agro-nt-tasks.py

# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os
from typing import List
from Bio import SeqIO

import datasets


# TODO: Add BibTeX citation
_CITATION = ''
# """\
# @InProceedings{huggingface:dataset,
# title = {A great new dataset},
# author={huggingface, Inc.
# },
# year={2020}
# }
# """

_DESCRIPTION = """\
This dataset comprises the various supervised learning tasks considered in the agro-nt 
paper. The task types include binary classification,multi-label classification, 
regression,and multi-output regression. The actual underlying genomic tasks range from 
predicting regulatory features, RNA processing sites, and gene expression values.
"""


# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

_TASK_NAMES =  ['poly_a.arabidopsis_thaliana',
                'poly_a.oryza_sativa_indica_group',
                'poly_a.trifolium_pratense',
                'poly_a.medicago_truncatula',
                'poly_a.chlamydomonas_reinhardtii',
                'poly_a.oryza_sativa_japonica_group']


_TASK_NAME_TO_TYPE = {'poly_a':'binary',
                      'lncrna':'binary',
                      'splice_site':'binary',}


class AgroNtTasksConfig(datasets.BuilderConfig):
    """BuilderConfig for the Agro NT supervised learning tasks dataset."""

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

        self.task,self.specie = task_name.split(".")
        self.task_type = _TASK_NAME_TO_TYPE[self.task]

        super().__init__(
            *args,
            name=f"{task_name}",
            **kwargs,
        )


class AgroNtTasks(datasets.GeneratorBasedBuilder):
    """GeneratorBasedBuilder for the Agro NT supervised learning tasks dataset."""

    BUILDER_CONFIG_CLASS = AgroNtTasksConfig
    VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [AgroNtTasksConfig(task_name=TASK_NAME) for TASK_NAME
                       in _TASK_NAMES]

    def _info(self):

        if self.config.task_type == 'binary':
            features = datasets.Features(
                {
                    "sequence": datasets.Value("string"),
                    "name": datasets.Value("string"),
                    "labels": datasets.Value("int8"),
                }
            )

        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,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

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

        train_file = dl_manager.download_and_extract(
            os.path.join(self.config.task, self.config.specie + "_train.fa"))
        test_file = dl_manager.download_and_extract(
            os.path.join(self.config.task, self.config.specie + "_test.fa"))

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": train_file,
                    "split": "train",
                },
            ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.VALIDATION,
            #     # These kwargs will be passed to _generate_examples
            #     gen_kwargs={
            #         "filepath": test_file,
            #         "split": "dev",
            #     },
            # ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": test_file,
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        with open(filepath, 'r') as f:
            key = 0
            for record in SeqIO.parse(f,'fasta'):
                    # Yields examples as (key, example) tuples

                split_name = record.name.split("|")
                name = split_name[0]
                labels = split_name[1]

                yield key, {
                    "sequence": str(record.seq),
                    "name": name,
                    "labels": labels,
                }
                key += 1