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pmc_open_access_figure / pmc_open_access_figure.py

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	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	#
	# This dataset script is based on pmc/open_access.py loading script.

	"""PMC Open Access Subset of figures with captions"""
	from huggingface_hub import hf_hub_url
	import datetime
	import pandas as pd
	import numpy as np
	from itertools import compress, chain
	from collections import defaultdict
	import os
	import re
	from lxml import etree
	import unicodedata
	import html
	import json
	from PIL import Image
	import tarfile

	import datasets
	from datasets.tasks import LanguageModeling

	from PIL import ImageFile # Important for error: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0
	import mimetypes
	ImageFile.LOAD_TRUNCATED_IMAGES = True

	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = ""

	_DESCRIPTION = """\
	The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under
	license terms that allow reuse.
	Not all articles in PMC are available for text mining and other reuse, many have copyright protection, however articles
	in the PMC Open Access Subset are made available under Creative Commons or similar licenses that generally allow more
	liberal redistribution and reuse than a traditional copyrighted work.
	The PMC Open Access Subset is one part of the PMC Article Datasets

	This version focus on associating the graphics of figures with their captions
	"""

	_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = """
	https://www.ncbi.nlm.nih.gov/pmc/about/copyright/

	Within the PMC Open Access Subset, there are three groupings:

	Commercial Use Allowed - CC0, CC BY, CC BY-SA, CC BY-ND licenses
	Non-Commercial Use Only - CC BY-NC, CC BY-NC-SA, CC BY-NC-ND licenses; and
	Other - no machine-readable Creative Commons license, no license, or a custom license.
	"""

	_URL_ROOT = "https://ftp.ncbi.nlm.nih.gov/pub/pmc/"
	_URL = _URL_ROOT+"oa_bulk/{subset}/xml/"

	_SUBSETS = {
	"commercial": "oa_comm",
	"non_commercial": "oa_noncomm",
	"other": "oa_other",
	}
	_BASELINE_DATE = "2023-12-18"

	begin_doc_rgx = re.compile("""<!DOCTYPE.*""")
	def clean_raw(xml_text):
	"""
	Fixes the formating of xml of files and returns it.
	Some have bad formating but they can be fixed/improved
	"""
	#Some XML can't be parsed because they are not starting with the DOCTYPE declaration
	# Could be disabled if we handle the parsing error (TBD, how many files would be trashed)

	begin_doc = begin_doc_rgx.search(xml_text)
	if begin_doc is None:
	return xml_text
	xml_text = xml_text[begin_doc.start():]

	return xml_text

	def get_extensions_for_type(general_type):
	for ext in mimetypes.types_map:
	if mimetypes.types_map[ext].split('/')[0] == general_type:
	yield ext

	IMAGE_EXT = list(get_extensions_for_type('image'))

	def extract_captions(article_tree):
	ref_el_l = article_tree.xpath(".//fig")
	figure_captions = []
	graphic_names = []
	for el in ref_el_l:
	graphic_l = el.xpath(".//graphic")
	if len(graphic_l) == 0:
	continue
	graphic_el = graphic_l[0]
	graphic_names.append(graphic_el.get("{http://www.w3.org/1999/xlink}href"))
	text = " ".join(el.itertext())
	text = unicodedata.normalize("NFKD", html.unescape(text))
	figure_captions.append(text)
	return figure_captions, graphic_names

	class OpenAccessFigureConfig(datasets.BuilderConfig):
	"""BuilderConfig for the PMC Open Access Subset."""

	def __init__(self, subsets=None, **kwargs):
	"""BuilderConfig for the PMC Open Access Subset.
	Args:
	subsets (:obj:`List[str]`): List of subsets/groups to load.
	**kwargs: Keyword arguments forwarded to super.
	"""
	subsets = [subsets] if isinstance(subsets, str) else subsets
	super().__init__(
	name="+".join(subsets), **kwargs,
	)
	self.subsets = subsets if self.name != "all" else list(_SUBSETS.keys())

	class OpenAccessFigure(datasets.GeneratorBasedBuilder):
	"""PMC Open Access Subset for figure and captions"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIG_CLASS = OpenAccessFigureConfig
	BUILDER_CONFIGS = [OpenAccessFigureConfig(subsets="all")] + [OpenAccessFigureConfig(subsets=subset) for subset in _SUBSETS]
	DEFAULT_CONFIG_NAME = "all"

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"accession_id": datasets.Value("string"),
	"pmid": datasets.Value("string"),
	"figure_idx": datasets.Value("int16"),
	"figure_fn": datasets.Value("string"),
	"figure": datasets.Image(),
	"caption": datasets.Value("string"),
	"license": datasets.Value("string"),
	"retracted": datasets.Value("string"),
	"last_updated": datasets.Value("string"),
	"citation": datasets.Value("string"),
	}
	),
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	task_templates=[LanguageModeling(text_column="content")],
	)

	def _split_generators(self, dl_manager):

	baseline_package_list = dl_manager.download(f"{_URL_ROOT}oa_file_list.csv")
	baseline_file_list_l, incremental_file_list_l = [], []
	for subset in self.config.subsets:
	url = _URL.format(subset=_SUBSETS[subset])
	basename = f"{_SUBSETS[subset]}_xml."
	baseline_file_list_urls = [f"{url}{basename}PMC00{i}xxxxxx.baseline.{_BASELINE_DATE}.filelist.csv" for i in range(10) if (subset!="non_commercial" or i>0)]
	baseline_file_list_l.extend(dl_manager.download(baseline_file_list_urls))

