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import pandas as pd
import os
import gzip
import random
import re
from tqdm import tqdm
def get_all_files_in_directory(directory):
all_files = []
for root, dirs, files in os.walk(directory):
root = root[len(directory):]
if root.startswith('\\') or root.startswith('/'):
root = root[1:]
for file in files:
file_path = os.path.join(root, file)
all_files.append(file_path)
return all_files
class Fileset(list):
def __init__(self, path, ext='', _read=None):
if isinstance(path, str):
self.root = path
self.extend(f for f in get_all_files_in_directory(self.root) if f.endswith(ext))
self._read = _read
def __getitem__(self, index):
if isinstance(index, int): # index是索引
if self._read:
return self._read(os.path.join(self.root, super().__getitem__(index)))
else:
return os.path.join(self.root, super().__getitem__(index))
else: # index是切片
fileset = Fileset(None)
fileset.root = self.root
fileset._read = self._read
fileset.extend(super().__getitem__(index))
return fileset
def readOne(filePath):
with gzip.open(filePath, 'rt', encoding='utf-8') if filePath.endswith('.gz') else open(filePath, encoding='utf-8') as f:
retn = [line.strip() for line in f]
return retn
rawcorpus = Fileset(r'D:\datasets\h-corpus\h-ss-corpus','.txt.gz', _read=readOne)
corpus = []
queries = []
qrels = []
reg_4 = re.compile(r'(.)\1{3,}') # 匹配四个或更多连续相同的字符
def has_four_or_more_repeated_chars(text):
return bool(reg_4.search(text))
def randsqidx(tmp):
for i in range(20): # 尝试20次
sqidx = random.randint(10, len(tmp) - 10)
if any(len(tmp[i]) < 20 or len(tmp[i]) > 512 or has_four_or_more_repeated_chars(tmp[i]) for i in range(sqidx-2, sqidx+3)):
continue
return sqidx
return -1
def appendqrels(tmp, sqidx, _range, sr):
qidx = len(queries)
queries.append((qidx, tmp[sqidx]))
if corpus:
cidx = corpus[-1][0] + 3
else:
cidx = 2
for k in _range:
corpus.append((cidx+k, tmp[sqidx+k]))
qrels.append((qidx, cidx+k, sr[k+2]))
def split3(s):
retn = []
cache = ''
for one in s:
cache += one
if len(cache) < 64:
continue
if one in ('?', '!', '。', '？', '！'):
retn.append(cache)
cache = ''
# print(retn)
return retn
def main():
for i in tqdm(range(len(rawcorpus)), desc="Converting"):
tmp = rawcorpus[i]
if len(tmp) < 30:
continue
if random.randint(0, 3):
sqidx = randsqidx(tmp)
if sqidx > 2:
appendqrels(tmp, sqidx, (-2, -1, 1, 2), (0.95, 0.97, 1, 0.97, 0.95))
continue
for s in tmp:
if len(s) <= 512:
continue
s = split3(s)
if len(s) < 3:
continue
sqidx = random.randint(1, len(s)-2)
appendqrels(s, sqidx, (-1, 1), (0.95, 1, 1, 1, 0.95))
break
main()
corpus_pd = pd.DataFrame(corpus, columns=['cid', 'text'], dtype=str)
queries_pd = pd.DataFrame(queries, columns=['qid', 'text'], dtype=str)
qrels_pd = pd.DataFrame(qrels, columns=['qid', 'cid', 'score'], dtype=str)
# def load_dataset(path):
# df = pd.read_parquet(path, engine="pyarrow")
# return df
# corpus_pd = load_dataset(r"D:\datasets\H2Retrieval\data\corpus.parquet.gz")
# queries_pd = load_dataset(r"D:\datasets\H2Retrieval\data\queries.parquet.gz")
# qrels_pd = load_dataset(r"D:\datasets\H2Retrieval\data\qrels.parquet.gz")
corpus_pd['cid'] = corpus_pd['cid'].astype(str)
queries_pd['qid'] = queries_pd['qid'].astype(str)
qrels_pd['qid'] = qrels_pd['qid'].astype(str)
qrels_pd['cid'] = qrels_pd['cid'].astype(str)
qrels_pd['score'] = (qrels_pd['score']*100).astype(int)
corpus_pd.to_parquet(
r"D:\datasets\H2Retrieval\data\corpus.parquet.gz",
engine="pyarrow",
compression="gzip",
)
queries_pd.to_parquet(
r"D:\datasets\H2Retrieval\data\queries.parquet.gz",
engine="pyarrow",
compression="gzip",
)
qrels_pd.to_parquet(
r"D:\datasets\H2Retrieval\data\qrels.parquet.gz",
engine="pyarrow",
compression="gzip",
) |