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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.
"""
This dataset contains annotations for a small corpus of full text journal
publications on the subject of inherited colorectal cancer. It is suitable for
Named Entity Recognition and Relation Extraction tasks. It uses the Variome
Annotation Schema, a schema that aims to capture the core concepts and
relations relevant to cataloguing and interpreting human genetic variation and
its relationship to disease, as described in the published literature. The
schema was inspired by the needs of the database curators of the International
Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is
intended to have application to genetic variation information in a range of
diseases.
"""
from pathlib import Path
from shutil import rmtree
from typing import Dict, List, 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{verspoor2013annotating,
title = {Annotating the biomedical literature for the human variome},
author = {
Verspoor, Karin and Jimeno Yepes, Antonio and Cavedon, Lawrence and
McIntosh, Tara and Herten-Crabb, Asha and Thomas, Zo{"e} and Plazzer,
John-Paul
},
year = 2013,
journal = {Database},
publisher = {Oxford Academic},
volume = 2013
}
"""
_DATASETNAME = "verspoor_2013"
_DISPLAYNAME = "Verspoor 2013"
_DESCRIPTION = """\
This dataset contains annotations for a small corpus of full text journal \
publications on the subject of inherited colorectal cancer. It is suitable for \
Named Entity Recognition and Relation Extraction tasks. It uses the Variome \
Annotation Schema, a schema that aims to capture the core concepts and \
relations relevant to cataloguing and interpreting human genetic variation and \
its relationship to disease, as described in the published literature. The \
schema was inspired by the needs of the database curators of the International \
Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is \
intended to have application to genetic variation information in a range of \
diseases.
"""
_HOMEPAGE = "NA"
_LICENSE = 'License information unavailable'
_URLS = ["http://github.com/rockt/SETH/zipball/master/"]
_SUPPORTED_TASKS = [
Tasks.NAMED_ENTITY_RECOGNITION,
Tasks.RELATION_EXTRACTION,
]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class Verspoor2013Dataset(datasets.GeneratorBasedBuilder):
"""\
This dataset contains annotations for a small corpus of full text journal publications
on the subject of inherited colorectal cancer. It is suitable for Named Entity Recognition and
Relation Extraction tasks. It uses the Variome Annotation Schema, a schema that aims to
capture the core concepts and relations relevant to cataloguing and interpreting human
genetic variation and its relationship to disease, as described in the published literature.
The schema was inspired by the needs of the database curators of the International Society
for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is intended to have
application to genetic variation information in a range of diseases.
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="verspoor_2013_source",
version=SOURCE_VERSION,
description="verspoor_2013 source schema",
schema="source",
subset_id="verspoor_2013",
),
BigBioConfig(
name="verspoor_2013_bigbio_kb",
version=BIGBIO_VERSION,
description="verspoor_2013 BigBio schema",
schema="bigbio_kb",
subset_id="verspoor_2013",
),
]
DEFAULT_CONFIG_NAME = "verspoor_2013_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"text": datasets.Value("string"),
"text_bound_annotations": [ # T line in brat, e.g. type or event trigger
{
"offsets": datasets.Sequence([datasets.Value("int32")]),
"text": datasets.Sequence(datasets.Value("string")),
"type": datasets.Value("string"),
"id": datasets.Value("string"),
}
],
"events": [ # E line in brat
{
"trigger": datasets.Value(
"string"
), # refers to the text_bound_annotation of the trigger,
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"arguments": datasets.Sequence(
{
"role": datasets.Value("string"),
"ref_id": datasets.Value("string"),
}
),
}
],
"relations": [ # R line in brat
{
"id": datasets.Value("string"),
"head": {
"ref_id": datasets.Value("string"),
"role": datasets.Value("string"),
},
"tail": {
"ref_id": datasets.Value("string"),
"role": datasets.Value("string"),
},
"type": datasets.Value("string"),
}
],
"equivalences": [ # Equiv line in brat
{
"id": datasets.Value("string"),
"ref_ids": datasets.Sequence(datasets.Value("string")),
}
],
"attributes": [ # M or A lines in brat
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"ref_id": datasets.Value("string"),
"value": datasets.Value("string"),
}
],
"normalizations": [ # N lines in brat
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"ref_id": datasets.Value("string"),
"resource_name": datasets.Value(
"string"
), # Name of the resource, e.g. "Wikipedia"
"cuid": datasets.Value(
"string"
), # ID in the resource, e.g. 534366
"text": datasets.Value(
"string"
), # Human readable description/name of the entity, e.g. "Barack Obama"
}
],
},
)
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."""
# Download gets entire git repo containing unused data from other datasets
repo_dir = Path(dl_manager.download_and_extract(_URLS[0]))
data_dir = repo_dir / "data"
data_dir.mkdir(exist_ok=True)
# Find the relevant files from Verspor2013 and move them to a new directory
verspoor_files = repo_dir.glob("*/*/*Verspoor2013/**/*")
for file in verspoor_files:
if file.is_file() and "readme" not in str(file):
file.rename(data_dir / file.name)
# Delete all unused files and directories from the original download
for x in repo_dir.glob("[!data]*"):
if x.is_file():
x.unlink()
elif x.is_dir():
rmtree(x)
data_files = {"text_files": list(data_dir.glob("*.txt"))}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# Whatever you put in gen_kwargs will be passed to _generate_examples
gen_kwargs={
"data_files": data_files,
"split": "train",
},
)
]
def _generate_examples(self, data_files, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
if self.config.schema == "source":
txt_files = data_files["text_files"]
for guid, txt_file in enumerate(txt_files):
example = parse_brat_file(txt_file)
example["id"] = str(guid)
yield guid, example
elif self.config.schema == "bigbio_kb":
txt_files = data_files["text_files"]
for guid, txt_file in enumerate(txt_files):
example = brat_parse_to_bigbio_kb(
parse_brat_file(txt_file)
)
example["id"] = str(guid)
yield guid, example
else:
raise ValueError(f"Invalid config: {self.config.name}")
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