Max-Constraint Margin Loss (functional)

Computes the hierarchy-constrained loss for multi-label classification. Enforces that if a class is predicted positive, all its ancestors must also be positive, using the ancestor matrix R.

Usage

nnf_mc_loss(
  output,
  target,
  R,
  to_eval = NULL,
  criterion = nnf_binary_cross_entropy_with_logits
)

Arguments

output

A torch_tensor of raw network outputs (pre-sigmoid), shape (batch_size, n_classes).

target

Binary target labels, shape (batch_size, n_classes).

R

Ancestor matrix tensor of shape (1, n_classes, n_classes) where R[1, i, j] = 1 iff class i is a descendant of class j.

to_eval

Optional logical tensor of shape (n_classes,) indicating which classes to include in the loss computation. If NULL, all classes are evaluated.

criterion

Loss function to apply after constraint propagation. Default: nnf_binary_cross_entropy_with_logits (expects raw logits).

Value

A scalar torch_tensor containing the computed loss, or a tensor of shape (batch_size, n_classes) if reduction = "none".

Details

The loss combines constrained outputs differently for positive and negative labels:

• For positive labels: uses constrained output of label-weighted predictions

• For negative labels: uses constrained raw predictions (penalizes ancestor violations)

See also

nn_mc_loss(), get_constr_output()