Add LogNormalBasisHazardLoss implementation and update training configuration

losses.py

@@ -1,5 +1,5 @@
 import math
-from typing import Optional, Sequence, Tuple
+from typing import Any, Dict, Optional, Sequence, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -258,3 +258,233 @@ class DiscreteTimeCIFNLLLoss(nn.Module):
                 F.nll_loss(logp_at_event_bin, target_events, reduction="mean")
 
         return nll, reg
+
+
+class LogNormalBasisHazardLoss(nn.Module):
+    """Event-only competing risks loss using lognormal basis (Gaussian on log-time).
+
+    This loss models cause-specific CIF as a mixture of lognormal basis CDFs:
+
+        F_j(t) = sum_r w_{j,r} * Phi((log t - mu_r) / sigma)
+
+    Training uses *bin probability mass* (Delta CIF / interval mass). There is
+    **no censoring**: every sample is an observed event with a valid cause.
+
+    Logits interface:
+        logits: (B, 1 + J*R)
+            logits[:, 0] -> w0 (survival mass / never-event)
+            logits[:, 1:] -> flattened (j,r) in row-major order: j then r
+                index = 1 + j*R + r
+
+    Forward interface (must match):
+        forward(logits, target_events, dt, reduction)
+    """
+
+    def __init__(
+        self,
+        bin_edges: Sequence[float],
+        centers: Sequence[float],
+        *,
+        eps: float = 1e-8,
+        bandwidth_init: float = 0.5,
+        bandwidth_min: float = 1e-3,
+        bandwidth_max: float = 10.0,
+        lambda_sigma_reg: float = 0.0,
+        sigma_reg_target: Optional[float] = None,
+        return_dict: bool = False,
+    ):
+        super().__init__()
+
+        if len(bin_edges) < 2:
+            raise ValueError("bin_edges must have length >= 2")
+        # allow last edge to be +inf
+        for i in range(1, len(bin_edges)):
+            prev = float(bin_edges[i - 1])
+            cur = float(bin_edges[i])
+            if math.isinf(prev):
+                raise ValueError(
+                    "bin_edges cannot have +inf except possibly as the last edge")
+            if i == len(bin_edges) - 1 and math.isinf(cur):
+                if not (cur > prev):
+                    raise ValueError("bin_edges must be strictly increasing")
+            else:
+                if not (cur > prev):
+                    raise ValueError("bin_edges must be strictly increasing")
+        if float(bin_edges[0]) < 0.0:
+            raise ValueError("bin_edges[0] must be >= 0")
+
+        if len(centers) < 1:
+            raise ValueError("centers must have length >= 1")
+
+        self.eps = float(eps)
+        self.bandwidth_min = float(bandwidth_min)
+        self.bandwidth_max = float(bandwidth_max)
+        self.lambda_sigma_reg = float(lambda_sigma_reg)
+        self.sigma_reg_target = None if sigma_reg_target is None else float(
+            sigma_reg_target)
+        self.bandwidth_init = float(bandwidth_init)
+        self.return_dict = bool(return_dict)
+
+        self.register_buffer(
+            "bin_edges",
+            torch.tensor([float(x) for x in bin_edges], dtype=torch.float32),
+            persistent=False,
+        )
+        self.register_buffer(
+            "centers",
+            torch.tensor([float(x) for x in centers], dtype=torch.float32),
+            persistent=False,
+        )
+
+        if self.bandwidth_init <= 0:
+            raise ValueError("bandwidth_init must be > 0")
+        self.log_sigma = nn.Parameter(torch.tensor(
+            math.log(self.bandwidth_init), dtype=torch.float32))
+
+    @staticmethod
+    def _normal_cdf(z: torch.Tensor) -> torch.Tensor:
+        # Stable normal CDF via erf.
+        z = torch.clamp(z, -12.0, 12.0)
+        return 0.5 * (1.0 + torch.erf(z / math.sqrt(2.0)))
+
+    @staticmethod
+    def _normal_sf(z: torch.Tensor) -> torch.Tensor:
+        # Stable normal survival function via erfc.
+        z = torch.clamp(z, -12.0, 12.0)
+        return 0.5 * torch.erfc(z / math.sqrt(2.0))
+
+    def forward(
+        self,
+        logits: torch.Tensor,
+        target_events: torch.Tensor,
+        dt: torch.Tensor,
+        reduction: str = "mean",
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Dict[str, Any]]:
+        if logits.ndim != 2:
+            raise ValueError(
+                f"logits must be 2D with shape (B, 1+J*R); got {tuple(logits.shape)}")
+        if target_events.ndim != 1 or dt.ndim != 1:
+            raise ValueError("target_events and dt must be 1D tensors")
+        if logits.shape[0] != target_events.shape[0] or logits.shape[0] != dt.shape[0]:
+            raise ValueError(
+                "Batch size mismatch among logits, target_events, dt")
+        if reduction not in {"mean", "sum", "none"}:
+            raise ValueError("reduction must be one of {'mean','sum','none'}")
+
