Datasets:

bigbio
/

verspoor_2013

Languages:

English

Multilinguality:

monolingual

License:

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verspoor_2013 / verspoor_2013.py

gabrielaltay

fix bigbio imports

ba68baa over 1 year ago

raw history blame contribute delete

No virus

10.8 kB

	# coding=utf-8
	# Copyright 2022 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 contains annotations for a small corpus of full text journal
	publications on the subject of inherited colorectal cancer. It is suitable for
	Named Entity Recognition and Relation Extraction tasks. It uses the Variome
	Annotation Schema, a schema that aims to capture the core concepts and
	relations relevant to cataloguing and interpreting human genetic variation and
	its relationship to disease, as described in the published literature. The
	schema was inspired by the needs of the database curators of the International
	Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is
	intended to have application to genetic variation information in a range of
	diseases.
	"""

	from pathlib import Path
	from shutil import rmtree
	from typing import Dict, List, Tuple

	import datasets

	from .bigbiohub import kb_features
	from .bigbiohub import BigBioConfig
	from .bigbiohub import Tasks
	from .bigbiohub import parse_brat_file
	from .bigbiohub import brat_parse_to_bigbio_kb

	_LANGUAGES = ['English']
	_PUBMED = True
	_LOCAL = False
	_CITATION = """\
	@article{verspoor2013annotating,
	title = {Annotating the biomedical literature for the human variome},
	author = {
	Verspoor, Karin and Jimeno Yepes, Antonio and Cavedon, Lawrence and
	McIntosh, Tara and Herten-Crabb, Asha and Thomas, Zo{"e} and Plazzer,
	John-Paul
	},
	year = 2013,
	journal = {Database},
	publisher = {Oxford Academic},
	volume = 2013
	}
	"""

	_DATASETNAME = "verspoor_2013"
	_DISPLAYNAME = "Verspoor 2013"

	_DESCRIPTION = """\
	This dataset contains annotations for a small corpus of full text journal \
	publications on the subject of inherited colorectal cancer. It is suitable for \
	Named Entity Recognition and Relation Extraction tasks. It uses the Variome \
	Annotation Schema, a schema that aims to capture the core concepts and \
	relations relevant to cataloguing and interpreting human genetic variation and \
	its relationship to disease, as described in the published literature. The \
	schema was inspired by the needs of the database curators of the International \
	Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is \
	intended to have application to genetic variation information in a range of \
	diseases.
	"""


	_HOMEPAGE = "NA"

	_LICENSE = 'License information unavailable'

	_URLS = ["http://github.com/rockt/SETH/zipball/master/"]

	_SUPPORTED_TASKS = [
	Tasks.NAMED_ENTITY_RECOGNITION,
	Tasks.RELATION_EXTRACTION,
	]

	_SOURCE_VERSION = "1.0.0"

	_BIGBIO_VERSION = "1.0.0"


	class Verspoor2013Dataset(datasets.GeneratorBasedBuilder):
	"""\
	This dataset contains annotations for a small corpus of full text journal publications
	on the subject of inherited colorectal cancer. It is suitable for Named Entity Recognition and
	Relation Extraction tasks. It uses the Variome Annotation Schema, a schema that aims to
	capture the core concepts and relations relevant to cataloguing and interpreting human
	genetic variation and its relationship to disease, as described in the published literature.
	The schema was inspired by the needs of the database curators of the International Society
	for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is intended to have
	application to genetic variation information in a range of diseases.
	"""

	SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
	BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

	BUILDER_CONFIGS = [
	BigBioConfig(
	name="verspoor_2013_source",
	version=SOURCE_VERSION,
	description="verspoor_2013 source schema",
	schema="source",
	subset_id="verspoor_2013",
	),
	BigBioConfig(
	name="verspoor_2013_bigbio_kb",
	version=BIGBIO_VERSION,
	description="verspoor_2013 BigBio schema",
	schema="bigbio_kb",
	subset_id="verspoor_2013",
	),
	]

