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()}
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,