def main():
'''main method.'''
args = parse_train_args()
logger = create_logger(name='train',
save_dir=args.save_dir,
quiet=args.quiet)
cross_validate(args, logger)
f'Seed {init_seed + fold_num} ==> test {args.metric} = {np.nanmean(scores):.6f}'
)
if args.show_individual_scores:
for task_name, score in zip(task_names, scores):
info(
f'Seed {init_seed + fold_num} ==> test {task_name} {args.metric} = {score:.6f}'
)
# Report scores across models
avg_scores = np.nanmean(
all_scores, axis=1) # average score for each model across tasks
mean_score, std_score = np.nanmean(avg_scores), np.nanstd(avg_scores)
info(f'Overall test {args.metric} = {mean_score:.6f} +/- {std_score:.6f}')
if args.show_individual_scores:
for task_num, task_name in enumerate(task_names):
info(
f'Overall test {task_name} {args.metric} = '
f'{np.nanmean(all_scores[:, task_num]):.6f} +/- {np.nanstd(all_scores[:, task_num]):.6f}'
)
return mean_score, std_score
if __name__ == '__main__':
args = parse_train_args()
logger = create_logger(name='train',
save_dir=args.save_dir,
quiet=args.quiet)
cross_validate(args, logger)