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 __train():
use_vr = True
# use_vr = False
use_hr = True
# use_hr = False
train_samples_file = os.path.join(config.DATA_DIR, 'figer/figer-dev-man-labeled.txt')
train_base_preds_file = os.path.join(config.DATA_DIR, 'figer/ftbfet7069-results-figer-wikival.txt')
train_srl_preds_file = os.path.join(config.DATA_DIR, 'figer/figer-train-srl0-preds.txt')
# train_srl_preds_file = os.path.join(config.DATA_DIR, 'figer/figer-train-srl-preds.txt')
train_hyp_file = os.path.join(config.DATA_DIR, 'figer/figer-wikival-all-fmm-hypext.txt')
train_verif_hyp_file = os.path.join(config.DATA_DIR, 'figer/figer-wikival-all-fmm-hypext-bbntyped.txt')
train_hypext_logits_file = os.path.join(config.DATA_DIR,
'figer/bert-tmhypextbbn-results-all-figer-wikival.txt')
train_pred_file_tup = (train_base_preds_file, train_srl_preds_file, train_hyp_file, train_verif_hyp_file,
train_hypext_logits_file)
gres = expdata.ResData(config.FIGER_FILES['type-vocab'], None)
type_infer_train = TypeInfer(gres.type_vocab, gres.type_id_dict, single_type_path=False)
_, _, child_type_vecs_train = fetutils.build_hierarchy_vecs(gres.type_vocab, gres.type_id_dict)
train_samples = expdata.load_labeled_samples(gres.type_id_dict, child_type_vecs_train,
train_samples_file, train_pred_file_tup, use_vr, use_hr)
print(len(train_samples), 'labeled samples')
test_mentions_file = config.FIGER_FILES['test-mentions']
test_sents_file = config.FIGER_FILES['test-sents']
test_base_preds_file = os.path.join(config.DATA_DIR, 'figer/ftbfet7069-results-figer.txt')
test_srl_preds_file = os.path.join(config.DATA_DIR, 'figer/figer-test-srl0-preds.txt')
# test_srl_preds_file = os.path.join(config.DATA_DIR, 'figer/figer-test-srl-preds.txt')
test_hyp_preds_file = os.path.join(config.DATA_DIR, 'figer/figer-test-all-fmm-hypext.txt')
test_verif_hypext_file = os.path.join(config.DATA_DIR, 'figer/figer-test-all-fmm-hypext-bbntyped.txt')
test_hypext_logits_file = os.path.join(config.DATA_DIR, 'figer/bert-tmhypextbbn-results-all-figer-test.txt')
test_file_tup = (test_mentions_file, test_sents_file, test_base_preds_file, test_srl_preds_file,
test_hyp_preds_file, test_verif_hypext_file, test_hypext_logits_file)
gres_test = expdata.ResData(config.FIGER_FILES['type-vocab'], None)
l1_type_indices_test, l1_type_vec_test, child_type_vecs_test = fetutils.build_hierarchy_vecs(
gres.type_vocab, gres.type_id_dict)
type_infer_test = TypeInfer(gres_test.type_vocab, gres_test.type_id_dict, single_type_path=False)
test_samples, test_true_labels_dict = expdata.samples_from_test(gres_test, child_type_vecs_test, test_file_tup)
stackexp.train_stacking(device, gres, use_vr, use_hr, type_infer_train, train_samples,
gres_test, type_infer_test, test_samples, test_true_labels_dict)
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)
def __eval():
dataset = 'figer'
# dataset = 'bbn'
datafiles = config.FIGER_FILES if dataset == 'figer' else config.BBN_FILES
word_vecs_file = config.WIKI_FETEL_WORDVEC_FILE
model_file_prefix = os.path.join(config.DATA_DIR,
'models/srl-{}'.format(dataset))
# sub_set = 'test'
# sub_set = 'train'
sub_sets = ['test', 'train']
for sub_set in sub_sets:
if sub_set == 'test':
mentions_file = datafiles['test-mentions']
sents_file = datafiles['test-sents']
srl_file = datafiles['test-srl']
dep_file = datafiles['test-sents-dep']
else:
if dataset == 'bbn':
mentions_file = datafiles['train-mentions']
sents_file = datafiles['train-sents']
srl_file = datafiles['train-srl']
dep_file = datafiles['train-sents-dep']
else:
mentions_file = os.path.join(
config.DATA_DIR, 'figer/wiki-valcands-figer-mentions.json')
sents_file = os.path.join(
config.DATA_DIR, 'figer/wiki-valcands-figer-sents.json')
srl_file = os.path.join(config.DATA_DIR,
'figer/wiki-valcands-figer-srl.txt')
dep_file = os.path.join(
config.DATA_DIR, 'figer/wiki-valcands-figer-tok-dep.txt')
output_preds_file = os.path.join(
config.DATA_DIR,
'{}/{}-{}-srl-preds.txt'.format(dataset, dataset, sub_set))
single_type_path = True if dataset == 'bbn' else False
gres = expdata.ResData(datafiles['type-vocab'], word_vecs_file)
srlfetexp.eval_trained(device, gres, model_file_prefix, mentions_file,
sents_file, srl_file, dep_file,
single_type_path, output_preds_file)