Fixing bug for metacat

medcat/utils/meta_cat/ml_utils.py

@@ -200,7 +200,7 @@ def train_model(model: nn.Module, data: List, config: ConfigMetaCAT, save_dir_pa
         if config.train['compute_class_weights'] is True:
             y_ = [x[2] for x in train_data]
             class_weights = compute_class_weight(class_weight="balanced", classes=np.unique(y_), y=y_)
-            config.train['class_weights'] = class_weights
+            config.train['class_weights'] = class_weights.tolist()
             logger.info(f"Class weights computed: {class_weights}")
 
             class_weights = torch.FloatTensor(class_weights).to(device)

medcat/utils/meta_cat/models.py

@@ -114,8 +114,13 @@ def __init__(self, config):
         self.fc3 = nn.Linear(hidden_size_2, hidden_size_2)
         # dense layer 3 (Output layer)
         model_arch_config = config.model.model_architecture_config
+
+        if model_arch_config['fc3'] is True and model_arch_config['fc2'] is False:
+            logger.warning("FC3 can only be used if FC2 is also enabled. Enabling FC2...")
+            config.model.model_architecture_config['fc2'] = True
+
         if model_arch_config is not None:
-            if model_arch_config['fc2'] is True or model_arch_config['fc3'] is True:
+            if model_arch_config['fc2'] is True:
                 self.fc4 = nn.Linear(hidden_size_2, self.num_labels)
             else:
                 self.fc4 = nn.Linear(config.model.hidden_size, self.num_labels)
@@ -190,11 +195,11 @@ def forward(
                 x = self.relu(x)
                 x = self.dropout(x)
 
-            if self.config.model.model_architecture_config['fc3'] is True:
-                # fc3
-                x = self.fc3(x)
-                x = self.relu(x)
-                x = self.dropout(x)
+                if self.config.model.model_architecture_config['fc3'] is True:
+                    # fc3
+                    x = self.fc3(x)
+                    x = self.relu(x)
+                    x = self.dropout(x)
         else:
             # fc2
             x = self.fc2(x)