+        device = logits.device
+        dtype = logits.dtype
+
+        bin_edges = self.bin_edges.to(device=device, dtype=dtype)
+        centers = self.centers.to(device=device, dtype=dtype)
+        bsz = logits.shape[0]
+        r = int(centers.numel())
+        jr = int(logits.shape[1] - 1)
+        if jr <= 0:
+            raise ValueError(
+                "logits.shape[1] must be >= 2 (w0 + at least one (j,r) weight)")
+        if jr % r != 0:
+            raise ValueError(
+                f"(logits.shape[1]-1) must be divisible by R={r}; got {jr}")
+        j = jr // r
+
+        # 1) Stable global weights (includes w0).
+        w_all = torch.softmax(logits, dim=-1)  # (B, 1+J*R)
+        w0 = w_all[:, 0]
+        w = w_all[:, 1:].view(bsz, j, r)
+
+        # 2) Determine event bin index.
+        k = int(bin_edges.numel() - 1)
+        if k < 1:
+            raise ValueError("bin_edges must define at least one bin")
+
+        # v2: dt is always continuous time (float), map to bin via searchsorted.
+        dt_f = dt.to(device=device, dtype=dtype)
+        bin_idx = torch.searchsorted(bin_edges, dt_f, right=True) - 1
+        bin_idx = torch.clamp(bin_idx, 0, k - 1).to(torch.long)
+
+        left = bin_edges[bin_idx]
+        right = bin_edges[bin_idx + 1]
+
+        # 3) Stable log(t) clamp.
+        if float(self.bin_edges[1]) > 0.0:
+            t_min = float(self.bin_edges[1]) * 1e-6
+        else:
+            t_min = 1e-12
+        t_min_t = torch.tensor(t_min, device=device, dtype=dtype)
+
+        left_is_zero = left <= 0
+
+        # For log() we still need a positive clamp, but we will treat CDF(left)=0 exactly
+        # when left<=0 (instead of approximating via t_min).
+        left_clamped = torch.clamp(left, min=t_min_t)
+        log_left = torch.log(left_clamped)
+        is_inf = torch.isinf(right)
+        # right might be +inf for last bin; avoid log(+inf) by substituting a safe finite value.
+        right_safe = torch.where(is_inf, left_clamped,
+                                 torch.clamp(right, min=t_min_t))
+        log_right = torch.log(right_safe)
+
+        sigma = torch.clamp(self.log_sigma.to(
+            device=device, dtype=dtype).exp(), self.bandwidth_min, self.bandwidth_max)
+
+        z_left = (log_left.unsqueeze(-1) - centers.unsqueeze(0)) / sigma
+        z_right = (log_right.unsqueeze(-1) - centers.unsqueeze(0)) / sigma
+        z_left = torch.clamp(z_left, -12.0, 12.0)
+        z_right = torch.clamp(z_right, -12.0, 12.0)
+
+        cdf_left = self._normal_cdf(z_left)
+        # Treat the first-bin left boundary exactly as 0 in CDF.
+        if left_is_zero.any():
+            cdf_left = torch.where(
+                left_is_zero.unsqueeze(-1), torch.zeros_like(cdf_left), cdf_left)
+        cdf_right = self._normal_cdf(z_right)
+        delta_finite = cdf_right - cdf_left
+        delta_last = self._normal_sf(z_left)
+        # If left<=0, SF(left)=1 exactly.
+        if left_is_zero.any():
+            delta_last = torch.where(
+                left_is_zero.unsqueeze(-1), torch.ones_like(delta_last), delta_last)
+        delta_basis = torch.where(
+            is_inf.unsqueeze(-1), delta_last, delta_finite)
+        delta_basis = torch.clamp(delta_basis, min=0.0)
+
+        # 4) Gather per-sample cause weights and compute event mass.
+        cause = target_events.to(device=device, dtype=torch.long)
+        if (cause < 0).any() or (cause >= j).any():
+            raise ValueError(f"target_events must be in [0, J-1] where J={j}")
+
+        b_idx = torch.arange(bsz, device=device)
+        w_cause = w[b_idx, cause, :]  # (B, R)
+
+        m = (w_cause * delta_basis).sum(dim=-1)
+        m = torch.clamp(m, min=self.eps)
+        nll_vec = -torch.log(m)
+
+        if reduction == "mean":
+            nll: torch.Tensor = nll_vec.mean()
+        elif reduction == "sum":
+            nll = nll_vec.sum()
+        else:
+            nll = nll_vec
+
+        sigma_penalty = torch.zeros((), device=device, dtype=dtype)
+        if self.lambda_sigma_reg > 0.0:
+            target = self.bandwidth_init if self.sigma_reg_target is None else self.sigma_reg_target
+            sigma_penalty = (self.log_sigma.to(
+                device=device, dtype=dtype) - math.log(float(target))) ** 2
+        reg = sigma_penalty * float(self.lambda_sigma_reg)
+
+        if not self.return_dict:
+            return nll, reg
+
+        return {
+            "nll": nll,
+            "reg": reg,
+            "nll_vec": nll_vec,
+            "sigma": sigma.detach(),
+            "avg_w0": w0.mean().detach(),
+            "min_delta_basis": delta_basis.min().detach(),
+            "mean_m": m.mean().detach(),
+            "sigma_penalty": sigma_penalty.detach(),
+            "bin_idx": bin_idx.detach(),
+        }