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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.
# TODO: Address all TODOs and remove all explanatory comments
"""Paired sequences from the Observed Antibody Space database"""
import csv
import json
import os
import datasets
_CITATION = """\
@article{Olsen_Boyles_Deane_2022,
title={Observed Antibody Space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences},
volume={31}, rights={© 2021 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.},
ISSN={1469-896X}, DOI={10.1002/pro.4205},
number={1}, journal={Protein Science}, author={Olsen, Tobias H. and Boyles, Fergus and Deane, Charlotte M.},
year={2022}, pages={141–146}, language={en} }
"""
_DESCRIPTION = """\
Paired heavy and light chain antibody sequences for multiple species.
"""
_HOMEPAGE = "https://opig.stats.ox.ac.uk/webapps/oas/"
_LICENSE = "cc-by-4.0"
_URLS = {
"human": "data/human/*.parquet",
"rat_SD": "data/rat_SD/*.parquet",
"mouse_BALB_c": "data/mouse_BALB_c/*.parquet",
"mouse_C57BL_6": "data/mouse_C57BL_6/*.parquet",
}
_FEATURES = datasets.Features(
{
"sequence_id_heavy": datasets.Value("string"),
"sequence_heavy": datasets.Value("string"),
"locus_heavy": datasets.Value("string"),
"stop_codon_heavy": datasets.Value("string"),
"productive_heavy": datasets.Value("string"),
"rev_comp_heavy": datasets.Value("string"),
"sequence_alignment_aa_heavy": datasets.Value("string"),
"fwr1_aa_heavy": datasets.Value("string"),
"cdr1_aa_heavy": datasets.Value("string"),
"fwr2_aa_heavy": datasets.Value("string"),
"cdr2_aa_heavy": datasets.Value("string"),
"fwr3_aa_heavy": datasets.Value("string"),
"cdr3_aa_heavy": datasets.Value("string"),
"junction_aa_heavy": datasets.Value("string"),
"sequence_id_light": datasets.Value("string"),
"sequence_light": datasets.Value("string"),
"locus_light": datasets.Value("string"),
"stop_codon_light": datasets.Value("string"),
"productive_light": datasets.Value("string"),
"rev_comp_light": datasets.Value("string"),
"sequence_alignment_aa_light": datasets.Value("string"),
"fwr1_aa_light": datasets.Value("string"),
"cdr1_aa_light": datasets.Value("string"),
"fwr2_aa_light": datasets.Value("string"),
"cdr2_aa_light": datasets.Value("string"),
"fwr3_aa_light": datasets.Value("string"),
"cdr3_aa_light": datasets.Value("string"),
"junction_aa_light": datasets.Value("string"),
}
)
class OasPairedSequenceData(datasets.GeneratorBasedBuilder):
"""OAS paired sequence data."""
VERSION = datasets.Version("1.1.0")
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="human", version=VERSION, description="Human"),
datasets.BuilderConfig(name="rat_SD", version=VERSION, description="rat_SD"),
datasets.BuilderConfig(
name="mouse_BALB_c", version=VERSION, description="mouse_BALB_c"
),
datasets.BuilderConfig(
name="mouse_C57BL_6", version=VERSION, description="mouse_C57BL_6"
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=_FEATURES,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
# def _split_generators(self, dl_manager):
# # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
# urls = _URLS[self.config.name]
# data_dir = dl_manager.download_and_extract(urls)
# return [
# datasets.SplitGenerator(
# name=datasets.Split.TRAIN,
# # These kwargs will be passed to _generate_examples
# gen_kwargs={
# "filepath": os.path.join(data_dir, "train.jsonl"),
# "split": "train",
# },
# ),
# datasets.SplitGenerator(
# name=datasets.Split.VALIDATION,
# # These kwargs will be passed to _generate_examples
# gen_kwargs={
# "filepath": os.path.join(data_dir, "dev.jsonl"),
# "split": "dev",
# },
# ),
# datasets.SplitGenerator(
# name=datasets.Split.TEST,
# # These kwargs will be passed to _generate_examples
# gen_kwargs={
# "filepath": os.path.join(data_dir, "test.jsonl"),
# "split": "test",
# },
# ),
# ]
# # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
# def _generate_examples(self, filepath, split):
# # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
# with open(filepath, encoding="utf-8") as f:
# for key, row in enumerate(f):
# data = json.loads(row)
# if self.config.name == "first_domain":
# # Yields examples as (key, example) tuples
# yield key, {
# "sentence": data["sentence"],
# "option1": data["option1"],
# "answer": "" if split == "test" else data["answer"],
# }
# else:
# yield key, {
# "sentence": data["sentence"],
# "option2": data["option2"],
# "second_domain_answer": ""
# if split == "test"
# else data["second_domain_answer"],
# }
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