| # 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: Add a description here.""" | |
| import json | |
| import datasets | |
| # TODO: Add BibTeX citation | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _CITATION = """\ | |
| @InProceedings{OOPSLA ’16, ACM, | |
| title = {Probabilistic Model for Code with Decision Trees.}, | |
| authors={Raychev, V., Bielik, P., and Vechev, M.}, | |
| year={2016} | |
| } | |
| """ | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| Dataset consisting of parsed ASTs that were used to train and | |
| evaluate the DeepSyn tool. | |
| The Python programs are collected from GitHub repositories | |
| by removing duplicate files, removing project forks (copy of another existing repository) | |
| ,keeping only programs that parse and have at most 30'000 nodes in the AST and | |
| we aim to remove obfuscated files""" | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "https://www.sri.inf.ethz.ch/py150" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "" | |
| # 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) | |
| _URLs = { | |
| "ast": "http://files.srl.inf.ethz.ch/data/py150.tar.gz", | |
| } | |
| # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case | |
| class PyAst(datasets.GeneratorBasedBuilder): | |
| """TODO: Short description of my dataset.""" | |
| VERSION = datasets.Version("1.0.0") | |
| # 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="ast", description="This part of the dataset contains 150 parsed python ASTs."), | |
| ] | |
| DEFAULT_CONFIG_NAME = "ast" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| # TODO: This method pecifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
| if self.config.name == "ast": # This is the name of the configuration selected in BUILDER_CONFIGS above | |
| features = datasets.Features( | |
| { | |
| "ast": datasets.Sequence( | |
| { | |
| "type": datasets.Value("string"), | |
| "value": datasets.Value("string"), | |
| "children": datasets.Sequence(datasets.Value("int32")), | |
| }, | |
| ) | |
| # 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=["ast"], | |
| # 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 | |
| my_urls = _URLs[self.config.name] | |
| archive = dl_manager.download(my_urls) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": "python100k_train.json", | |
| "files": dl_manager.iter_archive(archive), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": "python50k_eval.json", | |
| "files": dl_manager.iter_archive(archive), | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath, files): | |
| """Yields examples.""" | |
| # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method. | |
| # It is in charge of opening the given file and yielding (key, example) tuples from the dataset | |
| # The key is not important, it's more here for legacy reason (legacy from tfds) | |
| for path, f in files: | |
| if path == filepath: | |
| for id_, row in enumerate(f): | |
| row_data = json.loads(row.decode("utf-8")) | |
| for node in row_data: | |
| if "value" not in node: | |
| node["value"] = "N/A" | |
| if "children" not in node: | |
| node["children"] = [] | |
| yield id_, { | |
| "ast": row_data, | |
| } | |
| break | |