Reset features and targets after each fold

chemprop/data/data.py

@@ -93,6 +93,10 @@ def set_targets(self, targets: List[Optional[float]]):
         """
         self.targets = targets
 
+    def reset_features_and_targets(self) -> None:
+        """Resets the features and targets to their raw values."""
+        self.features, self.targets = self.raw_features, self.raw_targets
+
 
 class MoleculeDataset(Dataset):
     r"""A :class:`MoleculeDataset` contains a list of :class:`MoleculeDatapoint`\ s with access to their attributes."""
@@ -250,13 +254,10 @@ def set_targets(self, targets: List[List[Optional[float]]]) -> None:
         for i in range(len(self._data)):
             self._data[i].set_targets(targets[i])
 
-    def sort(self, key: Callable) -> None:
-        """
-        Sorts the dataset using the provided key.
-
-        :param key: A function on a :class:`MoleculeDatapoint` to determine the sorting order.
-        """
-        self._data.sort(key=key)
+    def reset_features_and_targets(self) -> None:
+        """Resets the features and targets to their raw values."""
+        for d in self._data:
+            d.reset_features_and_targets()
 
     def __len__(self) -> int:
         """

chemprop/train/cross_validate.py

@@ -1,5 +1,4 @@
 from collections import defaultdict
-from copy import deepcopy
 import csv
 from logging import Logger
 import os
@@ -72,6 +71,7 @@ def cross_validate(args: TrainArgs,
         args.seed = init_seed + fold_num
         args.save_dir = os.path.join(save_dir, f'fold_{fold_num}')
         makedirs(args.save_dir)
+        data.reset_features_and_targets()
         model_scores = train_func(args, data, logger)
         for metric, scores in model_scores.items():
             all_scores[metric].append(scores)

chemprop/train/run_training.py

@@ -91,7 +91,6 @@ def run_training(args: TrainArgs,
     if args.dataset_type == 'regression':
         debug('Fitting scaler')
         scaler = train_data.normalize_targets()
-        print(scaler.means, scaler.stds)
     else:
         scaler = None