Add binary metrics computation and refactor evaluation logic in age-bin evaluation

evaluation_age_time_dependent.py

@@ -20,6 +20,8 @@ from utils import (
     sample_context_in_fixed_age_bin,
 )
 
+from torch_metrics import compute_binary_metrics_torch
+
 
 def aggregate_age_bin_results(df_by_bin: pd.DataFrame) -> pd.DataFrame:
     """Aggregate per-bin age evaluation results.
@@ -173,105 +175,8 @@ def aggregate_age_bin_results(df_by_bin: pd.DataFrame) -> pd.DataFrame:
     return df_agg
 
 
-def _binary_roc_auc(y_true: np.ndarray, y_score: np.ndarray) -> float:
-    """Compute ROC AUC for binary labels with tie-aware ranking.
-
-    Returns NaN if y_true has no positives or no negatives.
-
-    Uses the Mann–Whitney U statistic with average ranks for ties.
-    """
-    y_true = np.asarray(y_true).astype(bool)
-    y_score = np.asarray(y_score).astype(float)
-
-    n = y_true.size
-    if n == 0:
-        return float("nan")
-
-    n_pos = int(y_true.sum())
-    n_neg = n - n_pos
-    if n_pos == 0 or n_neg == 0:
-        return float("nan")
-
-    # Rank scores ascending, average ranks for ties.
-    order = np.argsort(y_score, kind="mergesort")
-    sorted_scores = y_score[order]
-
-    ranks = np.empty(n, dtype=float)
-    i = 0
-    # 1-based ranks
-    while i < n:
-        j = i + 1
-        while j < n and sorted_scores[j] == sorted_scores[i]:
-            j += 1
-        avg_rank = 0.5 * ((i + 1) + j)  # ranks i+1 .. j
-        ranks[order[i:j]] = avg_rank
-        i = j
-
-    sum_ranks_pos = float(ranks[y_true].sum())
-    u = sum_ranks_pos - (n_pos * (n_pos + 1) / 2.0)
-    return float(u / (n_pos * n_neg))
-
-
-def _average_precision(y_true: np.ndarray, y_score: np.ndarray) -> float:
-    """Average precision (area under PR curve using step-wise interpolation).
-
-    Returns NaN if no positives.
-    """
-    y_true = np.asarray(y_true).astype(bool)
-    y_score = np.asarray(y_score).astype(float)
-
-    n = y_true.size
-    if n == 0:
-        return float("nan")
-
-    n_pos = int(y_true.sum())
-    if n_pos == 0:
-        return float("nan")
-
-    order = np.argsort(-y_score, kind="mergesort")
-    y = y_true[order]
-
-    tp = np.cumsum(y).astype(float)
-    fp = np.cumsum(~y).astype(float)
-    precision = tp / np.maximum(tp + fp, 1.0)
-
-    # AP = sum over each positive of precision at that point / n_pos
-    # (equivalent to ∑ Δrecall * precision)
-    ap = float(np.sum(precision[y]) / n_pos)
-    # handle potential tiny numerical overshoots
-    return float(max(0.0, min(1.0, ap)))
-
-
-def _precision_recall_at_k_percent(
-    y_true: np.ndarray,
-    y_score: np.ndarray,
-    k_percent: float,
-) -> Tuple[float, float]:
-    """Precision@K% and Recall@K% for binary labels.
-
-    Returns (precision, recall). Returns NaN for recall if no positives.
-    Returns NaN for precision if k leads to 0 selected.
-    """
-    y_true = np.asarray(y_true).astype(bool)
-    y_score = np.asarray(y_score).astype(float)
-
-    n = y_true.size
-    if n == 0:
-        return float("nan"), float("nan")
-
-    n_pos = int(y_true.sum())
-
-    k = int(math.ceil((float(k_percent) / 100.0) * n))
-    if k <= 0:
-        return float("nan"), float("nan")
-
-    order = np.argsort(-y_score, kind="mergesort")
-    top = order[:k]
-    tp_top = int(y_true[top].sum())
-
-    precision = tp_top / k
-    recall = float("nan") if n_pos == 0 else (tp_top / n_pos)
-    return float(precision), float(recall)
+# NOTE: metric computation is torch/GPU-native in `torch_metrics.py`.
+# NumPy/Pandas are only used for final CSV formatting/aggregation.
 
