def main():
setup_logging()
parser = argparse.ArgumentParser(description='Example on FB15k')
parser.add_argument('--config', default=DEFAULT_CONFIG,
help='Path to config file')
parser.add_argument('-p', '--param', action='append', nargs='*')
parser.add_argument('--data_dir', type=Path, default='data',
help='where to save processed data')
parser.add_argument('--no-filtered', dest='filtered', action='store_false',
help='Run unfiltered eval')
args = parser.parse_args()
if args.param is not None:
overrides = chain.from_iterable(args.param) # flatten
else:
overrides = None
# download data
data_dir = args.data_dir
fpath = download_url(FB15K_URL, data_dir)
extract_tar(fpath)
print('Downloaded and extracted file.')
loader = ConfigFileLoader()
config = loader.load_config(args.config, overrides)
set_logging_verbosity(config.verbose)
subprocess_init = SubprocessInitializer()
subprocess_init.register(setup_logging, config.verbose)
subprocess_init.register(add_to_sys_path, loader.config_dir.name)
input_edge_paths = [data_dir / name for name in FILENAMES]
output_train_path, output_valid_path, output_test_path = config.edge_paths
convert_input_data(
config.entities,
config.relations,
config.entity_path,
config.edge_paths,
input_edge_paths,
lhs_col=0,
rhs_col=2,
rel_col=1,
dynamic_relations=config.dynamic_relations,
)
train_config = attr.evolve(config, edge_paths=[output_train_path])
train(train_config, subprocess_init=subprocess_init)
relations = [attr.evolve(r, all_negs=True) for r in config.relations]
eval_config = attr.evolve(
config, edge_paths=[output_test_path], relations=relations, num_uniform_negs=0)
if args.filtered:
filter_paths = [output_test_path, output_valid_path, output_train_path]
do_eval(
eval_config,
evaluator=FilteredRankingEvaluator(eval_config, filter_paths),
subprocess_init=subprocess_init,
)
else:
do_eval(eval_config, subprocess_init=subprocess_init)
def main():
parser = argparse.ArgumentParser(description='Example on FB15k')
parser.add_argument('--config',
default='fb15k_config.py',
help='Path to config file')
parser.add_argument('-p', '--param', action='append', nargs='*')
parser.add_argument('--data_dir',
default='../data',
help='where to save processed data')
parser.add_argument('--no-filtered',
dest='filtered',
action='store_false',
help='Run unfiltered eval')
args = parser.parse_args()
if args.param is not None:
overrides = chain.from_iterable(args.param) # flatten
else:
overrides = None
# download data
data_dir = args.data_dir
fpath = utils.download_url(FB15K_URL, data_dir)
utils.extract_tar(fpath)
print('Downloaded and extracted file.')
edge_paths = [os.path.join(data_dir, name) for name in FILENAMES.values()]
convert_input_data(
args.config,
edge_paths,
lhs_col=0,
rhs_col=2,
rel_col=1,
)
config = parse_config(args.config, overrides)
train_path = [convert_path(os.path.join(data_dir, FILENAMES['train']))]
train_config = attr.evolve(config, edge_paths=train_path)
train(train_config)
eval_path = [convert_path(os.path.join(data_dir, FILENAMES['test']))]
relations = [attr.evolve(r, all_negs=True) for r in config.relations]
eval_config = attr.evolve(config,
edge_paths=eval_path,
relations=relations)
if args.filtered:
filter_paths = [
convert_path(os.path.join(data_dir, FILENAMES['test'])),
convert_path(os.path.join(data_dir, FILENAMES['valid'])),
convert_path(os.path.join(data_dir, FILENAMES['train'])),
]
do_eval(eval_config, FilteredRankingEvaluator(eval_config,
filter_paths))
else:
do_eval(eval_config)
def main():
setup_logging()
parser = argparse.ArgumentParser(description="Example on FB15k")
parser.add_argument("--config",
default=DEFAULT_CONFIG,
help="Path to config file")
parser.add_argument("-p", "--param", action="append", nargs="*")
parser.add_argument("--data_dir",
type=Path,
default="data",
help="where to save processed data")
parser.add_argument(
"--no-filtered",
dest="filtered",
action="store_false",
help="Run unfiltered eval",
)
args = parser.parse_args()
# download data
data_dir = args.data_dir
fpath = download_url(FB15K_URL, data_dir)
extract_tar(fpath)
print("Downloaded and extracted file.")
