mlos_core.optimizers.bayesian_optimizers package

Basic initializer module for the mlos_core Bayesian optimizers.

class mlos_core.optimizers.bayesian_optimizers.BaseBayesianOptimizer(*, parameter_space: ConfigurationSpace, optimization_targets: List[str], objective_weights: List[float] | None = None, space_adapter: BaseSpaceAdapter | None = None)

Bases: BaseOptimizer

Abstract base class defining the interface for Bayesian optimization.

Attributes:
space_adapter

Get the space adapter instance (if any).

Methods

acquisition_function(*, configs[, context])

Invokes the acquisition function from this Bayesian optimizer for the given configuration.

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.

surrogate_predict(*, configs[, context])

Obtain a prediction from this Bayesian optimizer's surrogate model for the given configuration(s).
abstract acquisition_function(*, configs: DataFrame, context: DataFrame | None = None) → ndarray[Any, dtype[_ScalarType_co]]

Invokes the acquisition function from this Bayesian optimizer for the given configuration.

Parameters:
configspd.DataFrame

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

contextpd.DataFrame

Not Yet Implemented.

abstract surrogate_predict(*, configs: DataFrame, context: DataFrame | None = None) → ndarray[Any, dtype[_ScalarType_co]]

Obtain a prediction from this Bayesian optimizer’s surrogate model for the given configuration(s).

Parameters:
configspd.DataFrame

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

contextpd.DataFrame

Not Yet Implemented.

class mlos_core.optimizers.bayesian_optimizers.SmacOptimizer(*, parameter_space: ConfigurationSpace, optimization_targets: List[str], objective_weights: List[float] | None = None, space_adapter: BaseSpaceAdapter | None = None, seed: int | None = 0, run_name: str | None = None, output_directory: str | None = None, max_trials: int = 100, n_random_init: int | None = None, max_ratio: float | None = None, use_default_config: bool = False, n_random_probability: float = 0.1)

Bases: BaseBayesianOptimizer

Wrapper class for SMAC based Bayesian optimization.

Attributes:
n_random_init

Gets the number of random samples to use to initialize the optimizer’s search space sampling.

space_adapter

Get the space adapter instance (if any).

Methods

acquisition_function(*, configs[, context])

Invokes the acquisition function from this Bayesian optimizer for the given configuration.

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.

surrogate_predict(*, configs[, context])

Obtain a prediction from this Bayesian optimizer's surrogate model for the given configuration(s).
acquisition_function(*, configs: DataFrame, context: DataFrame | None = None) → ndarray[Any, dtype[_ScalarType_co]]

Invokes the acquisition function from this Bayesian optimizer for the given configuration.

Parameters:
configspd.DataFrame

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

contextpd.DataFrame

Not Yet Implemented.

cleanup() → None

Remove temp files, release resources, etc.

after use. Default is no-op. Redefine this method in optimizers that require cleanup.

property n_random_init: int

Gets the number of random samples to use to initialize the optimizer’s search space sampling.

Note: This may not be equal to the value passed to the initializer, due to logic present in the SMAC. See Also: max_ratio

Returns:
int

The number of random samples used to initialize the optimizer’s search space sampling.

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.

surrogate_predict(*, configs: DataFrame, context: DataFrame | None = None) → ndarray[Any, dtype[_ScalarType_co]]

Obtain a prediction from this Bayesian optimizer’s surrogate model for the given configuration(s).

Parameters:
configspd.DataFrame

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

contextpd.DataFrame

Not Yet Implemented.

Submodules