def test_single_word(self):
"""
Issue#10: contains one character words like: 'I a'
:return:
"""
texts = ["I a", 'I']
aug = naw.WordNetAug()
for text in texts:
self.assertLess(0, len(text))
augmented_text = aug.augment(text)
self.assertNotEqual(text, augmented_text)
self.assertLess(0, len(texts))
texts = [
"a",
]
aug = naw.WordNetAug()
for text in texts:
self.assertLess(0, len(text))
augmented_text = aug.augment(text)
self.assertEqual(text, augmented_text)
self.assertLess(0, len(texts))
def __init__(self):
self.raw_data_path = './'
self.processed_data_path = './'
self.df = None
self.augmented_df = pd.DataFrame(columns=['text', 'label'])
self.no_of_augmentations = 5
self.augmentor = naw.WordNetAug(aug_p=0.5)
def test_stopwords(self):
text = 'The quick brown fox jumps over the lazy dog'
aug = naw.WordNetAug(stopwords=['quick', 'brown', 'fox'])
self.assertLess(0, len(text))
augmented_text = aug.augment(text)
self.assertNotEqual(text, augmented_text)
def test_antonyms(self):
texts = ['Good bad']
aug = naw.WordNetAug(is_synonym=False)
for text in texts:
self.assertLess(0, len(text))
augmented_text = aug.augment(text)
self.assertNotEqual(text, augmented_text)
self.assertLess(0, len(texts))
def test_substitute(self):
texts = ['The quick brown fox jumps over the lazy dog']
aug = naw.WordNetAug()
for text in texts:
self.assertLess(0, len(text))
augmented_text = aug.augment(text)
self.assertNotEqual(text, augmented_text)
self.assertLess(0, len(texts))
def test_no_separator(self):
"""
Pull#11: Remove seperator (underscore/ hyphen)
:return:
"""
texts = ["linguistic"]
aug = naw.WordNetAug()
for text in texts:
self.assertLess(0, len(text))
augmented_text = aug.augment(text)
for separator in ['-', '_']:
self.assertNotIn(separator, augmented_text)
self.assertNotEqual(text, augmented_text)
self.assertLess(0, len(texts))
