nucleotide_transformer_downstream_tasks / nucleotide_transformer_downstream_tasks.py
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1-fix-dataset-preview (#1)
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# 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 the 18 downstream tasks from the Nucleotide
Transformer paper."""
from typing import List
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.
def parse_fasta(fp):
name, seq = None, []
for line in fp:
line = line.rstrip()
if line.startswith(">"):
if name:
# Slice to remove '>'
yield (name[1:], "".join(seq))
name, seq = line, []
else:
seq.append(line)
if name:
# Slice to remove '>'
yield (name[1:], "".join(seq))
# 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 = """\
The 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):
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 = parse_fasta(f)
for name, seq in fasta_sequences:
# parse descriptions in the fasta file
sequence, name = str(seq), str(name)
label = int(name.split("|")[-1])
# yield example
yield key, {
"sequence": sequence,
"name": name,
"label": label,
}
key += 1