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Client ranker

LinearClientRanker

Bases: EntityProcessor

Sort and index clients by budget-to-weight ratio on each route.

For each route \((i,j)\), clients are sorted in descending order of \(b_{ij}^z / a_{ij}^z\) and assigned zero-based indices \(z = 0, 1, 2, \dots\). This ranking is essential for the [PrecendenceConstraint][oracle_paper.constraints.precedence_constraint.PrecendenceConstraint] and all Lagrange multiplier calculations.

Replaces the raw client entities (keyed by original client ID) with re-indexed entities keyed by \((i,j,z)\) tuples.

process(dataset)

Rank clients and re-index entities by \((i,j,z)\).

Parameters:

Name Type Description Default
dataset MultiEntityDataset

Dataset with client routes, budgets, and weights (modified in-place).

 required
Source code in src/oracle_paper/data/processor/client_ranker.py 
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def process(self, dataset: MultiEntityDataset) -> None:
    """Rank clients and re-index entities by $(i,j,z)$.

    Args:
        dataset: Dataset with client routes, budgets, and weights
            (modified in-place).
    """
    client_routes = dataset[BilevelDataCol.CLIENT_ROUTE]
    budgets = dataset[BilevelDataCol.BUDGET]
    weights = dataset[BilevelDataCol.TRANSPORT_WEIGHT_CLIENT]


    route_buckets = defaultdict(list)
    for (c_id,), (i, j) in client_routes.items():
        route_buckets[(i, j)].append(c_id)

    # mapping from client key -> (i,j,z)
    math_keys: Dict[Tuple, tuple] = {}

    math_weights: Dict[Tuple, float] = {}
    math_budgets: Dict[Tuple, float] = {}
    math_ratios: Dict[Tuple, float] = {}
    math_client_ids: Dict[Tuple, int] = {}

    for (i, j), clients in route_buckets.items():
        client_ratios = []
        for c_id in clients:
            ratio = budgets[c_id] / weights[c_id]
            client_ratios.append((c_id, ratio))

        client_ratios.sort(key=lambda x: x[1], reverse=True)


        for z, (c_id, ratio) in enumerate(client_ratios):
            math_key = (i, j, z)

            math_keys[(c_id,)] = math_key

            math_weights[math_key] = weights[c_id]
            math_budgets[math_key] = budgets[c_id]
            math_ratios[math_key] = ratio
            math_client_ids[math_key] = z


    math_indices = [DataCol.START_NODE,
                             DataCol.END_NODE,
                             BilevelDataCol.CLIENT_ID_ROUTE]

    dataset.add_entity(BilevelDataCol.CLIENT_KEY, [BilevelDataCol.CLIENT_KEY], math_keys)

    dataset.add_entity(BilevelDataCol.TRANSPORT_WEIGHT_CLIENT, math_indices, math_weights)
    dataset.add_entity(BilevelDataCol.BUDGET, math_indices, math_budgets)
    dataset.add_entity(BilevelDataCol.CLIENT_RATIO, math_indices, math_ratios)

    dataset.add_entity(BilevelDataCol.CLIENT_ID_ROUTE, math_indices, math_client_ids)