generate_pmc_open_access_section.py

# 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 sections parsed (plain text)"""

import datetime
import pandas as pd
import numpy as np
from itertools import compress, chain
from collections import defaultdict
import re
from lxml import etree
import json
import html
import unicodedata

import datasets
from datasets.tasks import LanguageModeling


# 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 takes XML version as source, benefiting from the structured text
to split the articles in sections, naming the introduction, methods, results,
discussion and conclusion, front, body and back. XML is then removed and format
it to plain text.
"""

_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)
 xml_text = xml_text[begin_doc.start():]

 #Some XML are poisoned with consecutive tabs and new lines
 xml_text = re.sub('\s+',' ',xml_text)
 return xml_text

def construct_datadict(article_tree):
 """
 Where the magic happens. A long script that:
 - Remove the references (and what is referenced to) from the text
 - Extract paragraphs and titles with their path in the document
 - Titles are used to identify ["introduction", "methods", "results" and "discussion"]
 - The path are then used to group paragraphs and titles into corresponding content.
 - Remaining p and title are put in three other section: front, body, back

 Returns:
 - content_d: Dictionnary with the content result

 Useful information about the tags can be found here: https://jats.nlm.nih.gov/archiving/tag-library/1.3/
 """
 res_content_d = {}

 refs_el = article_tree.find(".//ref-list")
 if refs_el is not None:
 refs_el.getparent().remove(refs_el)

 # One big query is faster than multiple small ones
 ref_el_l = article_tree.xpath(".//fig|.//table-wrap|.//array|.//supplementary-material\
 |.//inline-supplementary-material|.//disp-formula\
 |.//inline-formula|.//graphic|.//inline-graphic\
 |.//media|.//inline-media|.//boxed-text\
 |.//table-wrap-foot|.//fn-group|.//chem-struct-wrap\
 |.//code|.//disp-quote|.//speech")
 for el in ref_el_l[::-1]:
 repl_xref = etree.Element("xref")
 repl_xref.tail = el.tail
 el.addprevious(repl_xref)
 el.getparent().remove(el)

 path_l, text_l = [], []
 t_paths, t_texts_lowcase = [], []
 for part in ["front", "body", "back"]: #Iterate parts and insert first front and back
 tmp_path_l, tmp_text_l = [], []
 tmp_t_paths, tmp_t_texts_lowcase = [], []
 part_el = article_tree.find(".//"+part)
 if part_el is None:
 res_content_d[part] = []
 continue
 #Only the outermost p are kept, to prevent duplication.
 #Also seen title with p inside. not(ancestor::title) prevents duplication of that p
 for el in part_el.xpath(".//p[not(ancestor::p) and not(ancestor::title)]| .//title[not(ancestor::p) and not(ancestor::title)]"):
 new_text = " ".join(el.itertext())
 new_text = unicodedata.normalize("NFKD", html.unescape(new_text))
 tmp_path_l.append(article_tree.getelementpath(el))
 tmp_text_l.append(new_text)
 if el.tag=="title":
 tmp_t_paths.append(tmp_path_l[-1])
 tmp_t_texts_lowcase.append(new_text.lower())
 if part=="body": #We keep the body for processing right bellow.
 path_l, text_l = tmp_path_l, tmp_text_l
 t_paths, t_texts_lowcase = tmp_t_paths, tmp_t_texts_lowcase
 else:
 res_content_d[part] = tmp_text_l

 # Figuring from the titles which are the different categories
 mask_intro = np.array(["introduction" in t_text or "background" in t_text for t_text in t_texts_lowcase]).astype(bool)
 mask_metho = np.array(["method" in t_text for t_text in t_texts_lowcase]).astype(bool)
 mask_resul = np.array(["result" in t_text for t_text in t_texts_lowcase]).astype(bool)
 mask_discu = np.array(["discussion" in t_text for t_text in t_texts_lowcase]).astype(bool)
 mask_concl = np.array(["conclusion" in t_text for t_text in t_texts_lowcase]).astype(bool)
 processed_mask = np.zeros(len(text_l), dtype="bool")
 for mask, name_section in zip([mask_intro, mask_metho, mask_resul, mask_discu, mask_concl],
 ["introduction", "methods", "results", "discussion", "conclusion"]):
 if not np.any(mask):
 res_content_d[name_section] = []
 continue

