OptimizerBase

class OptimizerBase

Bases: ABC

Base class for black-box optimizer.

Methods

__init__	Initialize OptimizerBase.
fetch_history	Return the optimization history.
optimize	A method to execute optimization.
set_best	Set the current best solution and corresponding objective function value.
update_objective_weight	Update the weight for the objective function.

Attributes

best_objective	Objective function value corresponding to the current best solution (optimaized input).
best_solution	Current best solution (optimized input).
data	Current training dataset (both input and output of an individual objective function).
i_cycle	The number of current optimization cycle.
num_initial_data	A number of the samples in initial data.
objective	Objective function class instance.
objective_weight	The latest weight for the objective function.

__init__(

data: DataList,

objective: BlackBoxFuncBase,

objective_weight: float | Callable[[Any], float] = 1.0,

) → None

Initialize OptimizerBase.

Parameters:

• data (DataList) – Initial training dataset.

• objective (BlackBoxFuncBase) – Objective (black-box) function class instance created with blackbox decorator.

• objective_weight (float | Callable[[int, Any], float], optional) – A weight imposed on the objective function in amplify.Model. If a Callable object is passed, a weight is the return value of the object with the used optimizer instance as an argument at each optimization cycle. Generally, weight is only useful in case of multi-objective optimization using MultiObjectiveOptimizer. Defaults to 1.

fetch_history() → History

Return the optimization history.

Returns:

The optimization history.

Return type:

History

abstract optimize() → None

A method to execute optimization.

set_best(best_solution_dict: FlatSolutionDict | None = None) → None

Set the current best solution and corresponding objective function value.

Parameters:

best_solution_dict (FlatSolutionDict | None, optional) – A solution dictionary. This may contains solution values from other objective functions in case of multiple-objective optimization. In this case, ‘best’ means the variable value vector relevant to an individual objective function, that is a subset of the entire solution which achieve the best of multiple-objectives combined. Therefore, the set best here may not be the same as the best in OptimizerBase.data. If None is specified, search the best from OptimizerBase.data. Defaults to None.

update_objective_weight(optimizer: Any) → None

Update the weight for the objective function.

The weight is determined according to the weight function passed to OptimizerBase.__init__ (OptimizerBase.objective_weight is callable). In case of OptimizerBase.objective_weight being float, the weight is constant over optimization cycles. Expected to be executed at the beginning of each optmization cycle.

Parameters:

optimizer (Any) – An optimizer to be passed to the callable OptimizerBase.objective_weight.

__abstractmethods__ = frozenset({'optimize'})

__dict__ = mappingproxy({'__module__': 'amplify_bbopt.optimizer', '__doc__': 'Base class for black-box optimizer.', '__init__': <function OptimizerBase.__init__>, 'num_initial_data': <property object>, 'objective': <property object>, 'i_cycle': <property object>, 'objective_weight': <property object>, 'update_objective_weight': <function OptimizerBase.update_objective_weight>, 'best_objective': <property object>, 'best_solution': <property object>, 'set_best': <function OptimizerBase.set_best>, 'data': <property object>, 'fetch_history': <function OptimizerBase.fetch_history>, 'optimize': <function OptimizerBase.optimize>, '__dict__': <attribute '__dict__' of 'OptimizerBase' objects>, '__weakref__': <attribute '__weakref__' of 'OptimizerBase' objects>, '__abstractmethods__': frozenset({'optimize'}), '_abc_impl': <_abc._abc_data object>, '__annotations__': {'_objective_weight': 'float | None', '_best_objective': 'int | float | None'}})

__slots__ = ()

__weakref__

list of weak references to the object (if defined)

property best_objective: int | float

Objective function value corresponding to the current best solution (optimaized input).

property best_solution: StructuredSolutionDict

Current best solution (optimized input).

property data: DataList

Current training dataset (both input and output of an individual objective function).

property i_cycle: int

The number of current optimization cycle.

property num_initial_data: int

A number of the samples in initial data.

property objective: BlackBoxFuncBase

Objective function class instance.

property objective_weight: float | None

The latest weight for the objective function.