account for dataset structure if required model is triplet networks

flexynesis/main.py

@@ -237,6 +237,10 @@ def __init__(self, model, dataset, n_splits=5, batch_size=32, learning_rates=Non
                     {'encoders': False, 'supervisors': False}
                 ]
 
+        if model.__class__.__name__ == 'MultiTripletNetwork':
+            # modify dataset structure to accommodate TripletNetworks
+            self.dataset = TripletMultiOmicDataset(dataset, model.main_var)
+
     def apply_freeze_config(self, config):
         # Freeze or unfreeze encoders
         for encoder in self.model.encoders:
@@ -265,7 +269,7 @@ def training_step(self, batch, batch_idx):
 
     def validation_step(self, batch, batch_idx):
         # Call the model's validation step without logging
-        val_loss = self.model.validation_step(batch, batch_idx, log=False)  # Assuming you can disable logging
+        val_loss = self.model.validation_step(batch, batch_idx, log=False)  
         self.log("val_loss", val_loss, on_epoch=True, prog_bar=True)
         return val_loss