[FIX] remove .pth for early stopping

autoPyTorch/pipeline/components/training/trainer/__init__.py

@@ -1,6 +1,7 @@
 import collections
 import logging.handlers
 import os
+import shutil
 import tempfile
 import time
 from typing import Any, Dict, List, Optional, Tuple, cast
@@ -351,13 +352,35 @@ def _fit(self, X: Dict[str, Any], y: Any = None, **kwargs: Any) -> 'TrainerChoic
             )
             self.save_model_for_ensemble()
 
+        # As training have finished, load the best weight
+        if self.checkpoint_dir is not None:
+            self._load_best_weights_and_clean_checkpoints(X)
+
         self.logger.info(f"Finished training with {self.run_summary.repr_last_epoch()}")
 
         # Tag as fitted
         self.fitted_ = True
 
         return self
 
+    def _load_best_weights_and_clean_checkpoints(self, X: Dict[str, Any]) -> None:
+        """
+        Load the best model until the last epoch and delete all the files for checkpoints.
+
+        Args:
+            X (Dict[str, Any]): Dependencies needed by current component to perform fit
+        """
+        assert self.checkpoint_dir is not None  # mypy
+        assert self.run_summary is not None  # mypy
+
+        best_path = os.path.join(self.checkpoint_dir, 'best.pth')
+        self.logger.debug(f" Early stopped model {X['num_run']} on epoch {self.run_summary.get_best_epoch()}")
+        # We will stop the training. Load the last best performing weights
+        X['network'].load_state_dict(torch.load(best_path))
+
+        # Clean the temp dir
+        shutil.rmtree(self.checkpoint_dir)
+
     def early_stop_handler(self, X: Dict[str, Any]) -> bool:
         """
         If early stopping is enabled, this procedure stops the training after a
@@ -387,16 +410,7 @@ def early_stop_handler(self, X: Dict[str, Any]) -> bool:
         if epochs_since_best == 0:
             torch.save(X['network'].state_dict(), best_path)
 
-        if epochs_since_best > X['early_stopping']:
-            self.logger.debug(f" Early stopped model {X['num_run']} on epoch {self.run_summary.get_best_epoch()}")
-            # We will stop the training. Load the last best performing weights
-            X['network'].load_state_dict(torch.load(best_path))
-
-            # Let the tempfile module clean the temp dir
-            self.checkpoint_dir = None
-            return True
-
-        return False
+        return epochs_since_best > cast(int, X['early_stopping'])
 
     def eval_valid_each_epoch(self, X: Dict[str, Any]) -> bool:
         """

test/test_pipeline/components/training/test_training.py

@@ -1,6 +1,9 @@
 import copy
+import glob
 import os
+import shutil
 import sys
+import tempfile
 import unittest
 import unittest.mock
 
@@ -382,5 +385,53 @@ def test_get_set_config_space(self):
                 self.assertEqual(value, trainer_choice.choice.__dict__[key])
 
 
+def test_early_stopping():
+    dataset_properties = {'task_type': 'tabular_classification', 'output_type': 'binary'}
+    trainer_choice = TrainerChoice(dataset_properties=dataset_properties)
+
+    def dummy_performance(*args, **kwargs):
+        return (-time.time(), {'accuracy': -time.time()})
+
+    # Fake the training so that the first epoch was the best one
+    import time
+    trainer_choice.choice = unittest.mock.MagicMock()
+    trainer_choice.choice.train_epoch = dummy_performance
+    trainer_choice.choice.evaluate = dummy_performance
+    trainer_choice.choice.on_epoch_end.return_value = False
+
+    fit_dictionary = {
+        'logger_port': 1000,
+        'budget_type': 'epochs',
+        'epochs': 6,
+        'budget': 10,
+        'num_run': 1,
+        'torch_num_threads': 1,
+        'early_stopping': 5,
+        'metrics_during_training': True,
+        'dataset_properties': dataset_properties,
+        'split_id': 0,
+        'step_interval': StepIntervalUnit.batch
+    }
+    for item in ['backend', 'lr_scheduler', 'network', 'optimizer', 'train_data_loader', 'val_data_loader',
+                 'device', 'y_train']:
+        fit_dictionary[item] = unittest.mock.MagicMock()
+
+    fit_dictionary['backend'].temporary_directory = tempfile.mkdtemp()
+    fit_dictionary['network'].state_dict.return_value = {'dummy': 1}
+    trainer_choice.fit(fit_dictionary)
+    epochs_since_best = trainer_choice.run_summary.get_last_epoch() - trainer_choice.run_summary.get_best_epoch()
+
+    # Six epochs ran
+    assert len(trainer_choice.run_summary.performance_tracker['val_metrics']) == 6
+
+    # But the best performance was achieved on the first epoch
+    assert epochs_since_best == 0
+
+    # No files are left after training
+    left_files = glob.glob(f"{fit_dictionary['backend'].temporary_directory}/*")
+    assert len(left_files) == 0
+    shutil.rmtree(fit_dictionary['backend'].temporary_directory)
+
+
 if __name__ == '__main__':
     unittest.main()