Optimizer¶

class Optimizer¶

Bases: Generic[_T, _C]

Methods

`__init__`	Initialize the optimizer.
`add_random_training_data`	Add randomly generated solution vectors to the training data.
`add_solution`	Add a new solution to the training data.
`evaluate_objective`	Evaluate the objective function for a given solution.
`fallback_solution`	Try to find an alternative solution when only duplicate solutions are returned from annealing.
`find_unique_solution`	Find a unique solution from a list of solutions.
`minimize_surrogate`	Find solutions that minimize the surrogate model.
`optimize`	Optimize the black-box function.
`train_surrogate`	Train the surrogate model.

Attributes

`amplify_model`	Returns the amplify model.
`best`	Returns the best solution found so far.
`blackbox`	Returns the black-box function.
`encoding_info`	Returns the encoding information for the black-box function.
`history`	Returns the history of optimization iterations.
`initial_training_data`	Returns the initial training data for the black-box function.
`rng`	Returns the random number generator used in the optimizer.
`surrogate_data_transformer`	Returns the data transformer for the surrogate model training data.
`surrogate_model`	Returns the surrogate model.
`surrogate_training_data`	Returns the training data for the surrogate model (encoded if pre_encoding is True).
`training_data`	Returns the training data for the black-box function.

classmethod __class_getitem__(params)¶

__init__( blackbox: BlackBoxFuncBase, trainer: _T, client: _C = FixstarsClient({'url': 'https://optigan.fixstars.com', 'token': '', 'compression': true, 'parameters': {'outputs': {}}}), *, training_data: Dataset | None = None, constraints: amplify.Constraint | amplify.ConstraintList | None = None, pre_encoding: bool = True, seed: int = 0, surrogate_data_transformer: DataTransformer | None = None, ) → None¶

Initialize the optimizer.

Parameters:

blackbox (BlackBoxFuncBase) – A black-box function defined with the @blackbox decorator.
trainer (_T) – A Trainer class instance.
client (_C, optional) – A solver client defined with the Amplify SDK. Defaults to _default_client.
training_data (Dataset | None) – A training dataset for the surrogate model. Defaults to None.
constraints (amplify.Constraint | amplify.ConstraintList | None, optional) – Constraints. Defaults to None.
pre_encoding (bool, optional) – Whether to perform pre-encoding (True) or post-encoding (False). Defaults to True.
seed (int, optional) – A random seed. Defaults to 0.
surrogate_data_transformer (SurrogateDataTransformer | None, optional) – A data transformer for the surrogate model training data. Defaults to None.

Raises:

TypeError – If blackbox is not an subclass of BlackBoxFuncBase.
TypeError – If trainer is not an instance of Trainer.
TypeError – If client is not an instance of amplify.BaseClient.
TypeError – If training_data is not an instance of Dataset or None.
TypeError – If constraints is not an instance of amplify.Constraint, amplify.ConstraintList or None.

classmethod __init_subclass__(*args, **kwargs)¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

add_random_training_data(num_data: int, max_trials: int | None = None) → None¶

Add randomly generated solution vectors to the training data.

If user-defined constraints are given to the optimizer, the random solutions are generated to satisfy the constraints.

Parameters:

num_data (int) – The number of solutions to be added.
max_trials (int | None, optional) – The max number of trials. This value must be greater than or equal to
None. (num_data. If None is given max_trials is set as 2 * num_data. Defaults to)

Raises:

ValueError – If max_trials is less than num_data.

add_solution(solution: FlattenedSolution, objective: float) → None¶

Add a new solution to the training data.

Parameters:

solution (FlattenedSolution) – The solution to add.
objective (float) – The objective value for the solution.

evaluate_objective(solution: FlattenedSolution, use_cache: bool = False) → float¶

Evaluate the objective function for a given solution.

Parameters:

solution (FlattenedSolution) – The solution to evaluate.
use_cache (bool, optional) – Whether to use cached results. Defaults to False.

Returns:

The objective value for the given solution.

Return type:

float

fallback_solution( solutions: list[FlattenedSolution], max_trials: int, ) → FlattenedSolution | None¶

Try to find an alternative solution when only duplicate solutions are returned from annealing.

If user-defined constraints are given to the optimizer, the generated fallback solution is expected to satisfy the constraints.

Parameters:

solutions (list[FlattenedSolution]) – The duplicate input vectors (solutions of the optimization).
max_trials (int) – The max number of trials to find an alternative solution that satisfies the given constraints if any. If max_trials is 0 or max_trials runs out, the first solution (annealing best) is returned regardless of the uniqueness of the solution.

