Source code for immuneML.encodings.word2vec.model_creator.SequenceModelCreator

# quality: gold

from pathlib import Path

from gensim.models import Word2Vec

from immuneML.data_model.dataset.Dataset import Dataset
from immuneML.data_model.dataset.RepertoireDataset import RepertoireDataset
from immuneML.data_model.dataset.SequenceDataset import SequenceDataset
from immuneML.encodings.word2vec.model_creator.ModelCreatorStrategy import ModelCreatorStrategy
from immuneML.environment.EnvironmentSettings import EnvironmentSettings
from immuneML.environment.SequenceType import SequenceType
from immuneML.util.KmerHelper import KmerHelper


[docs]class SequenceModelCreator(ModelCreatorStrategy):
[docs] def create_model(self, dataset: Dataset, k: int, vector_size: int, batch_size: int, model_path: Path, sequence_type: SequenceType): print("starting to create model") model = Word2Vec(size=vector_size, min_count=1, window=self.window) # creates an empty model all_kmers = KmerHelper.create_all_kmers(k=k, alphabet=EnvironmentSettings.get_sequence_alphabet()) all_kmers = [[kmer] for kmer in all_kmers] model.build_vocab(all_kmers) if isinstance(dataset, RepertoireDataset): model = self._create_for_repertoire(dataset, batch_size, k, model, all_kmers, sequence_type) elif isinstance(dataset, SequenceDataset): model = self._create_for_sequences(dataset, batch_size, k, model, all_kmers, sequence_type) model.save(str(model_path)) return model
def _create_for_repertoire(self, dataset, batch_size, k, model, all_kmers, sequence_type): for example in dataset.get_data(batch_size=batch_size): sentences = KmerHelper.create_sentences_from_repertoire(repertoire=example, k=k, sequence_type=sequence_type) model.train(sentences=sentences, total_words=len(all_kmers), epochs=self.epochs) return model def _create_for_sequences(self, dataset: SequenceDataset, batch_size, k, model, all_kmers, sequence_type): for sequence_batch in dataset.get_batch(batch_size): sentences = [KmerHelper.create_kmers_from_sequence(seq, k, sequence_type) for seq in sequence_batch] model.train(sentences=sentences, total_words=len(all_kmers), epochs=self.epochs) return model