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Calm splits - #58

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taylormjs merged 11 commits into
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calm-splits
Apr 2, 2025
Merged

Calm splits#58
taylormjs merged 11 commits into
mainfrom
calm-splits

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@taylormjs taylormjs commented Apr 1, 2025

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  • Add calm heldout split from paper, create iid train, val splits (95/05)
  • Update calm data module
  • Change HF directory to taylor-joren/calm
  • Prevent caching in ~/.cache to avoid filling up homefs

Taylor Joren and others added 5 commits March 31, 2025 23:08
karinazad and others added 2 commits April 2, 2025 05:42
* config

* max length

* max length

* beignet
Co-authored-by: freyn6 <freyn6@gene.com>
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taylormjs marked this pull request as ready for review April 2, 2025 05:42
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taylormjs requested review from karinazad and ncfrey April 2, 2025 05:43
Comment thread src/lobster/data/_ume_datamodule.py Outdated
dataset_class=CalmIterableDataset,
modality=Modality.NUCLEOTIDE,
supported_splits={Split.TRAIN}, # TODO: add splits
supported_splits={"train_full", "train_iid", "val_iid", "heldout"},

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Updated the available splits for calm. Didn't add enum for these, but can add if we want

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I wonder whether for training we should just keep it simple as train, test and have more robust evaluation outside of the datamodule?

Comment thread src/lobster/datasets/_calm_dataset.py Outdated
Training data is originally from FASTA files containing coding DNA sequences.
"""

SUPPORTED_SPLITS: ClassVar[list[str]] = ["train_full", "train_iid", "val_iid", "heldout"]

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New calm splits

Comment thread src/lobster/data/_calm_datamodule.py Outdated
Function or transform to apply to the data, by default None.
split_mode : str, optional
Split mode to use. Options:
- "pre_split": Use pre-created IID splits (train_iid, val_iid) and heldout for test

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I think we could make this the only default option and not allow customized splits so it's more in line with the rest of the HF datasets?

@taylormjs taylormjs self-assigned this Apr 2, 2025
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taylormjs merged commit 3e71dbc into main Apr 2, 2025
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taylormjs deleted the calm-splits branch April 2, 2025 22:00
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3 participants