nucleotide_transformer_downstream_tasks.py

# Copyright 2020 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
# limitations under the License.
"""Script for the dataset containing all the downstream tasks from the Nucleotide
Transformer paper. There are 18 downstream tasks."""

from typing import List
import datasets
from Bio import SeqIO

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{dalla2023nucleotide,
  title={The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics},
  author={Dalla-Torre, Hugo and Gonzalez, Liam and Mendoza-Revilla, Javier and Carranza, Nicolas Lopez and Grzywaczewski, Adam Henryk and Oteri, Francesco and Dallago, Christian and Trop, Evan and Sirelkhatim, Hassan and Richard, Guillaume and others},
  journal={bioRxiv},
  pages={2023--01},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}
"""

# You can copy an official description
_DESCRIPTION = """\
18 classification downstream tasks from the Nucleotide Transformer paper. Each task
corresponds to a dataset configuration.
"""

_HOMEPAGE = "https://github.com/instadeepai/nucleotide-transformer"

_LICENSE = "https://github.com/instadeepai/nucleotide-transformer/LICENSE.md"

_TASKS = [
    'H4ac',
    'H3K36me3',
    'splice_sites_donors',
    'splice_sites_acceptors',
    'H3',
    'H4',
    'H3K4me3',
    'splice_sites_all',
    'H3K4me1',
    'H3K14ac',
    'enhancers_types',
    'promoter_no_tata',
    'H3K79me3',
    'H3K4me2',
    'promoter_tata',
    'enhancers',
    'H3K9ac',
    'promoter_all'
]


class NucleotideTransformerDownstreamTasksConfig(datasets.BuilderConfig):
    """BuilderConfig for The Nucleotide Transformer downstream 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 NucleotideTransformerDownstreamTasks(datasets.GeneratorBasedBuilder):
    """Genomes from 850 species, filtered and split into chunks of consecutive
    nucleotides. 50 genomes are taken for test, 50 for validation and 800
    for training."""

    VERSION = datasets.Version("1.1.0")
    BUILDER_CONFIG_CLASS = NucleotideTransformerDownstreamTasksConfig
    BUILDER_CONFIGS = [
        NucleotideTransformerDownstreamTasksConfig(task=task) for task in _TASKS
    ]
    DEFAULT_CONFIG_NAME = "enhancers"

    def _info(self):

        features = datasets.Features(
            {
                "sequence": datasets.Value("string"),
                "name": 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.fna')
        test_file = dl_manager.download_and_extract(self.config.task + '/test.fna')

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                    gen_kwargs={"file": train_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
        with open(file, 'rt') as f:
            fasta_sequences = SeqIO.parse(f, 'fasta')

            for record in fasta_sequences:

                # parse descriptions in the fasta file
                sequence, name = str(record.seq), str(record.name)
                label = int(name.split("|")[-1])

                # yield example
                yield key, {
                    'sequence': sequence,
                    'name': name,
                    'label': label,
                }
                key += 1