diff --git a/autoPyTorch/datasets/base_dataset.py b/autoPyTorch/datasets/base_dataset.py index f7a920efa..7db551a48 100644 --- a/autoPyTorch/datasets/base_dataset.py +++ b/autoPyTorch/datasets/base_dataset.py @@ -1,5 +1,5 @@ from abc import ABCMeta -from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast, Callable import numpy as np @@ -12,18 +12,16 @@ import torchvision from autoPyTorch.datasets.resampling_strategy import ( - CROSS_VAL_FN, + CrossValFuncs, CrossValTypes, DEFAULT_RESAMPLING_PARAMETERS, - HOLDOUT_FN, HoldoutValTypes, - get_cross_validators, - get_holdout_validators, - is_stratified, + HoldOutFuncs, ) 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: @@ -103,8 +101,8 @@ 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, CROSS_VAL_FN] = {} - self.holdout_validators: Dict[str, HOLDOUT_FN] = {} + self.cross_validators: Dict[str, SplitFunc] = {} + self.holdout_validators: Dict[str, SplitFunc] = {} self.rng = np.random.RandomState(seed=seed) self.shuffle = shuffle self.resampling_strategy = resampling_strategy @@ -121,8 +119,8 @@ def __init__( self.is_small_preprocess = True # Make sure cross validation splits are created once - self.cross_validators = get_cross_validators(*CrossValTypes) - self.holdout_validators = get_holdout_validators(*HoldoutValTypes) + self.cross_validators = CrossValFuncs.get_cross_validators(*CrossValTypes) + self.holdout_validators = HoldOutFuncs.get_holdout_validators(*HoldoutValTypes) self.splits = self.get_splits_from_resampling_strategy() # We also need to be able to transform the data, be it for pre-processing @@ -170,7 +168,7 @@ def __getitem__(self, index: int, train: bool = True) -> Tuple[np.ndarray, ...]: Returns: A transformed single point prediction """ - + X = self.train_tensors[0].iloc[[index]] if hasattr(self.train_tensors[0], 'loc') \ else self.train_tensors[0][index] @@ -249,7 +247,7 @@ def create_cross_val_splits( if not isinstance(cross_val_type, CrossValTypes): raise NotImplementedError(f'The selected `cross_val_type` "{cross_val_type}" is not implemented.') kwargs = {} - if is_stratified(cross_val_type): + if cross_val_type.is_stratified(): # we need additional information about the data for stratification kwargs["stratify"] = self.train_tensors[-1] splits = self.cross_validators[cross_val_type.name]( @@ -284,7 +282,7 @@ def create_holdout_val_split( if not isinstance(holdout_val_type, HoldoutValTypes): raise NotImplementedError(f'The specified `holdout_val_type` "{holdout_val_type}" is not supported.') kwargs = {} - if is_stratified(holdout_val_type): + if holdout_val_type.is_stratified(): # we need additional information about the data for stratification kwargs["stratify"] = self.train_tensors[-1] train, val = self.holdout_validators[holdout_val_type.name](val_share, self._get_indices(), **kwargs) diff --git a/autoPyTorch/datasets/resampling_strategy.py b/autoPyTorch/datasets/resampling_strategy.py index 1d0bc3077..7d5a29039 100644 --- a/autoPyTorch/datasets/resampling_strategy.py +++ b/autoPyTorch/datasets/resampling_strategy.py @@ -1,5 +1,5 @@ from enum import IntEnum -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union, Callable import numpy as np @@ -15,8 +15,12 @@ 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""" def __call__(self, num_splits: int, indices: np.ndarray, @@ -25,26 +29,59 @@ def __call__(self, class HOLDOUT_FN(Protocol): + """TODO: deprecate soon""" def __call__(self, val_share: float, indices: np.ndarray, stratify: Optional[Any] ) -> Tuple[np.ndarray, np.ndarray]: ... class CrossValTypes(IntEnum): + """The type of cross validation + + This class