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import json
import math
import zipfile
import bs4
import datasets
import dateutil.parser
import pandas as pd
from tqdm import tqdm
def yield_file_contents(zip_path, train_df, val_df):
with (zipfile.ZipFile(zip_path, 'r') as zip_file):
for file_info in zip_file.infolist():
with zip_file.open(file_info, 'r') as file:
content = file.read()
soup = bs4.BeautifulSoup(content, 'xml')
id_blk = soup.find('idno', type="titelcode")
text_id = id_blk.text.strip() if id_blk is not None else file_info.filename.replace('.xml', '')
ti_id = '_'.join(text_id.split('_')[:-1])
train_row = train_df[train_df['ti_id'] == ti_id]
val_row = val_df[val_df['ti_id'] == ti_id]
is_train = len(train_row) > 0
is_val = len(val_row) > 0
if is_train:
meta = train_row.iloc[0].to_dict()
split = 'train'
elif is_val:
meta = val_row.iloc[0].to_dict()
split = 'validation'
else:
print(f'Did not find meta for {text_id}!')
for key, value in list(meta.items()):
if isinstance(value, float) and math.isnan(value):
meta[key] = ''
edition_blk = soup.find('edition')
edition = edition_blk.text.strip() if edition_blk is not None else None
lang_blk = soup.find('language')
language = lang_blk.get('id').strip() if lang_blk is not None else None
date_blk = soup.find('revisionDesc')
if date_blk is not None:
date_blk = date_blk.find('date')
if date_blk is not None:
try:
date = dateutil.parser.parse(
date_blk.text.strip(),
yearfirst=True,
dayfirst=True
).isoformat() if date_blk is not None else None
except Exception:
date = None
else:
date = None
meta['revision_date'] = date
meta['edition'] = edition
meta['language'] = language
for chap_idx, chapter in enumerate(soup.find_all('div', type='chapter')):
meta['chapter'] = chap_idx + 1
for sec_idx, section in enumerate(chapter.find_all('div', type='section')):
meta['section'] = sec_idx + 1
text = section.text.strip()
yield {'meta': meta, 'text': text, 'id': f"{text_id}_{chap_idx}_{sec_idx}"}, split
if __name__ == '__main__':
train_fraction = 0.90
metadata_path = '../origin/titels_pd.csv'
meta_df = pd.read_csv(metadata_path, header=1, sep='|')
meta_df = meta_df.sample(frac=1, random_state=0)
num_train = round(train_fraction*len(meta_df))
train_df = meta_df.iloc[:num_train]
val_df = meta_df.iloc[num_train:]
with open('tmp/train.jsonl', 'w') as train_file:
with open('tmp/val.jsonl', 'w') as val_file:
for item, split in tqdm(yield_file_contents('../origin/xml_pd.zip', train_df, val_df)):
if split == 'train':
train_file.write('{}\n'.format(json.dumps(item)))
if split == 'validation':
val_file.write('{}\n'.format(json.dumps(item)))
datasets.Dataset.from_json('tmp/train.jsonl', split='train').to_parquet('../data/train.parquet')
datasets.Dataset.from_json('tmp/val.jsonl', split='validation').to_parquet('../data/validation.parquet')
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