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import argparse
import logging
import os
import glob
from collections import namedtuple
import re
from typing import Tuple
from tqdm import tqdm
from random import choices, shuffle
BsfInfo = namedtuple('BsfInfo', 'id, tag, start_idx, end_idx, token')
log = logging.getLogger(__name__)
log.setLevel(logging.INFO)
def format_token_as_beios(token: str, tag: str) -> list:
t_words = token.split()
res = []
if len(t_words) == 1:
res.append(token + ' S-' + tag)
else:
res.append(t_words[0] + ' B-' + tag)
for t_word in t_words[1: -1]:
res.append(t_word + ' I-' + tag)
res.append(t_words[-1] + ' E-' + tag)
return res
def format_token_as_iob(token: str, tag: str) -> list:
t_words = token.split()
res = []
if len(t_words) == 1:
res.append(token + ' B-' + tag)
else:
res.append(t_words[0] + ' B-' + tag)
for t_word in t_words[1:]:
res.append(t_word + ' I-' + tag)
return res
def convert_bsf(data: str, bsf_markup: str, converter: str = 'beios') -> str:
"""
Convert data file with NER markup in Brat Standoff Format to BEIOS or IOB format.
:param converter: iob or beios converter to use for document
:param data: tokenized data to be converted. Each token separated with a space
:param bsf_markup: Brat Standoff Format markup
:return: data in BEIOS or IOB format https://en.wikipedia.org/wiki/Inside–outside–beginning_(tagging)
"""
def join_simple_chunk(chunk: str) -> list:
if len(chunk.strip()) == 0:
return []
tokens = re.split(r'\s', chunk.strip())
return [token + ' O' if len(token.strip()) > 0 else token for token in tokens]
converters = {'beios': format_token_as_beios, 'iob': format_token_as_iob}
res = []
markup = parse_bsf(bsf_markup)
prev_idx = 0
m_ln: BsfInfo
for m_ln in markup:
res += join_simple_chunk(data[prev_idx:m_ln.start_idx])
convert_f = converters[converter]
res.extend(convert_f(m_ln.token, m_ln.tag))
prev_idx = m_ln.end_idx
if prev_idx < len(data) - 1:
res += join_simple_chunk(data[prev_idx:])
return '\n'.join(res)
def parse_bsf(bsf_data: str) -> list:
"""
Convert textual bsf representation to a list of named entities.
:param bsf_data: data in the format 'T9 PERS 778 783 токен'
:return: list of named tuples for each line of the data representing a single named entity token
"""
if len(bsf_data.strip()) == 0:
return []
ln_ptrn = re.compile(r'(T\d+)\s(\w+)\s(\d+)\s(\d+)\s(.+?)(?=T\d+\s\w+\s\d+\s\d+|$)', flags=re.DOTALL)
result = []
for m in ln_ptrn.finditer(bsf_data.strip()):
bsf = BsfInfo(m.group(1), m.group(2), int(m.group(3)), int(m.group(4)), m.group(5).strip())
result.append(bsf)
return result
CORPUS_NAME = 'Ukrainian-languk'
def convert_bsf_in_folder(src_dir_path: str, dst_dir_path: str, converter: str = 'beios',
doc_delim: str = '\n', train_test_split_file: str = None) -> None:
"""
:param doc_delim: delimiter to be used between documents
:param src_dir_path: path to directory with BSF marked files
:param dst_dir_path: where to save output data
:param converter: `beios` or `iob` output formats
:param train_test_split_file: path to file cotaining train/test lists of file names
:return:
"""
ann_path = os.path.join(src_dir_path, '*.tok.ann')
ann_files = glob.glob(ann_path)
ann_files.sort()
tok_path = os.path.join(src_dir_path, '*.tok.txt')
tok_files = glob.glob(tok_path)
tok_files.sort()
corpus_folder = os.path.join(dst_dir_path, CORPUS_NAME)
if not os.path.exists(corpus_folder):
os.makedirs(corpus_folder)
if len(ann_files) == 0 or len(tok_files) == 0:
raise FileNotFoundError(f'Token and annotation files are not found at specified path {ann_path}')
if len(ann_files) != len(tok_files):
raise RuntimeError(f'Mismatch between Annotation and Token files. Ann files: {len(ann_files)}, token files: {len(tok_files)}')
train_set = []
dev_set = []
test_set = []
data_sets = [train_set, dev_set, test_set]
split_weights = (8, 1, 1)
if train_test_split_file is not None:
train_names, dev_names, test_names = read_languk_train_test_split(train_test_split_file)
