precision and recall

cm =cm.astype('float')
#cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
precision = np.divide(np.diagonal(cm), (np.sum(cm, axis=0) ))
print(precision)
print('min precision', min(precision), 'index',  np.argmin(precision))
print('max precision', max(precision), 'index',np.argmax(precision))
recall = np.divide(np.diagonal(cm), (np.sum(cm, axis=1) ))
print(recall)
print('min recall', min(recall), 'index', np.argmin(recall))
print('max recall', max(recall), 'index', np.argmax(recall))