 
 @dataclass
@@ -341,18 +246,21 @@ def evaluate_time_dependent_age_bins(
             list(cfg.cause_ids), dtype=torch.long, device=device)
         n_causes_eval = int(cause_ids.numel())
 
-    # Storage: (mc, h, bin) -> list of CPU tensors (avoid .numpy() in inner loops)
-    y_true: List[List[List[List[torch.Tensor]]]] = [
-        [[[] for _ in range(len(age_bins))] for _ in range(len(horizons_years))]
-        for _ in range(int(cfg.n_mc))
-    ]
-    y_pred: List[List[List[List[torch.Tensor]]]] = [
-        [[[] for _ in range(len(age_bins))] for _ in range(len(horizons_years))]
-        for _ in range(int(cfg.n_mc))
-    ]
+    rows_by_bin: List[Dict[str, float | int]] = []
 
     for mc_idx in range(int(cfg.n_mc)):
         global_mc_idx = int(mc_offset) + int(mc_idx)
+
+        # Storage for this MC only: (tau, bin) -> list of GPU tensors.
+        # This keeps computations GPU-first while preventing a factor-n_mc
+        # blow-up in GPU memory.
+        y_true_mc: List[List[List[torch.Tensor]]] = [
+            [[] for _ in range(len(age_bins))] for _ in range(len(horizons_years))
+        ]
+        y_pred_mc: List[List[List[torch.Tensor]]] = [
+            [[] for _ in range(len(age_bins))] for _ in range(len(horizons_years))
+        ]
+
         # tqdm over batches; include MC idx in description.
         for batch_idx, batch in enumerate(
             tqdm(dataloader,
@@ -429,24 +337,17 @@ def evaluate_time_dependent_age_bins(
                         )
                         preds = cifs.index_select(dim=1, index=cause_ids)
 
-                    # Reduce CPU/NumPy conversion overhead: keep as CPU torch tensors
-                    # and convert to NumPy once during aggregation.
-                    y_true[mc_idx][tau_idx][bin_idx].append(
-                        y[keep].detach().to(dtype=torch.bool,
-                                            device="cpu", non_blocking=True)
+                    y_true_mc[tau_idx][bin_idx].append(
+                        y[keep].detach().to(dtype=torch.bool)
                     )
-                    y_pred[mc_idx][tau_idx][bin_idx].append(
-                        preds[keep].detach().to(dtype=torch.float32,
-                                                device="cpu", non_blocking=True)
+                    y_pred_mc[tau_idx][bin_idx].append(
+                        preds[keep].detach().to(dtype=torch.float32)
                     )
 
-    rows_by_bin: List[Dict[str, float | int]] = []
-
-    for mc_idx in range(int(cfg.n_mc)):
-        global_mc_idx = int(mc_offset) + int(mc_idx)
+        # Aggregate this MC immediately (frees GPU memory before next MC).
         for h_idx, tau_y in enumerate(horizons_years):
             for bin_idx, (a_lo, a_hi) in enumerate(age_bins):
-                if len(y_true[mc_idx][h_idx][bin_idx]) == 0:
+                if len(y_true_mc[h_idx][bin_idx]) == 0:
                     # No samples in this bin for this (mc, tau)
                     for cause_k in range(n_causes_eval):
                         cause_id = int(cause_k) if cause_ids is None else int(
@@ -472,34 +373,37 @@ def evaluate_time_dependent_age_bins(
                             )
                     continue
 
