Add support for evaluation indices in LandmarkEvaluator class

evaluate.py

@@ -221,6 +221,7 @@ class LandmarkEvaluator:
         head: torch.nn.Module,
         loss_fn: torch.nn.Module,
         dataset: HealthDataset,
+        eval_indices: Optional[List[int]] = None,
         device: str = 'cuda',
         batch_size: int = 256,
         num_workers: int = 4,
@@ -231,6 +232,7 @@ class LandmarkEvaluator:
         self.head.eval()
         self.loss_fn = loss_fn
         self.dataset = dataset
+        self.eval_indices = eval_indices
         self.device = device
         self.batch_size = batch_size
         self.num_workers = num_workers
@@ -430,7 +432,9 @@ class LandmarkEvaluator:
         labels_rows: List[np.ndarray] = []
         valid_rows: List[np.ndarray] = []
 
-        for idx in range(len(self.dataset)):
+        candidate_indices = self.eval_indices if self.eval_indices is not None else list(
+            range(len(self.dataset)))
+        for idx in candidate_indices:
             patient_id = self.dataset.patient_ids[idx]
             records = self.dataset.patient_events.get(patient_id, [])
             if not records:
@@ -965,23 +969,45 @@ def load_model_and_config(run_dir: str, device: str = 'cuda') -> Tuple:
     print(f"Model type: {config['model_type']}")
     print(f"Loss type: {config['loss_type']}")
 
-    # Load dataset to get dimensions
+    # Load dataset (same as training) and reproduce the train/val/test split.
+    # IMPORTANT: do NOT change data_prefix; train.py reads files like
+    #   <data_prefix>_basic_info.csv, <data_prefix>_table.csv, <data_prefix>_event_data.npy
     data_prefix = config['data_prefix']
 
-    # Determine covariate list based on full_cov
+    if config.get('full_cov', False):
-    if config['full_cov']:
+        covariate_list = None
-        covariate_list = None  # Use all covariates
     else:
-        # Use partial covariates (define your partial list here)
+        # Match train.py partial-cov settings
-        covariate_list = ['age_at_assessment',
+        covariate_list = ["bmi", "smoking", "alcohol"]
-                          'bmi', 'smoking_status']  # Example
 
     dataset = HealthDataset(
-        data_prefix=f"{data_prefix}_test",
+        data_prefix=data_prefix,
         covariate_list=covariate_list,
         cache_event_tensors=True,
     )
 
+    # Reproduce the random_split used in train.py to obtain the held-out test subset.
+    n_total = len(dataset)
+    train_ratio = float(config.get('train_ratio', 0.7))
+    val_ratio = float(config.get('val_ratio', 0.15))
+    seed = int(config.get('random_seed', 42))
+
+    n_train = int(n_total * train_ratio)
+    n_val = int(n_total * val_ratio)
+    n_test = n_total - n_train - n_val
+    if n_test < 0:
+        raise ValueError(
+            f"Invalid split sizes from config: n_total={n_total}, train_ratio={train_ratio}, val_ratio={val_ratio}"
+        )
+
+    from torch.utils.data import random_split
+    _, _, test_subset = random_split(
+        dataset,
+        [n_train, n_val, n_test],
+        generator=torch.Generator().manual_seed(seed),
+    )
+    test_indices = list(getattr(test_subset, 'indices', []))
+
     # Determine output dimensions based on loss type
     import math
     if config['loss_type'] == 'exponential':
@@ -1075,7 +1101,7 @@ def load_model_and_config(run_dir: str, device: str = 'cuda') -> Tuple:
     else:
         raise ValueError(f"Unknown loss type: {config['loss_type']}")
 
-    return model, head, loss_fn, dataset, config
+    return model, head, loss_fn, dataset, config, test_indices
 
 
 def print_summary(results: Dict):
@@ -1180,7 +1206,7 @@ def main():
     args = parser.parse_args()
 
     # Load model and dataset
-    model, head, loss_fn, dataset, config = load_model_and_config(
+    model, head, loss_fn, dataset, config, test_indices = load_model_and_config(
         args.run_dir, args.device)
 
     # Create evaluator
@@ -1189,6 +1215,7 @@ def main():
         head=head,
         loss_fn=loss_fn,
         dataset=dataset,
+        eval_indices=test_indices,
         device=args.device,
         batch_size=args.batch_size,
         num_workers=args.num_workers,