Пример #1
0
 def __init__(self):
     self.hierarchy = Hierarchy.from_graph(read_pickle(codes_file))
     self.batcher = Batcher(self.hierarchy)
     self.linearizations = {n:self.hierarchy.linearize(n) for n in self.hierarchy.descriptions.keys()}
Пример #2
0
                                   receiver_email=p.receiver_email,
                                   subject="%s: training %s model" %
                                   (socket.gethostname(), args.model_type))
        email_sender.send_email("Starting to train %s model." %
                                args.model_type)
        email_every = p.email_every
    else:
        email_sender = None
        email_every = None

    train_file = os.path.join(args.data_dir, 'train.data')
    val_file = os.path.join(args.data_dir, 'val.data')
    counts_file = os.path.join(args.data_dir, 'counts.pkl')
    used_targets_file = os.path.join(args.data_dir, 'used_targets.txt')

    hierarchy = Hierarchy.from_graph(read_pickle(args.code_graph_file))

    if args.save_checkpoint_folder is not None:
        write_pickle(
            hierarchy.to_dict(),
            os.path.join(args.save_checkpoint_folder, 'hierarchy.pkl'))
        if os.path.exists(counts_file):
            copyfile(counts_file,
                     os.path.join(args.save_checkpoint_folder, 'counts.pkl'))
        if os.path.exists(used_targets_file):
            copyfile(
                used_targets_file,
                os.path.join(args.save_checkpoint_folder, 'used_targets.txt'))

    if args.expensive_val_every is not None:
        supervised_val_file = os.path.join(args.supervised_data_dir,