oas-paired-sequence-data.py

# 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"],
    #                 }