def test_k_fold_cross(self):
method.configure(experiment.method_config)
result = experiment.k_fold_cross(2, 10, 'my_method', 'my-method')
self.assertEqual(len(result), 3)
file_pattern = 'data/2-fold-cross-*'
for f in glob(file_pattern):
os.remove(f)
def test_k_fold_cross_dataset(self):
method.configure(experiment.method_config)
result = experiment.k_fold_cross_dataset(2, 10)
self.assertEqual(len(result), 2)
for i in result:
for f in i.itervalues():
os.remove(f)
def test_analyze_feature(self):
method.configure(experiment.method_config)
feature_names = method.ImprovedMethod.feature_names
expectation = {'origin': 0}
i = 0
for feature_name in feature_names:
i += 1
expectation[feature_name] = i
mock_k_fold_cross = Mock()
mock_k_fold_cross.side_effect = range(0, 9)
experiment.k_fold_cross = mock_k_fold_cross
result = experiment.analyze_feature(2, 10, 'my_method')
self.assertDictEqual(result, expectation)
def test_has_pronoun(self):
method_config = {
'essentials': {
'third_person_pronoun': 'data/third-person-pronoun.txt',
'demonstrative_pronoun': 'data/demonstrative-pronoun.txt',
'cue_word': 'data/cue-word.txt',
'stop_word': 'data/stop-word.txt'
},
'word_similarity_calculators': {
'how_net': {
'class': 'HowNetCalculator',
'sememe_tree_file': 'data/whole.dat',
'glossary_file': 'data/glossary.dat'
}
},
'sentence_similarity_calculator': {
'ssc_with_how_net': {
'word_similarity_calculator': 'how_net',
'score_filename': 'data/how-net-sentence.score'
}
},
'feature_manager': {
'fm': {
'sentence_similarity_calculator': 'ssc_with_how_net'
}
},
'method': {
'de_boni': {
'class': 'DeBoni',
'feature_manager': 'fm',
'q_q_threshold': 0.89,
'q_a_threshold': 0.89
}
}
}
configure(method_config)
analyzed_sentence = AnalyzedSentence(
u'fb070223efa1b565414c30bd4343c56e',
u'[[[{"id": 0, "cont": "我", "pos": "r", "ne": "O", "parent": 1, "relate": "SBV", "arg": []},{ "id": 1, "cont": "是", "pos": "v", "ne": "O", "parent": -1, "relate": "HED", "arg": [{ "id": 0, "type": "A0", "beg": 0, "end": 0}, { "id": 1, "type": "A1", "beg": 2, "end": 3}]}, { "id": 2, "cont": "中国", "pos": "ns", "ne": "S-Ns", "parent": 3, "relate": "ATT", "arg": []}, { "id": 3, "cont": "人", "pos": "n", "ne": "O", "parent": 1, "relate": "VOB", "arg": []}], [{"cont": "那", "parent": 1, "relate": "ATT", "ne": "O", "pos": "r", "arg": [], "id": 0}, {"cont": "手机", "parent": 2, "relate": "SBV", "ne": "O", "pos": "n", "arg": [], "id": 1}, {"cont": "可以", "parent": -1, "relate": "HED", "ne": "O", "pos": "v", "arg": [], "id": 2}, {"cont": "吗", "parent": 2, "relate": "RAD", "ne": "O", "pos": "u", "arg": [], "id": 3}, {"cont": "？", "parent": 2, "relate": "WP", "ne": "O", "pos": "wp", "arg": [], "id": 4}]]]')
mock_index = Mock()
mock_index.return_value = (0, 11)
analyzed_sentence.index = mock_index
result = analyzed_sentence.has_pronoun()
self.assertFalse(result)