	#date_delta = datetime.date.today() - datetime.date.fromisoformat(_BASELINE_DATE)
	#incremental_dates = [
	# (datetime.date.fromisoformat(_BASELINE_DATE) + datetime.timedelta(days=i + 1)).isoformat()
	# for i in range(date_delta.days)
	# ]
	#incremental_urls = [f"{url}{basename}incr.{date}.filelist.csv" for date in incremental_dates]
	#for url in incremental_urls:
	# try:
	# incremental_file_list_l.append(dl_manager.download(url))
	# except FileNotFoundError: # Some increment don't exist
	# continue

	oa_package_list = pd.read_csv(baseline_package_list, index_col="Accession ID")
	oa_package_list = oa_package_list[["File"]]
	figure_archives = []
	df_l = []
	set_article = set()
	for l, baseline_file_list in enumerate(baseline_file_list_l): # incremental_file_list_l[::-1] +
	try:
	file_list = pd.read_csv(baseline_file_list, index_col="AccessionID")
	except FileNotFoundError: # File not found can happen here in stream mode
	continue
	file_list = file_list.join(oa_package_list).reset_index().set_index("Article File")
	file_list.File = file_list.File.fillna('')
	#mask = (~file_list.File.isin(set_article)) & (file_list.File!="")
	#file_list = file_list[mask]
	figure_url_l = list(_URL_ROOT + file_list.File) #[f"{_URL_ROOT}{figure_path}" for figure_path in file_list.File]

	#try
	figure_archives.append(dl_manager.download(figure_url_l))
	#if l < len(incremental_file_list_l): # Only adding the incrementals to the list, the rest don't have overlap in pmid
	# set_article.union(file_list.File[slc_])
	df_l.append(file_list)
	#except FileNotFoundError:
	# continue

	package_df = pd.concat(df_l).reset_index()
	figure_archives = list(chain(*figure_archives))

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"figure_archive_lists": self.archive_generator(dl_manager, figure_archives, "train"),
	"package_df": package_df[np.arange(len(package_df))%10 < 8],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"figure_archive_lists": self.archive_generator(dl_manager, figure_archives, "test"),
	"package_df": package_df[np.arange(len(package_df))%10 == 8],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"figure_archive_lists": self.archive_generator(dl_manager, figure_archives, "validation"),
	"package_df": package_df[np.arange(len(package_df))%10 == 9],
	},
	),
	]

	def archive_generator(self, dl_manager, figure_archives, name):
	if name == "train":
	for k, archive in enumerate(figure_archives):
	if k%10 < 8:
	yield dl_manager.iter_archive(archive)
	elif name == "test":
	for k, archive in enumerate(figure_archives[8::10]):
	yield dl_manager.iter_archive(archive)
	elif name == "validation":
	for k, archive in enumerate(figure_archives[9::10]):
	yield dl_manager.iter_archive(archive)

	def _generate_examples(self, figure_archive_lists, package_df):
	#Loading the file listing folders of individual PMC Article package (with medias and graphics)
	for i, figure_archive in enumerate(figure_archive_lists):
	data = package_df.iloc[i]
	f_d = defaultdict(lambda: {})
	file_xml = None
	try:
	for path, file in figure_archive:
	bn, ext = os.path.splitext(os.path.basename(path))
	if ext in [".nxml", ".xml"]:
	content = file.read()
	try:
	text = content.decode("utf-8").strip()
	except UnicodeDecodeError as e:
	text = content.decode("latin-1").strip()
	text = clean_raw(text)
	article_tree = etree.ElementTree(etree.fromstring(text))
	figure_captions, graphic_names = extract_captions(article_tree)
	break

	for path, file in figure_archive:
	bn, ext = os.path.splitext(os.path.basename(path))
	if ext in IMAGE_EXT and bn in graphic_names:
	f_d[ext][bn] = Image.open(file, mode="r")

	image_d = {}
	for ext in [".tif", ".jpg", ".png", ".gif"]:
	for bn, image in f_d[ext].items():
	if bn not in image_d.keys():
	image_d[bn] = image

	for j, (caption, graph_name) in enumerate(zip(figure_captions, graphic_names)):
	if graph_name in image_d.keys():
	yield (f"{data['AccessionID']}_{str(j+1)}",
	{"figure": image_d[graph_name],
	"caption":caption,
	"pmid": data["PMID"],
	"accession_id": data['AccessionID'],
	"figure_idx": j+1,
	"figure_fn": graph_name,
	"license": data["License"],
	"last_updated": data["LastUpdated (YYYY-MM-DD HH:MM:SS)"],
	"retracted": data["Retracted"],
	"citation": data["Article Citation"]})
	except: # (etree.XMLSyntaxError, tarfile.ReadError) In some files, xml is broken, and tarfile readerror may happen
	continue