is used to specify the cross validation function + and is not supposed to be instantiated. + + Examples: This class is supposed to be used as follows + >>> cv_type = CrossValTypes.k_fold_cross_validation + >>> print(cv_type.name) + + k_fold_cross_validation + + >>> for cross_val_type in CrossValTypes: + print(cross_val_type.name, cross_val_type.value) + + stratified_k_fold_cross_validation 1 + k_fold_cross_validation 2 + stratified_shuffle_split_cross_validation 3 + shuffle_split_cross_validation 4 + time_series_cross_validation 5 + """ stratified_k_fold_cross_validation = 1 k_fold_cross_validation = 2 stratified_shuffle_split_cross_validation = 3 shuffle_split_cross_validation = 4 time_series_cross_validation = 5 + def is_stratified(self) -> bool: + stratified = [self.stratified_k_fold_cross_validation, + self.stratified_shuffle_split_cross_validation] + return getattr(self, self.name) in stratified + class HoldoutValTypes(IntEnum): + """The type of hold out validation (refer to CrossValTypes' doc-string)""" holdout_validation = 6 stratified_holdout_validation = 7 + def is_stratified(self) -> bool: + stratified = [self.stratified_holdout_validation] + return getattr(self, self.name) in stratified + +"""TODO: deprecate soon""" RESAMPLING_STRATEGIES = [CrossValTypes, HoldoutValTypes] +"""TODO: deprecate soon""" DEFAULT_RESAMPLING_PARAMETERS = { HoldoutValTypes.holdout_validation: { 'val_share': 0.33, @@ -67,15 +104,8 @@ class HoldoutValTypes(IntEnum): } # type: Dict[Union[HoldoutValTypes, CrossValTypes], Dict[str, Any]] -def get_cross_validators(*cross_val_types: CrossValTypes) -> Dict[str, CROSS_VAL_FN]: - cross_validators = {} # type: Dict[str, CROSS_VAL_FN] - for cross_val_type in cross_val_types: - cross_val_fn = globals()[cross_val_type.name] - cross_validators[cross_val_type.name] = cross_val_fn - return cross_validators - - 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] @@ -84,70 +114,93 @@ def get_holdout_validators(*holdout_val_types: HoldoutValTypes) -> Dict[str, HOL 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") -def holdout_validation(val_share: float, indices: np.ndarray, **kwargs: Any) -> Tuple[np.ndarray, np.ndarray]: - train, val = train_test_split(indices, test_size=val_share, shuffle=False) - return train, val - - -def stratified_holdout_validation(val_share: float, indices: np.ndarray, **kwargs: Any) \ - -> Tuple[np.ndarray, np.ndarray]: - train, val = train_test_split(indices, test_size=val_share, shuffle=False, stratify=kwargs["stratify"]) - return train, val - - -def shuffle_split_cross_validation(num_splits: int, indices: np.ndarray, **kwargs: Any) \ - -> List[Tuple[np.ndarray, np.ndarray]]: - cv = ShuffleSplit(n_splits=num_splits) - splits = list(cv.split(indices)) - return splits - - -def stratified_shuffle_split_cross_validation(num_splits: int, indices: np.ndarray, **kwargs: Any) \ - -> List[Tuple[np.ndarray, np.ndarray]]: - cv = StratifiedShuffleSplit(n_splits=num_splits) - splits = list(cv.split(indices, kwargs["stratify"])) - return splits - - -def stratified_k_fold_cross_validation(num_splits: int, indices: np.ndarray, **kwargs: Any) \ - -> List[Tuple[np.ndarray, np.ndarray]]: - cv = StratifiedKFold(n_splits=num_splits) - splits = list(cv.split(indices, kwargs["stratify"])) - return splits - - -def k_fold_cross_validation(num_splits: int, indices: np.ndarray, **kwargs: Any) -> List[Tuple[np.ndarray, np.ndarray]]: - """ - Standard k fold cross validation. - - :param indices: array of indices to be split - :param num_splits: number of cross validation splits - :return: list of tuples of training and