postprocessing_stance.py 文件源码

def apply_metric_results_macro_average(results, metric,
        print_full_result=False):
    for method in results.keys():
        max_train = max(results[method].keys())
        for train_perc in sorted(results[method].keys()):
            samples = len(results[method][train_perc])
            if print_full_result:
                print ':'.join(map(str, [train_perc, method])) + ',' \
                    + ','.join(map(lambda x: '{:.2f}'.format(x),
                               [metric(a, b, train_perc=train_perc,
                               max_train=max_train) for (a, b) in
                               results[method][train_perc]]))
            metric_val = ' '.join(map(str, ['%.2f' % np.mean([metric(a,
                                  b, train_perc=train_perc,
                                  max_train=max_train) for (a, b) in
                                  results[method][train_perc]]), "\pm",
                                  '%.2f' % np.std([metric(a, b,
                                  train_perc=train_perc,
                                  max_train=max_train) for (a, b) in
                                  results[method][train_perc]])]))
            results[method][train_perc] = (metric_val, samples)