def test_tokenise_words(self):
test_sentences = [
('', []),
(' ', []),
("I don't like it. You'll agree?", ["I", "don", "'", "t", "like", "it", ".", "You", "'", "ll", "agree", "?"]),
(test_text,
['I', 'am', 'Sam', '.', 'I', 'am', 'Sam', '.', 'Sam', '-', 'I', '-', 'Am', '.',
'That', 'Sam', '-', 'I', '-', 'Am', '!', 'That', 'Sam', '-', 'I', '-', 'Am', '!',
'I', 'do', 'not', 'like', 'that', 'Sam', '-', 'I', '-', 'Am', '!',
'Do', 'you', 'like', 'green', 'eggs', 'and', 'ham', '?',
'I', 'do', 'not', 'like', 'them', ',', 'Sam', '-', 'I', '-', 'Am', '.',
'I', 'do', 'not', 'like', 'green', 'eggs', 'and', 'ham', '.'])
]
for test_sentence, result in test_sentences:
tokens = util.tokenize_words(test_sentence)
self.assertEquals(tokens, result, msg=(test_sentence, tokens, '!=', result))
def test_pos_distribution(self):
# TODO: more sentences
tag_list = ["NN", "DT", "VBD", "PRP", "VBP", "RB", "IN", "NNS", "CC", "NNP", "JJ", "VB"]
test_sentences = [
("dog",
{key+"_pos_relative_frequency": 0 if key is not "NN" else 1.0 for key in tag_list}),
('', {key+"_pos_relative_frequency": 0 for key in tag_list}),
('the quick brown fox jumped.',
{"PRP_pos_relative_frequency": 0,
"NN_pos_relative_frequency": 2.0/5,
"DT_pos_relative_frequency": 1.0/5,
"VBP_pos_relative_frequency": 0,
"RB_pos_relative_frequency": 0,
"IN_pos_relative_frequency": 0,
"NNS_pos_relative_frequency": 0,
"CC_pos_relative_frequency": 0,
"NNP_pos_relative_frequency": 0,
"JJ_pos_relative_frequency": 1.0/5,
"VB_pos_relative_frequency": 0,
"VBD_pos_relative_frequency": 1.0/5}),
(test_text,
{"PRP_pos_relative_frequency": 12.0/48,
"NN_pos_relative_frequency": 5.0/48,
"DT_pos_relative_frequency": 3.0/48,
"VBP_pos_relative_frequency": 8.0/48,
"RB_pos_relative_frequency": 3.0/48,
"IN_pos_relative_frequency": 3.0/48,
"NNS_pos_relative_frequency": 2.0/48,
"CC_pos_relative_frequency": 2.0/48,
"NNP_pos_relative_frequency": 7.0/48,
"JJ_pos_relative_frequency": 2.0/48,
"VB_pos_relative_frequency": 1.0/48,
"VBD_pos_relative_frequency": 0})
]
for test_sentence, result in test_sentences:
pos_freq_dis = compute_fingerprint.get_pos_counts(nltk.pos_tag(util.tokenize_words(test_sentence)), tag_list)
print
self.assertEquals(pos_freq_dis, result, msg=(test_sentence, pos_freq_dis, '!=', result))
def test_function_word_distribution(self):
# TODO: more sentences
test_sentences = [
("into the dog",
{"into_relative_frequency": 1.0/3,
"i_relative_frequency": 0,
"that_relative_frequency": 0,
"like_relative_frequency": 0,
"you_relative_frequency": 0,
"and_relative_frequency": 0,
"them_relative_frequency": 0}),
('', {"into_relative_frequency": 0,
"i_relative_frequency": 0,
"that_relative_frequency": 0,
"like_relative_frequency": 0,
"you_relative_frequency": 0,
"and_relative_frequency": 0,
"them_relative_frequency": 0}),
(test_text, {
"into_relative_frequency": 0,
"i_relative_frequency": 10.0/48,
"that_relative_frequency": 3.0/48,
"like_relative_frequency": 3.0/48,
"you_relative_frequency": 1.0/48,
"and_relative_frequency": 2.0/48,
"them_relative_frequency": 1.0/48})
]
tag_list = ["into", "i", "that", "like", "you", "and", "them"]
for test_sentence, result in test_sentences:
function_word_dist = compute_fingerprint.get_function_word_distribution(nltk.pos_tag(util.tokenize_words(test_sentence)), tag_list)
self.assertEquals(function_word_dist, result, msg=(test_sentence, function_word_dist, '!=', result))
