fix merge conflicts after rebase

Author: ravinkohli

autoPyTorch/api/base_task.py

@@ -299,14 +299,7 @@ def _get_dataset_input_validator(
         y_train: Union[List, pd.DataFrame, np.ndarray],
         X_test: Optional[Union[List, pd.DataFrame, np.ndarray]] = None,
         y_test: Optional[Union[List, pd.DataFrame, np.ndarray]] = None,
-<<<<<<< HEAD
         resampling_strategy: Optional[ResamplingStrategies] = None,
-=======
-        resampling_strategy: Optional[Union[
-            CrossValTypes,
-            HoldoutValTypes,
-            NoResamplingStrategyTypes]] = None,
->>>>>>> rebase and fix flake
         resampling_strategy_args: Optional[Dict[str, Any]] = None,
         dataset_name: Optional[str] = None,
     ) -> Tuple[BaseDataset, BaseInputValidator]:
@@ -348,14 +341,7 @@ def get_dataset(
         y_train: Union[List, pd.DataFrame, np.ndarray],
         X_test: Optional[Union[List, pd.DataFrame, np.ndarray]] = None,
         y_test: Optional[Union[List, pd.DataFrame, np.ndarray]] = None,
-<<<<<<< HEAD
         resampling_strategy: Optional[ResamplingStrategies] = None,
-=======
-        resampling_strategy: Optional[Union[
-            CrossValTypes,
-            HoldoutValTypes,
-            NoResamplingStrategyTypes]] = None,
->>>>>>> rebase and fix flake
         resampling_strategy_args: Optional[Dict[str, Any]] = None,
         dataset_name: Optional[str] = None,
     ) -> BaseDataset:
@@ -1403,14 +1389,7 @@ def fit_pipeline(
         X_test: Optional[Union[List, pd.DataFrame, np.ndarray]] = None,
         y_test: Optional[Union[List, pd.DataFrame, np.ndarray]] = None,
         dataset_name: Optional[str] = None,
-<<<<<<< HEAD
         resampling_strategy: Optional[Union[HoldoutValTypes, CrossValTypes, NoResamplingStrategyTypes]] = None,
-=======
-        resampling_strategy: Optional[Union[
-            CrossValTypes,
-            HoldoutValTypes,
-            NoResamplingStrategyTypes]] = None,
->>>>>>> rebase and fix flake
         resampling_strategy_args: Optional[Dict[str, Any]] = None,
         run_time_limit_secs: int = 60,
         memory_limit: Optional[int] = None,

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

@@ -516,13 +516,9 @@ def early_stop_handler(self, X: Dict[str, Any]) -> bool:
         if self.checkpoint_dir is None:
             self.checkpoint_dir = tempfile.mkdtemp(dir=X['backend'].temporary_directory)
 
-<<<<<<< HEAD
         last_epoch = self.run_summary.get_last_epoch()
         best_epoch = self.run_summary.get_best_epoch(split_type=self.early_stopping_split_type)
         epochs_since_best = last_epoch - best_epoch
-=======
-        epochs_since_best = self.run_summary.get_last_epoch() - self.run_summary.get_best_epoch()
->>>>>>> [FIX] Tests after rebase of `reg_cocktails` (#359)
 
         # Save the checkpoint if there is a new best epoch
         best_path = os.path.join(self.checkpoint_dir, 'best.pth')