import json import pandas as pd from Bio import Entrez from retry import retry from tqdm import tqdm import dask.dataframe as dd # provided your NIH credentials # read from .json file with open("credentials.json") as f: credentials = json.load(f) Entrez.email = credentials["email"] Entrez.api_key = credentials["api_key"] # change output file names here if necessary RAW_EVALUATION_DATASET = "./raw_data/training11b.json" PATH_TO_PASSAGE_DATASET = "./data/passages.parquet" PATH_TO_EVALUATION_DATASET = "./data/test.parquet" # only use questions that have at most MAX_PASSAGES passages to control the size of the dataset # set to None to use all questions MAX_PASSAGES = None @retry() def get_abstract(passage_id): with Entrez.efetch( db="pubmed", id=passage_id, rettype="abstract", retmode="text" ) as response: # get only the abstract - no metadata r = response.read() r = r.split("\n\n") abstract = max(r, key=len) return abstract if __name__ == "__main__": # load the training data containing the questions, answers and the ids of relevant passages # but lacks the actual passages with open(RAW_EVALUATION_DATASET) as f: eval_data = json.load(f)["questions"] eval_df = pd.DataFrame(eval_data, columns=["body", "documents", "ideal_answer"]) eval_df = eval_df.rename( columns={ "body": "question", "documents": "relevant_passage_ids", "ideal_answer": "answer", } ) eval_df.answer = eval_df.answer.apply(lambda x: x[0]) # get abstract id from url eval_df.relevant_passage_ids = eval_df.relevant_passage_ids.apply( lambda x: [int(url.split("/")[-1]) for url in x] ) if MAX_PASSAGES: eval_df["passage_count"] = eval_df.relevant_passage_ids.apply(lambda x: len(x)) eval_df = eval_df.drop(columns=["passage_count"]) # remove duplicate passage ids eval_df.relevant_passage_ids = eval_df.relevant_passage_ids.apply(lambda x: set(x)) eval_df.relevant_passage_ids = eval_df.relevant_passage_ids.apply(lambda x: list(x)) # get all passage ids that are relevant passage_ids = set().union(*eval_df.relevant_passage_ids) passage_ids = list(passage_ids) passages = pd.DataFrame(index=passage_ids) for i, passage_id in enumerate(tqdm(passages.index)): passages.loc[passage_id, "passage"] = get_abstract(passage_id) # intermediate save if i % 1000 == 0: dd.from_pandas(passages, npartitions=1).to_parquet(PATH_TO_PASSAGE_DATASET) # filter out the passages whos pmids (pubmed ids) where not available unavailable_passages = passages[passages["passage"] == "1. "] passages = passages[passages["passage"] != "1. "] passages.index.name = "id" dd.from_pandas(passages, npartitions=1).to_parquet(PATH_TO_PASSAGE_DATASET) # remove passages from evaluation dataset whose abstract could not be retrieved from pubmed website unavailable_ids = unavailable_passages.index.tolist() eval_df["relevant_passage_ids"] = eval_df["relevant_passage_ids"].apply( lambda x: [i for i in x if i not in unavailable_ids] ) eval_df.index.name = "id" eval_df = eval_df[["question", "answer", "relevant_passage_ids"]] dd.from_pandas(eval_df, npartitions=1).to_parquet(PATH_TO_EVALUATION_DATASET)