-                yb_t = torch.cat(y_true[mc_idx][h_idx][bin_idx], dim=0)
-                pb_t = torch.cat(y_pred[mc_idx][h_idx][bin_idx], dim=0)
+                yb_t = torch.cat(y_true_mc[h_idx][bin_idx], dim=0)
+                pb_t = torch.cat(y_pred_mc[h_idx][bin_idx], dim=0)
                 if tuple(yb_t.shape) != tuple(pb_t.shape):
                     raise ValueError(
                         f"Shape mismatch mc={mc_idx} tau={tau_y} bin={bin_idx}: y{tuple(yb_t.shape)} vs p{tuple(pb_t.shape)}"
                     )
 
-                yb = yb_t.numpy()
-                pb = pb_t.numpy()
+                n_samples = int(yb_t.size(0))
 
-                n_samples = int(yb.shape[0])
+                metrics = compute_binary_metrics_torch(
+                    y_true=yb_t,
+                    y_pred=pb_t,
+                    k_percents=topk_percents,
+                    tie_mode="exact",
+                    chunk_size=128,
+                    compute_ici=False,
+                )
+
+                # Move just the metric vectors to CPU once per (mc, tau, bin)
+                # for DataFrame construction.
+                auc = metrics.auc_per_cause.detach().cpu().numpy()
+                auprc = metrics.ap_per_cause.detach().cpu().numpy()
+                brier = metrics.brier_per_cause.detach().cpu().numpy()
+                n_pos = metrics.n_pos_per_cause.detach().cpu().numpy()
+                prec_at_k = metrics.precision_at_k.detach().cpu().numpy()  # (P,K)
+                rec_at_k = metrics.recall_at_k.detach().cpu().numpy()  # (P,K)
 
                 for cause_k in range(n_causes_eval):
-                    yk = yb[:, cause_k]
-                    pk = pb[:, cause_k]
-                    n_pos = int(yk.sum())
-
-                    auc = _binary_roc_auc(yk, pk)
-                    auprc = _average_precision(yk, pk)
-                    brier = float(np.mean(
-                        (yk.astype(float) - pk.astype(float)) ** 2)) if n_samples > 0 else float("nan")
-
                     cause_id = int(cause_k) if cause_ids is None else int(
                         cfg.cause_ids[cause_k])
-
-                    for k_percent in topk_percents:
-                        precision_k, recall_k = _precision_recall_at_k_percent(
-                            yk, pk, float(k_percent))
+                    for p_idx, k_percent in enumerate(topk_percents):
                         rows_by_bin.append(
                             dict(
                                 mc_idx=global_mc_idx,
@@ -510,12 +414,12 @@ def evaluate_time_dependent_age_bins(
                                 topk_percent=float(k_percent),
                                 cause_id=cause_id,
                                 n_samples=n_samples,
-                                n_positives=n_pos,
-                                auc=float(auc),
-                                auprc=float(auprc),
-                                recall_at_K=float(recall_k),
-                                precision_at_K=float(precision_k),
-                                brier_score=float(brier),
+                                n_positives=int(n_pos[cause_k]),
+                                auc=float(auc[cause_k]),
+                                auprc=float(auprc[cause_k]),
+                                recall_at_K=float(rec_at_k[p_idx, cause_k]),
+                                precision_at_K=float(prec_at_k[p_idx, cause_k]),
+                                brier_score=float(brier[cause_k]),
                             )
                         )
 

