def test_plot_confusion_matrix(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, name + '_confusion_matrix.png')
cf_cv.plot_confusion_matrix(savefile=filename)
self.assertTrue(os.path.isfile(filename))
def test_plot_confusion_matrix_eval_data_normalize(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,
name + 'eval_confusion_matrix.png')
cf_cv.labels_eval_real = ['pos', 'neg', 'neg', 'neg']
cf_cv.labels_eval_predicted = ['pos', 'neg', 'neg', 'neg']
cf_cv.plot_confusion_matrix(savefile=filename,
normalize=True,
use_evaluation_data=True)
self.assertTrue(os.path.isfile(filename))