"None" ensembling for classfication accuracy #290
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lauritowal
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Sep 3, 2023
AlexTMallen
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I am a bit confused why we're only evaluating the positive examples in our "none" ensembling case--it seems slightly arbitrary, but fine. I will also note that this function has always and continues to handle LM logits incorrectly. LM logits are log probs, so it does not make sense to apply a sigmoid to them and gives us slightly different ensembled results. Meanwhile, reporter logits are log odds, and applying a sigmoid to log odds gives probabilities.
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Closes NOT-372
ATM, accuracy for "none" ensembling == "partial" ensembling.
This PR implements a reasonable interpretation of what "No ensembling" would look like for classification accuracy: i.e. for accuracy and calibrated accuracy, use the positive hiddens for inference. I also added logging for
cal_thresh.