Add a Galaxy tool for TabPFN package (by Prof. Hutter's group) #1533
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bgruening
reviewed
Jan 14, 2025
tools/tabpfn/main.py
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| def separate_features_labels(data): | ||
| df = pd.read_csv(data, sep=",") |
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can we read tabular? or both ... but tabular would be nice.
bgruening
reviewed
Jan 14, 2025
| </tests> | ||
| <help> | ||
| <![CDATA[ | ||
| **What it does** |
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A slightly better help would be nice :)
bgruening
reviewed
Jan 14, 2025
tools/tabpfn/tabpfn.xml
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| ]]> | ||
| </help> | ||
| <citations> | ||
| <citation type="doi">10.48550/arXiv.2207.01848</citation> |
bgruening
reviewed
Jan 14, 2025
tools/tabpfn/tabpfn.xml
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| ]]> | ||
| </command> | ||
| <inputs> | ||
| <param name="train_data" type="data" format="csv" label="Train data" help="Please provide training data for training model."/> |
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Some more hints how the data needs to be structured would be good.
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The results seem to be a bit random, so we need to work with asserts. |
anuprulez
changed the title
bgruening
reviewed
Jan 15, 2025
bgruening
reviewed
Jan 15, 2025
bgruening
approved these changes
Jan 15, 2025
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The PR adds a Galaxy tool that uses the TabPFN package (DOI: 10.48550/arXiv.2207.01848) to create a general-purpose classifier.
Many more subsequent analyses can be added as required such as creating workflows with this tool.
A few challenges:
One challenge is the export this classifier to standard ONNX format. The standard export of Pytorch models to ONNX does not work with the trained TabPFN model. We may require it, otherwise, we may have to export the trained model as a zipped file.
Additionally, to move the discussion around integrating the
tabpfn_clientpackage - it requires a separate login before use, so integrating it to Galaxy would be complicated I think unless there is a way to bypass the login process.Thank you!
ping @bgruening