basename + '_test_' + str(index_run).zfill(3) + '.predict')
save_predictions(os.path.join(predictions_dir,
filename_test), Y_test)
current_num_models = len(dir_ensemble_list)
watch.stop_task('ensemble_iter_' + str(index_run))
time_iter = watch.get_wall_dur('ensemble_iter_' + str(index_run))
used_time = watch.wall_elapsed('ensemble_builder')
index_run += 1
return
if __name__ == '__main__':
seed = int(sys.argv[8])
predictions_dir = sys.argv[1]
logger = get_logger(os.path.basename(__file__))
add_file_handler(logger, os.path.join(predictions_dir, 'ensemble.log'))
logger.debug("Start script: %s" % __file__)
main(predictions_dir=sys.argv[1],
basename=sys.argv[2],
task_type=sys.argv[3],
metric=sys.argv[4],
limit=float(sys.argv[5]),
output_dir=sys.argv[6],
ensemble_size=int(sys.argv[7]),
logger=logger)
sys.exit(0)
def get_automl_logger(log_dir, basename, seed):
logger = get_logger(os.path.basename(__file__))
logger_file = os.path.join(log_dir, '%s.log' % str(
'AutoML_%s_%d' % (basename, seed)))
add_file_handler(logger, logger_file)
return logger