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anat_em

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anat_em / anat_em.py

gabrielaltay

update parsing imports (#2)

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	# 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.
	"""
	The extended Anatomical Entity Mention corpus (AnatEM) consists of 1212 documents
	(approx. 250,000 words) manually annotated to identify over 13,000 mentions of anatomical
	entities. Each annotation is assigned one of 12 granularity-based types such as Cellular
	component, Tissue and Organ, defined with reference to the Common Anatomy Reference Ontology
	(see https://bioportal.bioontology.org/ontologies/CARO).
	"""
	from pathlib import Path
	from typing import Dict, Iterator, 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{pyysalo2014anatomical,
	title={Anatomical entity mention recognition at literature scale},
	author={Pyysalo, Sampo and Ananiadou, Sophia},
	journal={Bioinformatics},
	volume={30},
	number={6},
	pages={868--875},
	year={2014},
	publisher={Oxford University Press}
	}
	"""

	_DATASETNAME = "anat_em"
	_DISPLAYNAME = "AnatEM"


	_DESCRIPTION = """\
	The extended Anatomical Entity Mention corpus (AnatEM) consists of 1212 \
	documents (approx. 250,000 words) manually annotated to identify over 13,000 \
	mentions of anatomical entities. Each annotation is assigned one of 12 \
	granularity-based types such as Cellular component, Tissue and Organ, defined \
	with reference to the Common Anatomy Reference Ontology.
	"""

	_HOMEPAGE = "http://nactem.ac.uk/anatomytagger/#AnatEM"

	_LICENSE = 'Creative Commons Attribution Share Alike 3.0 Unported'

	_URLS = {_DATASETNAME: "http://nactem.ac.uk/anatomytagger/AnatEM-1.0.2.tar.gz"}

	_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]

	_SOURCE_VERSION = "1.0.2"
	_BIGBIO_VERSION = "1.0.0"


	class AnatEMDataset(datasets.GeneratorBasedBuilder):
	"""The extended Anatomical Entity Mention corpus (AnatEM)"""

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

	BUILDER_CONFIGS = [
	BigBioConfig(
	name="anat_em_source",
	version=SOURCE_VERSION,
	description="AnatEM source schema",
	schema="source",
	subset_id="anat_em",
	),
	BigBioConfig(
	name="anat_em_bigbio_kb",
	version=BIGBIO_VERSION,
	description="AnatEM BigBio schema",
	schema="bigbio_kb",
	subset_id="anat_em",
	),
	]

	DEFAULT_CONFIG_NAME = "anat_em_source"

	def _info(self):
	if self.config.schema == "source":
	features = datasets.Features(
	{
	"document_id": datasets.Value("string"),
	"document_type": datasets.Value("string"), # Either PMC or PM
	"text": datasets.Value("string"),
	"text_type": datasets.Value(
	"string"
	), # Either abstract (for PM) or sec / caption (for PMC)
	"entities": [
	{
	"entity_id": datasets.Value("string"),
	"type": datasets.Value("string"),
	"offsets": datasets.Sequence([datasets.Value("int32")]),
	"text": datasets.Sequence(datasets.Value("string")),
	}
	],
	}
	)

	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):
	urls = _URLS[_DATASETNAME]
	data_dir = Path(dl_manager.download_and_extract(urls))

	standoff_dir = data_dir / "AnatEM-1.0.2" / "standoff"

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"split_dir": standoff_dir / "train"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"split_dir": standoff_dir / "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"split_dir": standoff_dir / "devel"},
	),
	]

	def _generate_examples(self, split_dir: Path) -> Iterator[Tuple[str, Dict]]:
	if self.config.name == "anat_em_source":
	for file in split_dir.iterdir():
	# Ignore hidden files and annotation files - we just consider the brat text files
	if file.name.startswith("._") or file.name.endswith(".ann"):
	continue

	# Read brat annotations for the given text file and convert example to the source format
	brat_example = parse_brat_file(file)
	source_example = self._to_source_example(file, brat_example)

	yield source_example["document_id"], source_example

	elif self.config.name == "anat_em_bigbio_kb":
	for file in split_dir.iterdir():
	# Ignore hidden files and annotation files - we just consider the brat text files
	if file.name.startswith("._") or file.name.endswith(".ann"):
	continue

	# Read brat annotations for the given text file and convert example to the BigBio-KB format
	brat_example = parse_brat_file(file)
	kb_example = brat_parse_to_bigbio_kb(brat_example)
	kb_example["id"] = kb_example["document_id"]

	# Fix text type annotation for the converted example
	_, text_type = self.get_document_type_and_text_type(file)
	kb_example["passages"][0]["type"] = text_type

	yield kb_example["id"], kb_example

	def _to_source_example(self, input_file: Path, brat_example: Dict) -> Dict:
	"""
	Converts an example extracted using the default brat parsing logic to the source format
	of the given corpus.
	"""
	document_type, text_type = self.get_document_type_and_text_type(input_file)

	source_example = {
	"document_id": brat_example["document_id"],
	"document_type": document_type,
	"text": brat_example["text"],
	"text_type": text_type,
	}

	id_prefix = brat_example["document_id"] + "_"

	source_example["entities"] = []
	for entity_annotation in brat_example["text_bound_annotations"]:
	entity_ann = entity_annotation.copy()

	entity_ann["entity_id"] = id_prefix + entity_ann["id"]
	entity_ann.pop("id")

	source_example["entities"].append(entity_ann)

	return source_example

	def get_document_type_and_text_type(self, input_file: Path) -> Tuple[str, str]:
	"""
	Extracts the document type (PubMed(PM) or PubMedCentral (PMC)) and the respective
	text type (abstract for PM and sec or caption for (PMC) from the name of the given
	file, e.g.:

	PMID-9778569.txt -> ("PM", "abstract")

	PMC-1274342-sec-02.txt -> ("PMC", "sec")

	PMC-1592597-caption-02.ann -> ("PMC", "caption")

	"""
	name_parts = str(input_file.stem).split("-")

	if name_parts[0] == "PMID":
	return "PM", "abstract"

	elif name_parts[0] == "PMC":
	return "PMC", name_parts[2]
	else:
	raise AssertionError(f"Unexpected file prefix {name_parts[0]}")