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# coding=utf-8
# 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: A dataset of protein sequences, ligand SMILES, binding affinities and contacts."""
import huggingface_hub
import os
import pyarrow.parquet as pq
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {jglaser/pdbbind_complexes},
author={Jens Glaser, ORNL
},
year={2022}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
A dataset to fine-tune language models on protein-ligand binding affinity and contact prediction.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "BSD two-clause"
# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://huggingface.co/datasets/jglaser/pdbbind_complexes/resolve/main/"
_data_dir = "data/"
_file_names = {'default': _data_dir+'pdbbind.parquet'}
_URLs = {name: _URL+_file_names[name] for name in _file_names}
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ProteinLigandContacts(datasets.ArrowBasedBuilder):
"""List of protein sequences, ligand SMILES, binding affinities and contacts."""
VERSION = datasets.Version("1.0")
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
#if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above
# features = datasets.Features(
# {
# "sentence": datasets.Value("string"),
# "option1": datasets.Value("string"),
# "answer": datasets.Value("string")
# # These are the features of your dataset like images, labels ...
# }
# )
#else: # This is an example to show how to have different features for "first_domain" and "second_domain"
features = datasets.Features(
{
"seq": datasets.Value("string"),
"smiles": datasets.Value("string"),
"ligand_xyz": datasets.Sequence(datasets.Sequence(datasets.Value('float32'))),
"receptor_xyz": datasets.Sequence(datasets.Sequence(datasets.Value('float32'))),
# These are the features of your dataset like images, labels ...
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# 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
files = dl_manager.download_and_extract(_URLs)
return [
datasets.SplitGenerator(
# These kwargs will be passed to _generate_examples
name=datasets.Split.TRAIN,
gen_kwargs={
'filepath': files["default"],
},
),
]
def _generate_tables(
self, filepath
):
from pyarrow import fs
local = fs.LocalFileSystem()
for i, f in enumerate([filepath]):
yield i, pq.read_table(f,filesystem=local)
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