Result

Benchmark result data class for a single model run.

BenchmarkResult(n_nodes, n_hubs, alpha, method, total_time, solving_time, lagrange_time, optimal, gap, node_count, n_clients, n_clients_route, random_n_clients_route, min_budget_factor, max_budget_factor, run_number, seed, sanctioned=False) dataclass

Stores the result of a single benchmark solve.

Captures the configuration parameters (nodes, hubs, clients, seed) together with the solution metrics returned by a specific model/solver run (solving time, optimality gap, node count, etc.).

from_solution(solution, config, n_nodes, n_clients, run, method) classmethod

Construct from a [BaseModelSolution][bilevelpy.solution.core.BaseModelSolution] and benchmark configuration.

Parameters:

Name	Type	Description	Default
solution	BaseModelSolution	The solution returned by the solver.	required
config	BenchmarkConfig	The benchmark configuration.	required
n_nodes	int	Number of nodes in the instance.	required
n_clients	int	Number of clients per route.	required
run	int	Run number (1-indexed, for seed lookup).	required
method	str	Method string key (e.g. "pc_hlp").	required

Source code in src/oracle_paper/benchmark/result.py

@classmethod
def from_solution(
    cls,
    solution: BaseModelSolution,
    config: BenchmarkConfig,
    n_nodes: int,
    n_clients: int,
    run: int,
    method: str,
) -> "BenchmarkResult":
    """Construct from a
    [`BaseModelSolution`][bilevelpy.solution.core.BaseModelSolution]
    and benchmark configuration.

    Args:
        solution: The solution returned by the solver.
        config: The benchmark configuration.
        n_nodes: Number of nodes in the instance.
        n_clients: Number of clients per route.
        run: Run number (1-indexed, for seed lookup).
        method: Method string key (e.g. ``"pc_hlp"``).
    """
    meta = solution.solution_metadata
    extra = meta.extra if hasattr(meta, "extra") else {}

    lagrange_time = extra.get("lagrange_time", 0.0) or 0.0
    total_time = float(meta.solving_time) + float(lagrange_time)

    return cls(
        n_nodes=n_nodes,
        n_hubs=config.n_hubs,
        alpha=config.alpha,
        method=method,
        total_time=total_time,
        solving_time=float(meta.solving_time),
        lagrange_time=float(lagrange_time),
        optimal=meta.is_optimal,
        gap=float(meta.mip_gap),
        node_count=int(meta.node_count),
        n_clients=extra.get("n_clients", 0),
        n_clients_route=n_clients,
        random_n_clients_route=config.random_n_clients_route,
        min_budget_factor=config.min_budget_factor,
        max_budget_factor=config.max_budget_factor,
        run_number=run,
        seed=config.seed_dict[run],
    )

sanctioned_method(config, method, n_nodes, n_clients, run) classmethod

Create a placeholder result for a sanctioned (skipped) method.

Source code in src/oracle_paper/benchmark/result.py

@classmethod
def sanctioned_method(
    cls,
    config: BenchmarkConfig,
    method: str,
    n_nodes: int,
    n_clients: int,
    run: int,
) -> "BenchmarkResult":
    """Create a placeholder result for a sanctioned (skipped) method."""
    return cls(
        n_nodes=n_nodes,
        n_hubs=config.n_hubs,
        alpha=config.alpha,
        method=method,
        total_time=None,
        solving_time=None,
        lagrange_time=None,
        optimal=None,
        gap=None,
        node_count=None,
        n_clients=None,
        n_clients_route=n_clients,
        random_n_clients_route=config.random_n_clients_route,
        min_budget_factor=config.min_budget_factor,
        max_budget_factor=config.max_budget_factor,
        run_number=run,
        seed=config.seed_dict[run],
        sanctioned=True,
    )

to_dict()

Convert to a plain dictionary.

Source code in src/oracle_paper/benchmark/result.py

def to_dict(self) -> Dict[str, Any]:
    """Convert to a plain dictionary."""
    return asdict(self)