Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
expert-generated
Annotations Creators:
no-annotation
Source Datasets:
original
License:
# 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 enriched from XML.""" | |
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 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 parts, naming the introduction, methods, results, | |
discussion and conclusion, and refers with keywords in the text to external or internal | |
resources (articles, figures, tables, formulas, boxed-text, quotes, code, footnotes, chemicals, graphics, medias). | |
""" | |
_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" | |
REFS_KEYS = ["pmid_ref", "unknown_pub_ref", "figure_ref", "table_ref", "formula_ref", "box_ref", "code_ref", | |
"quote_ref", "chemical_ref", "supplementary_ref", "footnote_ref", "graphic_ref", "media_ref"] | |
CONTENT_KEYS = ["introduction", "methods", "results", "discussion", "conclusion", | |
"front", "body", "back", "figure", "table", "formula", "box", | |
"code", "quote", "chemical", "supplementary", "footnote"] | |
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) # Commented because <code> requires those spacing | |
return xml_text | |
# Tag name to "reference type" linking | |
TAG_DIC = {"fig":("FIG","figure_ref"), "table-wrap":("TAB","table_ref"), | |
"array":("TAB","table_ref"), "boxed-text":("BOX","box_ref"), | |
"graphic":("GRAPH","graphic_ref"), "inline-graphic":("GRAPH","graphic_ref"), | |
"media":("MEDIA","media_ref"), "inline-media":("MEDIA","media_ref"), | |
"disp-formula":("FORMU","formula_ref"), "inline-formula":("FORMU","formula_ref"), | |
"table-wrap-foot":("FOOTN","footnote_ref"), "fn-group":("FOOTN","footnote_ref"), | |
"code":("CODE","code_ref"), "chem-struct-wrap":("CHEM","chemical_ref"), | |
"disp-quote":("QUOTE","quote_ref"), "speech":("QUOTE","quote_ref"), | |
"supplementary-material":("SUPPL","supplementary_ref"), | |
"inline-supplementary-material":("SUPPL","supplementary_ref")} | |
def get_ref_indexes(ref_el_l, refs_pmid, refs_nonpmid_keys): | |
""" | |
For all the element found as xref, give them an index to be later found in their corresponding section. | |
Also sort them into the different types of references (eg <array> and <table-wrap> are both | |
labeled as table_ref). | |
""" | |
count_ref_d = defaultdict(lambda:0) | |
reference_d = {} | |
for k, v in refs_pmid.items(): | |
reference_d[k] = (v, "REF", "pmid_ref") | |
for i, k in enumerate(refs_nonpmid_keys): | |
reference_d[k] = (i, "UREF", "unknown_pub_ref") | |
refs_key_l = [] | |
for el in ref_el_l: | |
keyword, ref_name = TAG_DIC[el.tag] | |
idx = count_ref_d[ref_name] | |
key = el.attrib["id"] if "id" in el.attrib.keys() else f"{el.tag}{idx}" | |
reference_d[key] = (idx, keyword, ref_name) | |
refs_key_l.append(key) | |
count_ref_d[ref_name]+=1 | |
return reference_d, refs_key_l | |
def parseout_el_refs(el, rids): | |
""" | |
Extract the text from the tag opening to its closing, discarding the tail's text. | |
Removes xml namespace from the text for storage savings, such as: | |
- xmlns:xlink="http://www.w3.org/1999/xlink" | |
- xmlns:mml="http://www.w3.org/1998/Math/MathML" | |
Extract then from the text all the references founds to the rids dictionnary, | |
and replace them by keywords of the corresponding family (eg "##FIG##4##Doe 2022##" for a figure, | |
"##TAB##0##Table 1##" for a table, or "##MATHS##1##(2)##" for mathematical formulas) | |
The range reference (e.g. 1-3 or 15-17) are replaced by their range (1,2,3 or 15,16,17) | |
Returns the parsed text | |
""" | |
for xref in el.xpath(".//xref"): | |
inner_text = "".join(xref.itertext()) | |
if inner_text == "": # Removing "empty" references | |
tail = xref.tail if xref.tail else "" | |
prev_el = xref.getprevious() | |
parent = xref.getparent() | |
if prev_el is None: | |
parent.text = "".join([(parent.text if parent.text else ""), tail]) | |
else: | |
prev_el.tail = "".join([(prev_el.tail if prev_el.tail else ""), tail]) | |
