[fix]: back to the renamed version of CROSS_VAL_FN from temporal SplitFunc typing.

Author: nabenabe0928

autoPyTorch/datasets/base_dataset.py

@@ -1,5 +1,5 @@
 from abc import ABCMeta
-from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast, Callable
+from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast
 
 import numpy as np
 
@@ -15,14 +15,15 @@
 from autoPyTorch.datasets.resampling_strategy import (
     CrossValFuncs,
     CrossValTypes,
+    CrossValFunc,
     DEFAULT_RESAMPLING_PARAMETERS,
     HoldoutValTypes,
     HoldOutFuncs,
+    HoldOutFunc
 )
 from autoPyTorch.utils.common import FitRequirement, hash_array_or_matrix
 
 BaseDatasetType = Union[Tuple[np.ndarray, np.ndarray], Dataset]
-SplitFunc = Callable[[Union[int, float], np.ndarray, Any], List[Tuple[np.ndarray, np.ndarray]]]
 
 
 def check_valid_data(data: Any) -> None:
@@ -102,8 +103,8 @@ def __init__(
         if not hasattr(train_tensors[0], 'shape'):
             type_check(train_tensors, val_tensors)
         self.train_tensors, self.val_tensors, self.test_tensors = train_tensors, val_tensors, test_tensors
-        self.cross_validators: Dict[str, SplitFunc] = {}
-        self.holdout_validators: Dict[str, SplitFunc] = {}
+        self.cross_validators: Dict[str, CrossValFunc] = {}
+        self.holdout_validators: Dict[str, HoldOutFunc] = {}
         self.rng = np.random.RandomState(seed=seed)
         self.shuffle = shuffle
         self.resampling_strategy = resampling_strategy

autoPyTorch/datasets/resampling_strategy.py

@@ -1,5 +1,5 @@
 from enum import IntEnum
-from typing import Any, Dict, List, Optional, Tuple, Union, Callable
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -15,21 +15,16 @@
 from typing_extensions import Protocol
 
 
-SplitFunc = Callable[[Union[int, float], np.ndarray, Any], List[Tuple[np.ndarray, np.ndarray]]]
-
-
 # Use callback protocol as workaround, since callable with function fields count 'self' as argument
-class CROSS_VAL_FN(Protocol):
-    """TODO: deprecate soon"""
+class CrossValFunc(Protocol):
     def __call__(self,
                  num_splits: int,
                  indices: np.ndarray,
                  stratify: Optional[Any]) -> List[Tuple[np.ndarray, np.ndarray]]:
         ...
 
 
-class HOLDOUT_FN(Protocol):
-    """TODO: deprecate soon"""
+class HoldOutFunc(Protocol):
     def __call__(self, val_share: float, indices: np.ndarray, stratify: Optional[Any]
                  ) -> Tuple[np.ndarray, np.ndarray]:
         ...
@@ -104,23 +99,6 @@ def is_stratified(self) -> bool:
 }  # type: Dict[Union[HoldoutValTypes, CrossValTypes], Dict[str, Any]]
 
 
-def get_holdout_validators(*holdout_val_types: HoldoutValTypes) -> Dict[str, HOLDOUT_FN]:
-    """TODO: deprecate soon"""
-    holdout_validators = {}  # type: Dict[str, HOLDOUT_FN]
-    for holdout_val_type in holdout_val_types:
-        holdout_val_fn = globals()[holdout_val_type.name]
-        holdout_validators[holdout_val_type.name] = holdout_val_fn
-    return holdout_validators
-
-
-def is_stratified(val_type: Union[str, CrossValTypes, HoldoutValTypes]) -> bool:
-    """TODO: deprecate soon"""
-    if isinstance(val_type, str):
-        return val_type.lower().startswith("stratified")
-    else:
-        return val_type.name.lower().startswith("stratified")
-
-
 class HoldOutFuncs():
     @staticmethod
     def holdout_validation(val_share: float, indices: np.ndarray, **kwargs: Any) -> Tuple[np.ndarray, np.ndarray]:
@@ -134,7 +112,7 @@ def stratified_holdout_validation(val_share: float, indices: np.ndarray, **kwarg
         return train, val
 
     @classmethod
-    def get_holdout_validators(cls, *holdout_val_types: Tuple[HoldoutValTypes]) -> Dict[str, SplitFunc]:
+    def get_holdout_validators(cls, *holdout_val_types: Tuple[HoldoutValTypes]) -> Dict[str, HoldOutFunc]:
 
         holdout_validators = {
             holdout_val_type.name: getattr(cls, holdout_val_type.name)
@@ -198,7 +176,7 @@ def time_series_cross_validation(num_splits: int, indices: np.ndarray, **kwargs:
         return splits
 
     @classmethod
-    def get_cross_validators(cls, *cross_val_types: CrossValTypes) -> Dict[str, SplitFunc]:
+    def get_cross_validators(cls, *cross_val_types: CrossValTypes) -> Dict[str, CrossValFunc]:
         cross_validators = {
             cross_val_type.name: getattr(cls, cross_val_type.name)
             for cross_val_type in cross_val_types