pmc_patients.py

# 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
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"""
PPS dataset is a list of triplets. Each entry is in format (patient_uid_1, patient_uid_2, similarity)
where similarity has three values:0, 1, 2, indicating corresponding similarity.
"""

import json
import os
from typing import Dict, List, Tuple

import datasets
import pandas as pd

from .bigbiohub import pairs_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks

_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@misc{zhao2022pmcpatients,
      title={PMC-Patients: A Large-scale Dataset of Patient Notes and Relations Extracted from Case
          Reports in PubMed Central},
      author={Zhengyun Zhao and Qiao Jin and Sheng Yu},
      year={2022},
      eprint={2202.13876},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}"""

_DATASETNAME = "pmc_patients"
_DISPLAYNAME = "PMC-Patients"

_DESCRIPTION = """\
This dataset is used for calculating the similarity between two patient descriptions.
"""

_HOMEPAGE = "https://github.com/zhao-zy15/PMC-Patients"

_LICENSE = 'Creative Commons Attribution Non Commercial Share Alike 4.0 International'

_URLS = {
    _DATASETNAME: "https://drive.google.com/u/0/uc?id=1vFCLy_CF8fxPDZvDtHPR6Dl6x9l0TyvW&export=download",
}

_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]

_SOURCE_VERSION = "1.2.0"

_BIGBIO_VERSION = "1.0.0"


class PMCPatientsDataset(datasets.GeneratorBasedBuilder):
    """PPS dataset is a list of triplets.
    Each entry is in format (patient_uid_1, patient_uid_2, similarity) and their
    respective texts.
    where similarity has three values:0, 1, 2, indicating corresponding similarity.
    """

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="pmc_patients_source",
            version=SOURCE_VERSION,
            description="pmc_patients source schema",
            schema="source",
            subset_id="pmc_patients",
        ),
        BigBioConfig(
            name="pmc_patients_bigbio_pairs",
            version=BIGBIO_VERSION,
            description="pmc_patients BigBio schema",
            schema="bigbio_pairs",
            subset_id="pmc_patients",
        ),
    ]

    DEFAULT_CONFIG_NAME = "pmc_patients_source"

    def _info(self) -> datasets.DatasetInfo:
        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "id_text1": datasets.Value("string"),
                    "id_text2": datasets.Value("string"),
                    "label": datasets.Value("int8"),
                }
            )

        elif self.config.schema == "bigbio_pairs":
            features = pairs_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": os.path.join(
                        data_dir,
                        "datasets/task_2_patient2patient_similarity/PPS_train.json",
                    ),
                    "split": "train",
                    "data_dir": data_dir,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir,
                        "datasets/task_2_patient2patient_similarity/PPS_test.json",
                    ),
                    "split": "test",
                    "data_dir": data_dir,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir,
                        "datasets/task_2_patient2patient_similarity/PPS_dev.json",
                    ),
                    "split": "dev",
                    "data_dir": data_dir,
                },
            ),
        ]

    def _generate_examples(
        self, filepath, split: str, data_dir: str
    ) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""

        uid = 0

        def lookup_text(patient_uid: str, df: pd.DataFrame) -> str:
            try:
                return df.loc[patient_uid]["patient"]
            except KeyError:
                return ""

        with open(filepath, "r") as j:
            ret_file = json.load(j)

        if self.config.schema == "source":

            for key, (id1, id2, label) in enumerate(ret_file):
                feature_dict = {
                    "id": uid,
                    "id_text1": id1,
                    "id_text2": id2,
                    "label": label,
                }
                uid += 1
                yield key, feature_dict

        elif self.config.schema == "bigbio_pairs":
            source_files = os.path.join(data_dir, f"datasets/PMC-Patients_{split}.json")
            src_frame = pd.read_json(source_files, encoding="utf8").set_index(
                "patient_uid"
            )
            for key, (id1, id2, label) in enumerate(ret_file):
                text_1 = lookup_text(id1, src_frame)
                text_2 = lookup_text(id2, src_frame)
                # test/dev splits are faulty and may not contain the patient_uid
                # if any of the lookup texts are empty skip the sample
                if text_1 == "" or text_2 == "":
                    continue
                feature_dict = {
                    "id": uid,
                    "document_id": "NULL",
                    "text_1": text_1,
                    "text_2": text_2,
                    "label": label,
                }
                uid += 1
                yield key, feature_dict