def test_sample_ranking_with_no_exceptions(self):
"""
Test if ranking is built without any exception.
"""
sample1 = Indexable(1, 'this is an indexable metadata')
sample2 = Indexable(2, 'this is an indexable super metadata')
sample3 = Indexable(3, 'this is another indexable metadata')
self.rank.build_rank([sample1, sample2, sample3])
def test_indexed_doc_count(self):
"""
Test if the number of indexed object is retrieved correctly.
"""
sample1 = Indexable(1, 'this is an indexable metadata')
sample2 = Indexable(2, 'this is an indexable super metadata')
sample3 = Indexable(3, 'this is another indexable metadata')
self.build_sample_index([sample1, sample2, sample3])
self.assertEqual(self.engine.count(), 3)
def test_non_existent_term_search(self):
"""
Test if search is correctly performed.
"""
sample1 = Indexable(1, 'this is an indexable metadata')
sample2 = Indexable(2, 'this is an indexable super metadata')
sample3 = Indexable(3, 'this is another indexable metadata')
self.build_sample_index([sample1, sample2, sample3])
expected_results = []
results = self.engine.search('asdasdasdas')
self.assertListEqual(results, expected_results)
def test_two_terms_search(self):
"""
Test if the search for two term returns expected results.
"""
sample1 = Indexable(1, 'this is an indexable simple metadata')
sample2 = Indexable(2, 'this is an indexable super metadata')
sample3 = Indexable(3, 'this is another indexable super metadata')
expected_indices = [1, 2]
self.index.build_index([sample1, sample2, sample3])
search_results = self.index.search_terms(['indexable', 'super'])
self.assertItemsEqual(search_results, expected_indices)
def test_stop_word_search(self):
"""
Test if stop words are correctly ignored.
"""
sample1 = Indexable(1, 'this is an indexable metadata')
sample2 = Indexable(2, 'this is an indexable super metadata')
sample3 = Indexable(3, 'this is another indexable super metadata')
expected_indices = []
self.index.build_index([sample1, sample2, sample3])
search_results = self.index.search_terms(['this'])
self.assertItemsEqual(search_results, expected_indices)
def test_mixed_valid_invalid_term_search(self):
"""
Test if the search returns when there are valid and invalid terms mixed.
"""
sample1 = Indexable(1, 'this is an indexable simple metadata')
sample2 = Indexable(2, 'this is an indexable super metadata')
sample3 = Indexable(3, 'this is another indexable metadata')
expected_indices = []
self.index.build_index([sample1, sample2, sample3])
search_results = self.index.search_terms(['not_valid_term', 'super'])
self.assertItemsEqual(search_results, expected_indices)
def test_one_term_search(self):
"""
Test if the search for one term returns expected results.
"""
sample1 = Indexable(1, "this is an indexable metadata")
sample2 = Indexable(2, "this is an indexable super metadata")
sample3 = Indexable(3, "this is another indexable super metadata")
expected_indices = [1, 2]
self.index.build_index([sample1, sample2, sample3])
search_results = self.index.search_terms(["super"])
self.assertItemsEqual(search_results, expected_indices)
def test_invalid_term_search(self):
"""
Test if the search returns when the term is not found.
"""
sample1 = Indexable(1, "this is an indexable simple metadata")
sample2 = Indexable(2, "this is an indexable super metadata")
sample3 = Indexable(3, "this is another indexable metadata")
expected_indices = []
self.index.build_index([sample1, sample2, sample3])
search_results = self.index.search_terms(["not_valid_term"])
self.assertItemsEqual(search_results, expected_indices)
def test_doc_frequency_matrix_with_sample2(self):
"""
Test if document frequency matrix is correctly built.
"""
sample1 = Indexable(1, 'the sky is blue')
sample2 = Indexable(2, 'the sun is bright')
self.rank.build_rank([sample1, sample2])
expected_vocab_indices = {'blue': 0, 'sun': 2, 'bright': 3, 'sky': 1}
expected_tf = np.array([[1, 1, 0, 0], [0, 0, 1, 1]])
self.assertEqual(self.rank.vocabulary, expected_vocab_indices)
np.testing.assert_array_equal(self.rank.ft_matrix.todense(),
expected_tf)
def test_search_result_limit(self):
"""
Test if search results can be limited.
"""
sample1 = Indexable(1, 'this is an indexable metadata')
sample2 = Indexable(2, 'this is an indexable super metadata')
sample3 = Indexable(3, 'this is another indexable metadata')
self.build_sample_index([sample1, sample2, sample3])
expected_results = [
IndexableResult(1.414214, sample1),
]
results = self.engine.search('indexable metadata', 1)
self.assertListEqual(results, expected_results)
def test_doc_inverse_term_frequency_vector2(self):
"""
Test if document inverse term frequency vector is correctly built.
