Use train/val split instead of k-folds#17
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JeremieGince merged 3 commits intodevfrom Feb 10, 2026
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Replace N_FOLDS/fold_id-based k-fold splitting with a fixed train/validation split. Add DEFAULT_TRAIN_VAL_SPLIT and new split_id parameter (used as RNG seed) and expose train_val_split in from_dataset_name and the DataModule constructor with validation assertions. _split_train_val_dataset now uses random_split into [train, val] lengths instead of concatenating k-fold subsets. Update type hint for train_dataset and adjust MaxcutDataModule to pass split_id. Remove N_FOLDS constant.
Add class and __init__ docstrings to DataModule to clarify responsibilities and parameters. Tighten type hints by changing _train_dataset to Optional[Subset] and making _split_train_val_dataset return Tuple[Subset, Subset] instead of generic Any. Update MaxcutDataModule.from_dataset_name signature: rename fold_id to split_id, add a train_val_split kw-only parameter, and default batch_size, random_state, and num_workers to DataModule's DEFAULT_* constants for consistent defaults and clearer API.
Replace fold_id with split_id in automl_pipeline_tutorial.ipynb and ligthning_pipeline_tutorial.ipynb. Updated the variable declaration and the argument passed to DataModule.from_dataset_name to match the newer API that expects split_id.
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Description
Replace N_FOLDS/fold_id-based k-fold splitting with a fixed train/validation split. Add DEFAULT_TRAIN_VAL_SPLIT and new split_id parameter (used as RNG seed) and expose train_val_split in from_dataset_name and the DataModule constructor with validation assertions. _split_train_val_dataset now uses random_split into [train, val] lengths instead of concatenating k-fold subsets. Update type hint for train_dataset and adjust MaxcutDataModule to pass split_id. Remove N_FOLDS constant.
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