ChEB-AI · sfluegel05 · Jan 22, 2026 · Jan 22, 2026 · Jan 22, 2026 · Jan 22, 2026
diff --git a/chebai/loss/bce_weighted.py b/chebai/loss/bce_weighted.py
@@ -33,8 +33,8 @@ def __init__(
         self.data_extractor = data_extractor
 
         assert (
-            isinstance(beta, float) and beta > 0.0
-        ), f"Beta parameter must be a float with value greater than 0.0, for loss class {self.__class__.__name__}."
+            isinstance(beta, float) and beta >= 0.0 and beta <= 1.0
+        ), f"Beta parameter must be a float with value between 0 and 1, for loss class {self.__class__.__name__}."
 
         assert (
             self.data_extractor is not None
@@ -63,13 +63,16 @@ def set_pos_weight(self, input: torch.Tensor) -> None:
             print(
                 f"Computing loss-weights based on v{self.data_extractor.chebi_version} dataset (beta={self.beta})"
             )
+            print(f"loading: {self.data_extractor.processed_file_names[0]}")
             complete_labels = torch.concat(
                 [
                     torch.stack(
                         [
                             torch.Tensor(row["labels"])
                             for row in self.data_extractor.load_processed_data(
-                                filename=file_name
+                                filename=os.path.join(
+                                    self.data_extractor.processed_dir, file_name
+                                )
                             )
                         ]
                     )

diff --git a/chebai/models/base.py b/chebai/models/base.py
@@ -278,7 +278,7 @@ def _execute(
                 loss_kwargs = dict()
                 if self.pass_loss_kwargs:
                     loss_kwargs = loss_kwargs_candidates
-                loss_kwargs["current_epoch"] = self.trainer.current_epoch
+                    loss_kwargs["current_epoch"] = self.trainer.current_epoch
                 loss = self.criterion(loss_data, loss_labels, **loss_kwargs)
                 if isinstance(loss, tuple):
                     unnamed_loss_index = 1

diff --git a/configs/loss/bce_weighted.yml b/configs/loss/bce_weighted.yml
@@ -1,3 +1,3 @@
 class_path: chebai.loss.bce_weighted.BCEWeighted
 init_args:
-  beta: 0.99
+  beta: 0.99 # this is the default weight, change this factor to increase/decrease the weighting effect
diff --git a/configs/loss/weighting_chebi100.yml b/configs/loss/weighting_chebi100.yml
diff --git a/...metrics/micro-macro-f1-roc-auc-binary.yml → configs/metrics/binary-f1-roc-auc.yml b/...metrics/micro-macro-f1-roc-auc-binary.yml → configs/metrics/binary-f1-roc-auc.yml
diff --git a/configs/metrics/single-class-f1.yml → configs/metrics/binary-f1.yml b/configs/metrics/single-class-f1.yml → configs/metrics/binary-f1.yml
diff --git a/configs/metrics/micro-macro-f1-roc-auc-17.yml b/configs/metrics/micro-macro-f1-roc-auc-17.yml
diff --git a/configs/metrics/micro-macro-f1-roc-auc-17_test.yml b/configs/metrics/micro-macro-f1-roc-auc-17_test.yml
diff --git a/configs/metrics/micro-macro-f1-roc-auc-2.yml b/configs/metrics/micro-macro-f1-roc-auc-2.yml
diff --git a/configs/metrics/micro-macro-f1-roc-auc-27.yml b/configs/metrics/micro-macro-f1-roc-auc-27.yml
diff --git a/configs/metrics/micro-macro-f1-roc-auc.yml b/configs/metrics/micro-macro-f1-roc-auc.yml
@@ -9,5 +9,3 @@ init_args:
       class_path: chebai.callbacks.epoch_metrics.MacroF1
     roc-auc:
       class_path: torchmetrics.classification.MultilabelAUROC
-      init_args:
-        num_labels: 12
diff --git a/configs/training/binary_callbacks.yml b/configs/training/binary_callbacks.yml
@@ -5,13 +5,6 @@
     filename: 'best_f1_{epoch:02d}_{val_loss:.4f}_{val_f1:.4f}_{val_roc-auc:.4f}'
     every_n_epochs: 1
     save_top_k: 1
-# - class_path: chebai.callbacks.model_checkpoint.CustomModelCheckpoint
-#   init_args:
-#     monitor: val_loss
-#     mode: 'min'
-#     filename: 'best_loss_{epoch:02d}_{val_loss:.4f}_{val_f1:.4f}_{val_roc-auc:.4f}'
-#     every_n_epochs: 1
-#     save_top_k: 1
 - class_path: chebai.callbacks.model_checkpoint.CustomModelCheckpoint
   init_args:
     monitor: val_roc-auc
@@ -23,21 +16,4 @@
   init_args:
     filename: 'per_{epoch:02d}_{val_loss:.4f}_{val_f1:.4f}_{val_roc-auc:.4f}'
     every_n_epochs: 25
-    save_top_k: 1
-
-# - class_path: lightning.pytorch.callbacks.early_stopping.EarlyStopping
-#   init_args:
-#     monitor: "val_roc-auc"
-#     min_delta: 0.0
-#     patience: 5
-#     verbose: False
-#     mode: "max"
-
-
-# - class_path: lightning.pytorch.callbacks.early_stopping.EarlyStopping
-#   init_args:
-#     monitor: "val_loss_epoch"
-#     min_delta: 0.0
-#     patience: 10
-#     verbose: False
-#     mode: "min"
+    save_top_k: -1
diff --git a/configs/training/default_callbacks.yml b/configs/training/default_callbacks.yml
@@ -2,32 +2,11 @@
   init_args:
     monitor: val_micro-f1
     mode: 'max'
-    filename: 'best_micro_f1_{epoch:02d}_{val_loss:.4f}_{val_macro-f1:.4f}_{val_micro-f1:.4f}_{val_roc-auc:.4f}'
+    filename: 'best_micro_f1_{epoch:02d}_{val_loss:.4f}_{val_macro-f1:.4f}_{val_micro-f1:.4f}'
     every_n_epochs: 1
-    save_top_k: 1
-# - class_path: chebai.callbacks.model_checkpoint.CustomModelCheckpoint
-#   init_args:
-#     monitor: val_loss
-#     mode: 'min'
-#     filename: 'best_loss_{epoch:02d}_{val_loss:.4f}_{val_macro-f1:.4f}_{val_micro-f1:.4f}_{val_roc-auc:.4f}'
-#     every_n_epochs: 1
-#     save_top_k: 1
+    save_top_k: 3
 - class_path: chebai.callbacks.model_checkpoint.CustomModelCheckpoint
   init_args:
-    monitor: val_roc-auc
-    mode: 'max'
-    filename: 'best_roc-auc_{epoch:02d}_{val_loss:.4f}_{val_macro-f1:.4f}_{val_micro-f1:.4f}_{val_roc-auc:.4f}'
-    every_n_epochs: 1
-    save_top_k: 1
-# - class_path: lightning.pytorch.callbacks.early_stopping.EarlyStopping
-#   init_args:
-#     monitor: "val_roc-auc"
-#     min_delta: 0.0
-#     patience: 5
-#     verbose: False
-#     mode: "max"
-- class_path: chebai.callbacks.model_checkpoint.CustomModelCheckpoint
-  init_args:
-    filename: 'per_{epoch:02d}_{val_loss:.4f}_{val_macro-f1:.4f}_{val_micro-f1:.4f}_{val_roc-auc:.4f}'
+    filename: 'per_{epoch:02d}_{val_loss:.4f}_{val_macro-f1:.4f}_{val_micro-f1:.4f}'
     every_n_epochs: 25
-    save_top_k: 1
+    save_top_k: -1
diff --git a/configs/training/default_trainer.yml b/configs/training/default_trainer.yml
@@ -1,4 +1,4 @@
-min_epochs: 20
+min_epochs: 100
 max_epochs: 100
 default_root_dir: &default_root_dir logs
 logger: csv_logger.yml

