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# | |
# Pyserini: Reproducible IR research with sparse and dense representations | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
# | |
# Starting point for writing this script | |
# https://github.com/jacklin64/pyserini/blob/msmarcov2/scripts/msmarco_v2/segment_docs.py | |
import argparse | |
import os | |
import sys | |
import gzip | |
import json | |
from tqdm import tqdm | |
import re | |
import glob | |
from multiprocessing import Pool, Manager | |
def read_doc_corpus(f_ins, docid_to_title, docid_to_headings, docid_to_url): | |
for f_in in f_ins: | |
print('read {}...'.format(f_in)) | |
with gzip.open(f_in, 'rt', encoding='utf8') as in_fh: | |
for json_string in tqdm(in_fh): | |
doc = json.loads(json_string) | |
docid = doc['docid'] | |
headings = doc['headings'] | |
title = doc['title'] | |
url = doc['url'] | |
docid_to_title[docid] = title | |
docid_to_headings[docid] = headings | |
docid_to_url[docid] = url | |
docid_to_pass_num[docid] = 0 | |
def passage_corpus_to_tsv(f_ins, f_out): | |
print('Output passages...') | |
output = open(f_out, 'w') | |
output_id = open(f_out.replace(".json", ".id"), 'w') | |
max_len = 0 | |
total_len = 0 | |
counter = 0 | |
for f_in in f_ins: | |
with gzip.open(f_in, 'rt', encoding='utf8') as in_fh: | |
for json_string in tqdm(in_fh): | |
input_dict = json.loads(json_string) | |
docid = input_dict['docid'] | |
pid = input_dict['pid'] | |
passage = input_dict['passage'] | |
passage_len = len(input_dict['passage']) | |
doc_url = docid_to_url[docid] | |
doc_title = docid_to_title[docid] | |
doc_headings = docid_to_headings[docid] | |
docid_to_pass_num[docid]+=1 | |
total_len += passage_len | |
if (passage_len > max_len): | |
max_len = passage_len | |
counter+=1 | |
output_dict = input_dict | |
output_dict['url'] = doc_url | |
output_dict['title'] = doc_title | |
output_dict['headings'] = doc_headings | |
output.write(json.dumps(output_dict) + '\n') | |
output_id.write(pid+'\n') | |
print('maximum passage length: {}'.format(max_len)) | |
print('average passage length: {}'.format(total_len/counter)) | |
output.close() | |
output_id.close() | |
print('Done!') | |
if __name__ == '__main__': | |
parser = argparse.ArgumentParser( | |
description='Concatenate MS MARCO original docs with predicted queries') | |
parser.add_argument('--original_psg_path', required=True, help='MS MARCO .tsv corpus file.') | |
parser.add_argument('--original_doc_path', required=True, help='doc json file path.') | |
parser.add_argument('--output_psg_path', required=True, help='Output file in the anserini jsonl format.') | |
parser.add_argument('--num_workers', default=1, type=int) | |
args = parser.parse_args() | |
os.makedirs(args.output_psg_path, exist_ok=True) | |
doc_files = glob.glob(os.path.join(args.original_doc_path, '*.gz')) | |
psg_files = glob.glob(os.path.join(args.original_psg_path, '*.gz')) | |
manager = Manager() | |
docid_to_title = manager.dict() | |
docid_to_headings = manager.dict() | |
docid_to_url = manager.dict() | |
docid_to_pass_num = manager.dict() | |
num_files = len(doc_files) | |
pool = Pool(args.num_workers) | |
num_files_per_worker=num_files//args.num_workers | |
for i in range(args.num_workers): | |
if i==(args.num_workers-1): | |
file_list = doc_files[i*num_files_per_worker:] | |
else: | |
file_list = doc_files[i*num_files_per_worker:((i+1)*num_files_per_worker)] | |
pool.apply_async(read_doc_corpus ,(file_list, docid_to_title, docid_to_headings, docid_to_url)) | |
pool.close() | |
pool.join() | |
print('Total document size: {}'.format(len(docid_to_title))) | |
num_files = len(psg_files) | |
pool = Pool(args.num_workers) | |
num_files_per_worker=num_files//args.num_workers | |
for i in range(args.num_workers): | |
f_out = os.path.join(args.output_psg_path, 'psg' + str(i) + '.json') | |
if i==(args.num_workers-1): | |
file_list = psg_files[i*num_files_per_worker:] | |
else: | |
file_list = psg_files[i*num_files_per_worker:((i+1)*num_files_per_worker)] | |
pool.apply_async(passage_corpus_to_tsv ,(file_list, f_out)) | |
pool.close() | |
pool.join() | |
print('Done!') | |