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_calc_evaluation_report(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)
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"))