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tenkgnad-clustering-p2p / extract_data.py

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add extraction script

e7c4c77 about 1 year ago

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	"""Script to generate splits for benchmarking text embedding clustering.
	Data and preprocessing based on 10kGNAD dataset (https://github.com/tblock/10kGNAD)."""

	import random
	import re
	import sqlite3
	import sys

	import jsonlines
	import numpy as np
	import pandas as pd
	from bs4 import BeautifulSoup
	from sklearn.model_selection import train_test_split
	from tqdm import tqdm

	random.seed(42)

	# path to corpus file, can be retrieved from here: https://github.com/tblock/10kGNAD/releases/download/v1.0/corpus.sqlite3
	DATA_PATH = sys.argv[1]

	INCLUDE_BODY = (
	True # True: combine title and article body (p2p), False: only title (s2s)
	)
	ARTICLE_QUERY = f"SELECT Path, Title{', Body' if INCLUDE_BODY else ''} FROM Articles WHERE PATH LIKE 'Newsroom/%' AND PATH NOT LIKE 'Newsroom/User%' ORDER BY Path"

	NUM_SPLITS = 10
	SPLIT_RANGE = np.array([0.1, 1.0])


	def get_split(frame, split_range=SPLIT_RANGE):
	samples = random.randint((split_range len(frame)).astype(int))
	return frame.sample(samples).to_dict("list")


	def write_sets(name, sets):
	with jsonlines.open(name, "w") as f_out:
	f_out.write_all(sets)


	conn = sqlite3.connect(DATA_PATH)
	cursor = conn.cursor()

	samples = []
	for row in tqdm(cursor.execute(ARTICLE_QUERY).fetchall(), unit_scale=True):
	path, title = row[0], row[1]

	text = title

	if INCLUDE_BODY:
	body = row[-1]
	soup = BeautifulSoup(body, "html.parser")

	# get description from subheadline
	description_obj = soup.find("h2", {"itemprop": "description"})
	if description_obj is not None:
	text += (
	" " + description_obj.text.replace("\n", " ").replace("\t", " ").strip()
	)

	# get text from paragraphs
	text_container = soup.find("div", {"class": "copytext"})
	if text_container is not None:
	for p in text_container.findAll("p"):
	text += " " + (
	p.text.replace("\n", " ")
	.replace("\t", " ")
	.replace('"', "")
	.replace("'", "")
	+ " "
	)
	text = text.strip()

	# remove article autors
	for author in re.findall(
	r"\.\ \(.+,.+2[0-9]+\)", text[-50:]
	): # some articles have a year of 21015..
	text = text.replace(author, ".")

	# get label from path
	label = path.split("/")[1]
	samples.append([text, label])

	conn.close()

	samples = pd.DataFrame(samples, columns=["sentences", "labels"])

	sets = []
	for _ in range(NUM_SPLITS):
	sets.append(get_split(samples))

	write_sets("test.jsonl", sets)