def preprocess(dataset_name, delete_data=False):
full_path = 'data/{0}/e1rel_to_e2_full.json'.format(dataset_name)
train_path = 'data/{0}/e1rel_to_e2_train.json'.format(dataset_name)
dev_ranking_path = 'data/{0}/e1rel_to_e2_ranking_dev.json'.format(
dataset_name)
test_ranking_path = 'data/{0}/e1rel_to_e2_ranking_test.json'.format(
dataset_name)
keys2keys = {}
keys2keys['e1'] = 'e1' # entities
keys2keys['rel'] = 'rel' # relations
keys2keys['e2'] = 'e1' # entities
keys2keys['e2_multi1'] = 'e1' # entity
keys2keys['e2_multi2'] = 'e1' # entity
input_keys = ['e1', 'rel', 'e2', 'e2_multi1', 'e2_multi2']
d = DatasetStreamer(input_keys)
d.add_stream_processor(JsonLoaderProcessors())
d.add_stream_processor(DictKey2ListMapper(input_keys))
# process full vocabulary and save it to disk
d.set_path(full_path)
p = Pipeline(Config.dataset,
delete_data,
keys=input_keys,
skip_transformation=True)
p.add_sent_processor(ToLower())
p.add_sent_processor(CustomTokenizer(lambda x: x.split(' ')),
keys=['e2_multi1', 'e2_multi2'])
p.add_token_processor(AddToVocab())
p.execute(d)
p.save_vocabs()
# process train, dev and test sets and save them to hdf5
p.skip_transformation = False
for path, name in zip([train_path, dev_ranking_path, test_ranking_path],
['train', 'dev_ranking', 'test_ranking']):
d.set_path(path)
p.clear_processors()
p.add_sent_processor(ToLower())
p.add_sent_processor(CustomTokenizer(lambda x: x.split(' ')),
keys=['e2_multi1', 'e2_multi2'])
p.add_post_processor(
ConvertTokenToIdx(keys2keys=keys2keys),
keys=['e1', 'rel', 'e2', 'e2_multi1', 'e2_multi2'])
p.add_post_processor(
StreamToHDF5(name,
samples_per_file=1500
if Config.dataset == 'YAGO3-10' else 1000,
keys=input_keys))
p.execute(d)
def preprocess(args, delete_data=False):
keys2keys = {}
keys2keys['e1'] = 'e1' # entities
keys2keys['rel'] = 'rel' # relations
keys2keys['rel_eval'] = 'rel' # relations
keys2keys['e2'] = 'e1' # entities
keys2keys['e2_multi1'] = 'e1' # entity
keys2keys['e2_multi2'] = 'e1' # entity
input_keys = ['e1', 'rel', 'rel_eval', 'e2', 'e2_multi1', 'e2_multi2']
print('create dataset streamer', flush=True)
d = DatasetStreamer(input_keys)
d.add_stream_processor(JsonLoaderProcessors())
d.add_stream_processor(DictKey2ListMapper(input_keys))
# process full vocabulary and save it to disk
d.set_path(args.train_path)
print('create pipeline', flush=True)
p = Pipeline(args.data,
delete_data,
keys=input_keys,
skip_transformation=True)
p.add_sent_processor(ToLower())
p.add_sent_processor(CustomTokenizer(lambda x: x.split(' ')),
keys=['e2_multi1', 'e2_multi2'])
p.add_token_processor(AddToVocab())
print('execute full vocabs', flush=True)
p.execute(d)
print('save full vocabs', flush=True)
p.save_vocabs()
# process train sets and save them to hdf5
p.skip_transformation = False
d.set_path(args.train_path)
p.clear_processors()
p.add_sent_processor(ToLower())
p.add_sent_processor(CustomTokenizer(lambda x: x.split(' ')),
keys=['e2_multi1', 'e2_multi2'])
p.add_post_processor(
ConvertTokenToIdx(keys2keys=keys2keys),
keys=['e1', 'rel', 'rel_eval', 'e2', 'e2_multi1', 'e2_multi2'])
p.add_post_processor(
StreamToHDF5('train', samples_per_file=1000, keys=input_keys))
print('execute and save train vocabs', flush=True)
p.execute(d)