torch_metrics.py

@@ -0,0 +1,524 @@
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import Dict, Iterable, List, Literal, Optional, Sequence, Tuple
+
+import torch
+
+
+TieMode = Literal["exact", "approx"]
+
+
+def _stable_sort(
+    x: torch.Tensor,
+    *,
+    dim: int,
+    descending: bool,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Stable torch sort when available.
+
+    Determinism notes:
+    - When `stable=True` is supported by the installed PyTorch, we request it.
+    - Otherwise we fall back to `torch.sort`. For identical inputs on the same
+      device/runtime, this is typically deterministic, but tie ordering is not
+      guaranteed to be stable across versions.
+    """
+    try:
+        return torch.sort(x, dim=dim, descending=descending, stable=True)
+    except TypeError:
+        return torch.sort(x, dim=dim, descending=descending)
+
+
+def _nanmean(x: torch.Tensor) -> torch.Tensor:
+    mask = torch.isfinite(x)
+    if not bool(mask.any()):
+        return torch.tensor(float("nan"), device=x.device, dtype=x.dtype)
+    return x[mask].mean()
+
+
+def _nanweighted_mean(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
+    x = x.to(torch.float32)
+    w = w.to(torch.float32)
+    mask = torch.isfinite(x) & torch.isfinite(w) & (w > 0)
+    if not bool(mask.any()):
+        return torch.tensor(float("nan"), device=x.device, dtype=torch.float32)
+    ww = w[mask]
+    return (x[mask] * ww).sum() / ww.sum()
+
+
+def _validate_binary_inputs(y_true: torch.Tensor, y_score: torch.Tensor) -> None:
+    if y_true.ndim != 2 or y_score.ndim != 2:
+        raise ValueError(
+            f"Expected y_true and y_score to be 2D (N,K); got {tuple(y_true.shape)} and {tuple(y_score.shape)}"
+        )
+    if tuple(y_true.shape) != tuple(y_score.shape):
+        raise ValueError(
+            f"Shape mismatch: y_true{tuple(y_true.shape)} vs y_score{tuple(y_score.shape)}"
+        )
+
+
+def brier_per_cause(y_true: torch.Tensor, y_score: torch.Tensor) -> torch.Tensor:
+    """Brier score per cause.
+
+    Args:
+        y_true: (N,K) bool/int tensor
+        y_score: (N,K) float tensor
+
+    Returns:
+        (K,) float32 tensor; NaN if N==0.
+    """
+    _validate_binary_inputs(y_true, y_score)
+    if y_true.numel() == 0:
+        return torch.full((y_true.size(1),), float("nan"), device=y_true.device, dtype=torch.float32)
+    yt = y_true.to(torch.float32)
+    ys = y_score.to(torch.float32)
+    return ((ys - yt) ** 2).mean(dim=0)
+
+
+def ici_per_cause_fixed_width(
+    y_true: torch.Tensor,
+    y_score: torch.Tensor,
+    *,
+    n_bins: int = 15,
+    chunk_size: int = 128,
+) -> torch.Tensor:
+    """Integrated Calibration Index (ICI) via fixed-width bins on [0,1].
+
+    ICI per cause = E[ |p_bin - y_bin| ] where bin stats are computed over
+    fixed-width probability bins.
+
+    This is deterministic and GPU-friendly (scatter_add based).
+
+    Returns:
+        (K,) float32 tensor; NaN when N==0.
+    """
+    _validate_binary_inputs(y_true, y_score)
+    if int(n_bins) <= 1:
+        raise ValueError("n_bins must be >= 2")
+
+    device = y_true.device
+    N, K = y_true.shape
+    if N == 0:
+        return torch.full((K,), float("nan"), device=device, dtype=torch.float32)
+
+    yt = y_true.to(torch.float32)
+    ys = y_score.to(torch.float32).clamp(0.0, 1.0)
+
+    out = torch.full((K,), float("nan"), device=device, dtype=torch.float32)
+
+    for start in range(0, K, int(chunk_size)):
+        end = min(K, start + int(chunk_size))
+        ys_c = ys[:, start:end]
