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,
