nnf_mc_loss: Max-Constraint Margin Loss (functional)

In tabnet: Fit 'TabNet' Models for Classification and Regression

Max-Constraint Margin Loss (functional)

Description

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).

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)

Value

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

See Also

nn_mc_loss(), get_constr_output()

tabnet documentation built on June 12, 2026, 5:06 p.m.