nn_mc_loss: Max-Constraint Margin Loss (module)

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

Max-Constraint Margin Loss (module)

Description

Module wrapper for nnf_mc_loss() with configurable parameters. Stores the ancestor matrix R and evaluation mask for reuse across batches.

Usage

nn_mc_loss(
  R,
  to_eval = NULL,
  criterion = nnf_binary_cross_entropy_with_logits,
  reduction = "mean"
)

Arguments

R	Ancestor matrix tensor of shape ⁠(1, n_classes, n_classes)⁠.
to_eval	Optional logical tensor of shape ⁠(n_classes,)⁠ indicating which classes to include in loss computation.
criterion	Loss function module or functional to apply after constraint propagation. Default: nn_binary_cross_entropy_with_logits().
reduction	(string, optional): Reduction method: 'none' | 'mean' | 'sum'.

Shape

• Input output: (N, C) where N = batch size, C = number of classes

• Input target: (N, C), same shape as output, binary values

• Output: scalar by default. If reduction = "none", then (N, C') where C' is the number of evaluated classes

See Also

nnf_mc_loss(), build_ancestor_matrix_from_outcomes(), get_constr_output()

Examples

## Not run: 
# Build ancestor matrix from hierarchy
R <- build_ancestor_matrix_from_outcomes(my_tree, processed$outcomes, device = "cuda")

# Create loss module
loss_fn <- nn_mc_loss(R = R, reduction = "mean")

# Forward pass
output <- model(x)  # (batch, n_classes)
loss <- loss_fn(output, labels)
loss$backward()

## End(Not run)

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