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 W2VSequenceEncoder(Word2VecEncoder):
def _encode_labels(self, dataset, params: EncoderParams):
labels = {name: [] for name in params.label_config.get_labels_by_name()}
for sequence in dataset.get_data():
for label_name in params.label_config.get_labels_by_name():
label = sequence.get_attribute(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):
kmers = KmerHelper.create_kmers_from_sequence(sequence=item, 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
return sequence_vector