EC2 Default User
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Parent(s):
4e30a61
Add load script
Browse files- human.parquet +0 -3
- mouse_BALB_c.parquet +0 -3
- mouse_C57BL_6.parquet +0 -3
- oas-paired-sequence-data.py +149 -0
- rat_SD.parquet +0 -3
human.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:385678cd991b8639196d97b51f1f2396d32ced223bdacd60ffb806cf3b568701
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size 92818695
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mouse_BALB_c.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:aadc6d7524d60d80109143772bc307bae06b8aabace45c87bdd2ca3aa7dd777f
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size 586744
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mouse_C57BL_6.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:f46d6e00e6bde03b91675d2ba03dcb3c6f67c944ae8dd9606a6c373a07c61ae3
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size 174681
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oas-paired-sequence-data.py
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# TODO: Address all TODOs and remove all explanatory comments
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"""Paired sequences from the Observed Antibody Space database"""
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import csv
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import json
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import os
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import pandas as pd
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import datasets
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_CITATION = """\
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@article{Olsen_Boyles_Deane_2022,
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title={Observed Antibody Space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences},
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volume={31}, rights={© 2021 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.},
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ISSN={1469-896X}, DOI={10.1002/pro.4205},
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number={1}, journal={Protein Science}, author={Olsen, Tobias H. and Boyles, Fergus and Deane, Charlotte M.},
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year={2022}, pages={141–146}, language={en} }
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"""
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_DESCRIPTION = """\
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Paired heavy and light chain antibody sequences for multiple species.
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"""
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_HOMEPAGE = "https://opig.stats.ox.ac.uk/webapps/oas/"
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_LICENSE = "cc-by-4.0"
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_URLS = {
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"human": "human.parquet",
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"rat_SD": "rat_SD.parquet",
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"mouse_BALB_c": "mouse_BALB_c.parquet",
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"mouse_C57BL_6": "mouse_C57BL_6.parquet",
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}
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_FEATURES = datasets.Features(
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{
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"pair_id": datasets.Value("string"),
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"sequence_alignment_aa_heavy": datasets.Value("string"),
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"cdr1_aa_heavy": datasets.Value("string"),
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"cdr2_aa_heavy": datasets.Value("string"),
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"cdr3_aa_heavy": datasets.Value("string"),
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"sequence_alignment_aa_light": datasets.Value("string"),
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"cdr1_aa_light": datasets.Value("string"),
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"cdr2_aa_light": datasets.Value("string"),
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"cdr3_aa_light": datasets.Value("string"),
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}
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)
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class OasPairedSequenceData(datasets.GeneratorBasedBuilder):
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"""OAS paired sequence data."""
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VERSION = datasets.Version("1.1.0")
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# You will be able to load one or the other configurations in the following list with
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# data = datasets.load_dataset('my_dataset', 'first_domain')
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# data = datasets.load_dataset('my_dataset', 'second_domain')
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="human", version=VERSION, description="Human"),
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datasets.BuilderConfig(name="rat_SD", version=VERSION, description="rat_SD"),
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datasets.BuilderConfig(
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name="mouse_BALB_c", version=VERSION, description="mouse_BALB_c"
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),
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datasets.BuilderConfig(
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name="mouse_C57BL_6", version=VERSION, description="mouse_C57BL_6"
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),
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=_FEATURES,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
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# 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.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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urls = _URLS[self.config.name]
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data = dl_manager.download_and_extract(urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": data,
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"split": "train",
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},
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),
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# datasets.SplitGenerator(
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# name=datasets.Split.VALIDATION,
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# # These kwargs will be passed to _generate_examples
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# gen_kwargs={
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# "filepath": os.path.join(data_dir, "dev.jsonl"),
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# "split": "dev",
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# },
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# ),
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# datasets.SplitGenerator(
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# name=datasets.Split.TEST,
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# # These kwargs will be passed to _generate_examples
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# gen_kwargs={
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# "filepath": os.path.join(data_dir, "test.jsonl"),
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# "split": "test",
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# },
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# ),
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]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, filepath, split):
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# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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with open(filepath, newline="") as f:
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reader = csv.reader(f, delimiter=",")
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for key, row in enumerate(reader):
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if key == 0:
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continue
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yield key, {
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"pair_id": row[0],
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"sequence_alignment_aa_heavy": row[1],
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"cdr1_aa_heavy": row[2],
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"cdr2_aa_heavy": row[3],
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"cdr3_aa_heavy": row[4],
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"sequence_alignment_aa_light": row[5],
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"cdr1_aa_light": row[6],
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"cdr2_aa_light": row[7],
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"cdr3_aa_light": row[8],
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}
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rat_SD.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:5b60b14ea7fe3c0216f8640b6cacd1e63543e15663e308d6998a8c6a0eac87e8
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size 2053567
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