loader = ConfigFileLoader()
config = loader.load_config(args.config, args.param)
set_logging_verbosity(config.verbose)
subprocess_init = SubprocessInitializer()
subprocess_init.register(setup_logging, config.verbose)
subprocess_init.register(add_to_sys_path, loader.config_dir.name)
input_edge_paths = [data_dir / name for name in FILENAMES]
output_train_path, output_valid_path, output_test_path = config.edge_paths
convert_input_data(
config.entities,
config.relations,
config.entity_path,
config.edge_paths,
input_edge_paths,
TSVEdgelistReader(lhs_col=0, rhs_col=2, rel_col=1),
dynamic_relations=config.dynamic_relations,
)
train_config = attr.evolve(config, edge_paths=[output_train_path])
train(train_config, subprocess_init=subprocess_init)
relations = [attr.evolve(r, all_negs=True) for r in config.relations]
eval_config = attr.evolve(config,
edge_paths=[output_test_path],
relations=relations,
num_uniform_negs=0)
if args.filtered:
filter_paths = [output_test_path, output_valid_path, output_train_path]
do_eval(
eval_config,
evaluator=FilteredRankingEvaluator(eval_config, filter_paths),
subprocess_init=subprocess_init,
)
else:
do_eval(eval_config, subprocess_init=subprocess_init)
def main():
parser = argparse.ArgumentParser(description='Example on FB15k')
parser.add_argument('--config',
default=DEFAULT_CONFIG,
help='Path to config file')
parser.add_argument('-p', '--param', action='append', nargs='*')
parser.add_argument('--data_dir',
default='data',
help='where to save processed data')
parser.add_argument('--no-filtered',
dest='filtered',
action='store_false',
help='Run unfiltered eval')
args = parser.parse_args()
if args.param is not None:
overrides = chain.from_iterable(args.param) # flatten
else:
overrides = None
# download data
data_dir = args.data_dir
fpath = utils.download_url(FB15K_URL, data_dir)
utils.extract_tar(fpath)
print('Downloaded and extracted file.')
loader = ConfigFileLoader()
config = loader.load_config(args.config, overrides)
edge_paths = [os.path.join(data_dir, name) for name in FILENAMES.values()]
convert_input_data(
config.entities,
config.relations,
config.entity_path,
edge_paths,
lhs_col=0,
rhs_col=2,
rel_col=1,
dynamic_relations=config.dynamic_relations,
)
train_path = [convert_path(os.path.join(data_dir, FILENAMES['train']))]
train_config = attr.evolve(config, edge_paths=train_path)
train(
train_config,
subprocess_init=partial(add_to_sys_path, loader.config_dir.name),
)
eval_path = [convert_path(os.path.join(data_dir, FILENAMES['test']))]
relations = [attr.evolve(r, all_negs=True) for r in config.relations]
eval_config = attr.evolve(config,
edge_paths=eval_path,
relations=relations,
num_uniform_negs=0)
if args.filtered:
filter_paths = [
convert_path(os.path.join(data_dir, FILENAMES['test'])),
convert_path(os.path.join(data_dir, FILENAMES['valid'])),
convert_path(os.path.join(data_dir, FILENAMES['train'])),
]
do_eval(
eval_config,
evaluator=FilteredRankingEvaluator(eval_config, filter_paths),
subprocess_init=partial(add_to_sys_path, loader.config_dir.name),
)
else:
do_eval(
eval_config,
subprocess_init=partial(add_to_sys_path, loader.config_dir.name),
)
