| # 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. | |
| """A tiny dataset containing 5 molecule configurations for fast inference example.""" | |
| import csv | |
| import json | |
| import os | |
| import torch | |
| import datasets | |
| # import pickle | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| This data is a trimmed version of the GEOM Drugs Dataset. """ | |
| _HOMEPAGE = "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JNGTDF" | |
| _LICENSE = "Creative Commons 1.0 Universal: https://creativecommons.org/publicdomain/zero/1.0/" | |
| _CITATION = """\ | |
| @data{DVN/JNGTDF_2021, | |
| author = {Axelrod, Simon and Gomez-Bombarelli, Rafael}, | |
| publisher = {Harvard Dataverse}, | |
| title = {{GEOM}}, | |
| year = {2021}, | |
| version = {V4}, | |
| doi = {10.7910/DVN/JNGTDF}, | |
| url = {https://doi.org/10.7910/DVN/JNGTDF} | |
| } | |
| """ | |
| # TODO: Add link to the official dataset URLs here | |
| # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. | |
| # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
| _URLS = { | |
| "drugs": "https://huggingface.co/datasets/fusing/geodiff-example-data/blob/main/data/molecules.pkl", | |
| } | |
| class GeoDiffExampleData(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.1.0") | |
| # This is an example of a dataset with multiple configurations. | |
| # If you don't want/need to define several sub-sets in your dataset, | |
| # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
| # If you need to make complex sub-parts in the datasets with configurable options | |
| # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
| # BUILDER_CONFIG_CLASS = MyBuilderConfig | |
| # 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="drugs", version=VERSION, description="This part of my dataset covers a first domain"), | |
| ] | |
| DEFAULT_CONFIG_NAME = "drugs" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| if self.config.name == "drugs": # This is the name of the configuration selected in BUILDER_CONFIGS above | |
| features = datasets.Features( | |
| { | |
| "num_molecules": 5, #datasets.Value("string"), | |
| # These are the features of your dataset like images, labels ... | |
| } | |
| ) | |
| else: | |
| raise NotImplementedError("Other Domains Not Added") | |
| 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, uncomment supervised_keys line below and | |
| # specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
| # supervised_keys=("sentence", "label"), | |
| # 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): | |
| # 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": data_dir, | |
| "split": "train", | |
| }, | |
| ) | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, filepath, split): | |
| # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. | |
| with open(filepath, "rb") as f: | |
| data = torch.load(f) | |
| # data = pickle.load(f) | |
| yield data | |