def test_cy_equal_np_ft_random(self):
ft = FastText(size=20, min_count=1)
ft.build_vocab(SENTENCES)
m1 = Average(ft)
m1.prep.prepare_vectors(sv=m1.sv,
total_sentences=len(self.sentences),
update=False)
m1._pre_train_calls()
from fse.models.average_inner import MAX_NGRAMS_IN_BATCH
m1.batch_ngrams = MAX_NGRAMS_IN_BATCH
mem1 = m1._get_thread_working_mem()
o1 = train_average_np(m1, self.sentences[:2], m1.sv.vectors, mem1)
m2 = Average(ft)
m2.prep.prepare_vectors(sv=m2.sv,
total_sentences=len(self.sentences),
update=False)
m2._pre_train_calls()
mem2 = m2._get_thread_working_mem()
from fse.models.average_inner import train_average_cy
o2 = train_average_cy(m2, self.sentences[:2], m2.sv.vectors, mem2)
self.assertEqual(o1, o2)
self.assertTrue(np.allclose(m1.sv.vectors, m2.sv.vectors, atol=1e-6))
def test_cy_equal_np_w2v_random(self):
w2v = Word2Vec(min_count=1, size=DIM)
# Random initialization
w2v.build_vocab(SENTENCES)
m1 = Average(w2v)
m1.prep.prepare_vectors(
sv=m1.sv, total_sentences=len(self.sentences), update=False
)
m1._pre_train_calls()
mem1 = m1._get_thread_working_mem()
o1 = train_average_np(m1, self.sentences, m1.sv.vectors, mem1)
m2 = Average(w2v)
m2.prep.prepare_vectors(
sv=m2.sv, total_sentences=len(self.sentences), update=False
)
m2._pre_train_calls()
mem2 = m2._get_thread_working_mem()
from fse.models.average_inner import train_average_cy
o2 = train_average_cy(m2, self.sentences, m2.sv.vectors, mem2)
self.assertTrue(np.allclose(m1.sv.vectors, m2.sv.vectors, atol=1e-6))
def test_average_train_np_w2v(self):
self.model.sv.vectors = np.zeros_like(self.model.sv.vectors, dtype=np.float32)
mem = self.model._get_thread_working_mem()
output = train_average_np(
self.model, self.sentences, self.model.sv.vectors, mem
)
self.assertEqual((4, 7), output)
self.assertTrue((183 == self.model.sv[0]).all())
self.assertTrue((164.5 == self.model.sv[1]).all())
self.assertTrue((self.model.wv.vocab["go"].index == self.model.sv[2]).all())
def test_average_train_np_ft(self):
ft = FastText(min_count=1, size=DIM)
ft.build_vocab(SENTENCES)
m = Average(ft)
m.prep.prepare_vectors(
sv=m.sv, total_sentences=len(self.sentences), update=False
)
m._pre_train_calls()
m.wv.vectors = m.wv.vectors_vocab = np.ones_like(m.wv.vectors, dtype=np.float32)
m.wv.vectors_ngrams = np.full_like(m.wv.vectors_ngrams, 2, dtype=np.float32)
mem = m._get_thread_working_mem()
output = train_average_np(m, self.sentences, m.sv.vectors, mem)
self.assertEqual((4, 10), output)
self.assertTrue((1.0 == m.sv[0]).all())
self.assertTrue((1.5 == m.sv[2]).all())
self.assertTrue((2 == m.sv[3]).all())
def test_cy_equal_np_w2v(self):
m1 = Average(W2V)
m1.prep.prepare_vectors(sv=m1.sv,
total_sentences=len(self.sentences),
update=False)
m1._pre_train_calls()
mem1 = m1._get_thread_working_mem()
o1 = train_average_np(m1, self.sentences, m1.sv.vectors, mem1)
m2 = Average(W2V)
m2.prep.prepare_vectors(sv=m2.sv,
total_sentences=len(self.sentences),
update=False)
m2._pre_train_calls()
mem2 = m2._get_thread_working_mem()
o2 = train_average_cy(m2, self.sentences, m2.sv.vectors, mem2)
self.assertEqual(o1, o2)
self.assertTrue((m1.sv.vectors == m2.sv.vectors).all())