Source code for bio_embeddings.embed.seqvec_embedder

import logging
from pathlib import Path
from typing import List, Optional, Generator

from allennlp.commands.elmo import ElmoEmbedder
from numpy import ndarray

from bio_embeddings.embed.embedder_interfaces import EmbedderWithFallback

logger = logging.getLogger(__name__)

# A random short sequence (T0922 from CASP2)

[docs]class SeqVecEmbedder(EmbedderWithFallback): """SeqVec Embedder Heinzinger, Michael, et al. "Modeling aspects of the language of life through transfer-learning protein sequences." BMC bioinformatics 20.1 (2019): 723. """ name = "seqvec" embedding_dimension = 1024 number_of_layers = 3 _weights_file: str _options_file: str _model: ElmoEmbedder # The fallback model running on the cpu, which will be initialized if needed _model_fallback: Optional[ElmoEmbedder] = None necessary_files = ["weights_file", "options_file"]
[docs] def __init__(self, warmup_rounds: int = 4, **kwargs): """ Initialize Elmo embedder. Can define non-positional arguments for paths of files and other settings. :param warmup_rounds: A sample sequence will be embedded this often to work around elmo's non-determinism ( :param weights_file: path of weights file :param options_file: path of options file :param model_directory: Alternative of weights_file/options_file :param max_amino_acids: max # of amino acids to include in embed_many batches. Default: 15k AA """ super().__init__(**kwargs) # Get file locations from kwargs if "model_directory" in self._options: self._weights_file = str( Path(self._options["model_directory"]).joinpath("weights_file") ) self._options_file = str( Path(self._options["model_directory"]).joinpath("options_file") ) else: self._weights_file = self._options["weights_file"] self._options_file = self._options["options_file"] if self._device.type == "cuda":"CUDA available, using the GPU") cuda_device = self._device.index or 0 else:"CUDA NOT available, using the CPU. This is slow") cuda_device = -1 self._model = ElmoEmbedder( weight_file=self._weights_file, options_file=self._options_file, cuda_device=cuda_device, ) self.warmup_rounds = warmup_rounds if self.warmup_rounds > 0:"Running ELMo warmup") for _ in range(self.warmup_rounds): self.embed(_warmup_seq)
[docs] def embed(self, sequence: str) -> ndarray: return self._model.embed_sentence(list(sequence))
def _get_fallback_model(self) -> ElmoEmbedder: if not self._model_fallback: logger.warning( "Loading model for CPU into RAM. Embedding on the CPU is very slow and you should avoid it." ) self._model_fallback = ElmoEmbedder( weight_file=self._weights_file, options_file=self._options_file, cuda_device=-1, ) if self.warmup_rounds > 0:"Running CPU ELMo warmup") for _ in range(self.warmup_rounds): self._model_fallback.embed_sentence(list(_warmup_seq)) return self._model_fallback def _embed_batch_impl( self, batch: List[str], model: ElmoEmbedder ) -> Generator[ndarray, None, None]: # elmo expect a `List[str]` as it was meant for tokens/words with more than one character. yield from model.embed_batch([list(seq) for seq in batch])
[docs] @staticmethod def reduce_per_protein(embedding): return embedding.sum(0).mean(0)