| 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): |
| 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: |
| 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,}') |
| def has_four_or_more_repeated_chars(text): |
| return bool(reg_4.search(text)) |
|
|
| def randsqidx(tmp): |
| for i in range(20): |
| 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 = '' |
| |
| 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) |
|
|
| |
| |
| |
| |
| |
| |
|
|
| 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", |
| ) |
