class RankTests(unittest.TestCase):
"""
Test case for Index class.
"""
def setUp(self):
"""
Setup ranker that will be subjected to the tests.
"""
self.rank = Rank(sample_stop_words())
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_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,
'metadata': 0,
'another': 4
}
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 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_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_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_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_debug_ft_matrix(self):
self.twitter = Twitter(CUR_DIR + "/test_crossfit.tweets",
CUR_DIR + "/test_stop_words.txt")
self.twitter.load_tweets_and_build_index()
class RankTests(unittest.TestCase):
"""
Test case for Index class.
"""
def setUp(self):
"""
Setup ranker that will be subjected to the tests.
"""
self.rank = Rank(sample_stop_words())
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_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, "metadata": 0, "another": 4}
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 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_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 = [1.0, 1.0, 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_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_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_debug_ft_matrix(self):
self.twitter = Twitter(CUR_DIR + "/test_crossfit.tweets", CUR_DIR + "/test_stop_words.txt")
self.twitter.load_tweets_and_build_index()
