PR brushing up abstract trainer class #18
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…clarity and facilitate easier subclassing.
… and functionality
…stopping criteria
…iner as logging will be made obligatory.
…it for better clarity on the minimal trainer
…Trainer for improved integration
…_split method and update AbstractTrainer to support specification of custom DataLoaders.
MikeLippincott
approved these changes
Oct 20, 2025
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| def default_random_split( | ||
| dataset: Dataset, | ||
| **kwargs | ||
| ) -> Tuple[DataLoader, DataLoader, DataLoader]: | ||
| """ | ||
| Randomly split a dataset into train, validation, and test sets. | ||
| :param dataset: The dataset to split. | ||
| :param train_frac: Fraction of data to use for training (default: 0.7). | ||
| :param val_frac: Fraction of data to use for validation (default: 0.15). | ||
| :param test_frac: Fraction of data to use for testing (default: remaining). | ||
| :param batch_size: Batch size for the DataLoaders (default: 4). | ||
| :param shuffle: Whether to shuffle the data in the DataLoaders | ||
| (default: True). | ||
| :return: A tuple of DataLoaders for (train, val, test) splits. | ||
| """ | ||
|
|
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| train_frac = kwargs.get("train_frac", 0.7) | ||
| val_frac = kwargs.get("val_frac", 0.15) | ||
| test_frac = kwargs.get( | ||
| "test_frac", 1.0 - train_frac - val_frac | ||
| ) |
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Are there hold out data that is removed prior to calling this function?
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Double checking these functions are supposed to be ... for now?
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Previously logger support is achieved by wrapping
AbstractTrainerwith yet another layer of abstract class, which is redundant, this PR adds logger support toAbstractTrainerdirectly along side a few minor refactoring (no change to functionality).Refactors:
AbstractTrainer.py:data_split.pyAdds:
AbstractTrainer.py:trainmethod support to logger classTrainerProtocol.py: