discrete_optimization.generic_tools.result_storage package

Submodules

discrete_optimization.generic_tools.result_storage.multiobj_utils module

class discrete_optimization.generic_tools.result_storage.multiobj_utils.TupleFitness(vector_fitness: ndarray, size: int)[source]

Bases: object

distance(other: TupleFitness) → floating[source]

size: int

vector_fitness: ndarray

discrete_optimization.generic_tools.result_storage.result_storage module

class discrete_optimization.generic_tools.result_storage.result_storage.ParetoFront(list_solution_fits: list[tuple[Solution, float | TupleFitness]], mode_optim: ModeOptim = ModeOptim.MAXIMIZATION)[source]

Bases: ResultStorage

add_point(solution: Solution, tuple_fitness: TupleFitness) → None[source]

compute_extreme_points() → list[tuple[Solution, TupleFitness]][source]

finalize() → None[source]

len_pareto_front() → int[source]

class discrete_optimization.generic_tools.result_storage.result_storage.ResultStorage(mode_optim: ModeOptim, list_solution_fits: list[tuple[Solution, float | TupleFitness]] | None = None)[source]

Bases: MutableSequence

Storage for solver results.

ResultStorage inherits from MutableSequence so you can - iterate over it (will iterate over tuples (sol, fit) - append directly to it (a tuple (sol, fit)) - pop, extend, …

get_best_solution() → Solution | None[source]

get_best_solution_fit(satisfying: Problem | None = None) → tuple[Solution, float | TupleFitness] | tuple[None, None][source]

get_last_best_solution() → tuple[Solution, float | TupleFitness][source]

get_n_best_solution(n_solutions: int) → list[tuple[Solution, float | TupleFitness]][source]

get_random_best_solution() → tuple[Solution, float | TupleFitness][source]

get_random_solution() → tuple[Solution, float | TupleFitness][source]

insert(index, value: tuple[Solution, float | TupleFitness]) → None[source]: S.insert(index, value) – insert value before index

list_solution_fits: list[tuple[Solution, float | TupleFitness]]

remove_duplicate_solutions(var_name: str) → None[source]

discrete_optimization.generic_tools.result_storage.result_storage.from_solutions_to_result_storage(list_solution: list[Solution], problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None) → ResultStorage[source]

discrete_optimization.generic_tools.result_storage.result_storage.merge_results_storage(result_1: ResultStorage, result_2: ResultStorage) → ResultStorage[source]

discrete_optimization.generic_tools.result_storage.result_storage.plot_fitness(result_storage: ResultStorage, ax: Any | None = None, color: str = 'b', title: str = '') → Any[source]

discrete_optimization.generic_tools.result_storage.result_storage.plot_pareto_2d(pareto_front: ParetoFront, name_axis: list[str], ax: Any | None = None, color: str = 'b') → Any[source]

discrete_optimization.generic_tools.result_storage.result_storage.plot_storage_2d(result_storage: ResultStorage, name_axis: list[str], ax: Any | None = None, color: str = 'r') → None[source]

discrete_optimization.generic_tools.result_storage.result_storage.result_storage_to_pareto_front(result_storage: ResultStorage, problem: Problem | None = None) → ParetoFront[source]

discrete_optimization.generic_tools.result_storage.resultcomparator module

class discrete_optimization.generic_tools.result_storage.resultcomparator.ResultComparator(list_result_storage: list[ResultStorage], result_storage_names: list[str], objectives_str: list[str], objective_weights: list[int], test_problems: list[Problem] | None = None)[source]