class TestTFIDF_InfoRetrieval(unittest.TestCase):
def setUp(self):
stopwords = "stop".split()
keywords = "information agency retrieval".split()
# documents = [
# ("Document 1", "information retrieval information retrieval"),
# ("Document 2", "retrieval retrieval retrieval retrieval"),
# ("Document 3", "agency information retrieval agency"),
# ("Document 4", "retrieval agency retrieval agency"),
# ]
documents = Loader.load_documents("data/documents-lab1.txt")
self.s = TFIDF(keywords, documents, Cleaner(stopwords))
def test_keyword_setup(self):
actual = self.s.keywords.items()
expected = [("agenc", 0), ("inform", 1), ("retriev", 2)]
self.assertEqual(actual, expected)
def test_documents_setup(self):
actual = self.s.document_vectors
expected = {0: [0, 2, 2], 1: [0, 0, 4], 2: [2, 1, 1], 3: [2, 0, 2]}
self.assertEqual(actual, expected)
def test_tf(self):
expected_results = [(0, [0, 1, 1]), (1, [0, 0, 1]), (2, [1, 0.5, 0.5]), (3, [1, 0, 1])]
for index, expected_vector in expected_results:
document = self.s.document_vectors[index]
for word, i in self.s.keywords.items():
actual = self.s.tf(document, word)
expected = expected_vector[i]
self.assertEqual(actual, expected)
def test_idf(self):
expected_results = [("inform", math.log(2, 10)), ("retriev", 0.0), ("agenc", math.log(2, 10))]
for term, expected in expected_results:
actual = self.s.idf(term)
self.assertAlmostEqual(actual, expected, places=6)
def test_tfidf(self):
expected_results = [
(0, [0, math.log(2, 10), 0]),
(1, [0, 0, 0]),
(2, [math.log(2, 10), 0.5 * math.log(2, 10), 0]),
(3, [math.log(2, 10), 0, 0]),
]
for index, expected_vector in expected_results:
document = self.s.document_vectors[index]
actual_vector = self.s.tfidf(document)
for actual, expected in zip(actual_vector, expected_vector):
self.assertAlmostEqual(actual, expected, places=6)
def test_similarity(self):
expected_results = [(0, 1), (1, 0), (2, math.sqrt(0.2)), (3, 0)]
question_vector = self.s.phrase_to_vector("information retrieval")
question_tfidfs = self.s.tfidf(question_vector)
for index, expected in expected_results:
actual = self.s.doc_question_similarity(index, question_tfidfs)
self.assertEqual(actual, expected)
def test_search(self):
expected = [("Document 1", 1.0, 0), ("Document 3", math.sqrt(0.2), 2)]
actual = self.s.search("information retrieval")
self.assertEqual(actual, expected)
class TestTFIDF_flies(unittest.TestCase):
def setUp(self):
stopwords = "stop".split()
keywords = "bee wasp fly fruit like".split()
documents = [
("D1", "Time fly like an arrow but fruit fly like a banana."),
("D2", "It's strange that bees and wasps don't like each other."),
("D3", "The fly attendant sprayed the cabin with a strange fruit " "aerosol."),
("D4", "Try not to carry a light, as wasps and bees may fly " "toward it."),
("D5", "Fruit fly fly around in swarms. When fly they flap their " "wings 220 times a second."),
]
self.s = TFIDF(keywords, documents, Cleaner(stopwords))
def test_keyword_setup(self):
actual = self.s.keywords.items()
expected = [("bee", 0), ("fly", 1), ("fruit", 2), ("like", 3), ("wasp", 4)]
self.assertEqual(actual, expected)
def test_documents_setup(self):
actual = self.s.document_vectors
expected = {0: [0, 2, 1, 2, 0], 1: [1, 0, 0, 1, 1], 2: [0, 1, 1, 0, 0], 3: [1, 1, 0, 0, 1], 4: [0, 3, 1, 0, 0]}
self.assertEqual(actual, expected)
def test_tf(self):
expected_results = [
(0, [0, 1, 0.5, 1, 0]),
(1, [1, 0, 0, 1, 1]),
(2, [0, 1, 1, 0, 0]),
(3, [1, 1, 0, 0, 1]),
(4, [0, 1, 0.333333333333333333, 0, 0]),
]
for index, expected_vector in expected_results:
document = self.s.document_vectors[index]
for word, i in self.s.keywords.items():
actual = self.s.tf(document, word)
expected = expected_vector[i]
self.assertEqual(actual, expected)
def test_idf(self):
expected_results = [
("bee", 0.397940009),
("fly", 0.096910013),
("fruit", 0.22184875),
("like", 0.397940009),
("wasp", 0.397940009),
]
for term, expected in expected_results:
actual = self.s.idf(term)
self.assertAlmostEqual(actual, expected, places=6)
def test_tfidf(self):
expected_results = [
(0, [0, 0.096910013, 0.110924375, 0.397940009, 0]),
(1, [0.397940009, 0, 0, 0.397940009, 0.397940009]),
(2, [0, 0.096910013, 0.22184875, 0, 0]),
(3, [0.397940009, 0.096910013, 0, 0, 0.397940009]),
(4, [0, 0.096910013, 0.073949583, 0, 0]),
]
for title, expected_vector in expected_results:
document = self.s.document_vectors[title]
actual_vector = self.s.tfidf(document)
for actual, expected in zip(actual_vector, expected_vector):
self.assertAlmostEqual(actual, expected, places=6)
