Refactor bias handling in AUC evaluation scripts to improve robustness and prevent errors when bias is not defined

evaluate_auc.py

@@ -632,6 +632,8 @@ def compute_logits_for_disease_chunk(
         device.type == "cuda" and use_amp) else torch.float32
     weight = model.risk_head.weight[disease_ids].detach().to(
         device=device, dtype=compute_dtype)
+    bias = None
+    if model.risk_head.bias is not None:
         bias = model.risk_head.bias[disease_ids].detach().to(
             device=device, dtype=compute_dtype)
 
@@ -640,7 +642,9 @@ def compute_logits_for_disease_chunk(
         end = min(start + logit_batch_size, n)
         h = torch.from_numpy(hidden_all[start:end]).to(
             device=device, dtype=compute_dtype, non_blocking=True)
-        logits = torch.matmul(h, weight.t()) + bias
+        logits = torch.matmul(h, weight.t())
+        if bias is not None:
+            logits = logits + bias
         parts.append(logits.float().cpu().numpy().astype(
             np.float32, copy=False))
         del h, logits

evaluate_auc_v2.py

@@ -839,6 +839,8 @@ def project_distribution_chunk(
         device.type == "cuda" and use_amp) else torch.float32
     weight = model.risk_head.weight[disease_ids].detach().to(
         device=device, dtype=compute_dtype)
+    bias = None
+    if model.risk_head.bias is not None:
         bias = model.risk_head.bias[disease_ids].detach().to(
             device=device, dtype=compute_dtype)
     rho_weight = None
@@ -868,7 +870,9 @@ def project_distribution_chunk(
         end = min(start + logit_batch_size, n)
         h = torch.from_numpy(hidden_all[start:end]).to(
             device=device, dtype=compute_dtype, non_blocking=True)
-        logits = torch.matmul(h, weight.t()) + bias
+        logits = torch.matmul(h, weight.t())
+        if bias is not None:
+            logits = logits + bias
         rho = None
         if dist_mode == "weibull":
             assert rho_weight is not None and rho_bias is not None