+        yt_c = yt[:, start:end]
+
+        # bin index in [0, n_bins-1]
+        bin_idx = torch.clamp(
+            (ys_c * float(n_bins)).to(torch.long), max=int(n_bins) - 1)
+
+        counts = torch.zeros((int(n_bins), end - start),
+                             device=device, dtype=torch.float32)
+        pred_sums = torch.zeros_like(counts)
+        true_sums = torch.zeros_like(counts)
+
+        ones = torch.ones_like(ys_c, dtype=torch.float32)
+        counts.scatter_add_(0, bin_idx, ones)
+        pred_sums.scatter_add_(0, bin_idx, ys_c)
+        true_sums.scatter_add_(0, bin_idx, yt_c)
+
+        denom = counts.clamp(min=1.0)
+        pred_mean = pred_sums / denom
+        true_mean = true_sums / denom
+
+        abs_gap = (pred_mean - true_mean).abs()
+        # sample-weighted average of bin gap
+        total = counts.sum(dim=0).clamp(min=1.0)
+        ici = (abs_gap * counts).sum(dim=0) / total
+
+        # If a cause has no samples (shouldn't happen when N>0), mark NaN.
+        out[start:end] = torch.where(
+            total > 0, ici.to(torch.float32), out[start:end])
+
+    return out
+
+
+def average_precision_per_cause(
+    y_true: torch.Tensor,
+    y_score: torch.Tensor,
+    *,
+    tie_mode: TieMode = "exact",
+    chunk_size: int = 128,
+) -> torch.Tensor:
+    """Average precision (AP) per cause.
+
+    Definition matches sklearn's `average_precision_score` (step-wise PR integral):
+        AP = \\sum_i (R_i - R_{i-1}) * P_i
+    where i iterates over unique score thresholds.
+
+    tie_mode:
+      - "exact": tie-invariant AP by grouping identical scores (recommended)
+      - "approx": mean precision at positive ranks; can differ under ties
+
+    Returns:
+        (K,) float32 tensor with NaN for causes with 0 positives.
+    """
+    _validate_binary_inputs(y_true, y_score)
+
+    device = y_true.device
+    N, K = y_true.shape
+    if N == 0:
+        return torch.full((K,), float("nan"), device=device, dtype=torch.float32)
+
+    yt = y_true.to(torch.bool)
+    ys = y_score.to(torch.float32)
+
+    n_pos_all = yt.sum(dim=0).to(torch.float32)
+    out = torch.full((K,), float("nan"), device=device, dtype=torch.float32)
+
+    for start in range(0, K, int(chunk_size)):
+        end = min(K, start + int(chunk_size))
+        yt_c = yt[:, start:end]
+        ys_c = ys[:, start:end]
+
+        # For exact mode we need per-cause tie grouping; do per-cause loops
+        # within a chunk to keep memory bounded and stay on GPU.
+        for j in range(end - start):
+            n_pos = n_pos_all[start + j]
+
+            scores = ys_c[:, j]
+            labels = yt_c[:, j]
+
+            if tie_mode == "approx":
+                _, order = _stable_sort(scores, dim=0, descending=True)
+                y_sorted = labels.gather(0, order).to(torch.float32)
+                tp = y_sorted.cumsum(dim=0)
+                denom = torch.arange(
+                    1, N + 1, device=device, dtype=torch.float32)
+                precision = tp / denom
+                n_pos_safe = torch.clamp(n_pos, min=1.0)
+                ap = (precision * y_sorted).sum() / n_pos_safe
+                out[start + j] = torch.where(n_pos > 0.0,
+                                             ap.to(torch.float32), out[start + j])
+                continue
+
+            # exact: group by unique score thresholds
+            sorted_scores, order = _stable_sort(scores, dim=0, descending=True)
+            y_sorted = labels.gather(0, order).to(torch.float32)
+
+            # group boundaries where score changes
