ml4co_kit.task.routing.tsp.spctsp

Stochastic Prize Collecting Traveling Salesman Problem (SPCTSP).

The SPCTSP is a variant of PCTSP where the actual prize values are revealed only when nodes are visited, making it a stochastic optimization problem.

Classes

SPCTSPTask(distance_type, round_type, ...)

class ml4co_kit.task.routing.tsp.spctsp.SPCTSPTask(distance_type: ~ml4co_kit.task.routing.base.DISTANCE_TYPE = DISTANCE_TYPE.EUC_2D, round_type: ~ml4co_kit.task.routing.base.ROUND_TYPE = ROUND_TYPE.NO, precision: ~numpy.float32 | ~numpy.float64 = <class 'numpy.float32'>, threshold: float = 1e-05)

Bases: RoutingTaskBase

check_constraints(sol: ndarray) → bool

Check if the solution is valid.

evaluate(sol: ndarray, check_constr: bool = True) → floating

Evaluate the total cost of the SPCTSP solution.

from_data(depots: ndarray | None = None, points: ndarray | None = None, penalties: ndarray | None = None, expected_prizes: ndarray | None = None, actual_prizes: ndarray | None = None, required_prize: float | None = None, sol: ndarray | None = None, ref: bool = False, normalize: bool = False, name: str | None = None)

Create a problem instance from raw data. To be implemented by subclasses.

render(save_path: Path, with_sol: bool = True, figsize: tuple = (5, 5), node_color: str = 'darkblue', edge_color: str = 'darkblue', node_size: int = 50)

Render the SPCTSP problem instance with or without solution.