 filtered_path_l = list(compress(t_paths, mask))
 levels = np.array([len(path.split("/")) for path in filtered_path_l])
 root_path = filtered_path_l[np.argmin(levels)]
 root_path = root_path[:root_path.rindex("/")]
 mask_contents = np.array([path.startswith(root_path) for path in path_l]).astype(bool)
 processed_mask |= mask_contents
 res_content_d[name_section] = list(compress(text_l, mask_contents))

 processed_mask = ~processed_mask #Finally, add the body part as everything that don't belong to previous categories
 res_content_d["body"] = list(compress(text_l, processed_mask))

 return res_content_d

class OpenAccessXMLConfig(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 OpenAccessXML(datasets.GeneratorBasedBuilder):
 """PMC Open Access Subset enriched from XML files."""

 VERSION = datasets.Version("1.0.0")
 BUILDER_CONFIG_CLASS = OpenAccessXMLConfig
 BUILDER_CONFIGS = [OpenAccessXMLConfig(subsets="all")] + [OpenAccessXMLConfig(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"),

 "introduction": datasets.Value("string"),
 "methods": datasets.Value("string"),
 "results": datasets.Value("string"),
 "discussion": datasets.Value("string"),
 "conclusion": datasets.Value("string"),

 "front": datasets.Value("string"),
 "body": datasets.Value("string"),
 "back": datasets.Value("string"),

 "license": datasets.Value("string"),
 "retracted": datasets.Value("string"),
 "last_updated": datasets.Value("string"),
 "citation": datasets.Value("string"),
 "package_file": datasets.Value("string"),
 }
 ),
 homepage=_HOMEPAGE,
 license=_LICENSE,
 citation=_CITATION,
 task_templates=[LanguageModeling(text_column="content")],
 )

 def _split_generators(self, dl_manager):

 incremental_paths = {
 "incremental_file_lists": [],
 "incremental_archives": []
 }

 baseline_package_list = dl_manager.download(f"{_URL_ROOT}oa_file_list.csv")

 baseline_file_lists = []
 baseline_archives = []
 for subset in self.config.subsets:
 url = _URL.format(subset=_SUBSETS[subset])
 basename = f"{_SUBSETS[subset]}_xml."
 # Baselines non-commercial PMC000xxxxxx baseline does not exist
 baselines = [f"PMC00{i}xxxxxx.baseline.{_BASELINE_DATE}" for i in range(10) if (subset != "non_commercial" or i > 0)]
 
 for baseline in baselines:
 baseline_file_list_url = f"{url}{basename}{baseline}.filelist.csv"
 baseline_archive_url = f"{url}{basename}{baseline}.tar.gz"
 baseline_file_list = dl_manager.download(baseline_file_list_url)
 baseline_archive = dl_manager.download(baseline_archive_url)

 baseline_file_lists.append(baseline_file_list)
 baseline_archives.append(baseline_archive)

 baseline_file_list_url = f"{url}{basename}{baseline}.filelist.csv"

 # Incremental commented because some articles are already in the main parts (updates?)
 # Need to find a way to add them to the dataset without duplicating the articles.
 # Also adding them would mean that each new day the dataset is loaded, the whole dataset is recreated.
 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)
 ]
 incrementals = [f"incr.{date}" for date in incremental_dates]
 for incremental in incrementals:
 incremental_file_list_url = f"{url}{basename}{incremental}.filelist.csv"
 incremental_archive_url = f"{url}{basename}{incremental}.tar.gz"
 try:
 incremental_file_list = dl_manager.download(incremental_file_list_url)
 incremental_archive = dl_manager.download(incremental_archive_url)
 except FileNotFoundError: # Some increment might not exist
 continue
 incremental_paths["incremental_file_lists"].append(incremental_file_list)
 incremental_paths["incremental_archives"].append(incremental_archive)

 return [
 datasets.SplitGenerator(
 name=datasets.Split.TRAIN,
 gen_kwargs={
 "baseline_file_lists": baseline_file_lists,
 "baseline_archives": [dl_manager.iter_archive(archive) for archive in baseline_archives],
 "baseline_package_list": baseline_package_list,
 "incremental_file_lists": incremental_paths["incremental_file_lists"],
 "incremental_archives": [dl_manager.iter_archive(archive) for archive in incremental_paths["incremental_archives"]],
 },
 ),
 ]

 def _generate_examples(self, baseline_file_lists, baseline_archives, baseline_package_list, incremental_file_lists, incremental_archives):
 #Loading the file listing folders of individual PMC Article package (with medias and graphics)
 oa_package_list = pd.read_csv(baseline_package_list, index_col="Accession ID")
 oa_package_list = oa_package_list[["File"]]
 oa_package_list.sort_index(inplace=True)
 processed_ids = set()

