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bigbio
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bioasq_task_c_2017

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bioasq_task_c_2017 / bioasq_task_c_2017.py

gabrielaltay

Fix text_features typo (#1)

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	# coding=utf-8
	# Copyright 2022 The HuggingFace Datasets Authors and
	#
	# 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.

	import json
	import os
	import xml.etree.ElementTree as ET
	from dataclasses import dataclass
	from typing import List

	import datasets

	from .bigbiohub import text_features
	from .bigbiohub import BigBioConfig
	from .bigbiohub import Tasks

	_LANGUAGES = ['English']
	_PUBMED = True
	_LOCAL = True
	_CITATION = """\
	@article{nentidis-etal-2017-results,
	title = {Results of the fifth edition of the {B}io{ASQ} Challenge},
	author = {
	Nentidis, Anastasios and Bougiatiotis, Konstantinos and Krithara,
	Anastasia and Paliouras, Georgios and Kakadiaris, Ioannis
	},
	year = 2007,
	journal = {},
	volume = {BioNLP 2017},
	doi = {10.18653/v1/W17-2306},
	url = {https://aclanthology.org/W17-2306},
	biburl = {},
	bibsource = {https://aclanthology.org/W17-2306}
	}

	"""

	_DATASETNAME = "bioasq_task_c_2017"
	_DISPLAYNAME = "BioASQ Task C 2017"

	_DESCRIPTION = """\
	The training data set for this task contains annotated biomedical articles
	published in PubMed and corresponding full text from PMC. By annotated is meant
	that GrantIDs and corresponding Grant Agencies have been identified in the full
	text of articles
	"""

	_HOMEPAGE = "http://participants-area.bioasq.org/general_information/Task5c/"

	_LICENSE = 'National Library of Medicine Terms and Conditions'

	# Website contains all data, but login required
	_URLS = {_DATASETNAME: "http://participants-area.bioasq.org/datasets/"}

	_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]

	_SOURCE_VERSION = "1.0.0"
	_BIGBIO_VERSION = "1.0.0"


	@dataclass
	class BioASQTaskC2017BigBioConfig(BigBioConfig):
	schema: str = "source"
	name: str = "bioasq_task_c_2017_source"
	version: datasets.Version = datasets.Version(_SOURCE_VERSION)
	description: str = "bioasq_task_c_2017 source schema"
	subset_id: str = "bioasq_task_c_2017"


	class BioASQTaskC2017(datasets.GeneratorBasedBuilder):
	"""
	BioASQ Task C Dataset for 2017
	"""

	DEFAULT_CONFIG_NAME = "bioasq_task_c_2017_source"
	SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
	BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

	BUILDER_CONFIGS = [
	BioASQTaskC2017BigBioConfig(
	name="bioasq_task_c_2017_source",
	version=SOURCE_VERSION,
	description="bioasq_task_c_2017 source schema",
	schema="source",
	subset_id="bioasq_task_c_2017",
	),
	BioASQTaskC2017BigBioConfig(
	name="bioasq_task_c_2017_bigbio_text",
	version=BIGBIO_VERSION,
	description="bioasq_task_c_2017 BigBio schema",
	schema="bigbio_text",
	subset_id="bioasq_task_c_2017",
	),
	]

	BUILDER_CONFIG_CLASS = BioASQTaskC2017BigBioConfig

	def _info(self) -> datasets.DatasetInfo:

	# BioASQ Task C source schema
	if self.config.schema == "source":
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"document_id": datasets.Value("string"),
	"pmid": datasets.Value("string"),
	"pmcid": datasets.Value("string"),
	"grantList": [
	{
	"agency": datasets.Value("string"),
	}
	],
	"text": datasets.Value("string"),
	}
	)

	# For example bigbio_kb, bigbio_t2t
	elif self.config.schema == "bigbio_text":
	features = text_features

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=str(_LICENSE),
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:

	if self.config.data_dir is None:
	raise ValueError(
	"This is a local dataset. Please pass the data_dir kwarg to load_dataset."
	)
	else:
	data_dir = self.config.data_dir

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "taskCTrainingData2017.json"),
	"filespath": os.path.join(data_dir, "Train_Text"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "taskc_golden2.json"),
	"filespath": os.path.join(data_dir, "Final_Text"),
	"split": "test",
	},
	),
	]

	def _generate_examples(self, filepath, filespath, split):

	with open(filepath) as f:
	task_data = json.load(f)

	if self.config.schema == "source":
	for article in task_data["articles"]:

	with open(filespath + "/" + article["pmcid"] + ".xml") as f:
	text = f.read()
	pmid = article["pmid"]

	yield pmid, {
	"text": text, # articles[pmid],
	"document_id": pmid,
	"id": str(pmid),
	"pmid": pmid,
	"pmcid": article["pmcid"],
	"grantList": [
	{"agency": grant["agency"]} for grant in article["grantList"]
	],
	}

	elif self.config.schema == "bigbio_text":

	for article in task_data["articles"]:

	with open(filespath + "/" + article["pmcid"] + ".xml") as f:
	xml_string = f.read()

	try:
	article_body = ET.fromstring(xml_string).find("./article/body")
	except ET.ParseError:

	# PubMed XML might not contain namespace which results in parse error, add manually
	xml_string = xml_string.replace(
	"</pmc-articleset>",
	# xlink namespace
	'<article xmlns:xlink="http://www.w3.org/1999/xlink"' # mml namespace
	' xmlns:mml="http://www.w3.org/1998/Math/MathML"'
	' article-type="research-article">',
	)
	xml_string = xml_string + "</article></pmc-articleset>"
	article_body = ET.fromstring(xml_string).find("./article/body")

	text = ET.tostring(article_body, encoding="utf8", method="text")

	yield article["pmid"], {
	"text": text,
	"id": str(article["pmid"]),
	"document_id": article["pmid"],
	"labels": [grant["agency"] for grant in article["grantList"]],
	}