import json import datasets _CITATION = """\ """ _DESCRIPTION = """\ SwissIPG dataset containing three subsets: GO, IPR, and IPR_GO, each with training and test sets. """ class SwissProtIPG(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ datasets.BuilderConfig( name="GO", version=datasets.Version("1.0.0"), description="GO subset", ), datasets.BuilderConfig( name="IPR", version=datasets.Version("1.0.0"), description="IPR subset", ), datasets.BuilderConfig( name="IPR_GO", version=datasets.Version("1.0.0"), description="IPR+GO subset", ), ] def _info(self): if self.config.name == "GO": features = datasets.Features( { "Gene Ontology (molecular function)": datasets.Sequence( { "GO-ID": datasets.Value("string"), "GO-Name": datasets.Value("string"), } ), "sequence": datasets.Value("string"), "instruction": datasets.Value("string"), } ) elif self.config.name == "IPR": features = datasets.Features( { "InterPro": datasets.Sequence( { "InterPro-ID": datasets.Value("string"), "InterPro-Name": datasets.Value("string"), "InterPro-Type": datasets.Value("string"), "Beg": datasets.Value("int32"), "End": datasets.Value("int32"), } ), "sequence": datasets.Value("string"), "instruction": datasets.Value("string"), } ) else: # IPR_GO features = datasets.Features( { "Gene Ontology (molecular function)": datasets.Sequence( { "GO-ID": datasets.Value("string"), "GO-Name": datasets.Value("string"), } ), "InterPro": datasets.Sequence( { "InterPro-ID": datasets.Value("string"), "InterPro-Name": datasets.Value("string"), "InterPro-Type": datasets.Value("string"), "Beg": datasets.Value("int32"), "End": datasets.Value("int32"), } ), "sequence": datasets.Value("string"), "instruction": datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, citation=_CITATION, ) def _split_generators(self, dl_manager): if self.config.name == "GO": train_file = dl_manager.download_and_extract("go_train.json") test_file = dl_manager.download_and_extract("go_test.json") elif self.config.name == "IPR": train_file = dl_manager.download_and_extract("ipr_train.json") test_file = dl_manager.download_and_extract("ipr_test.json") elif self.config.name == "IPR_GO": train_file = dl_manager.download_and_extract("ipr_go_train.json") test_file = dl_manager.download_and_extract("ipr_go_test.json") else: raise ValueError(f"Invalid config: {self.config.name}") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"filepath": train_file}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"filepath": test_file}, ), ] def _generate_examples(self, filepath): with open(filepath, encoding="utf-8") as f: data = json.load(f) for i, row in enumerate(data): yield i, row