def main():
config_help = "\n\nConfig parameters:\n\n" + "\n".join(ConfigSchema.help())
parser = argparse.ArgumentParser(
epilog=config_help,
# Needed to preserve line wraps in epilog.
formatter_class=argparse.RawDescriptionHelpFormatter,
)
parser.add_argument("config", help="Path to config file")
parser.add_argument("-p", "--param", action="append", nargs="*")
parser.add_argument("edge_paths",
type=Path,
nargs="*",
help="Input file paths")
parser.add_argument(
"-l",
"--lhs-col",
type=str,
required=True,
help="Column index for source entity",
)
parser.add_argument(
"-r",
"--rhs-col",
type=str,
required=True,
help="Column index for target entity",
)
parser.add_argument("--rel-col",
type=str,
help="Column index for relation entity")
parser.add_argument(
"--relation-type-min-count",
type=int,
default=1,
help="Min count for relation types",
)
parser.add_argument("--entity-min-count",
type=int,
default=1,
help="Min count for entities")
opt = parser.parse_args()
loader = ConfigFileLoader()
config_dict = loader.load_raw_config(opt.config, opt.param)
entity_configs, relation_configs, entity_path, edge_paths, dynamic_relations = parse_config_partial( # noqa
config_dict)
convert_input_data(
entity_configs,
relation_configs,
entity_path,
edge_paths,
opt.edge_paths,
ParquetEdgelistReader(opt.lhs_col, opt.rhs_col, opt.rel_col),
opt.entity_min_count,
opt.relation_type_min_count,
dynamic_relations,
)
def main():
config_help = '\n\nConfig parameters:\n\n' + '\n'.join(ConfigSchema.help())
parser = argparse.ArgumentParser(
epilog=config_help,
# Needed to preserve line wraps in epilog.
formatter_class=argparse.RawDescriptionHelpFormatter,
)
parser.add_argument('config', help='Path to config file')
parser.add_argument('-p', '--param', action='append', nargs='*')
parser.add_argument('edge_paths',
type=Path,
nargs='*',
help='Input file paths')
parser.add_argument('-l',
'--lhs-col',
type=str,
required=True,
help='Column index for source entity')
parser.add_argument('-r',
'--rhs-col',
type=str,
required=True,
help='Column index for target entity')
parser.add_argument('--rel-col',
type=str,
help='Column index for relation entity')
parser.add_argument('--relation-type-min-count',
type=int,
default=1,
help='Min count for relation types')
parser.add_argument('--entity-min-count',
type=int,
default=1,
help='Min count for entities')
opt = parser.parse_args()
loader = ConfigFileLoader()
config_dict = loader.load_raw_config(opt.config, opt.param)
entity_configs, relation_configs, entity_path, edge_paths, dynamic_relations = \
parse_config_partial(config_dict)
convert_input_data(
entity_configs,
relation_configs,
entity_path,
edge_paths,
opt.edge_paths,
ParquetEdgelistReader(opt.lhs_col, opt.rhs_col, opt.rel_col),
opt.entity_min_count,
opt.relation_type_min_count,
dynamic_relations,
)
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():
setup_logging()
parser = argparse.ArgumentParser(description='Example on Livejournal')
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')
args = parser.parse_args()
# download data
data_dir = args.data_dir
data_dir.mkdir(parents=True, exist_ok=True)
fpath = download_url(URL, data_dir)
fpath = extract_gzip(fpath)
print('Downloaded and extracted file.')
# random split file for train and test
random_split_file(fpath)
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_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=1, rel_col=None),
dynamic_relations=config.dynamic_relations,
)
train_config = attr.evolve(config, edge_paths=[output_train_path])
train(train_config, subprocess_init=subprocess_init)
eval_config = attr.evolve(config, edge_paths=[output_test_path])
do_eval(eval_config, subprocess_init=subprocess_init)
# data/example_2/entity_names_merchant_0.json
# data/example_2/entity_names_user_0.json
#
# and this file with data:
# data/example_2/edges_partitioned/edges_0_0.h5
# =================================================
setup_logging()
config = parse_config(raw_config)
subprocess_init = SubprocessInitializer()
input_edge_paths = [Path(GRAPH_PATH)]
convert_input_data(
config.entities,
config.relations,
config.entity_path,
config.edge_paths,
input_edge_paths,
TSVEdgelistReader(lhs_col=0, rel_col=1, rhs_col=2),
dynamic_relations=config.dynamic_relations,
)
# ===============================================
# 3. TRAIN THE EMBEDDINGS
# files generated in this step:
#
# checkpoint_version.txt
# config.json
# embeddings_item_0.v7.h5
# embeddings_merchant_0.v7.h5
# embeddings_user_0.v7.h5
# model.v7.h5
