def test_max_score(self):
"""
Test that the maximum score is 1 when normalization is enabled.
"""
"""
Create the test data.
"""
tokenizer = Tokenizer(stem=False)
posts = [
"Erdogan with threats to attack regime forces 'everywhere' in Syria",
"Damascus says Erdogan 'disconnected from reality' after threats",
]
corpus = [Document(post, tokenizer.tokenize(post)) for post in posts]
extractor = TokenExtractor()
scorer = LogTFScorer()
candidates = extractor.extract(corpus)
scores = scorer.score(candidates)
self.assertTrue(all(score <= 1 for score in scores.values()))
def test_score_of_unknown_token(self):
"""
Test that the score of an unknown token is 0.
"""
"""
Create the test data.
"""
tokenizer = Tokenizer(stem=False)
posts = [
"Erdogan with threats to attack regime forces 'everywhere' in Syria",
"Damascus says Erdogan 'disconnected from reality' after threats",
]
corpus = [Document(post, tokenizer.tokenize(post)) for post in posts]
extractor = TokenExtractor()
scorer = LogTFScorer()
candidates = extractor.extract(corpus)
scores = scorer.score(candidates)
self.assertFalse(scores.get('unknown'))
def test_repeated_tokens(self):
"""
Test that when tokens are repeated, the frequency that is returned is the document frequency.
"""
"""
Create the test data.
"""
tokenizer = Tokenizer(stem=False)
posts = [
"After Erdogan's statement, Damascus says Erdogan 'disconnected from reality' after threats",
]
corpus = [Document(post, tokenizer.tokenize(post)) for post in posts]
extractor = TokenExtractor(tokenizer=tokenizer)
scorer = LogTFScorer()
candidates = extractor.extract(corpus)
scores = scorer.score(candidates, normalize_scores=False)
self.assertEqual(math.log(2 + 1, 10),
scores.get('erdogan')) # apply Laplace smoothing
def test_logarithm_base(self):
"""
Test that when a logarithmic base is provided, it is used instead of the default base.
"""
"""
Create the test data.
"""
tokenizer = Tokenizer(stem=False)
posts = [
"Erdogan with threats to attack regime forces 'everywhere' in Syria",
"After Erdogan's statement, Damascus says Erdogan 'disconnected from reality' after threats",
]
corpus = [Document(post, tokenizer.tokenize(post)) for post in posts]
extractor = TokenExtractor()
scorer = LogTFScorer(base=2)
candidates = extractor.extract(corpus)
scores = scorer.score(candidates, normalize_scores=False)
self.assertEqual(math.log(2 + 1, 2),
scores.get('erdogan')) # apply Laplace smoothing
def test_normalization(self):
"""
Test that when normalization is disabled, the returned scores are integers.
"""
"""
Create the test data.
"""
tokenizer = Tokenizer(stem=False)
posts = [
"Erdogan with threats to attack regime forces 'everywhere' in Syria",
"After Erdogan's statement, Damascus says Erdogan 'disconnected from reality' after threats",
]
corpus = [Document(post, tokenizer.tokenize(post)) for post in posts]
extractor = TokenExtractor()
scorer = LogTFScorer()
candidates = extractor.extract(corpus)
scores = scorer.score(candidates, normalize_scores=False)
self.assertEqual(math.log(2 + 1, 10),
scores.get('erdogan')) # apply Laplace smoothing
def test_score_across_multiple_documents(self):
"""
Test that the score is based on document frequency.
"""
"""
Create the test data.
"""
tokenizer = Tokenizer(stem=False)
posts = [
"Erdogan with threats to attack regime forces 'everywhere' in Syria",
"After Erdogan's statement, Damascus says Erdogan 'disconnected from reality' after threats",
]
corpus = [Document(post, tokenizer.tokenize(post)) for post in posts]
extractor = TokenExtractor(tokenizer=tokenizer)
scorer = LogTFScorer()
candidates = extractor.extract(corpus)
scores = scorer.score(candidates, normalize_scores=False)
self.assertEqual(math.log(3 + 1, 10),
scores.get('erdogan')) # apply Laplace smoothing
def test_log_tf_scorer(self):
"""
Test the basic functionality of the logarithmic TF scorer.
"""
"""
Create the test data.
"""
tokenizer = Tokenizer(stem=False)
posts = [
"Erdogan with threats to attack regime forces 'everywhere' in Syria",
"Damascus says Erdogan 'disconnected from reality' after threats",
]
corpus = [Document(post, tokenizer.tokenize(post)) for post in posts]
extractor = TokenExtractor()
scorer = LogTFScorer()
candidates = extractor.extract(corpus)
scores = scorer.score(candidates)
self.assertEqual(1, scores.get('erdogan', 0))
self.assertEqual(
math.log(1 + 1, 10) / math.log(2 + 1, 10),
scores.get('damascus', 0))
self.assertEqual(1, scores.get('threats', 0))