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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}")
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