def test_create_kmers_within_HD(self):
kmers = KmerHelper.create_kmers_within_HD("ACT", list("ACTEF"), 1)
self.assertEqual(15, len(kmers))
for i in range(15):
self.assertTrue(set("ACT").intersection(set(kmers[i][1])))
def create_model(self, dataset: RepertoireDataset, k: int,
vector_size: int, batch_size: int, model_path: Path):
model = Word2Vec(size=vector_size, min_count=1,
window=5) # 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)
for kmer in all_kmers:
sentences = KmerHelper.create_kmers_within_HD(
kmer=kmer[0],
alphabet=EnvironmentSettings.get_sequence_alphabet(),
distance=1)
model.train(sentences=sentences,
total_words=len(all_kmers),
epochs=model.epochs)
model.save(str(model_path))
return model