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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
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