|
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" |
|
|
|
_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 = [ |
|
|
|
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) |
|
] + [ |
|
|
|
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) |
|
] |
|
|
|
|
|
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): |
|
|
|
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}") |
|
|
