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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
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