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# 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.
"""
super().__init__(
*args,
name=f"{task_name}",
**kwargs,
)
self.task,self.name = task_name.split(".")
self.task_type = _TASK_NAME_TO_TYPE[self.task]
class AgroNtTasks(datasets.GeneratorBasedBuilder):
"""GeneratorBasedBuilder for the Agro NT supervised learning tasks dataset."""
BUILDER_CONFIGS = [AgroNtTasksConfig(task_name=TASK_NAME) for TASK_NAME
in _TASK_NAMES]
DEFAULT_CONFIG_NAME = _TASK_NAMES[0]
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.name + "_train.fa"))
test_file = dl_manager.download_and_extract(
os.path.join(self.config.task, self.config.name + "_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 |