| """ACP Bench dataset.""" | |
| import json | |
| import datasets | |
| import itertools | |
| _CITATION = """\ | |
| @article{kokel2024acp, | |
| title={ACPBench: Reasoning about Action, Change, and Planning}, | |
| author={Kokel, Harsha and Katz, Michael and Srinivas, Kavitha and Sohrabi, Shirin}, | |
| journal={arXiv}, | |
| year={2024} | |
| } | |
| """ | |
| _DESCRIPTION = """ACPBench consists of 7 reasoning tasks over 13 domains. The 13 domains include 11 classical planning domains, ALFWorld, and a novel Swap domain. The 7 tasks included in ACPBench are Action Applicability (app), Progression (prog), Atom Reachability (reach), Validation (val), Action Reachability (areach), Justification (just), and Landmarks (land).""" | |
| _HOMEPAGE = "https://ibm.github.io/ACPBench/" | |
| _LICENSE = "MIT" | |
| _BASE_URL = "https://raw.github.com/ibm/ACPBench/main/dataset" | |
| task_list = [ | |
| "app", | |
| "areach", | |
| "just", | |
| "land", | |
| "prog", | |
| "reach", | |
| "val" | |
| ] | |
| format_list = [ | |
| "bool", "mcq" | |
| ] | |
| class ACPConfig(datasets.BuilderConfig): | |
| def __init__(self, urls, **kwargs): | |
| """ | |
| urls: *dict[string]*, the urls for each split of the ACPBench set. | |
| """ | |
| super().__init__(version=datasets.Version("1.0.0"), **kwargs) | |
| self.urls = urls | |
| class ACP(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| ACPConfig( | |
| name=f"acp_{task_name}_{format_name}", | |
| urls={ | |
| "test": f"{_BASE_URL}/{task_name}/test.{format_name}.json.gz", | |
| "val": f"{_BASE_URL}/{task_name}/dev.{format_name}.json", | |
| }, | |
| ) | |
| for task_name, format_name in itertools.product(task_list,format_list) | |
| ] | |
| def _info(self): | |
| features = { | |
| "context": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "answer": datasets.Value("string"), | |
| "group": datasets.Value("string"), | |
| "id": datasets.Value("string") | |
| } | |
| if 'mcq' in self.config.name: | |
| features["query"]= datasets.Value("string") | |
| features["choices"]= datasets.features.Sequence(feature={'text': datasets.Value(dtype='string', id=None), 'label': datasets.Value(dtype='string', id=None)}, length=-1, id=None) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features(features), | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_dir = dl_manager.download_and_extract(self.config.urls) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": data_dir["test"], | |
| }, | |
| ),datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "filepath": data_dir["val"], | |
| }, | |
| ) | |
| ] | |
| def _generate_examples(self, filepath): | |
| with open(filepath) as f: | |
| examples = json.load(f) | |
| for i, instance in enumerate(examples): | |
| yield i, instance |