"""
sample1 = Indexable(1, 'the sky is blue')
sample2 = Indexable(2, 'the sun is bright')
self.rank.build_rank([sample1, sample2])
expected_idf = [1.40546511, 1.40546511, 1.40546511, 1.40546511]
expected_tf_idf = [[0.70710678, 0.70710678, 0, 0],
[0, 0, 0.70710678, 0.70710678]]
np.testing.assert_almost_equal(self.rank.ifd_diag_matrix.diagonal(),
expected_idf, 4)
np.testing.assert_almost_equal(self.rank.tf_idf_matrix.todense(),
expected_tf_idf, 4)
def test_existent_term_search(self):
"""
Test if search is correctly performed.
"""
sample1 = Indexable(1, 'this is an indexable metadata')
sample2 = Indexable(2, 'this is an indexable super metadata')
sample3 = Indexable(3, 'this is another indexable metadata')
self.build_sample_index([sample1, sample2, sample3])
expected_results = [
IndexableResult(1.414214, sample1),
IndexableResult(0.906589, sample2),
IndexableResult(0.906589, sample3),
]
results = self.engine.search('indexable metadata')
self.assertListEqual(results, expected_results)
def test_doc_inverse_term_frequency_vector1(self):
"""
Test if document inverse term frequency vector is correctly built.
"""
sample1 = Indexable(1, 'this is an indexable metadata')
sample2 = Indexable(2, 'this is an indexable super metadata')
sample3 = Indexable(3, 'this is another indexable metadata')
self.rank.build_rank([sample1, sample2, sample3])
expected_idf = [1., 1., 1.28768207, 1.69314718, 1.69314718]
expected_tf_idf = [[0.52284231, 0.52284231, 0.67325467, 0, 0],
[0.39148397, 0.39148397, 0.50410689, 0.66283998, 0],
[0.45329466, 0.45329466, 0, 0, 0.76749457]]
np.testing.assert_almost_equal(self.rank.ifd_diag_matrix.diagonal(),
expected_idf, 4)
np.testing.assert_almost_equal(self.rank.tf_idf_matrix.todense(),
expected_tf_idf, 4)
def test_score_computation(self):
"""
Test if document score is correctly calculated.
"""
sample1 = Indexable(1, 'the sky is blue')
self.rank.build_rank([sample1])
np.testing.assert_almost_equal(self.rank.compute_rank(0, ['blue']),
0.707106, 5)
np.testing.assert_almost_equal(self.rank.compute_rank(0, ['sky']),
0.7071067, 5)
np.testing.assert_almost_equal(
self.rank.compute_rank(0, ['blue', 'sky']), 1.414213, 5)
def test_doc_frequency_matrix_with_sample1(self):
"""
Test if document frequency matrix is correctly built.
"""
sample1 = Indexable(1, 'this is an indexable metadata')
sample2 = Indexable(2, 'this is an indexable super metadata')
sample3 = Indexable(3, 'this is another indexable metadata')
self.rank.build_rank([sample1, sample2, sample3])
expected_vocab_indices = {
'an': 2,
'super': 3,
'indexable': 1,
'another': 4,
'metadata': 0
}
expected_tf = np.array([[1, 1, 1, 0, 0], [1, 1, 1, 1, 0],
[1, 1, 0, 0, 1]])
self.assertEqual(self.rank.vocabulary, expected_vocab_indices)
np.testing.assert_array_equal(self.rank.ft_matrix.todense(),
expected_tf)
def __init__(self, iid, word):
Indexable.__init__(self, iid, word)
# self.title = title
# self.singer = singer
self.word = word
def __init__(self, iid, title, author, metadata):
Indexable.__init__(self, iid, metadata)
self.title = title
self.author = author
def __init__(self, iid, word, isBinaryWord):
Indexable.__init__(self, iid, word, isBinaryWord)
# self.title = title
# self.singer = singer
self.word = word
def __init__(self, iid, title, author, metadata):
Indexable.__init__(self, iid, metadata)
self.title = title
self.author = author
def test_sample_indexing_with_no_exceptions(self):
sample1 = Indexable(1, 'this is an indexable metadata')
sample2 = Indexable(2, 'this is an indexable super metadata')
sample3 = Indexable(3, 'this is another indexable metadata')
self.index.build_index([sample1, sample2, sample3])
