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_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_ft_weights(self):
clf1 = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', LogisticRegression())])
clf2 = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', MultinomialNB())])
clf3 = FasttextClassifier(epoch=2, output=self.ft_output)
eclf = EnsembleClassifier(clfs=[clf1, clf2, clf3], weights=[1,1,0.5])
cf_cv = ClassifierCv(self.labels, self.texts)
name = 'ensemble_clf'
metric = 'f1'
cf_cv.train_save_metrics([('clf', eclf)],
metric, name,
self.test_dir,
self.test_dir)
self.assertTrue(os.path.isfile(os.path.join(self.test_dir, name + '_' + metric + '.png')))
self.assertTrue(os.path.isfile(os.path.join(self.test_dir, name + 'ROC_AUC.png')))
self.assertTrue(os.path.isfile(os.path.join(self.test_dir, name + 'prec_recall.png')))
self.assertTrue(os.path.isfile(os.path.join(self.test_dir, name + '.xlsx')))
self.assertTrue(os.path.isfile(os.path.join(self.test_dir, name + '_average.xlsx')))
self.assertEqual(type(cf_cv.roc_auc), dict)
self.assertEqual(type(cf_cv.tpr), dict)
self.assertEqual(type(cf_cv.fpr), dict)
self.assertEqual(type(cf_cv.metrics_average_df), pd.DataFrame)
self.assertEqual(type(cf_cv.metrics_df), pd.DataFrame)
self.assertEqual(type(cf_cv.metrics_per_class), list)
self.assertEqual(type(cf_cv.metrics_average), list)
def test_init(self):
ft_clf = FasttextClassifier()
self.assertEqual(ft_clf.lr, 0.1)
self.assertEqual(ft_clf.lr_update_rate, 100)
self.assertEqual(ft_clf.dim, 100)
self.assertEqual(ft_clf.ws, 5)
self.assertEqual(ft_clf.epoch, 100)
def test_xft_classifiercv(self):
cf_cv = ClassifierCv(self.labels, self.texts)
name = 'ft'
metric = 'f1'
cf_cv.train_save_metrics(
[('clf', FasttextClassifier(output=self.output, epoch=1))], metric,
name, self.test_dir, self.test_dir)
filename = os.path.join(self.test_dir, '_eval_report')
cf_cv.calc_evaluation_report(self.df_test['text'],
self.df_test['class'],
savefile=filename)
self.assertTrue(os.path.isfile(filename + "_" + name + ".csv"))
self.assertTrue(os.path.isfile(filename + "_" + name + "_average.csv"))
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']))