parent.remove(xref) | |
res_rid = defaultdict(list) | |
res_reftext = defaultdict(list) | |
ref_rstart, ref_rstop = None, None | |
has_ref_range = None | |
for xref in el.xpath(".//xref[not(ancestor::xref)]"): #Ignore innermost of imbricated references | |
inner_text = "".join(xref.itertext()) | |
parent = xref.getparent() | |
rid = xref.get("rid") | |
if rid in rids.keys(): | |
ref_idx, ref_kword, ref_class = rids[rid] | |
res_rid[ref_class].append(ref_idx) | |
res_reftext[ref_class].append(inner_text) | |
tail = xref.tail if xref.tail else "" | |
#### START HANDLING REF RANGE ######## | |
try: | |
if has_ref_range is None: | |
if ref_kword in ["UREF", "REF"]: # Otherwise it's a year | |
has_ref_range = res_reftext[ref_class][-1].isnumeric() and int(res_reftext[ref_class][-1]) < 500 | |
if has_ref_range and ref_kword in ["UREF", "REF"]: | |
if tail=="-": | |
ref_rstart = int(res_reftext[ref_class][-1]) | |
tail = ", " | |
elif ref_rstart is not None: | |
ref_rstop = int(res_reftext[ref_class][-1]) | |
new_ref_kwords = [f"##{ref_kword}##{ref_idx}##{inner_text}##"] | |
for i in range(ref_rstart+1, ref_rstop): | |
new_rid = re.sub(str(ref_rstop), str(i), rid, count=1) | |
ref_idx_, ref_kword_, ref_class_ = rids[new_rid] | |
res_rid[ref_class_].insert(-1, ref_idx_) | |
res_reftext[ref_class_].insert(-1, str(i)) | |
new_ref_kwords.insert(-1, f"##{ref_kword_}##{ref_idx_}##{str(i)}##") | |
ref_kword = ", ".join(new_ref_kwords) | |
ref_rstart = None | |
except (KeyError, ValueError): | |
ref_rstart = None | |
continue # The substitution failed, happen when text don't match the rid | |
#### END HANDLING REF RANGE ######## | |
prev_el = xref.getprevious() | |
if prev_el is None: | |
parent.text = "".join([(parent.text if parent.text else ""), f"##{ref_kword}##{ref_idx}##{inner_text}##", tail]) | |
else: | |
prev_el.tail = "".join([(prev_el.tail if prev_el.tail else ""), f"##{ref_kword}##{ref_idx}##{inner_text}##", tail]) | |
parent.remove(xref) | |
text = etree.tostring(el, with_tail=False, encoding='unicode', method='xml') | |
#Removing the xml namespace, (otherwise they would be everywhere) | |
tag_start = text.find(">")+1 | |
tag_txt = text[:tag_start] | |
for k, v in el.nsmap.items(): | |
tag_txt = tag_txt.replace(f' xmlns:{k}="{v}"', "", 1) | |
text = "".join([tag_txt, text[tag_start:]]) | |
return text | |
def get_references(article_tree): | |
""" | |
Retrieve two dictionnaries of the bibr references for that article. | |
The first has the references' PMID for those having one. | |
The second contains the <ref> tag fields, that could be identified to retrieve the | |
referenced documents (some have PMID that could be found from the title and authors of a document). | |
""" | |
references_pmid = {} | |
references_nonpmid = [] | |
references_nonpmid_keys = [] | |
refs = article_tree.find(".//ref-list") | |
if refs is None: #Some don't have any references | |
return {}, [], [] | |
refs = refs.findall("ref") | |
for i, ref in enumerate(refs): | |
pmid = None | |
for pubid in ref.findall(".//pub-id"): | |
if pubid.get("pub-id-type") == "pmid": | |
pmid = int(pubid.text) | |
break | |
if pmid is not None and pmid<100000000: | |
#In an article (oa_comm:PMC2679651), broken PMID were found (>10e9). | |
#May be several of those. Not sure what to do with them, and what threshold to use | |
#Keeping them would result in loosing info about the reference (article title, authors, ...) | |
#Only the PMID is kept, as it links to the documents in pubmed abstract dataset. | |
references_pmid[ref.attrib["id"]] = str(pmid) | |
else: | |
ref_key = ref.attrib["id"] if "id" in ref.attrib.keys() else f"URef{i+1}" | |
citation_d = defaultdict(list) | |
#Authors are the only elements that can come in multiples (I could be wrong) | |
for el in ref.iterdescendants(): | |
if isinstance(el.text, str) and isinstance(el.tag, str): | |
citation_d[el.tag].append(el.text) | |
references_nonpmid.append(dict(citation_d)) | |
