Switch to binary classification for PU #402
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jpata
reviewed
Mar 6, 2025
parameters/pytorch/pyg-cms.yaml
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| @@ -118,9 +118,6 @@ train_dataset: | |||
| cms_pf_qcd: | |||
| version: 2.5.0 | |||
| splits: [1,2,3,4,5,6,7,8,9,10] | |||
| cms_pf_ztt: | |||
| version: 2.5.0 | |||
| splits: [1,2,3,4,5,6,7,8,9,10] | |||
|
Thanks, looks good! Just had a minor question inline (see above): #402 (comment) |
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I think it was simply because we didn't copy the ztt dataset. I reverted it. Thanks. |
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This PR switches PU regression to binary classification, as the majority of particles are either isPU=1 or 0. Focal loss was tried but do not offer any benefits compared to cross entropy. The final output of the PU branch may be tuned for a more optimized threshold for PU identification.