log.info(f'Found {len(tok_files)} files in data folder "{src_dir_path}"')
for (tok_fname, ann_fname) in tqdm(zip(tok_files, ann_files), total=len(tok_files), unit='file'):
if tok_fname[:-3] != ann_fname[:-3]:
tqdm.write(f'Token and Annotation file names do not match ann={ann_fname}, tok={tok_fname}')
continue
with open(tok_fname) as tok_file, open(ann_fname) as ann_file:
token_data = tok_file.read()
ann_data = ann_file.read()
out_data = convert_bsf(token_data, ann_data, converter)
if train_test_split_file is None:
target_dataset = choices(data_sets, split_weights)[0]
else:
target_dataset = train_set
fkey = os.path.basename(tok_fname)[:-4]
if fkey in dev_names:
target_dataset = dev_set
elif fkey in test_names:
target_dataset = test_set
target_dataset.append(out_data)
log.info(f'Data is split as following: train={len(train_set)}, dev={len(dev_set)}, test={len(test_set)}')
# writing data to {train/dev/test}.bio files
names = ['train', 'dev', 'test']
if doc_delim != '\n':
doc_delim = '\n' + doc_delim + '\n'
for idx, name in enumerate(names):
fname = os.path.join(corpus_folder, name + '.bio')
with open(fname, 'w') as f:
f.write(doc_delim.join(data_sets[idx]))
log.info('Writing to ' + fname)
log.info('All done')
def read_languk_train_test_split(file_path: str, dev_split: float = 0.1) -> Tuple:
"""
Read predefined split of train and test files in data set.
Originally located under doc/dev-test-split.txt
:param file_path: path to dev-test-split.txt file (should include file name with extension)
:param dev_split: 0 to 1 float value defining how much to allocate to dev split
:return: tuple of (train, dev, test) each containing list of files to be used for respective data sets
"""
log.info(f'Trying to read train/dev/test split from file "{file_path}". Dev allocation = {dev_split}')
train_files, test_files, dev_files = [], [], []
container = test_files
with open(file_path, 'r') as f:
for ln in f:
ln = ln.strip()
if ln == 'DEV':
container = train_files
elif ln == 'TEST':
container = test_files
elif ln == '':
pass
else:
container.append(ln)
# split in file only contains train and test split.
# For Stanza training we need train, dev, test
# We will take part of train as dev set
# This way anyone using test set outside of this code base can be sure that there was no data set polution
shuffle(train_files)
dev_files = train_files[: int(len(train_files) * dev_split)]
train_files = train_files[int(len(train_files) * dev_split):]
assert len(set(train_files).intersection(set(dev_files))) == 0
log.info(f'Files in each set: train={len(train_files)}, dev={len(dev_files)}, test={len(test_files)}')
return train_files, dev_files, test_files
if __name__ == '__main__':
logging.basicConfig()
parser = argparse.ArgumentParser(description='Convert lang-uk NER data set from BSF format to BEIOS format compatible with Stanza NER model training requirements.\n'
'Original data set should be downloaded from https://github.com/lang-uk/ner-uk\n'
'For example, create a directory extern_data/lang_uk, then run "git clone git@github.com:lang-uk/ner-uk.git')
parser.add_argument('--src_dataset', type=str, default='extern_data/ner/lang-uk/ner-uk/data', help='Dir with lang-uk dataset "data" folder (https://github.com/lang-uk/ner-uk)')
parser.add_argument('--dst', type=str, default='data/ner', help='Where to store the converted dataset')
parser.add_argument('-c', type=str, default='beios', help='`beios` or `iob` formats to be used for output')
parser.add_argument('--doc_delim', type=str, default='\n', help='Delimiter to be used to separate documents in the output data')
parser.add_argument('--split_file', type=str, help='Name of a file containing Train/Test split (files in train and test set)')
parser.print_help()
args = parser.parse_args()
convert_bsf_in_folder(args.src_dataset, args.dst, args.c, args.doc_delim, train_test_split_file=args.split_file)
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