# 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 IEPA benchmark PPI corpus is designed for relation extraction. It was created from 303 PubMed abstracts, each of which contains a specific pair of co-occurring chemicals. """ # Comment from Author # BigBio schema fixes offsets of entities to an offset where 0 is the start of the document. # (In source offsets of entities start from 0 for each passage in document) # Offsets of entities in source remain unchanged. import xml.dom.minidom as xml from typing import Dict, List, Tuple import datasets from .bigbiohub import kb_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @ARTICLE{ding2001mining, title = "Mining {MEDLINE}: abstracts, sentences, or phrases?", author = "Ding, J and Berleant, D and Nettleton, D and Wurtele, E", journal = "Pac Symp Biocomput", pages = "326--337", year = 2002, address = "United States", language = "en" } """ _DATASETNAME = "iepa" _DISPLAYNAME = "IEPA" _DESCRIPTION = """\ The IEPA benchmark PPI corpus is designed for relation extraction. It was \ created from 303 PubMed abstracts, each of which contains a specific pair of \ co-occurring chemicals. """ _HOMEPAGE = "http://psb.stanford.edu/psb-online/proceedings/psb02/abstracts/p326.html" _LICENSE = 'License information unavailable' _URLS = { _DATASETNAME: { "train": "https://raw.githubusercontent.com/metalrt/ppi-dataset/master/csv_output/IEPA-train.xml", "test": "https://raw.githubusercontent.com/metalrt/ppi-dataset/master/csv_output/IEPA-test.xml", }, } _SUPPORTED_TASKS = [Tasks.RELATION_EXTRACTION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class IepaDataset(datasets.GeneratorBasedBuilder): """The IEPA benchmark PPI corpus is designed for relation extraction.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="iepa_source", version=SOURCE_VERSION, description="IEPA source schema", schema="source", subset_id="iepa", ), BigBioConfig( name="iepa_bigbio_kb", version=BIGBIO_VERSION, description="IEPA BigBio schema", schema="bigbio_kb", subset_id="iepa", ), ] DEFAULT_CONFIG_NAME = "iepa_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "PMID": datasets.Value("string"), "origID": datasets.Value("string"), "sentences": [ { "id": datasets.Value("string"), "origID": datasets.Value("string"), "offsets": [datasets.Value("int32")], "text": datasets.Value("string"), "entities": [ { "id": datasets.Value("string"), "origID": datasets.Value("string"), "text": datasets.Value("string"), "offsets": [datasets.Value("int32")], } ], "interactions": [ { "id": datasets.Value("string"), "e1": datasets.Value("string"), "e2": datasets.Value("string"), "type": 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) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_dir["train"], }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_dir["test"], }, ), ] def _generate_examples(self, filepath) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" collection = xml.parse(filepath).documentElement if self.config.schema == "source": for id, document in self._parse_documents(collection): yield id, document elif self.config.schema == "bigbio_kb": for id, document in self._parse_documents(collection): yield id, self._source_to_bigbio(document) def _parse_documents(self, collection): for document in collection.getElementsByTagName("document"): pmid_doc = self._strict_get_attribute(document, "PMID") id_doc = self._strict_get_attribute(document, "id") origID_doc = self._strict_get_attribute(document, "origID") sentences = [] for sentence in document.getElementsByTagName("sentence"): offsets_sent = self._strict_get_attribute(sentence, "charOffset").split( "-" ) id_sent = self._strict_get_attribute(sentence, "id") origID_sent = self._strict_get_attribute(sentence, "origID") text_sent = self._strict_get_attribute(sentence, "text") entities = [] for entity in sentence.getElementsByTagName("entity"): id_ent = self._strict_get_attribute(entity, "id") origID_ent = self._strict_get_attribute(entity, "origID") text_ent = self._strict_get_attribute(entity, "text") offsets_ent = self._strict_get_attribute( entity, "charOffset" ).split("-") entities.append( { "id": id_ent, "origID": origID_ent, "text": text_ent, "offsets": offsets_ent, } ) interactions = [] for interaction in sentence.getElementsByTagName("interaction"): id_int = self._strict_get_attribute(interaction, "id") e1_int = self._strict_get_attribute(interaction, "e1") e2_int = self._strict_get_attribute(interaction, "e2") type_int = self._strict_get_attribute(interaction, "type") interactions.append( {"id": id_int, "e1": e1_int, "e2": e2_int, "type": type_int} ) sentences.append( { "id": id_sent, "origID": origID_sent, "offsets": offsets_sent, "text": text_sent, "entities": entities, "interactions": interactions, } ) yield id_doc, { "id": id_doc, "PMID": pmid_doc, "origID": origID_doc, "sentences": sentences, } def _strict_get_attribute(self, element, key): if element.hasAttribute(key): return element.getAttribute(key) else: raise ValueError(f"No such key exists in element: {element.tagName} {key}") def _source_to_bigbio(self, document_): document = {} document["id"] = document_["id"] document["document_id"] = document_["PMID"] passages = [] entities = [] relations = [] for sentence_ in document_["sentences"]: for entity_ in sentence_["entities"]: entity_["type"] = "" entity_["normalized"] = [] entity_.pop("origID") entity_["text"] = [entity_["text"]] entity_["offsets"] = [ [ int(sentence_["offsets"][0]) + int(entity_["offsets"][0]), int(sentence_["offsets"][0]) + int(entity_["offsets"][1]), ] ] entities.append(entity_) for relation_ in sentence_["interactions"]: relation_["arg1_id"] = relation_.pop("e1") relation_["arg2_id"] = relation_.pop("e2") relation_["normalized"] = [] relations.append(relation_) sentence_.pop("entities") sentence_.pop("interactions") sentence_.pop("origID") sentence_["type"] = "" sentence_["text"] = [sentence_["text"]] sentence_["offsets"] = [sentence_["offsets"]] passages.append(sentence_) document["passages"] = passages document["entities"] = entities document["relations"] = relations document["events"] = [] document["coreferences"] = [] return document