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1 | 1 | from abc import ABCMeta
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2 |
| -from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast, Callable |
| 2 | +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast |
3 | 3 |
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4 | 4 | import numpy as np
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5 | 5 |
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17 | 17 | CrossValFunc,
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18 | 18 | DEFAULT_RESAMPLING_PARAMETERS,
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19 | 19 | HoldoutValTypes,
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20 |
| - HoldOutValFunc |
| 20 | + HoldoutValFunc |
21 | 21 | )
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22 | 22 | from autoPyTorch.utils.common import FitRequirement, hash_array_or_matrix
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23 | 23 |
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24 | 24 | BaseDatasetType = Union[Tuple[np.ndarray, np.ndarray], Dataset]
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25 |
| -SplitFunc = Callable[[Union[int, float], np.ndarray, Any], List[Tuple[np.ndarray, np.ndarray]]] |
26 | 25 |
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27 | 26 |
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28 | 27 | def check_valid_data(data: Any) -> None:
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@@ -111,7 +110,7 @@ def __init__(
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111 | 110 | type_check(train_tensors, val_tensors)
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112 | 111 | self.train_tensors, self.val_tensors, self.test_tensors = train_tensors, val_tensors, test_tensors
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113 | 112 | self.cross_validators: Dict[str, CrossValFunc] = {}
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114 |
| - self.holdout_validators: Dict[str, HoldOutValFunc] = {} |
| 113 | + self.holdout_validators: Dict[str, HoldoutValFunc] = {} |
115 | 114 | self.rng = np.random.RandomState(seed=seed)
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116 | 115 | self.shuffle = shuffle
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117 | 116 | self.resampling_strategy = resampling_strategy
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