def test_score_sequence(self):
# Network predicts <unk> probability.
scorer = TextScorer(self.dummy_network)
word_ids = numpy.arange(6)
class_ids = numpy.arange(6)
membership_probs = numpy.ones(6, dtype='float32')
logprob = scorer.score_sequence(word_ids, class_ids, membership_probs)
correct = word_ids[1:].astype('float32')
correct = correct / 5
correct = numpy.log(correct).sum()
self.assertAlmostEqual(logprob, correct, places=5)
# <unk> is removed from the resulting logprobs.
scorer = TextScorer(self.dummy_network, ignore_unk=True)
word_ids = numpy.arange(6)
word_ids[3] = self.vocabulary.word_to_id['<unk>']
class_ids = numpy.arange(6)
membership_probs = numpy.ones(6, dtype='float32')
logprob = scorer.score_sequence(word_ids, class_ids, membership_probs)
correct = word_ids[[1, 2, 4, 5]].astype('float32')
correct = correct / 5
correct = numpy.log(correct).sum()
self.assertAlmostEqual(logprob, correct, places=5)
# <unk> is assigned a constant logprob.
scorer = TextScorer(self.dummy_network, ignore_unk=False, unk_penalty=-5)
word_ids = numpy.arange(6)
word_ids[3] = self.vocabulary.word_to_id['<unk>']
class_ids = numpy.arange(6)
membership_probs = numpy.ones(6, dtype='float32')
logprob = scorer.score_sequence(word_ids, class_ids, membership_probs)
correct = word_ids[[1, 2, 4, 5]].astype('float32')
correct = correct / 5
correct = numpy.log(correct).sum() - 5
self.assertAlmostEqual(logprob, correct, places=5)
def test_score_sequence(self):
# Network predicts <unk> probability.
scorer = TextScorer(self.dummy_network)
word_ids = numpy.arange(6)
class_ids = numpy.arange(6)
membership_probs = numpy.ones(6, dtype='float32')
logprob = scorer.score_sequence(word_ids, class_ids, membership_probs)
correct = word_ids[1:].astype('float32')
correct = correct / 5
correct = numpy.log(correct).sum()
self.assertAlmostEqual(logprob, correct, places=5)
# <unk> is removed from the resulting logprobs.
scorer = TextScorer(self.dummy_network, ignore_unk=True)
word_ids = numpy.arange(6)
word_ids[3] = self.vocabulary.word_to_id['<unk>']
class_ids = numpy.arange(6)
membership_probs = numpy.ones(6, dtype='float32')
logprob = scorer.score_sequence(word_ids, class_ids, membership_probs)
correct = word_ids[[1, 2, 4, 5]].astype('float32')
correct = correct / 5
correct = numpy.log(correct).sum()
self.assertAlmostEqual(logprob, correct, places=5)
# <unk> is assigned a constant logprob.
scorer = TextScorer(self.dummy_network, ignore_unk=False, unk_penalty=-5)
word_ids = numpy.arange(6)
word_ids[3] = self.vocabulary.word_to_id['<unk>']
class_ids = numpy.arange(6)
membership_probs = numpy.ones(6, dtype='float32')
logprob = scorer.score_sequence(word_ids, class_ids, membership_probs)
correct = word_ids[[1, 2, 4, 5]].astype('float32')
correct = correct / 5
correct = numpy.log(correct).sum() - 5
self.assertAlmostEqual(logprob, correct, places=5)
def test_score_sequence(self):
# Network predicts <unk> probability.
scorer = TextScorer(self.dummy_network, use_shortlist=False)
word_ids = numpy.arange(15)
class_ids, _ = self.vocabulary.get_class_memberships(word_ids)
membership_probs = numpy.ones_like(word_ids).astype('float32')
logprob = scorer.score_sequence(word_ids, class_ids, membership_probs)
correct = word_ids[1:].astype('float32')
correct /= 100.0
correct[12] = 12.0 / 100.0
correct[13] = 12.0 / 100.0
correct = numpy.log(correct).sum()
self.assertAlmostEqual(logprob, correct, places=4)
# Network predicts <unk> probability. This is distributed for
# out-of-shortlist words according to word frequency.
scorer = TextScorer(self.dummy_network, use_shortlist=True)
word_ids = numpy.arange(15)
class_ids, _ = self.vocabulary.get_class_memberships(word_ids)
membership_probs = numpy.ones_like(word_ids).astype('float32')
logprob = scorer.score_sequence(word_ids, class_ids, membership_probs)
correct = word_ids[1:].astype('float32')
correct /= 100.0
correct[11] = 1.0 # <unk> is ignored
correct[12] = 12.0 / 100.0 * 0.3
correct[13] = 12.0 / 100.0 * 0.7
correct = numpy.log(correct).sum()
self.assertAlmostEqual(logprob, correct, places=5)
# OOV and OOS words are excluded from the resulting logprobs.
scorer = TextScorer(self.dummy_network,
use_shortlist=False,
exclude_unk=True)
word_ids = numpy.arange(15)
class_ids, _ = self.vocabulary.get_class_memberships(word_ids)
membership_probs = numpy.ones_like(word_ids).astype('float32')
logprob = scorer.score_sequence(word_ids, class_ids, membership_probs)
correct = word_ids[1:12].astype('float32')
correct /= 100.0
correct = numpy.log(correct).sum()
self.assertAlmostEqual(logprob, correct, places=5)
