jijzepttools.blackbox_optimization.bbo_ommx

jijzepttools.blackbox_optimization.bbo_ommx#

Classes#

BlackboxOptState

BBOMethod

Generic enumeration.

BlackboxOptimization

Blackbox optimization using surrogate models.

Module Contents#

class BlackboxOptState#
instance: ommx.v1.Instance#
class BBOMethod#

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

FMO = 'FMO'#
BOCS = 'BOCS'#
BayesianFMO = 'BayesianFMO'#
TPE = 'TPE'#
class BlackboxOptimization(bb_model: jijzepttools.blackbox_optimization.problem.BlackboxProblem)#

Blackbox optimization using surrogate models.

This class provides a unified interface for optimizing blackbox functions using different surrogate modeling approaches such as Factorization Machine and BOCS.

The typical workflow is: 1. Initialize with a BlackboxProblem 2. Setup with initial data and objectives 3. Run optimization with specified method and parameters

bb_model#
X: numpy.ndarray | None = None#
y: numpy.ndarray | None = None#
experiment_details: minto.Experiment | None = None#
setup(dataset: pandas.DataFrame | tuple[list[dict], list[dict]], objectives: list[str], instance_data: dict | None = None, record_experiment_details: bool = False, record_instance_in_experiment_details: bool = False, record_solution_in_experiment_details: bool = False, experiment_name: str = 'blackbox_optimization')#

Setup the optimization with initial data and objectives. Creates a minto.Experiment and logs initial data.

Parameters:

  • dataset (pd.DataFrame or tuple[list[dict], list[dict]]) – Initial dataset containing decision variables and objective values. Can be either a DataFrame or tuple of (input_dicts, output_dicts).

  • objectives (list[str]) – Names of objective functions to optimize.

  • instance_data (dict, optional) – Additional data for creating the problem instance.

  • record_experiment_details (bool, default=False) – Whether to record the experiment details. If True, the experiment details will be recorded and you can see this by using get_experiment_details() method.

  • record_instance_in_experiment_details (bool, default=False) – Whether to record the instance in the experiment details. If True, the instance will be recorded in the experiment details. Instance Recording can be so heavy.

  • record_solution_in_experiment_details (bool, default=False) – Whether to record the solution in the experiment details. If True, the solution will be recorded in the experiment details. Solution Recording can be so heavy.

  • experiment_name (str, default="blackbox_optimization") – Name for the minto experiment.

run(n_iter: int, blackbox_func: Callable[[dict], dict[str, float]] | None = None, solver: Callable | None = None, methods: List[BBOMethod] | None = None, surrogate_params: jijzepttools.blackbox_optimization.surrogate_model.SurrogateModelParams | None = None, enable_binary_conversion: bool = False, resolution_continuous_to_integer: int | None = None) → minto.Experiment#

Run blackbox optimization iterations using the existing experiment.

# TODO: FMOやBOCSのサロゲートベースの方法だけでなく、Optunaにも対応できるようにする。 # TODO: 複数メソッドを使ってブラックボックス最適化できるようにする。またそれをデフォルトにする。 # TODO: 連続変数の問題に対して、FMのfit前にバイナリ変数への変換を行う機能を実装する。 # TODO: enable_binary_conversionが現在FMOのみ対応だが、他の必要なメソッドにも対応できるようにする。 # TODO: 多目的最適化への対応を行う。

Parameters:

  • n_iter (int) – Number of optimization iterations.

  • methods (list of BBOMethod, optional) – List of surrogate model methods. Currently only supports a list with one method. #TODO: 複数メソッドに対応させる Defaults to [BBOMethod.FMO].

  • blackbox_func (callable, optional) – Function to evaluate new candidates. Required if n_iter > 1.

  • solver (callable, optional) – OMMX solver function. Defaults to ommx_pyscipopt_adapter.OMMXPySCIPOptAdapter.solve.

  • surrogate_params (SurrogateModelParams, optional) – Parameters for the surrogate model. Uses defaults if None.

  • enable_binary_conversion (bool, default=False) – Whether to use binary variable conversion (now, support only BBOMethod.FMO).

  • resolution_continuous_to_integer (int, optional) – Resolution for continuous to integer conversion (required if enable_binary_conversion=True).

Returns:

The experiment object containing all optimization history.

Return type:

minto.Experiment

Raises:

ValueError – If setup() hasn’t been called or invalid parameters are provided.

get_experiment_details() → minto.Experiment#
Contents