Returns:

The alternative solution if found, otherwise None.

Return type:

FlattenedSolution | None

Raises:

ValueError – If max_trials is less than 0.

find_unique_solution( solutions: list[FlattenedSolution], ) → FlattenedSolution | None¶

Find a unique solution from a list of solutions.

Parameters:: solutions (list[FlattenedSolution]) – The list of solutions to search.
Returns:: The unique solution if found, otherwise None.
Return type:: FlattenedSolution | None

minimize_surrogate(max_retries: int = 10) → tuple[list[FlattenedSolution], Result]¶

Find solutions that minimize the surrogate model.

Parameters:: max_retries (int, optional) – Maximum number of retries for annealing. Defaults to 10.
Returns:: List of optimized solutions and the Amplify SDK’s raw result. Solutions are in order of the objective value (from lowest to highest).
Return type:: tuple[list[FlattenedSolution], amplify.Result]

optimize(num_iterations: int, max_deduplication_trials: int | None = None) → None¶

Optimize the black-box function.

Parameters:

num_iterations (int) – The number of iterations to perform.
max_deduplication_trials (int | None, optional) – The maximum number of trials to find an alternative solution when duplicate solutions are returned from annealing. If 0 is set, the first solution (annealing best) is returned regardless of the uniqueness of the solution. If None is given, it is set as the number of variables in the black-box function. Defaults to None.

Raises:

ValueError – If num_iterations is less than or equal to 0.
ValueError – If max_deduplication_trials is less than 0.
RuntimeError – If no feasible solution was found in the iteration.

train_surrogate() → None¶: Train the surrogate model.

__dict__ = mappingproxy({'__module__': 'amplify_bbopt.optimizer', '__init__': <function Optimizer.__init__>, 'blackbox': <property object>, 'training_data': <property object>, 'surrogate_training_data': <property object>, 'initial_training_data': <property object>, 'surrogate_model': <property object>, 'amplify_model': <property object>, 'encoding_info': <property object>, 'surrogate_data_transformer': <property object>, '_transform_surrogate_training_data': <function Optimizer._transform_surrogate_training_data>, '_solution_from_flattened_values': <function Optimizer._solution_from_flattened_values>, 'best': <property object>, 'history': <property object>, 'rng': <property object>, '_mutate_solution': <function Optimizer._mutate_solution>, '_generate_mutated_solutions': <function Optimizer._generate_mutated_solutions>, '_generate_random_solutions': <function Optimizer._generate_random_solutions>, '_encode_blackbox_solution': <function Optimizer._encode_blackbox_solution>, '_search_dataset': <function Optimizer._search_dataset>, 'train_surrogate': <function Optimizer.train_surrogate>, 'minimize_surrogate': <function Optimizer.minimize_surrogate>, 'evaluate_objective': <function Optimizer.evaluate_objective>, 'add_solution': <function Optimizer.add_solution>, 'find_unique_solution': <function Optimizer.find_unique_solution>, 'fallback_solution': <function Optimizer.fallback_solution>, 'optimize': <function Optimizer.optimize>, 'add_random_training_data': <function Optimizer.add_random_training_data>, '__orig_bases__': (typing.Generic[~_T, ~_C],), '__dict__': <attribute '__dict__' of 'Optimizer' objects>, '__weakref__': <attribute '__weakref__' of 'Optimizer' objects>, '__doc__': None, '__parameters__': (~_T, ~_C), '__annotations__': {'_history': 'list[IterationResult]', '_pre_encoding': 'Final[bool]', '_num_initial_data': 'Final[int]'}})¶

__orig_bases__ = (typing.Generic[~_T, ~_C],)¶

__parameters__ = (~_T, ~_C)¶

__slots__ = ()¶

__weakref__¶: list of weak references to the object (if defined)

property amplify_model: AmplifyModel¶: Returns the amplify model.

property best: Solution¶: Returns the best solution found so far.

property blackbox: BlackBoxFuncBase¶: Returns the black-box function.

property encoding_info: EncodingInfo¶: Returns the encoding information for the black-box function.

property history: list[IterationResult]¶: Returns the history of optimization iterations.

property initial_training_data: Dataset¶: Returns the initial training data for the black-box function.

property rng: Generator¶: Returns the random number generator used in the optimizer.

property surrogate_data_transformer: DataTransformer | None¶: Returns the data transformer for the surrogate model training data.

property surrogate_model: SurrogateModel¶: Returns the surrogate model.

property surrogate_training_data: Dataset¶: Returns the training data for the surrogate model (encoded if pre_encoding is True).

property training_data: Dataset¶: Returns the training data for the black-box function.