references_nonpmid_keys.append(ref_key) | |
return references_pmid, references_nonpmid, references_nonpmid_keys | |
def construct_datadict(article_tree): | |
""" | |
Where the magic happens. A long script that: | |
- Get the external references (from pmid if present) | |
- Get glossary and remove it from the document | |
- Find internal references (figures, tables, ...) and build a xref dictionary | |
- 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 | |
- reference_d: The references of each kind (figure, table, ...) for each content type (intro, figure caption, ...) | |
- reference_text_d: The replaced text by the keywords of the references, with keys matching reference_d. | |
- reference_count: The count of unique external-document references. | |
Useful information about the tags can be found here: https://jats.nlm.nih.gov/archiving/tag-library/1.3/ | |
""" | |
res_content_d = {} | |
refs_pmid, refs_nonpmid, refs_nonpmid_keys = get_references(article_tree) | |
reference_count = len(refs_pmid)+len(refs_nonpmid) | |
res_content_d["unknown_pub"] = json.dumps(refs_nonpmid) | |
refs_el = article_tree.find(".//ref-list") | |
if refs_el is not None: | |
refs_el.getparent().remove(refs_el) | |
# Extracts the glossary if exists, and removes it from the tree | |
glossary = {} | |
def search_def(el): | |
for item in el.findall(".//def-item"): | |
abbrev = item.find(".//term") | |
if abbrev is None: | |
continue | |
k = "".join(abbrev.itertext()) | |
definition = item.find(".//def") | |
definition = "".join(definition.itertext()) if definition is not None else "" | |
glossary[k] = definition | |
for el in article_tree.findall(".//glossary"): | |
search_def(el) | |
el.getparent().remove(el) | |
for el in article_tree.findall(".//def-list"): | |
search_def(el) #There may be still more def-list outside of a glossary | |
el.getparent().remove(el) | |
res_content_d["glossary"] = glossary | |
# After testing, no question were found in the dataset, so I commented that part | |
# question_l = [] | |
# for el in article_tree.xpath(".//question-preamble|.//question|.//answer|.//explanation"): | |
# text = parseout_el_refs(el, {}) | |
# question_l.append(text) | |
# res_content_d["question"] = "\n".join(question_l) | |
# for el in article_tree.xpath(".//question-wrap-group|.//question-wrap|.//answer-set|.//explanation"): | |
# el.getparent().remove(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") | |
rids, key_l = get_ref_indexes(ref_el_l, refs_pmid, refs_nonpmid_keys) | |
text_l_d = defaultdict(list) | |
for el, key in zip(ref_el_l[::-1], key_l[::-1]): | |
#The iteration is done backward to always process first the most inner reference, | |
# it makes the processing is agnostic to structure rules differences between articles | |
new_text = parseout_el_refs(el, rids) | |
ref_class = rids[key][2] | |
text_l_d[ref_class].insert(0, new_text) | |
repl_xref = etree.Element("xref", attrib={"rid":key}) | |
repl_xref.tail = el.tail | |
el.addprevious(repl_xref) | |
el.getparent().remove(el) | |
# Finally, the discovered references and text are added to the result | |
for ref_k in REFS_KEYS[2:]: #Slicing from 2, to not add pmid and unknown ref here | |
res_content_d[ref_k[:-4]] = text_l_d[ref_k]#"\n".join(text_l_d[ref_k]) | |
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 = parseout_el_refs(el, rids) | |
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, reference_count) | |
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.features.Sequence(datasets.Value("string")), | |
"methods": datasets.features.Sequence(datasets.Value("string")), | |
"results": datasets.features.Sequence(datasets.Value("string")), | |
"discussion": datasets.features.Sequence(datasets.Value("string")), | |
"conclusion": datasets.features.Sequence(datasets.Value("string")), | |
"front": datasets.features.Sequence(datasets.Value("string")), | |
"body": datasets.features.Sequence(datasets.Value("string")), | |
"back": datasets.features.Sequence(datasets.Value("string")), | |
