Datasets:
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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)
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