Datasets:

bigbio
/

distemist

Languages:

Spanish

Multilinguality:

monolingual

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distemist / distemist.py

gabrielaltay

upload hub_repos/distemist/distemist.py to hub from bigbio repo

fcf2be2 about 1 year ago

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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.

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

	import datasets
	import pandas as pd

	from .bigbiohub import kb_features
	from .bigbiohub import BigBioConfig
	from .bigbiohub import Tasks

	_LANGUAGES = ['Spanish']
	_PUBMED = False
	_LOCAL = False
	_CITATION = """\
	@article{miranda2022overview,
	title={Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases
	from clinical texts: results, methods, evaluation and multilingual resources},
	author={Miranda-Escalada, Antonio and Gascó, Luis and Lima-López, Salvador and Farré-Maduell,
	Eulàlia and Estrada, Darryl and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras,
	Georgios and Paliouras, Georgios and Krallinger, Martin},
	booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum.
	CEUR Workshop Proceedings},
	year={2022}
	}
	"""

	_DATASETNAME = "distemist"
	_DISPLAYNAME = "DisTEMIST"

	_DESCRIPTION = """\
	The DisTEMIST corpus is a collection of 1000 clinical cases with disease annotations linked with Snomed-CT concepts.
	All documents are released in the context of the BioASQ DisTEMIST track for CLEF 2022.
	"""

	_HOMEPAGE = "https://zenodo.org/record/7614764"

	_LICENSE = 'CC_BY_4p0'

	_URLS = {
	_DATASETNAME: "https://zenodo.org/record/7614764/files/distemist_zenodo.zip?download=1",
	}

	_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]

	_SOURCE_VERSION = "5.1.0"
	_BIGBIO_VERSION = "1.0.0"


	class DistemistDataset(datasets.GeneratorBasedBuilder):
	"""
	The DisTEMIST corpus is a collection of 1000 clinical cases with disease annotations linked with Snomed-CT
	concepts.
	"""

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

	BUILDER_CONFIGS = [
	BigBioConfig(
	name="distemist_entities_source",
	version=SOURCE_VERSION,
	description="DisTEMIST (subtrack 1: entities) source schema",
	schema="source",
	subset_id="distemist_entities",
	),
	BigBioConfig(
	name="distemist_linking_source",
	version=SOURCE_VERSION,
	description="DisTEMIST (subtrack 2: linking) source schema",
	schema="source",
	subset_id="distemist_linking",
	),
	BigBioConfig(
	name="distemist_entities_bigbio_kb",
	version=BIGBIO_VERSION,
	description="DisTEMIST (subtrack 1: entities) BigBio schema",
	schema="bigbio_kb",
	subset_id="distemist_entities",
	),
	BigBioConfig(
	name="distemist_linking_bigbio_kb",
	version=BIGBIO_VERSION,
	description="DisTEMIST (subtrack 2: linking) BigBio schema",
	schema="bigbio_kb",
	subset_id="distemist_linking",
	),
	]

	DEFAULT_CONFIG_NAME = "distemist_entities_source"

	def _info(self) -> datasets.DatasetInfo:

	if self.config.schema == "source":
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"document_id": datasets.Value("string"),
	"passages": [
	{
	"id": datasets.Value("string"),
	"type": datasets.Value("string"),
	"text": datasets.Sequence(datasets.Value("string")),
	"offsets": datasets.Sequence([datasets.Value("int32")]),
	}
	],
	"entities": [
	{
	"id": datasets.Value("string"),
	"type": datasets.Value("string"),
	"text": datasets.Sequence(datasets.Value("string")),
	"offsets": datasets.Sequence([datasets.Value("int32")]),
	"concept_codes": datasets.Sequence(datasets.Value("string")),
	"semantic_relations": 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) -> List[datasets.SplitGenerator]:
	"""Returns SplitGenerators."""
	urls = _URLS[_DATASETNAME]
	data_dir = dl_manager.download_and_extract(urls)
	base_bath = Path(data_dir) / "distemist_zenodo"
	track = self.config.subset_id.split('_')[1]

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"split": "train",
	"track": track,
	"base_bath": base_bath,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"split": "test",
	"track": track,
	"base_bath": base_bath,
	},
	),
	]

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

	tsv_files = {
	('entities', 'train'): [
	base_bath / "training" / "subtrack1_entities" / "distemist_subtrack1_training_mentions.tsv"
	],
	('entities', 'test'): [
	base_bath / "test_annotated" / "subtrack1_entities" / "distemist_subtrack1_test_mentions.tsv"
	],
	('linking', 'train'): [
	base_bath / "training" / "subtrack2_linking" / "distemist_subtrack2_training1_linking.tsv",
	base_bath / "training" / "subtrack2_linking" / "distemist_subtrack2_training2_linking.tsv",
	],
	('linking', 'test'): [
	base_bath / "test_annotated" / "subtrack2_linking" / "distemist_subtrack2_test_linking.tsv"
	],
	}
	entity_mapping_files = tsv_files[(track, split)]

	if split == "train":
	text_files_dir = base_bath / "training" / "text_files"
	elif split == "test":
	text_files_dir = base_bath / "test_annotated" / "text_files"

	entities_mapping = pd.concat([pd.read_csv(file, sep="\t") for file in entity_mapping_files])
	entity_file_names = entities_mapping["filename"].unique()

	for uid, filename in enumerate(entity_file_names):
	text_file = text_files_dir / f"{filename}.txt"

	doc_text = text_file.read_text(encoding='utf8')
	# doc_text = doc_text.replace("\n", "")

	entities_df: pd.DataFrame = entities_mapping[entities_mapping["filename"] == filename]

	example = {
	"id": f"{uid}",
	"document_id": filename,
	"passages": [
	{
	"id": f"{uid}_{filename}_passage",
	"type": "clinical_case",
	"text": [doc_text],
	"offsets": [[0, len(doc_text)]],
	}
	],
	}
	if self.config.schema == "bigbio_kb":
	example["events"] = []
	example["coreferences"] = []
	example["relations"] = []

	entities = []
	for row in entities_df.itertuples(name="Entity"):
	entity = {
	"id": f"{uid}_{row.filename}_{row.Index}_entity_id_{row.mark}",
	"type": row.label,
	"text": [row.span],
	"offsets": [[row.off0, row.off1]],
	}
	if self.config.schema == "source":
	entity["concept_codes"] = []
	entity["semantic_relations"] = []
	if self.config.subset_id == "distemist_linking":
	entity["concept_codes"] = row.code.split("+")
	entity["semantic_relations"] = row.semantic_rel.split("+")

	elif self.config.schema == "bigbio_kb":
	if self.config.subset_id == "distemist_linking":
	entity["normalized"] = [
	{"db_id": code, "db_name": "SNOMED_CT"} for code in row.code.split("+")
	]
	else:
	entity["normalized"] = []

	entities.append(entity)

	example["entities"] = entities
	yield uid, example