def __train():
log_file = os.path.join(config.LOG_DIR, '{}-{}-{}-{}.log'.format(os.path.splitext(
os.path.basename(__file__))[0], args.idx, str_today, config.MACHINE_NAME))
# log_file = None
init_universal_logging(log_file, mode='a', to_stdout=True)
logging.info('logging to {}'.format(log_file))
train_config = srlfetexp.TrainConfig(loss_name='mm', neg_scale=0.1, n_steps=70000)
lstm_dim = 250
mlp_hidden_dim = 500
type_embed_dim = 500
word_vecs_file = config.WIKI_FETEL_WORDVEC_FILE
# dataset = 'figer'
dataset = 'bbn'
datafiles = config.FIGER_FILES if dataset == 'figer' else config.BBN_FILES
data_prefix = datafiles['srl-train-data-prefix']
dev_data_pkl = data_prefix + '-dev.pkl'
train_data_pkl = data_prefix + '-train.pkl'
test_file_tup = (datafiles['test-mentions'], datafiles['test-sents'],
datafiles['test-sents-dep'], datafiles['test-srl'])
single_type_path = False if dataset == 'figer' else True
# output_model_file = None
save_model_file_prefix = os.path.join(config.DATA_DIR, 'models/srl-{}'.format(dataset))
gres = expdata.ResData(datafiles['type-vocab'], word_vecs_file)
logging.info('dataset={} {}'.format(dataset, data_prefix))
srlfetexp.train_srlfet(device, gres, train_data_pkl, dev_data_pkl, None, test_file_tup, lstm_dim, mlp_hidden_dim,
type_embed_dim, train_config, single_type_path,
save_model_file_prefix=save_model_file_prefix)
def __train3():
log_file = os.path.join(config.LOG_DIR, '{}-{}-{}-{}.log'.format(os.path.splitext(
os.path.basename(__file__))[0], args.idx, str_today, config.MACHINE_NAME))
# log_file = None
init_universal_logging(log_file, mode='a', to_stdout=True)
logging.info('logging to {}'.format(log_file))
margin = 1.0
train_config = srlfetexp.TrainConfig(
pos_margin=margin, neg_margin=margin, neg_scale=1.0, batch_size=128, schedule_lr=True, n_steps=70000)
lstm_dim = 250
mlp_hidden_dim = 500
type_embed_dim = 500
word_vecs_file = config.WIKI_FETEL_WORDVEC_FILE
dataset = 'figer'
# dataset = 'bbn'
datafiles = config.FIGER_FILES if dataset == 'figer' else config.BBN_FILES
data_prefix = datafiles['srl-train-data-prefix']
dev_data_pkl = data_prefix + '-dev.pkl'
train_data_pkl = data_prefix + '-train.pkl'
test_file_tup = (datafiles['test-mentions'], datafiles['test-sents'],
datafiles['test-sents-dep'], datafiles['test-srl'])
single_type_path = False if dataset == 'figer' else True
# output_model_file = None
save_model_file_prefix = os.path.join(config.DATA_DIR, 'models/srl3-{}'.format(dataset))
val_mentions_file = os.path.join(config.DATA_DIR, 'figer/wiki-valcands-figer-mentions.json')
val_sents_file = os.path.join(config.DATA_DIR, 'figer/wiki-valcands-figer-sents.json')
val_srl_file = os.path.join(config.DATA_DIR, 'figer/wiki-valcands-figer-srl.txt')
val_dep_file = os.path.join(config.DATA_DIR, 'figer/wiki-valcands-figer-tok-dep.txt')
val_manual_label_file = os.path.join(config.DATA_DIR, 'figer/figer-dev-man-labeled.txt')
# manual_val_file_tup = (val_mentions_file, val_sents_file, val_dep_file, val_srl_file, val_manual_label_file)
manual_val_file_tup = None
gres = expdata.ResData(datafiles['type-vocab'], word_vecs_file)
logging.info('dataset={} {}'.format(dataset, data_prefix))
srlfetexp.train_srlfet(
device, gres, train_data_pkl, dev_data_pkl, manual_val_file_tup, test_file_tup, lstm_dim, mlp_hidden_dim,
type_embed_dim, train_config, single_type_path, save_model_file_prefix=save_model_file_prefix)