chg: small corrections

frame/evaluate.py

@@ -53,14 +53,16 @@ def main():
 
     # * Get checkpoint and prepare Explainer
     model = models.select_model(model_name, tune)
-    model.load_state_dict(torch.load(path_checkpoint))
+    model.load_state_dict(torch.load(path_checkpoint,
+                                     map_location=device,
+                                     weights_only=True))
     model.eval()
 
     agg_pred = []
     agg_lbl = []
     agg_true = []
     for data in tqdm(test_loader, ncols=120, desc="Explaining"):
-        data.to(device)
+        data = data.to(device)
 
         # * Make predictions
         model_out = model(x=data.x.float(),

frame/explain.py

@@ -60,7 +60,9 @@ def main():
 
     # * Get checkpoint and prepare Explainer
     model = models.select_model(model_name, tune)
-    model.load_state_dict(torch.load(path_checkpoint))
+    model.load_state_dict(torch.load(path_checkpoint,
+                                     map_location=device,
+                                     weights_only=True))
     model.eval()
 
     if task == "classification":
@@ -77,7 +79,7 @@ def main():
                                             return_type="raw"))
 
     for data in tqdm(dataloader, ncols=120, desc="Explaining"):
-        data.to(device)
+        data = data.to(device)
 
         # * Make predictions
         model_out = model(x=data.x.float(),

frame/generate.py

@@ -42,7 +42,11 @@ def main():
         raise NotImplementedError("Loader not available")
 
     # * Export
-    bce_weight = (len(dataset.y) - sum(dataset.y)) / sum(dataset.y)
+    task = params["Data"].get("task", "classification").lower()
+    if task == "classification" and sum(dataset.y) > 0:
+        bce_weight = (len(dataset.y) - sum(dataset.y)) / sum(dataset.y)
+    else:
+        bce_weight = torch.tensor(1.0)
     metadata = {"feat_size": dataset.num_node_features,
                 "edge_dim": dataset.num_edge_features,
                 "bce_weight": bce_weight,

frame/source/datasets/decompose.py

@@ -59,7 +59,7 @@ def process_data(self):
         # * Iterate
         data_list = []
         for line in tqdm(dataset, ncols=120, desc="Creating graphs"):
-            line = re.sub(r"\'.*\'", "", line)  # Replace ".*" strings.
+            line = re.sub(r"\'.*?\'", "", line)  # Replace '...' strings.
             line = line.split(",")
 
             # Get label
@@ -206,7 +206,7 @@ def _gen_features(smiles):
     #         [single, double, triple, aromatic, conjugation, ring] + stereo)
 
     #     edge_attrs += [edge_attr, edge_attr]
-    #     frag_edge_attr = torch.stack(edge_attrs, dim=0)
+    # frag_edge_attr = torch.stack(edge_attrs, dim=0)
 
     agg_x = torch.sum(frag_x, dim=0)
     return agg_x

frame/source/datasets/default.py

@@ -59,7 +59,7 @@ def process_data(self):
         # * Iterate
         data_list = []
         for line in tqdm(dataset, ncols=120, desc="Creating graphs"):
-            line = re.sub(r"\'.*\'", "", line)  # Replace ".*" strings.
+            line = re.sub(r"\'.*?\'", "", line)  # Replace '...' strings.
             line = line.split(",")
 
             # Get label

frame/source/train/epoch.py

@@ -26,12 +26,12 @@ def train_epoch(model, optim, scheduler, lossfn, loader):
     model = model.train()
     for batch in loader:
         batch = batch.to(device)
-        optim.zero_grad(batch)
+        optim.zero_grad()
 
         # * Make predictions
         out = model(x=batch.x.float(),
                     edge_index=batch.edge_index,
-                    edge_attr=batch.edge_attr,
+                    edge_attr=batch.edge_attr.float(),
                     batch=batch.batch)
 
         # * Compute loss
@@ -64,7 +64,7 @@ def valid_epoch(model, task, loader):
         # * Make predictions
         out = model(x=batch.x.float(),
                     edge_index=batch.edge_index,
-                    edge_attr=batch.edge_attr,
+                    edge_attr=batch.edge_attr.float(),
                     batch=batch.batch)
 
         # * Read prediction values

frame/tune.py

@@ -64,8 +64,8 @@ def objective(trial, params, dataset):
             patience_counter = 0
             best_model_state = None
 
+            start = time.time()
             for epoch in tqdm(range(epochs), ncols=120, desc="Training"):
-                start = time.time()
                 _ = train.train_epoch(model, optim, schdlr,
                                       lossfn, train_loader)
                 val_metrics = train.valid_epoch(model, task, valid_loader)
@@ -82,7 +82,7 @@ def objective(trial, params, dataset):
                 if patience_counter >= patience:
                     break
 
-                fit_time = time.time() - start
+            fit_time = time.time() - start
 
             # Prepare best model
             model.load_state_dict(best_model_state)
@@ -153,7 +153,7 @@ def main():
         params = yaml.safe_load(stream)
 
     # * Initialize
-    task = name = params["Data"]["task"]
+    task = params["Data"]["task"]
     name = params["Data"]["name"]
     if name.lower() == "none":
         name = str(uuid.uuid4()).split("-")[0]