import math from typing import Optional, Sequence, Tuple import torch import torch.nn as nn import torch.nn.functional as F # ============================================================ # Pair extraction (utility; not used by the losses below) # ============================================================ def get_valid_pairs_and_dt( event_seqs: torch.Tensor, time_seqs: torch.Tensor, n_tech_tokens: int ) -> Optional[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]]: """ Extract valid event pairs (prev -> next) and compute dt in years. Args: event_seqs (torch.Tensor): Event sequences. time_seqs (torch.Tensor): Time sequences. n_tech_tokens (int): Number of technical tokens. Returns: Optional[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]]: (dt, b_prev, t_prev, b_next, t_next) if valid pairs exist, else None. Notes: - Assumes strict right-padding. - Filters to next events that are disease tokens: token_id >= n_tech_tokens. - Filters to strictly positive dt. """ real_mask = event_seqs >= 1 idx = real_mask.nonzero(as_tuple=False) if idx.size(0) <= 1: return None same_batch = idx[1:, 0] == idx[:-1, 0] if not same_batch.any(): return None prev_idx = idx[:-1][same_batch] next_idx = idx[1:][same_batch] b_next, t_next = next_idx[:, 0], next_idx[:, 1] valid_target = event_seqs[b_next, t_next] >= n_tech_tokens if not valid_target.any(): return None prev_idx = prev_idx[valid_target] next_idx = next_idx[valid_target] b_prev, t_prev = prev_idx[:, 0], prev_idx[:, 1] b_next, t_next = next_idx[:, 0], next_idx[:, 1] dt = (time_seqs[b_next, t_next] - time_seqs[b_prev, t_prev]).to(torch.float32) / 365.25 valid_dt = dt > 0 if not valid_dt.any(): return None dt = dt[valid_dt] b_prev = b_prev[valid_dt] t_prev = t_prev[valid_dt] b_next = b_next[valid_dt] t_next = t_next[valid_dt] return dt, b_prev, t_prev, b_next, t_next # ============================================================ # Losses (clean interface): loss_fn(preds, target_events, dt) -> (nll, regularization) # ============================================================ class ExponentialNLLLoss(nn.Module): """ Competing risks exponential likelihood. The negative log-likelihood is given by: .. math:: \\text{nll} = -\\log \\lambda_{k^*} + \\left(\\sum_k \\lambda_k\\right) \\cdot dt Args: eps (float): Small epsilon for numerical stability. """ def __init__( self, lambda_reg: float = 0.0, eps: float = 1e-6, ): super().__init__() self.eps = eps self.lambda_reg = lambda_reg def forward( self, logits: torch.Tensor, target_events: torch.Tensor, dt: torch.Tensor, reduction: str = "mean", ) -> Tuple[torch.Tensor, torch.Tensor]: """ Forward pass. Args: logits (torch.Tensor): (M, K) tensor of logits. target_events (torch.Tensor): (M,) int64 tensor of target events in [0, K). dt (torch.Tensor): (M,) float tensor of time intervals (years), strictly positive. reduction (str): 'mean', 'sum', or 'none'. Returns: Tuple[torch.Tensor, torch.Tensor]: (nll, regularization) where regularization is 0. """ logits = logits.squeeze(-1) if logits.dim() == 3 else logits hazards = F.softplus(logits) + self.eps # (M,K) hazard_event = hazards.gather( 1, target_events.unsqueeze(1)).squeeze(1) # (M,) total_hazard = hazards.sum(dim=1) # (M,) log_hazards = torch.log(hazards) # (M, K) nll = -torch.log(hazard_event) + total_hazard * dt if reduction == "mean": nll = nll.mean() elif reduction == "sum": nll = nll.sum() reg = F.cross_entropy(log_hazards, target_events, reduction="mean") * self.lambda_reg return nll, reg class DiscreteTimeCIFNLLLoss(nn.Module): """Direct discrete-time CIF negative log-likelihood (no censoring). This loss assumes the model outputs per-bin logits over (K causes + 1 complement) channels, where the complement channel (index K) represents survival across bins. Per-sample likelihood for observed cause k at time bin j: p = \\prod_{u=1}^{j-1} p(comp at u) * p(k at j) Args: bin_edges: Increasing sequence of floats of length (n_bins + 1) with bin_edges[0] == 0. eps: Unused; kept for interface compatibility / future numerical tweaks. lambda_reg: Optional regularization strength. """ def __init__( self, bin_edges: Sequence[float], eps: float = 1e-6, lambda_reg: float = 0.0, ): super().