v0.2

Summary of changes:

Major change - Variant Scoring

• The ability to change the expert variant scoring strategy has been added. There is now a class VariantScoring which can be configured with a scoring_strategy argument (currently supported: attribute_value, pseudolikelihood_ratio, and mutant_marginal (NEW)). Each expert has an instance of a VariantScoring class. It is defined in evo_prot_grad.common.variant_scoring.
• The main entry point for instantiating an expert, get_expert, now has a scoring_strategy argument for configuring the expert.
• The use_without_wildtype argument of the Expert class has been removed. Each scoring strategy normalizes the score with respect to the wildtype score, so this was superflous. If you want to instantiate an expert and use it outside of the DirectedEvolution class, you have to explicitly call expert.init_wildtype(wt_seq) before calling the expert to cache the wildtype score (see below).
• Expert private class method _model_output_to_scalar_score has been removed in favor of a public facing method get_model_output. This method can be used to directly get expert scores for sequences.
• The Expert class no longer has a wt_score attribute. The wildtype score is now stored in the VariantScoring class (wt_score_cache).

Minor changes

• The Expert abstract class now publicly exposes the following methods: init_wildtype, for storing the wildtype string sequence and caching the WT score, tokenize for tokenizing a sequence, get_model_output which accepts a list of protein sequence strings and returns the one-hot encoded sequences and the expert model's predictions.
• Renamed experts.base_experts.HuggingFaceExpert to experts.base_experts.ProteinLMExpert
• Improved error message reporting for get_expert
• Upgraded transformers[torch] to 4.38.0
• Check for size of preserved region fixed to reflect 1 AA instead of 2 by @EvanKomp in #6

New Contributors

• @EvanKomp made their first contribution in #6