File size: 12,806 Bytes
bee03a2 c1ab669 bee03a2 f7838cd bee03a2 21f36f5 bee03a2 f7838cd bee03a2 4f3b68b bee03a2 f7838cd bee03a2 f7838cd bee03a2 1088738 bee03a2 f7838cd bee03a2 1088738 5a9abed f7838cd 34dc2c6 bee03a2 5a9abed bee03a2 1088738 bee03a2 1088738 bee03a2 1088738 bee03a2 1088738 bee03a2 f8eeb59 bee03a2 f8eeb59 b9fb443 f8eeb59 b9fb443 f8eeb59 f7838cd f8eeb59 47569c1 f8eeb59 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 |
# 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 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,
)
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
|