def test_basic(self):
config = ConfigSchema(
entities={"e": EntitySchema(num_partitions=1)},
relations=[RelationSchema(name="r", lhs="e", rhs="e")],
dimension=1,
entity_path="foo", edge_paths=["bar"], checkpoint_path="baz")
metadata = ConfigMetadataProvider(config).get_checkpoint_metadata()
self.assertIsInstance(metadata, dict)
self.assertCountEqual(metadata.keys(), ["config/json"])
self.assertEqual(
config, ConfigSchema.from_dict(json.loads(metadata["config/json"])))
def read_config(self) -> ConfigSchema:
config_json = self.storage.load_config()
return ConfigSchema.from_dict(json.loads(config_json))
lr=0.1,
num_uniform_negs=50,
eval_fraction=
0, # to reproduce results, we need to use all training data
workers=1,
distributed_init_method="tpc://localhost:30050",
)
for num_part in args.num_parts:
datadir = "{}_big_{}".format(args.dataset, num_part)
config_dict['entity_path'] = os.path.join(args.root_output,
datadir)
config_dict['entities']['all']['num_partitions'] = num_part
config_dict['edge_paths'] = [
os.path.join(args.root_output, datadir, datadir)
]
config = ConfigSchema.from_dict(config_dict)
convert_input_data(
config.entities,
config.relations,
config.entity_path,
config.edge_paths,
[
Path(
os.path.join(
args.root_output,
"{}_text/edgelist_pybig.txt".format(args.dataset)))
],
lhs_col=0,
rhs_col=2,
rel_col=1,
def read_config(self) -> ConfigSchema:
with open(os.path.join(self.path, CONFIG_FILE), "rt") as tf:
return ConfigSchema.from_dict(json.load(tf))