+            change = torch.empty((N,), device=device, dtype=torch.bool)
+            change[0] = True
+            if N > 1:
+                change[1:] = sorted_scores[1:] != sorted_scores[:-1]
+            group_starts = change.nonzero(as_tuple=False).squeeze(1)
+            group_ends = torch.cat(
+                [group_starts[1:], torch.tensor(
+                    [N], device=device, dtype=group_starts.dtype)]
+            ) - 1
+
+            tp = y_sorted.cumsum(dim=0)
+            fp = torch.arange(1, N + 1, device=device, dtype=torch.float32) - tp
+
+            tp_end = tp[group_ends]
+            fp_end = fp[group_ends]
+            precision = tp_end / torch.clamp(tp_end + fp_end, min=1.0)
+            n_pos_safe = torch.clamp(n_pos, min=1.0)
+            recall = tp_end / n_pos_safe
+
+            recall_prev = torch.cat(
+                [torch.zeros((1,), device=device,
+                             dtype=torch.float32), recall[:-1]]
+            )
+            ap = ((recall - recall_prev) * precision).sum()
+            out[start + j] = torch.where(n_pos > 0.0,
+                                         ap.to(torch.float32), out[start + j])
+
+    return out
+
+
+def auroc_per_cause(
+    y_true: torch.Tensor,
+    y_score: torch.Tensor,
+    *,
+    tie_mode: TieMode = "exact",
+    chunk_size: int = 128,
+) -> torch.Tensor:
+    """AUROC per cause via Mann–Whitney U.
+
+    AUC = (sum_ranks_pos - n_pos*(n_pos+1)/2) / (n_pos*n_neg)
+
+    tie_mode:
+      - "exact": average ranks for ties (matches typical sklearn tie behavior)
+      - "approx": ordinal ranks (faster, differs under ties)
+
+    Returns:
+        (K,) float32 tensor; NaN when a cause has n_pos==0 or n_neg==0.
+    """
+    _validate_binary_inputs(y_true, y_score)
+
+    device = y_true.device
+    N, K = y_true.shape
+    if N == 0:
+        return torch.full((K,), float("nan"), device=device, dtype=torch.float32)
+
+    yt = y_true.to(torch.bool)
+    ys = y_score.to(torch.float32)
+
+    n_pos_all = yt.sum(dim=0).to(torch.float32)
+    n_neg_all = (float(N) - n_pos_all).to(torch.float32)
+
+    out = torch.full((K,), float("nan"), device=device, dtype=torch.float32)
+
+    for start in range(0, K, int(chunk_size)):
+        end = min(K, start + int(chunk_size))
+        yt_c = yt[:, start:end]
+        ys_c = ys[:, start:end]
+
+        for j in range(end - start):
+            n_pos = n_pos_all[start + j]
+            n_neg = n_neg_all[start + j]
+
+            scores = ys_c[:, j]
+            labels = yt_c[:, j]
+
+            sorted_scores, order = _stable_sort(scores, dim=0, descending=False)
+            y_sorted = labels.gather(0, order).to(torch.float32)
+
+            if tie_mode == "approx":
+                ranks = torch.arange(
+                    1, N + 1, device=device, dtype=torch.float32)
+            else:
+                # average ranks for ties
+                change = torch.empty((N,), device=device, dtype=torch.bool)
+                change[0] = True
+                if N > 1:
+                    change[1:] = sorted_scores[1:] != sorted_scores[:-1]
+                group_starts = change.nonzero(as_tuple=False).squeeze(1)
+                group_ends = torch.cat(
+                    [group_starts[1:], torch.tensor(
+                        [N], device=device, dtype=group_starts.dtype)]
+                ) - 1
+
+                lengths = (group_ends - group_starts + 1).to(torch.long)
+                start_rank = (group_starts + 1).to(torch.float32)
+                end_rank = (group_ends + 1).to(torch.float32)
