BaseOptimizer

Abstract base class for optimization algorithms.

Module: aisp.base
Import: from aisp.base import BaseOptimizer

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Overview

This class defines the core interface for optimization strategies. It keeps track of cost history, evaluated solutions, and the best solution found during the optimization process. Subclasses must implement optimize and affinity_function.

Use cases:

• Abstract base class for extending optimization algorithm classes.

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Attributes

Name	Type	Default	Description
cost_history	List[float]	[]	History of best costs found at each iteration.
solution_history	List	[]	History of the best solution found at each iteration.
best_solution	Any	None	The best solution found.
best_cost	Optional[float]	None	Cost of the best solution found.
mode	{"min", "max"}	'min'	Defines whether the algorithm minimizes or maximizes the cost function.

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Abstract Methods

optimize

@abstractmethod
def optimize(
    self,
    max_iters: int = 50,
    n_iter_no_change: int = 10,
    verbose: bool = True
) -> Any:
    ...

Execute the optimization process.
This abstract method must be implemented by the subclass, defining how the optimization strategy explores the search space.

Parameters

Name	Type	Default	Description
max_iters	int	50	Maximum number of iterations
n_iter_no_change	int	10	the maximum number of iterations without updating the best
verbose	bool	True	Flag to enable or disable detailed output during training.

Returns

Type	Description
Self	Returns the instance of the class that implements this method.

---

affinity_function

@abstractmethod
def affinity_function(self, solution: Any) -> float:
    ...

Evaluate the affinity of a candidate solution.

This method must be implemented according to the specific optimization problem, defining how the solution will be evaluated. The returned value should represent the quality of the evaluated solution.

Parameters

Name	Type	Default	Description
solution	Any	-	Candidate solution to be evaluated.

Returns

Type	Description
float	Cost value associated with the given solution.

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Public Methods

get_report

def get_report(self) -> str:
    ...

Generate a formatted summary report of the optimization process.
The report includes the best solution, its associated cost, and the evolution of cost values per iteration.

Returns

Type	Description
str	A formatted string containing the optimization summary.

---

register

def register(self, alias: str, function: Callable[..., Any]) -> None:
    ...

Register a function dynamically in the optimizer instance.

Parameters

Name	Type	Default	Description
alias	str	-	Name used to access the function as an attribute.
function	Callable[..., Any]	-	Callable to be registered.

Raises

Exception	Description
TypeError	If the provided function is not callable.
AttributeError	If alias is protected and cannot be modified, or does not exist in the class.

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reset

def reset(self):
    ...

Reset the object's internal state, clearing history and resetting values.