File size: 5,439 Bytes
b47b74e f5ded62 c5ef047 b47b74e 1eb5300 b47b74e 2657c8e b47b74e f5ded62 b47b74e 1eb5300 b47b74e 1eb5300 b47b74e 1eb5300 b47b74e 1eb5300 b47b74e 1eb5300 b47b74e 1eb5300 2657c8e b47b74e 2657c8e b47b74e 2657c8e b47b74e 1eb5300 b47b74e 1eb5300 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 |
# 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
|