Datasets:

bloyal
/

oas-paired-sequence-data

@@ -1,141 +0,0 @@
-# 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": "human.parquet",
-    "rat_SD": "rat_SD.parquet",
-    "mouse_BALB_c": "mouse_BALB_c.parquet",
-    "mouse_C57BL_6": "mouse_C57BL_6.parquet",
-}
-_FEATURES = datasets.Features(
-    {
-        "pair_id": datasets.Value("string"),
-        "sequence_alignment_aa_heavy": datasets.Value("string"),
-        "cdr1_aa_heavy": datasets.Value("string"),
-        "cdr2_aa_heavy": datasets.Value("string"),
-        "cdr3_aa_heavy": datasets.Value("string"),
-        "sequence_alignment_aa_light": datasets.Value("string"),
-        "cdr1_aa_light": datasets.Value("string"),
-        "cdr2_aa_light": datasets.Value("string"),
-        "cdr3_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 = 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"),
-                    "filepath": data,
-                    "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)
-                yield key, data
-                # yield key, {
-                #     "sentence": data["sentence"],
-                #     "option2": data["option2"],
-                #     "second_domain_answer": ""
-                # }