registered yet.

Return type:

Tuple[Dict[str, float], TunableGroups]

register(tunables: mlos_bench.tunables.tunable_groups.TunableGroups, status: mlos_bench.environments.status.Status, score: Dict[str, mlos_bench.tunables.tunable.TunableValue] | None = None) → Dict[str, float] | None[source]

Register the observation for the given configuration.

Parameters:

  • tunables (TunableGroups) – The configuration that has been benchmarked. Usually it’s the same config that the .suggest() method returned.

  • status (Status) – Final status of the experiment (e.g., SUCCEEDED or FAILED).

  • score (Optional[Dict[str, TunableValue]]) – A dict with the final benchmark results. None if the experiment was not successful.

Returns:

value – Benchmark scores extracted (and possibly transformed) from the dataframe that’s being MINIMIZED.

Return type:

Optional[Dict[str, float]]