"figure": datasets.features.Sequence(datasets.Value("string")), | |
"table": datasets.features.Sequence(datasets.Value("string")), | |
"formula": datasets.features.Sequence(datasets.Value("string")), | |
"box": datasets.features.Sequence(datasets.Value("string")), | |
"code": datasets.features.Sequence(datasets.Value("string")), | |
"quote": datasets.features.Sequence(datasets.Value("string")), | |
"chemical": datasets.features.Sequence(datasets.Value("string")), | |
"supplementary": datasets.features.Sequence(datasets.Value("string")), | |
"footnote": datasets.features.Sequence(datasets.Value("string")), | |
"graphic": datasets.features.Sequence(datasets.Value("string")), | |
"media": datasets.features.Sequence(datasets.Value("string")), | |
"unknown_pub": datasets.Value("string"), | |
# "question": datasets.Value("string"), | |
"glossary": datasets.features.Sequence( | |
{"acronym": datasets.Value("string"), "definition": datasets.Value("string")} | |
), | |
"n_references": datasets.Value("int32"), | |
"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 | |
baselines = [f"PMC00{i}xxxxxx.baseline.{_BASELINE_DATE}" for i in range(9)] | |
for baseline in baselines: | |
baseline_file_list_url = f"{url}{basename}{baseline}.filelist.csv" | |
baseline_archive_url = f"{url}{basename}{baseline}.tar.gz" | |
try: | |
baseline_file_list = dl_manager.download(baseline_file_list_url) | |
baseline_archive = dl_manager.download(baseline_archive_url) | |
except FileNotFoundError: # non-commercial PMC000xxxxxx baseline does not exist | |
continue | |
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, n_ref = construct_datadict(article_tree) | |
glossary = np.array([[k,v] for k,v in content_d["glossary"].items()]) | |
data = { | |
"introduction": content_d["introduction"], | |
"methods": content_d["methods"], | |
"results": content_d["results"], | |
"discussion": content_d["discussion"], | |
"conclusion": content_d["conclusion"], | |
"front": content_d["front"], | |
"body": content_d["body"], | |
"back": content_d["back"], | |
"figure": content_d["figure"], | |
"table": content_d["table"], | |
"formula": content_d["formula"], | |
"box": content_d["box"], | |
"code": content_d["code"], | |
"quote": content_d["quote"], | |
"chemical": content_d["chemical"], | |
"supplementary": content_d["supplementary"], | |
"footnote": content_d["footnote"], | |
"graphic": content_d["graphic"], | |
"media": content_d["media"], | |
# "question": content_d["question"], | |
"unknown_pub": content_d["unknown_pub"], | |
"glossary": {"acronym":glossary[:,0], "definition":glossary[:,1]} if len(glossary)>0 else {"acronym":[], "definition":[]}, | |
"n_references": n_ref, | |
"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, n_ref = construct_datadict(article_tree) | |
glossary = np.array([[k,v] for k,v in content_d["glossary"].items()]) | |
data = { | |
"introduction": content_d["introduction"], | |
"methods": content_d["methods"], | |
"results": content_d["results"], | |
"discussion": content_d["discussion"], | |
"conclusion": content_d["conclusion"], | |
"front": content_d["front"], | |
"body": content_d["body"], | |
"back": content_d["back"], | |
"figure": content_d["figure"], | |
"table": content_d["table"], | |
"formula": content_d["formula"], | |
"box": content_d["box"], | |
"code": content_d["code"], | |
"quote": content_d["quote"], | |
"chemical": content_d["chemical"], | |
"supplementary": content_d["supplementary"], | |
"footnote": content_d["footnote"], | |
"graphic": content_d["graphic"], | |
"media": content_d["media"], | |
# "question": content_d["question"], | |
"unknown_pub": content_d["unknown_pub"], | |
"glossary": {"acronym":glossary[:,0], "definition":glossary[:,1]} if len(glossary)>0 else {"acronym":[], "definition":[]}, | |
"n_references": n_ref, | |
"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 | |
#except FileNotFoundError: # non-commercial PMC000xxxxxx baseline does not exist | |
# continue | |