# 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. """ The DrugProt corpus consists of a) expert-labelled chemical and gene mentions, and (b) all binary relationships between them corresponding to a specific set of biologically relevant relation types. The corpus was introduced in context of the BioCreative VII Track 1 (Text mining drug and chemical-protein interactions). For further information see: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/ """ import collections from pathlib import Path from typing import Dict, Iterator, Tuple import datasets from .bigbiohub import kb_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @inproceedings{miranda2021overview, title={Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of \ drug-gene/protein relations}, author={Miranda, Antonio and Mehryary, Farrokh and Luoma, Jouni and Pyysalo, Sampo and Valencia, Alfonso \ and Krallinger, Martin}, booktitle={Proceedings of the seventh BioCreative challenge evaluation workshop}, year={2021} } """ _DATASETNAME = "drugprot" _DISPLAYNAME = "DrugProt" _DESCRIPTION = """\ The DrugProt corpus consists of a) expert-labelled chemical and gene mentions, and (b) all binary relationships \ between them corresponding to a specific set of biologically relevant relation types. """ _HOMEPAGE = "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/" _LICENSE = 'Creative Commons Attribution 4.0 International' _URLS = {_DATASETNAME: "https://zenodo.org/record/5119892/files/drugprot-training-development-test-background.zip?download=1"} _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION] _SOURCE_VERSION = "1.0.2" _BIGBIO_VERSION = "1.0.0" class DrugProtDataset(datasets.GeneratorBasedBuilder): """ The DrugProt corpus consists of a) expert-labelled chemical and gene mentions, and \ (b) all binary relationships between them. """ SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="drugprot_source", version=SOURCE_VERSION, description="DrugProt source schema", schema="source", subset_id="drugprot", ), BigBioConfig( name="drugprot_bigbio_kb", version=BIGBIO_VERSION, description="DrugProt BigBio schema", schema="bigbio_kb", subset_id="drugprot", ), ] DEFAULT_CONFIG_NAME = "drugprot_source" def _info(self): if self.config.schema == "source": features = datasets.Features( { "document_id": datasets.Value("string"), "title": datasets.Value("string"), "abstract": datasets.Value("string"), "text": datasets.Value("string"), "entities": [ { "id": datasets.Value("string"), "type": datasets.Value("string"), "text": datasets.Value("string"), "offset": datasets.Sequence(datasets.Value("int32")), } ], "relations": [ { "id": datasets.Value("string"), "type": datasets.Value("string"), "arg1_id": datasets.Value("string"), "arg2_id": 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): urls = _URLS[_DATASETNAME] data_dir = Path(dl_manager.download_and_extract(urls)) data_dir = data_dir / "drugprot-gs-training-development" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"data_dir": data_dir, "split": "training"}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"data_dir": data_dir, "split": "development"}, ), ] def _generate_examples(self, data_dir: Path, split: str) -> Iterator[Tuple[str, Dict]]: if self.config.name == "drugprot_source": documents = self._read_source_examples(data_dir, split) for document_id, document in documents.items(): yield document_id, document elif self.config.name == "drugprot_bigbio_kb": documents = self._read_source_examples(data_dir, split) for document_id, document in documents.items(): yield document_id, self._transform_source_to_kb(document) def _read_source_examples(self, input_dir: Path, split: str) -> Dict: """ """ split_dir = input_dir / split abstracts_file = split_dir / f"drugprot_{split}_abstracs.tsv" entities_file = split_dir / f"drugprot_{split}_entities.tsv" relations_file = split_dir / f"drugprot_{split}_relations.tsv" document_to_entities = collections.defaultdict(list) for line in entities_file.read_text().splitlines(): columns = line.split("\t") document_id = columns[0] document_to_entities[document_id].append( { "id": document_id + "_" + columns[1], "type": columns[2], "offset": [columns[3], columns[4]], "text": columns[5], } ) document_to_relations = collections.defaultdict(list) for line in relations_file.read_text().splitlines(): columns = line.split("\t") document_id = columns[0] document_relations = document_to_relations[document_id] document_relations.append( { "id": document_id + "_" + str(len(document_relations)), "type": columns[1], "arg1_id": document_id + "_" + columns[2][5:], "arg2_id": document_id + "_" + columns[3][5:], } ) document_to_source = {} for line in abstracts_file.read_text().splitlines(): document_id, title, abstract = line.split("\t") document_to_source[document_id] = { "document_id": document_id, "title": title, "abstract": abstract, "text": " ".join([title, abstract]), "entities": document_to_entities[document_id], "relations": document_to_relations[document_id], } return document_to_source def _transform_source_to_kb(self, source_document: Dict) -> Dict: document_id = source_document["document_id"] offset = 0 passages = [] for text_field in ["title", "abstract"]: text = source_document[text_field] passages.append( { "id": document_id + "_" + text_field, "type": text_field, "text": [text], "offsets": [[offset, offset + len(text)]], } ) offset += len(text) + 1 entities = [ { "id": entity["id"], "type": entity["type"], "text": [entity["text"]], "offsets": [entity["offset"]], "normalized": [], } for entity in source_document["entities"] ] relations = [ { "id": relation["id"], "type": relation["type"], "arg1_id": relation["arg1_id"], "arg2_id": relation["arg2_id"], "normalized": [], } for relation in source_document["relations"] ] return { "id": document_id, "document_id": document_id, "passages": passages, "entities": entities, "relations": relations, "events": [], "coreferences": [], }