diff --git a/configs/training/early_stop_callbacks_regression.yml b/configs/training/early_stop_callbacks_regression.yml
diff --git a/...s/training/early_stop_callbacks_tox21.yml → ...training/early_stop_callbacks_roc-auc.yml b/...s/training/early_stop_callbacks_tox21.yml → ...training/early_stop_callbacks_roc-auc.yml
diff --git a/configs/training/solCur_callbacks.yml → configs/training/regression_callbacks.yml b/configs/training/solCur_callbacks.yml → configs/training/regression_callbacks.yml
@@ -1,10 +1,3 @@
-# - class_path: chebai.callbacks.model_checkpoint.CustomModelCheckpoint
-#   init_args:
-#     monitor: val_loss
-#     mode: 'min'
-#     filename: 'best_loss_{epoch:02d}_{val_loss:.4f}_{val_mse:.4f}_{val_rmse:.4f}_{val_r2:.4f}'
-#     every_n_epochs: 1
-#     save_top_k: 1
 - class_path: chebai.callbacks.model_checkpoint.CustomModelCheckpoint
   init_args:
     monitor: val_r2
@@ -19,16 +12,8 @@
     filename: 'best_rmse_{epoch:02d}_{val_loss:.4f}_{val_mse:.4f}_{val_rmse:.4f}_{val_r2:.4f}'
     every_n_epochs: 1
     save_top_k: 1
-# - class_path: lightning.pytorch.callbacks.early_stopping.EarlyStopping
-#   init_args:
-#     monitor: "val_rmse"
-#     min_delta: 0.0
-#     patience: 5
-#     verbose: False
-#     mode: "min"
-
 - class_path: chebai.callbacks.model_checkpoint.CustomModelCheckpoint
   init_args:
     filename: 'per_{epoch:02d}_{val_loss:.4f}_{val_mse:.4f}_{val_rmse:.4f}_{val_r2:.4f}'
     every_n_epochs: 25
-    save_top_k: 1
+    save_top_k: -1
diff --git a/configs/training/single_class_callbacks.yml b/configs/training/single_class_callbacks.yml
diff --git a/configs/weightings/chebi100.yml b/configs/weightings/chebi100.yml