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import argparse
import json
import os
import time
import numpy as np
import pandas as pd
import nltk
from rank_bm25 import BM25Okapi
from multiprocessing import Pool, cpu_count, Manager, Lock
from functools import partial
import heapq
from threading import Thread, Event
import queue
from datetime import datetime, timedelta
def download_nltk_data(package_name, download_dir='nltk_data'):
# Ensure the download directory exists
os.makedirs(download_dir, exist_ok=True)
# Set NLTK data path
nltk.data.path.append(download_dir)
try:
# Try to find the resource
nltk.data.find(f'tokenizers/{package_name}')
print(f"Package '{package_name}' is already downloaded")
except LookupError:
# If resource isn't found, download it
print(f"Downloading {package_name}...")
nltk.download(package_name, download_dir=download_dir)
print(f"Successfully downloaded {package_name}")
def combine_all_sentences(knowledge_file):
sentences, urls = [], []
with open(knowledge_file, "r", encoding="utf-8") as json_file:
for i, line in enumerate(json_file):
data = json.loads(line)
sentences.extend(data["url2text"])
urls.extend([data["url"] for _ in range(len(data["url2text"]))])
return sentences, urls, i + 1
def remove_duplicates(sentences, urls):
df = pd.DataFrame({"document_in_sentences":sentences, "sentence_urls":urls})
df['sentences'] = df['document_in_sentences'].str.strip().str.lower()
df = df.drop_duplicates(subset="sentences").reset_index()
return df['document_in_sentences'].tolist(), df['sentence_urls'].tolist()
def retrieve_top_k_sentences(query, document, urls, top_k):
tokenized_docs = [nltk.word_tokenize(doc) for doc in document[:top_k]]
bm25 = BM25Okapi(tokenized_docs)
scores = bm25.get_scores(nltk.word_tokenize(query))
top_k_idx = np.argsort(scores)[::-1][:top_k]
return [document[i] for i in top_k_idx], [urls[i] for i in top_k_idx]
def process_single_example(idx, example, args, result_queue, counter, lock):
try:
with lock:
current_count = counter.value + 1
counter.value = current_count
print(f"\nProcessing claim {idx}... Progress: {current_count} / {args.total_examples}")
# start_time = time.time()
document_in_sentences, sentence_urls, num_urls_this_claim = combine_all_sentences(
os.path.join(args.knowledge_store_dir, f"{idx}.jsonl")
)
print(f"Obtained {len(document_in_sentences)} sentences from {num_urls_this_claim} urls.")
document_in_sentences, sentence_urls = remove_duplicates(document_in_sentences, sentence_urls)
query = example["claim"] + " " + " ".join(example['hypo_fc_docs'])
top_k_sentences, top_k_urls = retrieve_top_k_sentences(
query, document_in_sentences, sentence_urls, args.top_k
)
result = {
"claim_id": idx,
"claim": example["claim"],
f"top_{args.top_k}": [
{"sentence": sent, "url": url}
for sent, url in zip(top_k_sentences, top_k_urls)
],
"hypo_fc_docs": example['hypo_fc_docs']
}
result_queue.put((idx, result))
return True
except Exception as e:
print(f"Error processing example {idx}: {str(e)}")
result_queue.put((idx, None))
return False
def writer_thread(output_file, result_queue, total_examples, stop_event):
next_index = 0
pending_results = []
with open(output_file, "w", encoding="utf-8") as f:
while not (stop_event.is_set() and result_queue.empty()):
try:
idx, result = result_queue.get(timeout=1)
if result is not None:
heapq.heappush(pending_results, (idx, result))
while pending_results and pending_results[0][0] == next_index:
_, result_to_write = heapq.heappop(pending_results)
f.write(json.dumps(result_to_write, ensure_ascii=False) + "\n")
f.flush()
next_index += 1
except queue.Empty:
continue
# def format_time(seconds):
# """Format time duration nicely."""
# return str(timedelta(seconds=round(seconds)))
def main(args):
download_nltk_data('punkt')
download_nltk_data('punkt_tab')
with open(args.target_data, "r", encoding="utf-8") as json_file:
target_examples = json.load(json_file)
if args.end == -1:
args.end = len(target_examples)
print(f"Total examples to process: {args.end - args.start}")
files_to_process = list(range(args.start, args.end))
examples_to_process = [(idx, target_examples[idx]) for idx in files_to_process]
num_workers = min(args.workers if args.workers > 0 else cpu_count(), len(files_to_process))
print(f"Using {num_workers} workers to process {len(files_to_process)} examples")
with Manager() as manager:
counter = manager.Value('i', 0)
lock = manager.Lock()
args.total_examples = len(files_to_process)
result_queue = manager.Queue()
stop_event = Event()
writer = Thread(
target=writer_thread,
args=(args.json_output, result_queue, len(files_to_process), stop_event)
)
writer.start()
process_func = partial(
process_single_example,
args=args,
result_queue=result_queue,
counter=counter,
lock=lock
)
with Pool(num_workers) as pool:
results = pool.starmap(process_func, examples_to_process)
stop_event.set()
writer.join()
# successful = sum(1 for r in results if r)
# print(f"\nSuccessfully processed {successful} out of {len(files_to_process)} examples")
# print(f"Results written to {args.json_output}")
# # Calculate and display timing information
# total_time = time.time() - script_start
# avg_time = total_time / len(files_to_process)
# end_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
# print("\nTiming Summary:")
# print(f"Start time: {start_time}")
# print(f"End time: {end_time}")
# print(f"Total runtime: {format_time(total_time)} (HH:MM:SS)")
# print(f"Average time per example: {avg_time:.2f} seconds")
# if successful > 0:
# print(f"Processing speed: {successful / total_time:.2f} examples per second")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Get top 10000 sentences with BM25 in the knowledge store using parallel processing."
)
parser.add_argument(
"-k",
"--knowledge_store_dir",
type=str,
default="data_store/knowledge_store",
help="The path of the knowledge_store_dir containing json files with all the retrieved sentences.",
)
parser.add_argument(
"--target_data",
type=str,
default="data_store/hyde_fc.json",
help="The path of the file that stores the claim.",
)
parser.add_argument(
"-o",
"--json_output",
type=str,
default="data_store/dev_retrieval_top_k.json",
help="The output dir for JSON files to save the top 100 sentences for each claim.",
)
parser.add_argument(
"--top_k",
default=5000,
type=int,
help="How many documents should we pick out with BM25.",
)
parser.add_argument(
"-s",
"--start",
type=int,
default=0,
help="Starting index of the files to process.",
)
parser.add_argument(
"-e",
"--end",
type=int,
default=-1,
help="End index of the files to process.",
)
parser.add_argument(
"-w",
"--workers",
type=int,
default=0,
help="Number of worker processes (default: number of CPU cores)",
)
args = parser.parse_args()
main(args)
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