# 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