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
Changes for v0.2
Major change - Variant Scoring
VariantScoringwhich can be configured with ascoring_strategyargument (currently supported:attribute_value,pseudolikelihood_ratio, andmutant_marginal(NEW)). Each expert has an instance of aVariantScoringclass. It is defined inevo_prot_grad.common.variant_scoring.get_expert, now has ascoring_strategyargument for configuring the expert.use_without_wildtypeargument 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 callexpert.init_wildtype(wt_seq)before calling the expert to cache the wildtype score (see below).Expertprivate class method_model_output_to_scalar_scorehas been removed in favor of a public facing methodget_model_output. This method can be used to directly get expert scores for sequences.Expertclass no longer has awt_scoreattribute. The wildtype score is now stored in theVariantScoringclass (wt_score_cache).Minor changes
Expertabstract class now publicly exposes the following methods:init_wildtype, for storing the wildtype string sequence and caching the WT score,tokenizefor tokenizing a sequence,get_model_outputwhich accepts a list of protein sequence strings and returns the one-hot encoded sequences and the expert model's predictions.experts.base_experts.HuggingFaceExperttoexperts.base_experts.ProteinLMExpertget_experttransformers[torch]to4.38.0