Source code for immuneML.encodings.word2vec.W2VRepertoireEncoder

import numpy as np

from immuneML.encodings.EncoderParams import EncoderParams
from immuneML.encodings.word2vec.Word2VecEncoder import Word2VecEncoder
from immuneML.environment.SequenceType import SequenceType
from immuneML.util.KmerHelper import KmerHelper


[docs]class W2VRepertoireEncoder(Word2VecEncoder): def _encode_labels(self, dataset, params: EncoderParams): label_config = params.label_config labels = {name: [] for name in label_config.get_labels_by_name()} for repertoire in dataset.get_data(): for label_name in label_config.get_labels_by_name(): label = repertoire.metadata[label_name] labels[label_name].append(label) return np.array([labels[name] for name in labels.keys()]) def _encode_item(self, item, vectors, sequence_type: SequenceType): repertoire_vector = np.zeros(vectors.vector_size) for (index2, sequence) in enumerate(item.sequences): kmers = KmerHelper.create_kmers_from_sequence(sequence=sequence, k=self.k, sequence_type=sequence_type) sequence_vector = np.zeros(vectors.vector_size) for kmer in kmers: try: word_vector = vectors.get_vector(kmer) sequence_vector = np.add(sequence_vector, word_vector) except KeyError: pass repertoire_vector = np.add(repertoire_vector, sequence_vector) return repertoire_vector