# 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 dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator. """ import os from typing import Dict, List, Tuple import datasets import pandas as pd from .bigbiohub import qa_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @inproceedings{deyoung-etal-2020-evidence, title = "Evidence Inference 2.0: More Data, Better Models", author = "DeYoung, Jay and Lehman, Eric and Nye, Benjamin and Marshall, Iain and Wallace, Byron C.", booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.bionlp-1.13", pages = "123--132", } """ _DATASETNAME = "evidence_inference" _DISPLAYNAME = "Evidence Inference 2.0" _DESCRIPTION = """\ The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator. """ _HOMEPAGE = "https://github.com/jayded/evidence-inference" _LICENSE = 'MIT License' _URLS = { _DATASETNAME: "http://evidence-inference.ebm-nlp.com/v2.0.tar.gz", } _SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING] _SOURCE_VERSION = "2.0.0" _BIGBIO_VERSION = "1.0.0" QA_CHOICES = [ "significantly increased", "no significant difference", "significantly decreased", ] # Some examples are removed due to comments on the dataset's github page # https://github.com/jayded/evidence-inference/blob/master/annotations/README.md#caveat INCORRECT_PROMPT_IDS = set([ 911, 912, 1262, 1261, 3044, 3248, 3111, 3620, 4308, 4490, 4491, 4324, 4325, 4492, 4824, 5000, 5001, 5002, 5046, 5047, 4948, 5639, 5710, 5752, 5775, 5782, 5841, 5843, 5861, 5862, 5863, 5964, 5965, 5966, 5975, 4807, 5776, 5777, 5778, 5779, 5780, 5781, 6034, 6065, 6066, 6666, 6667, 6668, 6669, 7040, 7042, 7944, 8590, 8605, 8606, 8639, 8640, 8745, 8747, 8749, 8877, 8878, 8593, 8631, 8635, 8884, 8886, 8773, 10032, 10035, 8876, 8875, 8885, 8917, 8921, 8118, 10885, 10886, 10887, 10888, 10889, 10890 ]) QUESTIONABLE_PROMPT_IDS = set([ 7811, 7812, 7813, 7814, 7815, 8197, 8198, 8199, 8200, 8201, 9429, 9430, 9431, 8536, 9432 ]) SOMEWHAT_MALFORMED_PROMPT_IDS = set([ 3514, 346, 5037, 4715, 8767, 9295, 9297, 8870, 9862 ]) SKIP_PROMPT_IDS = INCORRECT_PROMPT_IDS | QUESTIONABLE_PROMPT_IDS | SOMEWHAT_MALFORMED_PROMPT_IDS class EvidenceInferenceDataset(datasets.GeneratorBasedBuilder): f"""{_DESCRIPTION}""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="evidence-inference_source", version=SOURCE_VERSION, description="evidence-inference source schema", schema="source", subset_id="evidence-inference", ), BigBioConfig( name="evidence-inference_bigbio_qa", version=BIGBIO_VERSION, description="evidence-inference BigBio schema", schema="bigbio_qa", subset_id="evidence-inference", ), ] DEFAULT_CONFIG_NAME = "evidence-inference_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("int64"), "prompt_id": datasets.Value("int64"), "pmcid": datasets.Value("int64"), "label": datasets.Value("string"), "evidence": datasets.Value("string"), "intervention": datasets.Value("string"), "comparator": datasets.Value("string"), "outcome": datasets.Value("string"), } ) elif self.config.schema == "bigbio_qa": features = qa_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={ "filepaths": [ os.path.join(data_dir, "annotations_merged.csv"), os.path.join(data_dir, "prompts_merged.csv"), ], "datapath": os.path.join(data_dir, "txt_files"), "split": "train", "datadir": data_dir, }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepaths": [ os.path.join(data_dir, "annotations_merged.csv"), os.path.join(data_dir, "prompts_merged.csv"), ], "datapath": os.path.join(data_dir, "txt_files"), "split": "validation", "datadir": data_dir, }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepaths": [ os.path.join(data_dir, "annotations_merged.csv"), os.path.join(data_dir, "prompts_merged.csv"), ], "datapath": os.path.join(data_dir, "txt_files"), "split": "test", "datadir": data_dir, }, ), ] def _generate_examples( self, filepaths, datapath, split, datadir ) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" with open(f"{datadir}/splits/{split}_article_ids.txt", "r") as f: ids = [int(i.strip()) for i in f.readlines()] prompts = pd.read_csv(filepaths[-1], encoding="utf8") prompts = prompts[prompts["PMCID"].isin(ids)] annotations = pd.read_csv(filepaths[0], encoding="utf8").set_index("PromptID") evidences = pd.read_csv(filepaths[0], encoding="utf8").set_index("PMCID") evidences = evidences[evidences["Evidence Start"] != -1] uid = 0 def lookup(df: pd.DataFrame, id, col) -> str: try: label = df.loc[id][col] if isinstance(label, pd.Series): return label.values[0] else: return label except KeyError: return -1 def extract_evidence(doc_id, start, end): p = f"{datapath}/PMC{doc_id}.txt" with open(p, "r") as f: return f.read()[start:end] for key, sample in prompts.iterrows(): pid = sample["PromptID"] pmcid = sample["PMCID"] label = lookup(annotations, pid, "Label") start = lookup(evidences, pmcid, "Evidence Start") end = lookup(evidences, pmcid, "Evidence End") if pid in SKIP_PROMPT_IDS: continue if label == -1: continue evidence = extract_evidence(pmcid, start, end) if self.config.schema == "source": feature_dict = { "id": uid, "pmcid": pmcid, "prompt_id": pid, "intervention": sample["Intervention"], "comparator": sample["Comparator"], "outcome": sample["Outcome"], "evidence": evidence, "label": label, } uid += 1 yield key, feature_dict elif self.config.schema == "bigbio_qa": context = evidence question = ( f"Compared to {sample['Comparator']} " f"what was the result of {sample['Intervention']} on {sample['Outcome']}?" ) feature_dict = { "id": uid, "question_id": pid, "document_id": pmcid, "question": question, "type": "multiple_choice", "choices": QA_CHOICES, "context": context, "answer": [label], } uid += 1 yield key, feature_dict