+                avg_rank = 0.5 * (start_rank + end_rank)
+                ranks = avg_rank.repeat_interleave(lengths)
+
+            sum_ranks_pos = (ranks * y_sorted).sum()
+            u = sum_ranks_pos - (n_pos * (n_pos + 1.0) / 2.0)
+            denom = n_pos * n_neg
+            auc = u / torch.clamp(denom, min=1.0)
+            valid = (n_pos > 0.0) & (n_neg > 0.0)
+            out[start + j] = torch.where(valid,
+                                         auc.to(torch.float32), out[start + j])
+
+    return out
+
+
+def precision_recall_at_k_percents_per_cause(
+    y_true: torch.Tensor,
+    y_score: torch.Tensor,
+    k_percents: Sequence[float],
+    *,
+    chunk_size: int = 128,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Precision@K% and Recall@K% per cause.
+
+    Uses stable sort (descending) to match deterministic tie behavior.
+
+    Returns:
+        precision: (P,K) float32
+        recall: (P,K) float32
+    """
+    _validate_binary_inputs(y_true, y_score)
+
+    device = y_true.device
+    N, K = y_true.shape
+    P = int(len(k_percents))
+
+    precision = torch.full((P, K), float(
+        "nan"), device=device, dtype=torch.float32)
+    recall = torch.full((P, K), float(
+        "nan"), device=device, dtype=torch.float32)
+
+    if N == 0:
+        return precision, recall
+
+    yt = y_true.to(torch.bool)
+    ys = y_score.to(torch.float32)
+
+    n_pos_all = yt.sum(dim=0).to(torch.float32)
+
+    ks: List[int] = []
+    for kp in k_percents:
+        k = int(math.ceil((float(kp) / 100.0) * float(N)))
+        ks.append(k)
+
+    for start in range(0, K, int(chunk_size)):
+        end = min(K, start + int(chunk_size))
+        yt_c = yt[:, start:end]
+        ys_c = ys[:, start:end]
+
+        for j in range(end - start):
+            scores = ys_c[:, j]
+            labels = yt_c[:, j]
+            n_pos = n_pos_all[start + j]
+
+            # stable descending order
+            _, order = _stable_sort(scores, dim=0, descending=True)
+            y_sorted = labels.gather(0, order).to(torch.float32)
+            tp = y_sorted.cumsum(dim=0)
+
+            for p_idx, k in enumerate(ks):
+                if k <= 0:
+                    continue
+                tp_k = tp[min(k, N) - 1]
+                precision[p_idx, start + j] = tp_k / float(k)
+                recall[p_idx, start + j] = torch.where(
+                    n_pos > 0.0,
+                    tp_k / n_pos,
+                    torch.tensor(float("nan"), device=device,
+                                 dtype=torch.float32),
+                )
+
+    return precision, recall
+
+
+@dataclass
+class BinaryMetricsResult:
+    auc_per_cause: torch.Tensor  # (K,)
+    ap_per_cause: torch.Tensor  # (K,)
+    brier_per_cause: torch.Tensor  # (K,)
+    precision_at_k: torch.Tensor  # (P,K)
+    recall_at_k: torch.Tensor  # (P,K)
+    n_pos_per_cause: torch.Tensor  # (K,)
+    n_neg_per_cause: torch.Tensor  # (K,)
+    ici_per_cause: Optional[torch.Tensor] = None  # (K,)
+
+
+@torch.inference_mode()
+def compute_binary_metrics_torch(
+    y_true: torch.Tensor,
+    y_pred: torch.Tensor,
+    *,
+    device: str | torch.device | None = None,
+    k_percents: Sequence[float] = (1.0, 5.0, 10.0, 20.0, 50.0),
+    tie_mode: TieMode = "exact",
+    chunk_size: int = 128,
+    compute_ici: bool = False,
+    ici_bins: int = 15,
+) -> BinaryMetricsResult:
+    """Compute per-cause binary ranking metrics on GPU using torch.
+
+    Inputs must be (N,K) and live on the device you want to compute on.
+
+    Performance notes:
