class TestLidstoneBigram(metaclass=ParametrizedTests):
"""Unit tests for Lidstone class"""
score_tests = [
# count(d | c) = 1
# *count(d | c) = 1.1
# Count(w | c for w in vocab) = 1
# *Count(w | c for w in vocab) = 1.8
("d", ["c"], 1.1 / 1.8),
# Total unigrams: 14
# Vocab size: 8
# Denominator: 14 + 0.8 = 14.8
# count("a") = 2
# *count("a") = 2.1
("a", None, 2.1 / 14.8),
# in vocabulary but unseen
# count("z") = 0
# *count("z") = 0.1
("z", None, 0.1 / 14.8),
# out of vocabulary should use "UNK" score
# count("<UNK>") = 3
# *count("<UNK>") = 3.1
("y", None, 3.1 / 14.8),
]
@classmethod
def setup_method(self):
vocab, training_text = _prepare_test_data(2)
self.model = Lidstone(0.1, 2, vocabulary=vocab)
self.model.fit(training_text)
def test_gamma(self):
assert 0.1 == self.model.gamma
def test_entropy_perplexity(self):
text = [
("<s>", "a"),
("a", "c"),
("c", "<UNK>"),
("<UNK>", "d"),
("d", "c"),
("c", "</s>"),
]
# Unlike MLE this should be able to handle completely novel ngrams
# Ngram = score, log score
# <s>, a = 0.3929, -1.3479
# a, c = 0.0357, -4.8074
# c, UNK = 0.0(5), -4.1699
# UNK, d = 0.0263, -5.2479
# d, c = 0.0357, -4.8074
# c, </s> = 0.0(5), -4.1699
# TOTAL logscore: −24.5504
# - AVG logscore: 4.0917
H = 4.0917
perplexity = 17.0504
assert pytest.approx(self.model.entropy(text), 1e-4) == H
assert pytest.approx(self.model.perplexity(text), 1e-4) == perplexity
class LidstoneBigramTests(unittest.TestCase):
"""unit tests for Lidstone class"""
score_tests = [
# count(d | c) = 1
# *count(d | c) = 1.1
# Count(w | c for w in vocab) = 1
# *Count(w | c for w in vocab) = 1.8
("d", ["c"], 1.1 / 1.8),
# Total unigrams: 14
# Vocab size: 8
# Denominator: 14 + 0.8 = 14.8
# count("a") = 2
# *count("a") = 2.1
("a", None, 2.1 / 14.8),
# in vocabulary but unseen
# count("z") = 0
# *count("z") = 0.1
("z", None, 0.1 / 14.8),
# out of vocabulary should use "UNK" score
# count("<UNK>") = 3
# *count("<UNK>") = 3.1
("y", None, 3.1 / 14.8),
]
def setUp(self):
vocab, training_text = _prepare_test_data(2)
self.model = Lidstone(0.1, 2, vocabulary=vocab)
self.model.fit(training_text)
def test_gamma(self):
self.assertEqual(0.1, self.model.gamma)
def test_entropy_perplexity(self):
text = [
("<s>", "a"),
("a", "c"),
("c", "<UNK>"),
("<UNK>", "d"),
("d", "c"),
("c", "</s>"),
]
# Unlike MLE this should be able to handle completely novel ngrams
# Ngram = score, log score
# <s>, a = 0.3929, -1.3479
# a, c = 0.0357, -4.8074
# c, UNK = 0.0(5), -4.1699
# UNK, d = 0.0263, -5.2479
# d, c = 0.0357, -4.8074
# c, </s> = 0.0(5), -4.1699
# TOTAL logscore: −24.5504
# - AVG logscore: 4.0917
H = 4.0917
perplexity = 17.0504
self.assertAlmostEqual(H, self.model.entropy(text), places=4)
self.assertAlmostEqual(perplexity, self.model.perplexity(text), places=4)
class LidstoneBigramTests(unittest.TestCase):
"""unit tests for Lidstone class"""
score_tests = [
# count(d | c) = 1
# *count(d | c) = 1.1
# Count(w | c for w in vocab) = 1
# *Count(w | c for w in vocab) = 1.8
("d", ["c"], 1.1 / 1.8),
# Total unigrams: 14
# Vocab size: 8
# Denominator: 14 + 0.8 = 14.8
# count("a") = 2
# *count("a") = 2.1
("a", None, 2.1 / 14.8),
# in vocabulary but unseen
# count("z") = 0
# *count("z") = 0.1
("z", None, 0.1 / 14.8),
# out of vocabulary should use "UNK" score
# count("<UNK>") = 3
# *count("<UNK>") = 3.1
("y", None, 3.1 / 14.8),
]
def setUp(self):
vocab, training_text = _prepare_test_data(2)
self.model = Lidstone(0.1, 2, vocabulary=vocab)
self.model.fit(training_text)
def test_gamma(self):
self.assertEqual(0.1, self.model.gamma)
def test_entropy_perplexity(self):
text = [
("<s>", "a"),
("a", "c"),
("c", "<UNK>"),
("<UNK>", "d"),
("d", "c"),
("c", "</s>"),
]
# Unlike MLE this should be able to handle completely novel ngrams
# Ngram = score, log score
# <s>, a = 0.3929, -1.3479
# a, c = 0.0357, -4.8074
# c, UNK = 0.0(5), -4.1699
# UNK, d = 0.0263, -5.2479
# d, c = 0.0357, -4.8074
# c, </s> = 0.0(5), -4.1699
# TOTAL logscore: −24.5504
# - AVG logscore: 4.0917
H = 4.0917
perplexity = 17.0504
self.assertAlmostEqual(H, self.model.entropy(text), places=4)
self.assertAlmostEqual(perplexity, self.model.perplexity(text), places=4)