def attribute_evaluate_subfunc(feat_func, test_set, **test_kwargs):
""" evaluate the attribute recognition precision """
result = attribute_evaluate(feat_func, test_set, **test_kwargs)
print('-' * 60)
print('Evaluation on %s set:' % (cfg.test_split))
print('Label-based evaluation: \n mA: %.4f'%(np.mean(result['label_acc'])))
print('Instance-based evaluation: \n Acc: %.4f, Prec: %.4f, Rec: %.4f, F1: %.4f'\
% (result['instance_acc'], result['instance_precision'], result['instance_recall'], result['instance_F1']))
print('-' * 60)
def attribute_evaluate_subfunc(feat_func, test_set, **test_kwargs):
""" evaluate the attribute recognition precision """
result = attribute_evaluate(feat_func, test_set, **test_kwargs)
print('-' * 60)
print('Evaluation on %s set:' % (cfg.test_split))
print('Label-based evaluation: \n mA: %.4f' %
(np.mean(result['label_acc'])))
print('Instance-based evaluation: \n Acc: %.4f, Prec: %.4f, Rec: %.4f, F1: %.4f' \
%(result['instance_acc'], result['instance_precision'], result['instance_recall'], result['instance_F1']))
print("Label-based evaluation in details:")
print(" {:>25s} | {:^5s} | {:^5s} | {:^5s} ".format(
"Attr Name", "P", "N", "A"))
print("=" * 48)
for idx in range(len(test_set.att_name)):
print(" {:>25s} | {:.2f} | {:.2f} | {:.2f} ".format(
test_set.att_name[idx],
100 * result[test_set.att_name[idx]]['label_pos_acc'],
100 * result[test_set.att_name[idx]]['label_neg_acc'],
100 * result[test_set.att_name[idx]]['label_acc']))
print('=' * 48)