def test_predict_labels(self):
cf_cv = ClassifierCv(self.labels, self.texts)
name = 'MultinomialNB'
metric = 'f1'
cf_cv.train_save_metrics([
('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', MultinomialNB(alpha=.05)),
], metric, name, self.test_dir, self.test_dir)
labels = cf_cv.predict(['bad', 'good'])
self.assertTrue(all(labels == ['neg', 'pos']))
def test_predict_labels_probas(self):
cf_cv = ClassifierCv(self.labels, self.texts)
name = 'MultinomialNB'
metric = 'f1'
cf_cv.train_save_metrics([
('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', MultinomialNB(alpha=.05)),
], metric, name, self.test_dir, self.test_dir)
labels = cf_cv.predict(['bad', 'good'], proba=True)
self.assertTrue(labels.shape == (2, 2))
self.assertEqual(type(labels), pd.DataFrame)
self.assertEqual(len(labels['pos'].values), 2)
self.assertEqual(len(labels['neg'].values), 2)
self.assertEqual(type(labels['neg'].values[0]), np.float64)
def test_unpickle(self):
cf_cv = ClassifierCv(self.labels, self.texts)
name = 'MultinomialNB'
metric = 'f1'
cf_cv.train_save_metrics([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', MultinomialNB(alpha=.05))], metric,
name, self.test_dir, self.test_dir)
savefile = os.path.join(self.test_dir, 'clf_cv.cv')
cf_cv.pickle(savefile)
new_cf_cv = ClassifierCv.unpickle(savefile)
texts = ['dont know that', 'nice good and bad']
predicted_labels_orig = cf_cv.predict(texts, proba=True)
predicted_labesl_new = new_cf_cv.predict(texts, proba=True)
self.assertTrue(all(predicted_labels_orig == predicted_labesl_new))