GUE_public.py

"""Script for the dataset containing the 28 downstream tasks from the DNABertv2 paper."""

from typing import List
import csv
import datasets


# This function is a basic reimplementation of SeqIO's parse method. This allows the
# dataset viewer to work as it does not require an external package.

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = ''

# You can copy an official description
_DESCRIPTION = ''

_HOMEPAGE = ""

_LICENSE = ""

_TASKS = [
    "splice_reconstructed",
    "mouse0",
    "mouse1",
    "mouse2",
    "mouse3",
    "mouse4",
    'covid',
    'prom_core_tata',
    'prom_core_notata',
    'prom_core_all',
    'prom_300_tata',
    'prom_300_notata',
    'prom_300_all',
    'tf0',
    'tf1',
    'tf2',
    'tf3',
    'tf4',
    'H3',
    'H3K14ac',
    'H3K36me3',
    'H3K4me1',
    'H3K4me2',
    'H3K4me3',
    'H3K79me3',
    'H3K9ac',
    'H4',
    'H4ac',
]


class GUEConfig(datasets.BuilderConfig):
    """BuilderConfig for GUE taks dataset."""

    def __init__(self, *args, task: str, **kwargs):
        """BuilderConfig downstream tasks dataset.
        Args:
            task (:obj:`str`): Task name.
            **kwargs: keyword arguments forwarded to super.
        """
        super().__init__(
            *args,
            name=f"{task}",
            **kwargs,
        )
        self.task = task


class GUEDownstreamTasks(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.1.0")
    BUILDER_CONFIG_CLASS = GUEConfig
    BUILDER_CONFIGS = [GUEConfig(task=task) for task in _TASKS]
    DEFAULT_CONFIG_NAME = "reconstructed"

    def _info(self):

        features = datasets.Features(
            {
                "sequence": datasets.Value("string"),
                "label": datasets.Value("int32"),
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

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

        train_file = dl_manager.download_and_extract(self.config.task + "/train.csv")
        valid_file = dl_manager.download_and_extract(self.config.task + "/dev.csv")
        test_file = dl_manager.download_and_extract(self.config.task + "/test.csv")

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"file": train_file}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={"file": valid_file}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"file": test_file}
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, file):
        key = 0
        print(file)
        with open(file, "r") as f:
            csv_reader = csv.reader(f)
            head = next(csv_reader)
            for row in csv_reader:
                # yield example
                yield key, {
                    "sequence": row[0],
                    "label": row[1],
                }
                key += 1