	DEFAULT_CONFIG_NAME = "verspoor_2013_source"

	def _info(self) -> datasets.DatasetInfo:

	if self.config.schema == "source":
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"document_id": datasets.Value("string"),
	"text": datasets.Value("string"),
	"text_bound_annotations": [ # T line in brat, e.g. type or event trigger
	{
	"offsets": datasets.Sequence([datasets.Value("int32")]),
	"text": datasets.Sequence(datasets.Value("string")),
	"type": datasets.Value("string"),
	"id": datasets.Value("string"),
	}
	],
	"events": [ # E line in brat
	{
	"trigger": datasets.Value(
	"string"
	), # refers to the text_bound_annotation of the trigger,
	"id": datasets.Value("string"),
	"type": datasets.Value("string"),
	"arguments": datasets.Sequence(
	{
	"role": datasets.Value("string"),
	"ref_id": datasets.Value("string"),
	}
	),
	}
	],
	"relations": [ # R line in brat
	{
	"id": datasets.Value("string"),
	"head": {
	"ref_id": datasets.Value("string"),
	"role": datasets.Value("string"),
	},
	"tail": {
	"ref_id": datasets.Value("string"),
	"role": datasets.Value("string"),
	},
	"type": datasets.Value("string"),
	}
	],
	"equivalences": [ # Equiv line in brat
	{
	"id": datasets.Value("string"),
	"ref_ids": datasets.Sequence(datasets.Value("string")),
	}
	],
	"attributes": [ # M or A lines in brat
	{
	"id": datasets.Value("string"),
	"type": datasets.Value("string"),
	"ref_id": datasets.Value("string"),
	"value": datasets.Value("string"),
	}
	],
	"normalizations": [ # N lines in brat
	{
	"id": datasets.Value("string"),
	"type": datasets.Value("string"),
	"ref_id": datasets.Value("string"),
	"resource_name": datasets.Value(
	"string"
	), # Name of the resource, e.g. "Wikipedia"
	"cuid": datasets.Value(
	"string"
	), # ID in the resource, e.g. 534366
	"text": datasets.Value(
	"string"
	), # Human readable description/name of the entity, e.g. "Barack Obama"
	}
	],
	},
	)

	elif self.config.schema == "bigbio_kb":
	features = kb_features

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

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

	# Download gets entire git repo containing unused data from other datasets
	repo_dir = Path(dl_manager.download_and_extract(_URLS[0]))
	data_dir = repo_dir / "data"
	data_dir.mkdir(exist_ok=True)

	# Find the relevant files from Verspor2013 and move them to a new directory
	verspoor_files = repo_dir.glob("//Verspoor2013//")
	for file in verspoor_files:
	if file.is_file() and "readme" not in str(file):
	file.rename(data_dir / file.name)

	# Delete all unused files and directories from the original download
	for x in repo_dir.glob("[!data]*"):
	if x.is_file():
	x.unlink()
	elif x.is_dir():
	rmtree(x)

	data_files = {"text_files": list(data_dir.glob("*.txt"))}

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# Whatever you put in gen_kwargs will be passed to _generate_examples
	gen_kwargs={
	"data_files": data_files,
	"split": "train",
	},
	)
	]

	def _generate_examples(self, data_files, split: str) -> Tuple[int, Dict]:
	"""Yields examples as (key, example) tuples."""

	if self.config.schema == "source":
	txt_files = data_files["text_files"]
	for guid, txt_file in enumerate(txt_files):
	example = parse_brat_file(txt_file)
	example["id"] = str(guid)
	yield guid, example

	elif self.config.schema == "bigbio_kb":
	txt_files = data_files["text_files"]
	for guid, txt_file in enumerate(txt_files):
	example = brat_parse_to_bigbio_kb(
	parse_brat_file(txt_file)
	)
	example["id"] = str(guid)
	yield guid, example
	else:
	raise ValueError(f"Invalid config: {self.config.name}")