def test_load_pretrained(self):
ft_clf = FasttextClassifier(output=self.output)
ft_clf.fit(self.texts, self.labels)
loaded_ft_clf = FasttextClassifier()
loaded_ft_clf.loadpretrained(self.output + '.bin')
labels = loaded_ft_clf.predict(['very bad', 'very good'])
self.assertTrue(all(labels == ['neg', 'pos']))
def test_predict_wrong_type(self):
ft_clf = FasttextClassifier(output=self.output)
ft_clf.fit(self.texts, self.labels)
labels = ft_clf.predict("text")
self.assertEqual(labels, None)
def test_predict(self):
ft_clf = FasttextClassifier(output=self.output)
ft_clf.fit(self.texts, self.labels)
labels = ft_clf.predict(['very bad', 'very good'])
self.assertTrue(all(labels == ['neg', 'pos']))