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"))