EpiClassify4GARD.py

# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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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# Lint as: python3
"""EpiClassify4GARD dataset."""


import csv
import datasets
from datasets.tasks import TextClassification


_DESCRIPTION = """\
INSERT DESCRIPTION
"""
_CITATION = """\
John JN, Sid E, Zhu Q. Recurrent Neural Networks to Automatically Identify Rare Disease Epidemiologic Studies from PubMed. AMIA Jt Summits Transl Sci Proc. 2021 May 17;2021:325-334. PMID: 34457147; PMCID: PMC8378621.
"""

_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/ncats/epi4GARD/master/dataset/train.tsv"
_VAL_DOWNLOAD_URL = "https://raw.githubusercontent.com/ncats/epi4GARD/master/dataset/val.tsv"
_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/ncats/epi4GARD/master/dataset/test.tsv"


class EpiClassify4GARD(datasets.GeneratorBasedBuilder):
    """EpiClassify4GARD text classification dataset."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "abstract": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=["1 = IsEpi", "0 = IsNotEpi"]),
                }
            ),
            homepage="https://github.com/ncats/epi4GARD/tree/master/Epi4GARD#epi4gard",
            citation=_CITATION,
            task_templates=[TextClassification(text_column="abstract", label_column="label")],
        )

    def _split_generators(self, dl_manager):
        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
        val_path = dl_manager.download_and_extract(_VAL_DOWNLOAD_URL)
        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path }),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
        ]

    def _generate_examples(self, filepath):
        """Generate examples."""
        with open(filepath, encoding="utf-8") as csv_file:
            csv_reader = csv.reader(
                csv_file, quotechar='"', delimiter="\t", quoting=csv.QUOTE_ALL, skipinitialspace=True
            )
            next(csv_reader)
            for id_, row in enumerate(csv_reader):
                abstract = row[0]
                label = row[1]
                yield id_, {"abstract": abstract, "label": int(label)}