+    - Computation is chunked over causes to bound peak memory.
+    - For `tie_mode="exact"`, AP and AUROC are computed with tie grouping, which
+      is more correct under ties but uses per-cause loops (still GPU-resident).
+
+    Determinism:
+    - Uses stable sorts where available.
+    - Avoids nondeterministic selection ops for ties (no `topk`).
+    """
+    _validate_binary_inputs(y_true, y_pred)
+
+    if device is not None:
+        device = torch.device(device)
+        y_true = y_true.to(device)
+        y_pred = y_pred.to(device)
+
+    N, K = y_true.shape
+
+    yt = y_true.to(torch.bool)
+    yp = y_pred.to(torch.float32)
+
+    n_pos = yt.sum(dim=0).to(torch.long)
+    n_neg = (int(N) - n_pos).to(torch.long)
+
+    auc = auroc_per_cause(yt, yp, tie_mode=tie_mode, chunk_size=chunk_size)
+    ap = average_precision_per_cause(
+        yt, yp, tie_mode=tie_mode, chunk_size=chunk_size)
+    brier = brier_per_cause(yt, yp)
+
+    prec_k, rec_k = precision_recall_at_k_percents_per_cause(
+        yt, yp, k_percents, chunk_size=chunk_size
+    )
+
+    ici = None
+    if compute_ici:
+        ici = ici_per_cause_fixed_width(
+            yt, yp, n_bins=int(ici_bins), chunk_size=chunk_size)
+
+    return BinaryMetricsResult(
+        auc_per_cause=auc,
+        ap_per_cause=ap,
+        brier_per_cause=brier,
+        precision_at_k=prec_k,
+        recall_at_k=rec_k,
+        n_pos_per_cause=n_pos,
+        n_neg_per_cause=n_neg,
+        ici_per_cause=ici,
+    )
+
+
+@torch.inference_mode()
+def compute_metrics_torch(
+    y_true: torch.Tensor,
+    y_pred: torch.Tensor,
+    *,
+    device: str | torch.device | None = None,
+    weights: Optional[torch.Tensor] = None,
+    k_percents: Sequence[float] = (1.0, 5.0, 10.0, 20.0, 50.0),
+    tie_mode: TieMode = "exact",
+    chunk_size: int = 128,
+    compute_ici: bool = False,
+    ici_bins: int = 15,
+) -> Dict[str, object]:
+    """Convenience API: per-cause + macro/weighted aggregations.
+
+    Returns a dict compatible with downstream reporting:
+      - per-cause tensors under `per_cause`
+      - macro + weighted summaries (NaN-aware)
+
+    If `weights` is None, uses number of positives per cause as weights.
+    """
+    res = compute_binary_metrics_torch(
+        y_true,
+        y_pred,
+        device=device,
+        k_percents=k_percents,
+        tie_mode=tie_mode,
+        chunk_size=chunk_size,
+        compute_ici=compute_ici,
+        ici_bins=ici_bins,
+    )
+
+    w = res.n_pos_per_cause.to(
+        torch.float32) if weights is None else weights.to(torch.float32)
+
+    out: Dict[str, object] = {
+        "auc_macro": _nanmean(res.auc_per_cause),
+        "auc_weighted": _nanweighted_mean(res.auc_per_cause, w),
+        "ap_macro": _nanmean(res.ap_per_cause),
+        "ap_weighted": _nanweighted_mean(res.ap_per_cause, w),
+        "brier_macro": _nanmean(res.brier_per_cause),
+        "brier_weighted": _nanweighted_mean(res.brier_per_cause, w),
+        "per_cause": {
+            "auc": res.auc_per_cause,
+            "ap": res.ap_per_cause,
+            "brier": res.brier_per_cause,
+            "precision_at_k": res.precision_at_k,
+            "recall_at_k": res.recall_at_k,
+            "n_pos": res.n_pos_per_cause,
+            "n_neg": res.n_neg_per_cause,
+        },
+    }
+
+    if res.ici_per_cause is not None:
+        out["ici_macro"] = _nanmean(res.ici_per_cause)
+        out["ici_weighted"] = _nanweighted_mean(res.ici_per_cause, w)
+        out["per_cause"]["ici"] = res.ici_per_cause
+
+    return out