DeepHealth/losses.py

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


class PiecewiseExponentialCIFNLLLoss(nn.Module):
    """
    Piecewise-Exponential (PWE) cause-specific hazards with discrete-time CIF likelihood.
    - No censoring
    - No regularization (reg always 0)
    - Forward signature matches DiscreteTimeCIFNLLLoss:
        forward(logits, target_events, dt, reduction) -> (nll, reg)

    Expected shapes:
        logits: (M, K, n_bins)  # hazard logits per cause per bin
        target_events: (M,) long in [0, K-1]
        dt: (M,) event times (strictly > 0)

    bin_edges:
        length n_bins+1, strictly increasing, bin_edges[0]==0,
        and MUST be finite at the last edge (no +inf) for PWE.
    """

    def __init__(
        self,
        bin_edges: Sequence[float],
        eps: float = 1e-6,
        lambda_reg: float = 0.0,  # kept for signature compatibility; UNUSED
    ):
        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 0.0")
        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")
        if math.isinf(float(bin_edges[-1])):
            raise ValueError(
                "PiecewiseExponentialCIFNLLLoss requires a finite last bin edge (no +inf). "
                "Use a finite truncation horizon for PWE."
            )

        self.eps = float(eps)
        # unused, kept only for interface compatibility
        self.lambda_reg = float(lambda_reg)

        self.register_buffer(
            "bin_edges",
            torch.tensor([float(x) for x in 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 reduction not in {"mean", "sum", "none"}:
            raise ValueError("reduction must be one of {'mean','sum','none'}")

        if logits.ndim != 3:
            raise ValueError(
                f"logits must be 3D (M, K, n_bins); got shape={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 not torch.all(dt > 0):
            raise ValueError(
                "dt must be strictly positive (no censoring supported here)")

        M, K, n_bins = logits.shape

        if target_events.dtype != torch.long:
            target_events = target_events.to(torch.long)
        if (target_events < 0).any() or (target_events >= K).any():
            raise ValueError(f"target_events must be in [0, {K-1}]")

        # Prepare bin_edges / bin widths
        bin_edges = self.bin_edges.to(device=dt.device, dtype=dt.dtype)
        if bin_edges.numel() != n_bins + 1:
            raise ValueError(
                f"bin_edges length must be n_bins+1={n_bins+1}; got {bin_edges.numel()}"
            )

        dt_bins = (bin_edges[1:] - bin_edges[:-1]
                   ).to(device=logits.device, dtype=logits.dtype)  # (n_bins,)
        if not torch.isfinite(dt_bins).all():
            raise ValueError("All bin widths must be finite for PWE.")
        if not (dt_bins > 0).all():
            raise ValueError(
                "All bin widths must be strictly positive for PWE.")

        # Map event time -> bin index k* in {1..n_bins}
        # (same convention as your discrete_time_cif: clamp to [1, n_bins])
        time_bin = torch.bucketize(dt, bin_edges)
        time_bin = torch.clamp(
            time_bin, min=1, max=n_bins).to(torch.long)  # (M,)
        k0 = time_bin - 1  # 0..n_bins-1

        # Nonnegative hazards per cause per bin
        hazards = F.softplus(logits) + self.eps  # (M, K, n_bins)

        # Integrated hazards H_{j,k} = lambda_{j,k} * Δt_k
        H_jk = hazards * dt_bins.view(1, 1, n_bins)  # (M, K, n_bins)
        H_k = H_jk.sum(dim=1)  # (M, n_bins)

        # Previous survival term: Σ_{u<k*} H_u
        bins = torch.arange(
            1, n_bins + 1, device=logits.device).unsqueeze(0)  # (1, n_bins)
        mask_prev = bins < time_bin.unsqueeze(1)  # (M, n_bins)
        loss_prev = (H_k * mask_prev.to(H_k.dtype)).sum(dim=1)  # (M,)

        # Event term at k*: -log p_{k*}(cause)
        m_idx = torch.arange(M, device=logits.device)

        H_event_total = torch.clamp(H_k[m_idx, k0], min=self.eps)  # (M,)
        H_event_cause = torch.clamp(
            H_jk[m_idx, target_events, k0], min=self.eps)  # (M,)

        # log(1 - exp(-H)) stable
        log1mexp = torch.log(-torch.expm1(-H_event_total))  # (M,)
        loss_event = -log1mexp - \
            torch.log(H_event_cause) + torch.log(H_event_total)

        loss_vec = loss_prev + loss_event  # (M,)

        if reduction == "mean":
            nll = loss_vec.mean()
        elif reduction == "sum":
            nll = loss_vec.sum()
        else:
            nll = loss_vec

        if self.lambda_reg > 0.0 and n_bins >= 3:
            log_h = torch.log(hazards)  # (M, K, n_bins)
            d2 = log_h[:, :, 2:] - 2.0 * log_h[:, :, 1:-1] + \
                log_h[:, :, :-2]  # (M, K, n_bins-2)
            reg = self.lambda_reg * (d2.pow(2).mean())
        else:
            reg = torch.zeros((), device=logits.device, dtype=loss_vec.dtype)

        return nll, reg