Use population bias correction in StandardizeYTransform std computation #589
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
facebook-github-bot
added
the
CLA Signed
|
@Balandat has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator. |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
torch.stdby default uses the Bessel correction (normalize byN-1), butnumpy.stddoes not (normalize by N). This causes the botorch models to warn about unnormalized data even though data is being normalized in the modelbridge.This won't matter much from a statistical perspective (this is just data normalization and not inference after all), but it feels like addressing this in Ax may be more reasonable, since in BoTorch when people are asked to standardize their data they will most likely use
torch.stdwith default args and it would be very confusing if that would not get rid of the warning.