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_fit(self):
ft_clf = FasttextClassifier(output=self.output)
ft_clf.fit(self.texts, self.labels)
self.assertEqual(ft_clf.classes_, list(self.labels.unique()))
self.assertTrue(all(ft_clf.x == self.texts))
self.assertTrue(all(ft_clf.y == self.labels))
self.assertTrue(os.path.isfile(self.output + '.bin'))
def test_predict_proba_wrong_type(self):
ft_clf = FasttextClassifier(output=self.output)
ft_clf.fit(self.texts, self.labels)
probas = ft_clf.predict_proba('text')
self.assertEqual(probas, None)
def test_predict_proba(self):
ft_clf = FasttextClassifier(output=self.output)
ft_clf.fit(self.texts, self.labels)
probas = ft_clf.predict_proba(['very bad', 'very good'])
self.assertTrue(probas[0][0] <= probas[0][1])
self.assertTrue(probas[1][0] >= probas[1][1])
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']))