Datasets:

bigbio
/

gad

	from pathlib import Path
	from typing import List

	import datasets
	import pandas as pd

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


	_SOURCE_VIEW_NAME = "source"
	_UNIFIED_VIEW_NAME = "bigbio"

	_LANGUAGES = ["English"]
	_PUBMED = True
	_LOCAL = False
	_CITATION = """\
	@article{Bravo2015,
	doi = {10.1186/s12859-015-0472-9},
	url = {https://doi.org/10.1186/s12859-015-0472-9},
	year = {2015},
	month = feb,
	publisher = {Springer Science and Business Media {LLC}},
	volume = {16},
	number = {1},
	author = {{\`{A}}lex Bravo and Janet Pi{\~{n}}ero and N{\'{u}}ria Queralt-Rosinach and Michael Rautschka and Laura I Furlong},
	title = {Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research},
	journal = {{BMC} Bioinformatics}
	}
	"""

	_DESCRIPTION = """\
	A corpus identifying associations between genes and diseases by a semi-automatic
	annotation procedure based on the Genetic Association Database
	"""

	_DATASETNAME = "gad"
	_DISPLAYNAME = "GAD"

	_HOMEPAGE = "https://github.com/dmis-lab/biobert" # This data source is used by the BLURB benchmark

	_LICENSE = "CC_BY_4p0"

	_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]

	_SOURCE_VERSION = "1.0.0"
	_BIGBIO_VERSION = "1.0.0"


	class GAD(datasets.GeneratorBasedBuilder):
	"""GAD is a weakly labeled dataset for Entity Relations (REL) task which is treated as a sentence classification task."""

	BUILDER_CONFIGS = [
	# 10-fold source schema
	BigBioConfig(
	name=f"gad_fold{i}_source",
	version=datasets.Version(_SOURCE_VERSION),
	description="GAD source schema",
	schema="source",
	subset_id=f"gad_fold{i}",
	)
	for i in range(10)
	] + [
	# 10-fold bigbio schema
	BigBioConfig(
	name=f"gad_fold{i}_bigbio_text",
	version=datasets.Version(_BIGBIO_VERSION),
	description="GAD BigBio schema",
	schema="bigbio_text",
	subset_id=f"gad_fold{i}",
	)
	for i in range(10)
	]

	# BLURB Benchmark config https://microsoft.github.io/BLURB/
	BUILDER_CONFIGS.append(
	BigBioConfig(
	name=f"gad_blurb_bigbio_text",
	version=datasets.Version(_BIGBIO_VERSION),
	description=f"GAD BLURB benchmark in simplified BigBio schema",
	schema="bigbio_text",
	subset_id=f"gad_blurb",
	)
	)

	DEFAULT_CONFIG_NAME = "gad_fold0_source"

	def _info(self):
	if self.config.schema == "source":
	features = datasets.Features(
	{
	"index": datasets.Value("string"),
	"sentence": datasets.Value("string"),
	"label": datasets.Value("int32"),
	}
	)
	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: datasets.DownloadManager
	) -> List[datasets.SplitGenerator]:

	data_dir = Path(dl_manager.download_and_extract("data/REdata.zip"))

	if "blurb" in self.config.name:
	data_files = {
	"train": data_dir / "GAD" / "blurb" / "train.tsv",
	"validation": data_dir / "GAD" / "blurb" / "dev.tsv",
	"test": data_dir / "GAD" / "blurb" / "test.tsv",
	}

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": data_files["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": data_files["validation"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": data_files["test"]},
	),
	]

	else:

	fold_id = int(self.config.subset_id.split("_fold")[1][0]) + 1

	data_files = {
	"train": data_dir / "GAD" / str(fold_id) / "train.tsv",
	"test": data_dir / "GAD" / str(fold_id) / "test.tsv",
	}

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

	def _generate_examples(self, filepath: Path):
	# train files in non-blurb splits don't have headers for some reason
	if "train.tsv" in str(filepath) and "blurb" not in self.config.name:
	df = pd.read_csv(filepath, sep="\t", header=None).reset_index()
	else:
	df = pd.read_csv(filepath, sep="\t")
	df.columns = ["id", "sentence", "label"]

	if self.config.schema == "source":
	for id, row in enumerate(df.itertuples()):
	ex = {
	"index": row.id,
	"sentence": row.sentence,
	"label": int(row.label),
	}
	yield id, ex
	elif self.config.schema == "bigbio_text":
	for id, row in enumerate(df.itertuples()):
	ex = {
	"id": id,
	"document_id": row.id,
	"text": row.sentence,
	"labels": [str(row.label)],
	}
	yield id, ex
	else:
	raise ValueError(f"Invalid config: {self.config.name}")