bioasq_task_c_2017.py

# coding=utf-8
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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import json
import os
import xml.etree.ElementTree as ET
from dataclasses import dataclass
from typing import List

import datasets

from .bigbiohub import text_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks

_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = True
_CITATION = """\
@article{nentidis-etal-2017-results,
  title        = {Results of the fifth edition of the {B}io{ASQ} Challenge},
  author       = {
    Nentidis, Anastasios  and Bougiatiotis, Konstantinos  and Krithara,
    Anastasia  and Paliouras, Georgios  and Kakadiaris, Ioannis
  },
  year         = 2007,
  journal      = {},
  volume       = {BioNLP 2017},
  doi          = {10.18653/v1/W17-2306},
  url          = {https://aclanthology.org/W17-2306},
  biburl       = {},
  bibsource    = {https://aclanthology.org/W17-2306}
}

"""

_DATASETNAME = "bioasq_task_c_2017"
_DISPLAYNAME = "BioASQ Task C 2017"

_DESCRIPTION = """\
The training data set for this task contains annotated biomedical articles
published in PubMed and corresponding full text from PMC. By annotated is meant
that GrantIDs and corresponding Grant Agencies have been identified in the full
text of articles
"""

_HOMEPAGE = "http://participants-area.bioasq.org/general_information/Task5c/"

_LICENSE = 'National Library of Medicine Terms and Conditions'

# Website contains all data, but login required
_URLS = {_DATASETNAME: "http://participants-area.bioasq.org/datasets/"}

_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]

_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"


@dataclass
class BioASQTaskC2017BigBioConfig(BigBioConfig):
    schema: str = "source"
    name: str = "bioasq_task_c_2017_source"
    version: datasets.Version = datasets.Version(_SOURCE_VERSION)
    description: str = "bioasq_task_c_2017 source schema"
    subset_id: str = "bioasq_task_c_2017"


class BioASQTaskC2017(datasets.GeneratorBasedBuilder):
    """
    BioASQ Task C Dataset for 2017
    """

    DEFAULT_CONFIG_NAME = "bioasq_task_c_2017_source"
    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

    BUILDER_CONFIGS = [
        BioASQTaskC2017BigBioConfig(
            name="bioasq_task_c_2017_source",
            version=SOURCE_VERSION,
            description="bioasq_task_c_2017 source schema",
            schema="source",
            subset_id="bioasq_task_c_2017",
        ),
        BioASQTaskC2017BigBioConfig(
            name="bioasq_task_c_2017_bigbio_text",
            version=BIGBIO_VERSION,
            description="bioasq_task_c_2017 BigBio schema",
            schema="bigbio_text",
            subset_id="bioasq_task_c_2017",
        ),
    ]

    BUILDER_CONFIG_CLASS = BioASQTaskC2017BigBioConfig

    def _info(self) -> datasets.DatasetInfo:

        # BioASQ Task C source schema
        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "document_id": datasets.Value("string"),
                    "pmid": datasets.Value("string"),
                    "pmcid": datasets.Value("string"),
                    "grantList": [
                        {
                            "agency": datasets.Value("string"),
                        }
                    ],
                    "text": datasets.Value("string"),
                }
            )

        # For example bigbio_kb, bigbio_t2t
        elif self.config.schema == "bigbio_text":
            features = text_features

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=str(_LICENSE),
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:

        if self.config.data_dir is None:
            raise ValueError(
                "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
            )
        else:
            data_dir = self.config.data_dir

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "taskCTrainingData2017.json"),
                    "filespath": os.path.join(data_dir, "Train_Text"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "taskc_golden2.json"),
                    "filespath": os.path.join(data_dir, "Final_Text"),
                    "split": "test",
                },
            ),
        ]

    def _generate_examples(self, filepath, filespath, split):

        with open(filepath) as f:
            task_data = json.load(f)

        if self.config.schema == "source":
            for article in task_data["articles"]:

                with open(filespath + "/" + article["pmcid"] + ".xml") as f:
                    text = f.read()
                pmid = article["pmid"]

                yield pmid, {
                    "text": text,  # articles[pmid],
                    "document_id": pmid,
                    "id": str(pmid),
                    "pmid": pmid,
                    "pmcid": article["pmcid"],
                    "grantList": [
                        {"agency": grant["agency"]} for grant in article["grantList"]
                    ],
                }

        elif self.config.schema == "bigbio_text":

            for article in task_data["articles"]:

                with open(filespath + "/" + article["pmcid"] + ".xml") as f:
                    xml_string = f.read()

                try:
                    article_body = ET.fromstring(xml_string).find("./article/body")
                except ET.ParseError:

                    # PubMed XML might not contain namespace which results in parse error, add manually
                    xml_string = xml_string.replace(
                        "</pmc-articleset>",
                        # xlink namespace
                        '<article xmlns:xlink="http://www.w3.org/1999/xlink"'  # mml namespace
                        ' xmlns:mml="http://www.w3.org/1998/Math/MathML"'
                        ' article-type="research-article">',
                    )
                    xml_string = xml_string + "</article></pmc-articleset>"
                    article_body = ET.fromstring(xml_string).find("./article/body")

                text = ET.tostring(article_body, encoding="utf8", method="text")

                yield article["pmid"], {
                    "text": text,
                    "id": str(article["pmid"]),
                    "document_id": article["pmid"],
                    "labels": [grant["agency"] for grant in article["grantList"]],
                }