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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 tasks for genome origin detection."""
from typing import List
import datasets
# Custom function to parse lines in the dataset
def parse_text(fp):
for line in fp:
line = line.strip()
if not line: # Skip empty lines
continue
seq, label = line.split(",")
yield seq, label
# Dataset metadata
_CITATION = """TBD"""
_DESCRIPTION = """TBD"""
_HOMEPAGE = "TBD"
_LICENSE = "TBD"
# Tasks available in the dataset
_TASKS = [
"four_kingdoms",
"plasmid_detection",
]
class GenomeOriginTasksConfig(datasets.BuilderConfig):
"""BuilderConfig for Genome Origin tasks dataset."""
def __init__(self, *args, task: str, **kwargs):
"""
BuilderConfig for Genome Origin tasks dataset.
Args:
task (str): Task name.
**kwargs: Additional keyword arguments forwarded to super.
"""
super().__init__(
*args,
name=f"{task}",
**kwargs,
)
self.task = task
class GenomeOriginTasks(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIG_CLASS = GenomeOriginTasksConfig
BUILDER_CONFIGS = [GenomeOriginTasksConfig(task=task) for task in _TASKS]
DEFAULT_CONFIG_NAME = "four_kingdoms"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"sequence": datasets.Value("string"),
"label": datasets.Value("int32"),
}
),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
train_file = dl_manager.download_and_extract(self.config.task + "/train.txt")
test_file = dl_manager.download_and_extract(self.config.task + "/test.txt")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"file_path": train_file}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"file_path": test_file}
),
]
def _generate_examples(self, file_path):
"""Generates examples from a given file."""
key = 0
with open(file_path, "r") as f:
for seq, label in parse_text(f):
yield key, {
"sequence": seq.upper(), # Ensure sequences are uppercase
"label": int(label), # Convert label to integer
}
key += 1
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