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[doc] Add the reference to the fit_dictionary
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autoPyTorch/evaluation/train_evaluator.py

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@@ -362,6 +362,8 @@ def _fit_and_predict(self, pipeline: BaseEstimator, fold: int, train_indices: Un
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self.indices[fold] = ((train_indices, test_indices))
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# See autoPyTorch/pipeline/components/base_component.py::autoPyTorchComponent for more details
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# about fit_dictionary
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X = {'train_indices': train_indices,
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'val_indices': test_indices,
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'split_id': fold,

autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/utils.py

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@@ -12,8 +12,12 @@ def get_tabular_preprocessers(X: Dict[str, Any]) -> Dict[str, List[BaseEstimator
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Creates a dictionary with two keys,
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numerical- containing list of numerical preprocessors
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categorical- containing list of categorical preprocessors
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Args:
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X: fit dictionary
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See autoPyTorch/pipeline/components/base_component.py::autoPyTorchComponent for more details
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about fit_dictionary
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Returns:
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(Dict[str, List[BaseEstimator]]): dictionary with list of numerical and categorical preprocessors
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"""

autoPyTorch/utils/common.py

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@@ -167,6 +167,8 @@ def get_device_from_fit_dictionary(X: Dict[str, Any]) -> torch.device:
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Args:
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X (Dict[str, Any]): A fit dictionary to control how the pipeline is fitted
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See autoPyTorch/pipeline/components/base_component.py::autoPyTorchComponent for more details
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about fit_dictionary
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Returns:
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torch.device: Device to be used for training/inference

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