Training a Normalizing Flow: “module 'selfies' has no attribute 'multiple_selfies_to_hot'”

Hey everyone,

I’m modifying @ncfrey ’s Normalizing Flow tutorial to work on the MolNet BBBP dataset, and I’m facing an issue.

The code block that throws the error is: onehots = sf.multiple_selfies_to_hot(selfies_list, largest_selfie_len, selfies_alphabet)

The error thrown is: module 'selfies' has no attribute 'multiple_selfies_to_hot'

Was wondering if someone knew why that could be the case?

[alat-rights]

That PR failed horribly, accidentally checked in a Conda installer, but I WILL MAKE ANOTHER ONE, I already have all the necessary changes made on a Colab doc, this issue will be closed soon y’all!

[ncfrey]

There is a new example in the SELFIES repo here: https://github.com/aspuru-guzik-group/selfies#label-integer-encoding-selfies that hopefully will be of some help.

It seems that there’s an extra step now, after constructing the alphabet you have to do vocab_stoi = {s: i for i, s in enumerate(alphabet)} to get vocab_stoi. Then I think you’ll have to specify enc_type='one_hot' to get one hot encodings used in the rest of the tutorial, rather than integer labels.

My apologies I haven’t had a chance to test this out myself, but thanks for taking this on! If you can implement a fix and submit a PR, that would be great. It’s probably a good idea to pin the selfies version with pip install selfies==1.0.2. I think that the version of selfies I was using at the time wasn’t a major release, so that’s what’s causing the dependency issues.