__init__() if len(bin_edges) < 2: raise ValueError("bin_edges must have length >= 2 (n_bins >= 1)") if float(bin_edges[0]) != 0.0: raise ValueError("bin_edges[0] must equal 0") for i in range(1, len(bin_edges)): if not (float(bin_edges[i]) > float(bin_edges[i - 1])): raise ValueError("bin_edges must be strictly increasing") self.eps = float(eps) self.lambda_reg = float(lambda_reg) self.register_buffer( "bin_edges", torch.tensor(bin_edges, dtype=torch.float32), persistent=False, ) def forward( self, logits: torch.Tensor, target_events: torch.Tensor, dt: torch.Tensor, reduction: str = "mean", ) -> Tuple[torch.Tensor, torch.Tensor]: if logits.ndim != 3: raise ValueError( f"logits must have ndim==3 with shape (M, K+1, n_bins+1); got {tuple(logits.shape)}" ) if target_events.ndim != 1 or dt.ndim != 1: raise ValueError( f"target_events and dt must be 1D tensors; got target_events.ndim={target_events.ndim}, dt.ndim={dt.ndim}" ) if logits.shape[0] != target_events.shape[0] or logits.shape[0] != dt.shape[0]: raise ValueError( "Batch size mismatch: logits.shape[0] must equal target_events.shape[0] and dt.shape[0]" ) if reduction not in {"mean", "sum", "none"}: raise ValueError("reduction must be one of {'mean','sum','none'}") if not torch.all(dt > 0): raise ValueError("dt must be strictly positive") # Infer K and n_bins from logits and bin_edges. m, k_plus_1, n_bins_plus_1 = logits.shape k_comp = k_plus_1 - 1 if k_comp < 1: raise ValueError( "logits.shape[1] must be at least 2 (K>=1 plus complement channel)") n_bins = int(self.bin_edges.numel() - 1) if n_bins_plus_1 != n_bins + 1: raise ValueError( f"logits.shape[2] must equal n_bins+1={n_bins + 1} based on bin_edges; got {n_bins_plus_1}" ) if target_events.dtype != torch.long: target_events = target_events.to(torch.long) if (target_events < 0).any() or (target_events >= k_comp).any(): raise ValueError( f"target_events must be in [0, K-1] where K={k_comp}; got min={int(target_events.min())}, max={int(target_events.max())}" ) # Map continuous dt to discrete bins j in {1..n_bins}. bin_edges = self.bin_edges.to(device=dt.device, dtype=dt.dtype) # (M,), may be n_bins+1 if dt > bin_edges[-1] time_bin = torch.bucketize(dt, bin_edges) time_bin = torch.clamp(time_bin, min=1, max=n_bins).to( torch.long) # ensure valid event bins # Log-probabilities across causes+complement for each bin. logp = F.log_softmax(logits, dim=1) # (M, K+1, n_bins+1) # Previous survival term: sum_{u=1}^{j-1} -log p(comp at u) bins = torch.arange(n_bins + 1, device=logits.device) # (n_bins+1,) mask = (bins.unsqueeze(0) >= 1) & (bins.unsqueeze( 0) < time_bin.unsqueeze(1)) # (M, n_bins+1) logp_comp = logp[:, k_comp, :] # (M, n_bins+1) loss_prev = -(logp_comp * mask.to(logp_comp.dtype)).sum(dim=1) # (M,) # Event term at bin j: -log p(k at j) m_idx = torch.arange(m, device=logits.device) loss_event = -logp[m_idx, target_events, time_bin] # (M,) loss = loss_prev + loss_event if reduction == "mean": nll = loss.mean() elif reduction == "sum": nll = loss.sum() else: nll = loss reg = torch.zeros((), device=logits.device, dtype=loss.dtype) if self.lambda_reg > 0.0: # Regularize the cause distribution at the event bin using NLL on log-probs. logp_causes = logp[:, :k_comp, :] # (M, K, n_bins+1) idx = time_bin.view(m, 1, 1).expand(-1, k_comp, 1) logp_at_event_bin = logp_causes.gather( dim=2, index=idx).squeeze(2) # (M, K) reg = self.lambda_reg * \ F.nll_loss(logp_at_event_bin, target_events, reduction="mean") return nll, reg