mlos_core.optimizers.flaml_optimizer module

Contains the FlamlOptimizer class.

class mlos_core.optimizers.flaml_optimizer.EvaluatedSample(config: dict, score: float)

Bases: NamedTuple

A named tuple representing a sample that has been evaluated.

Methods

count(value, /)	Return number of occurrences of value.
index(value[, start, stop])	Return first index of value.

config: dict

Alias for field number 0

score: float

Alias for field number 1

class mlos_core.optimizers.flaml_optimizer.FlamlOptimizer(*, parameter_space: ConfigurationSpace, optimization_targets: List[str], objective_weights: List[float] | None = None, space_adapter: BaseSpaceAdapter | None = None, low_cost_partial_config: dict | None = None, seed: int | None = None)

Bases: BaseOptimizer

Wrapper class for FLAML Optimizer: A fast library for AutoML and tuning.

Attributes:

space_adapter

Get the space adapter instance (if any).

Methods

cleanup()	Remove temp files, release resources, etc.
get_best_observations(*[, n_max])	Get the N best observations so far as a triplet of DataFrames (config, score, context).
get_observations()	Returns the observations as a triplet of DataFrames (config, score, context).
register(*, configs, scores[, context, metadata])	Wrapper method, which employs the space adapter (if any), before registering the configs and scores.
register_pending(*, configs[, context, metadata])	Registers the given configs as "pending".
suggest(*[, context, defaults])	Wrapper method, which employs the space adapter (if any), after suggesting a new configuration.

register_pending(*, configs: DataFrame, context: DataFrame | None = None, metadata: DataFrame | None = None) → None

Registers the given configs as “pending”. That is it say, it has been suggested by the optimizer, and an experiment trial has been started. This can be useful for executing multiple trials in parallel, retry logic, etc.

Parameters:

configspd.DataFrame

Dataframe of configs / parameters. The columns are parameter names and the rows are the configs.

contextpd.DataFrame

Not Yet Implemented.

metadataOptional[pd.DataFrame]

Metadata returned by the backend optimizer’s suggest method.