 # Incrementals
 if incremental_file_lists:
 for incremental_file_list, incremental_archive in zip(incremental_file_lists[::-1], incremental_archives[::-1]):
 try:
 incrementals = pd.read_csv(incremental_file_list, index_col="AccessionID")
 except FileNotFoundError: # File not found can happen here in stream mode
 continue
 incrementals = incrementals.join(oa_package_list).reset_index().set_index("Article File")
 incrementals.File = incrementals.File.fillna('')
 incrementals = incrementals.to_dict(orient="index")

 for path, file in incremental_archive:
 data = incrementals.pop(path)
 pmcid = data["AccessionID"]
 if pmcid in processed_ids: #oa_package_list.loc[pmcid, "yet_processed"]:
 continue
 content = file.read()
 try:
 text = content.decode("utf-8").strip()
 except UnicodeDecodeError as e:
 text = content.decode("latin-1").strip()
 text = clean_raw(text)
 try:
 article_tree = etree.ElementTree(etree.fromstring(text))
 except etree.XMLSyntaxError: #In some files, xml is broken
 continue

 content_d = construct_datadict(article_tree)
 data = {
 "introduction": "\n".join(content_d["introduction"]),
 "methods": "\n".join(content_d["methods"]),
 "results": "\n".join(content_d["results"]),
 "discussion": "\n".join(content_d["discussion"]),
 "conclusion": "\n".join(content_d["conclusion"]),
 "front": "\n".join(content_d["front"]),
 "body": "\n".join(content_d["body"]),
 "back": "\n".join(content_d["back"]),
 "pmid": data["PMID"],
 "accession_id": pmcid,
 "license": data["License"],
 "last_updated": data["LastUpdated (YYYY-MM-DD HH:MM:SS)"],
 "retracted": data["Retracted"],
 "citation": data["Article Citation"],
 "package_file": data["File"],
 }
 processed_ids.add(pmcid)
 yield pmcid, data

 # Baselines
 for baseline_file_list, baseline_archive in zip(baseline_file_lists, baseline_archives):

 #try:
 baselines = pd.read_csv(baseline_file_list, index_col="AccessionID")
 baselines = baselines.join(oa_package_list).reset_index().set_index("Article File")
 baselines.File = baselines.File.fillna('')
 baselines = baselines.to_dict(orient="index")

 for path, file in baseline_archive:
 data = baselines.pop(path)
 pmcid = data["AccessionID"]
 if pmcid in processed_ids:
 continue
 content = file.read()
 try:
 text = content.decode("utf-8").strip()
 except UnicodeDecodeError as e:
 text = content.decode("latin-1").strip()
 text = clean_raw(text)
 try:
 article_tree = etree.ElementTree(etree.fromstring(text))
 except etree.XMLSyntaxError: #In some files, xml is broken
 continue

 content_d = construct_datadict(article_tree)
 data = {
 "introduction": "\n".join(content_d["introduction"]),
 "methods": "\n".join(content_d["methods"]),
 "results": "\n".join(content_d["results"]),
 "discussion": "\n".join(content_d["discussion"]),
 "conclusion": "\n".join(content_d["conclusion"]),
 "front": "\n".join(content_d["front"]),
 "body": "\n".join(content_d["body"]),
 "back": "\n".join(content_d["back"]),
 "pmid": data["PMID"],
 "accession_id": pmcid,
 "license": data["License"],
 "last_updated": data["LastUpdated (YYYY-MM-DD HH:MM:SS)"],
 "retracted": data["Retracted"],
 "citation": data["Article Citation"],
 "package_file": data["File"],
 }
 processed_ids.add(pmcid)
 yield pmcid, data