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import glob |
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import os |
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import datasets |
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from huggingface_hub import snapshot_download |
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_CITATION = """TO BE ADDED.""" |
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_DESCRIPTION = """ |
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HeuriGen is a collection of combinatorial-optimization problems |
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for benchmarking heuristic-program generation by LLMs. |
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""" |
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REPO_ID = "heurigen/heurigen-data" |
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class HeuriGenConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super().__init__(version=datasets.Version("0.1.0"), **kwargs) |
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_BUILDER_CONFIGS = [ |
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HeuriGenConfig( |
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name="operator_scheduling", description="DFG operator-scheduling instances" |
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), |
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HeuriGenConfig( |
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name="technology_mapping", |
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description="Logic-network technology-mapping instances", |
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), |
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HeuriGenConfig( |
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name="global_routing", description="Netlist global-routing instances" |
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), |
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HeuriGenConfig( |
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name="egraph_extraction", description="E-graph extraction instances" |
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), |
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HeuriGenConfig(name="intra_op_parallel", description="Intra-op parallel instances"), |
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HeuriGenConfig( |
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name="protein_sequence_design", description="Protein sequence design instances" |
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), |
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HeuriGenConfig(name="pedigree", description="Pedigree problem instances"), |
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HeuriGenConfig( |
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name="pickup_delivery_time_windows", description="Pickup-delivery-time-windows (PDPTW) instances" |
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), |
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HeuriGenConfig(name="crew_pairing", description="Crew pairing instances"), |
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HeuriGenConfig(name="frequency_assignment", description="Frequency assignment instances"), |
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] |
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class HeuriGen(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = _BUILDER_CONFIGS |
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DEFAULT_CONFIG_NAME = "operator_scheduling" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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citation=_CITATION, |
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features=datasets.Features( |
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{ |
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"file_path": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage=f"https://huggingface.co/datasets/{REPO_ID}", |
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) |
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def _split_generators(self, dl_manager): |
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local_root = snapshot_download( |
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repo_id=REPO_ID, |
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repo_type="dataset", |
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revision="main", |
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allow_patterns=[f"{self.config.name}/**"], |
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local_dir=dl_manager.manual_dir, |
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) |
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base = os.path.join(local_root, self.config.name) |
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def files(pattern): |
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all_files = [] |
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for path in glob.glob(os.path.join(base, pattern), recursive=True): |
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if os.path.isfile(path): |
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all_files.append(os.path.abspath(path)) |
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return sorted(all_files) |
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demo_paths = files("demo/**/*") |
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eval_paths = files("eval/**/*") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"files": demo_paths}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"files": eval_paths}, |
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), |
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] |
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def _generate_examples(self, files): |
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for idx, path in enumerate(files): |
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yield idx, {"file_path": path} |
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