validation indices - """ - cv = KFold(n_splits=num_splits) - splits = list(cv.split(indices)) - return splits - - -def time_series_cross_validation(num_splits: int, indices: np.ndarray, **kwargs: Any) \ - -> List[Tuple[np.ndarray, np.ndarray]]: - """ - Returns train and validation indices respecting the temporal ordering of the data. - Dummy example: [0, 1, 2, 3] with 3 folds yields - [0] [1] - [0, 1] [2] - [0, 1, 2] [3] - - :param indices: array of indices to be split - :param num_splits: number of cross validation splits - :return: list of tuples of training and validation indices - """ - cv = TimeSeriesSplit(n_splits=num_splits) - splits = list(cv.split(indices)) - return splits +class HoldOutFuncs(): + @staticmethod + def holdout_validation(val_share: float, indices: np.ndarray, **kwargs: Any) -> Tuple[np.ndarray, np.ndarray]: + train, val = train_test_split(indices, test_size=val_share, shuffle=False) + return train, val + + @staticmethod + def stratified_holdout_validation(val_share: float, indices: np.ndarray, **kwargs: Any) \ + -> Tuple[np.ndarray, np.ndarray]: + train, val = train_test_split(indices, test_size=val_share, shuffle=False, stratify=kwargs["stratify"]) + return train, val + + @classmethod + def get_holdout_validators(cls, *holdout_val_types: Tuple[HoldoutValTypes]) -> Dict[str, SplitFunc]: + + holdout_validators = { + holdout_val_type.name: getattr(cls, holdout_val_type.name) + for holdout_val_type in holdout_val_types + } + return holdout_validators + + +class CrossValFuncs(): + @staticmethod + def shuffle_split_cross_validation(num_splits: int, indices: np.ndarray, **kwargs: Any) \ + -> List[Tuple[np.ndarray, np.ndarray]]: + cv = ShuffleSplit(n_splits=num_splits) + splits = list(cv.split(indices)) + return splits + + @staticmethod + def stratified_shuffle_split_cross_validation(num_splits: int, indices: np.ndarray, **kwargs: Any) \ + -> List[Tuple[np.ndarray, np.ndarray]]: + cv = StratifiedShuffleSplit(n_splits=num_splits) + splits = list(cv.split(indices, kwargs["stratify"])) + return splits + + @staticmethod + def stratified_k_fold_cross_validation(num_splits: int, indices: np.ndarray, **kwargs: Any) \ + -> List[Tuple[np.ndarray, np.ndarray]]: + cv = StratifiedKFold(n_splits=num_splits) + splits = list(cv.split(indices, kwargs["stratify"])) + return splits + + @staticmethod + def k_fold_cross_validation(num_splits: int, indices: np.ndarray, **kwargs: Any) \ + -> List[Tuple[np.ndarray, np.ndarray]]: + """ + Standard k fold cross validation. + + :param indices: array of indices to be split + :param num_splits: number of cross validation splits + :return: list of tuples of training and validation indices + """ + cv = KFold(n_splits=num_splits) + splits = list(cv.split(indices)) + return splits + + @staticmethod + def time_series_cross_validation(num_splits: int, indices: np.ndarray, **kwargs: Any) \ + -> List[Tuple[np.ndarray, np.ndarray]]: + """ + Returns train and validation indices respecting the temporal ordering of the data. + Dummy example: [0, 1, 2, 3] with 3 folds yields + [0] [1] + [0, 1] [2] + [0, 1, 2] [3] + + :param indices: array of indices to be split + :param num_splits: number of cross validation splits + :return: list of tuples of training and validation indices + """ + cv = TimeSeriesSplit(n_splits=num_splits) + splits = list(cv.split(indices)) + return splits + + @classmethod + def get_cross_validators(cls, *cross_val_types: CrossValTypes) -> Dict[str, SplitFunc]: + cross_validators = { + cross_val_type.name: getattr(cls, cross_val_type.name) + for cross_val_type in cross_val_types + } + return cross_validators