def main():
setup_logging()
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('--rank',
type=int,
default=0,
help="For multi-machine, this machine's rank")
opt = parser.parse_args()
if opt.param is not None:
overrides = chain.from_iterable(opt.param) # flatten
else:
overrides = None
loader = ConfigFileLoader()
config = loader.load_config(opt.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)
train(config, rank=Rank(opt.rank), subprocess_init=subprocess_init)
def main():
setup_logging()
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(
"--rank",
type=int,
default=SINGLE_TRAINER,
help="For multi-machine, this machine's rank",
)
opt = parser.parse_args()
loader = ConfigFileLoader()
config = loader.load_config(opt.config, opt.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)
train(config, rank=opt.rank, subprocess_init=subprocess_init)
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():
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()
if args.param is not None:
overrides = chain.from_iterable(args.param) # flatten
else:
overrides = None
# 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, 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)
edge_paths = [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=1,
rel_col=None,
dynamic_relations=config.dynamic_relations,
)
train_path = [str(convert_path(data_dir / FILENAMES['train']))]
train_config = attr.evolve(config, edge_paths=train_path)
train(train_config, subprocess_init=subprocess_init)
eval_path = [str(convert_path(data_dir / FILENAMES['test']))]
eval_config = attr.evolve(config, edge_paths=eval_path)
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)
def main():
# Late import to avoid circular dependency.
from torchbiggraph.util import set_logging_verbosity, setup_logging
setup_logging()
parser = argparse.ArgumentParser()
parser.add_argument('config', help="Path to config file")
parser.add_argument('query', help="Name of param to retrieve")
parser.add_argument('-p', '--param', action='append', nargs='*')
opt = parser.parse_args()
if opt.param is not None:
overrides = chain.from_iterable(opt.param) # flatten
else:
overrides = None
loader = ConfigFileLoader()
config = loader.load_config(opt.config, overrides)
set_logging_verbosity(config.verbose)
print(config[opt.query])
def main():
setup_logging()
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='*')
opt = parser.parse_args()
loader = ConfigFileLoader()
config = loader.load_config(opt.config, opt.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)
do_eval(config, subprocess_init=subprocess_init)
# =================================================
# 2. TRANSFORM GRAPH TO A BIGGRAPH-FRIENDLY FORMAT
# This step generates the following metadata files:
#
# data/example_2/entity_count_item_0.txt
# data/example_2/entity_count_merchant_0.txt
# data/example_2/entity_count_user_0.txt
# data/example_2/entity_names_item_0.json
# 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,
)
# ===============================================
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 run(input_file: KGTKFiles,
output_file: KGTKFiles,
verbose: bool = False,
very_verbose: bool = False,
**kwargs):
"""
**kwargs stores all parameters providing by user
"""
# print(kwargs)
# import modules locally
import sys
import typing
import os
import logging
from pathlib import Path
import json, os, h5py, gzip, torch, shutil
from torchbiggraph.config import parse_config
from kgtk.exceptions import KGTKException
# copy missing file under kgtk/graph_embeddings
from kgtk.templates.kgtkcopytemplate import KgtkCopyTemplate
from kgtk.graph_embeddings.importers import TSVEdgelistReader, convert_input_data
from torchbiggraph.train import train
from torchbiggraph.util import SubprocessInitializer, setup_logging
from kgtk.graph_embeddings.export_to_tsv import make_tsv
# from torchbiggraph.converters.export_to_tsv import make_tsv
try:
input_kgtk_file: Path = KGTKArgumentParser.get_input_file(input_file)
output_kgtk_file: Path = KGTKArgumentParser.get_output_file(
output_file)
# store the data into log file, then the console will not output anything
if kwargs['log_file_path'] != None:
log_file_path = kwargs['log_file_path']
logging.basicConfig(
format='%(asctime)s - %(filename)s[line:%(lineno)d] \
- %(levelname)s: %(message)s',
level=logging.DEBUG,
filename=str(log_file_path),
filemode='w')
print(
f'In Processing, Please go to {kwargs["log_file_path"]} to check details',
file=sys.stderr,
flush=True)
tmp_folder = kwargs['temporary_directory']
tmp_tsv_path: Path = tmp_folder / f'tmp_{input_kgtk_file.name}'
# tmp_tsv_path:Path = input_kgtk_file.parent/f'tmp_{input_kgtk_file.name}'
# make sure the tmp folder exists, otherwise it will raise an exception
if not os.path.exists(tmp_folder):
os.makedirs(tmp_folder)
try: #if output_kgtk_file is not empty, delete it
output_kgtk_file.unlink()
except:
pass # didn't find, then let it go
# *********************************************
# 0. PREPARE PBG TSV FILE
# *********************************************
reader_options: KgtkReaderOptions = KgtkReaderOptions.from_dict(kwargs)
value_options: KgtkValueOptions = KgtkValueOptions.from_dict(kwargs)
error_file: typing.TextIO = sys.stdout if kwargs.get(
"errors_to_stdout") else sys.stderr
kct: KgtkCopyTemplate = KgtkCreateTmpTsv(
input_file_path=input_kgtk_file,
output_file_path=tmp_tsv_path,
reader_options=reader_options,
value_options=value_options,
error_file=error_file,
verbose=verbose,
very_verbose=very_verbose,
)
# prepare the graph file
# create a tmp tsv file for PBG embedding
logging.info('Generate the valid tsv format for embedding ...')
kct.process()
logging.info('Embedding file is ready...')
# *********************************************
# 1. DEFINE CONFIG
# *********************************************
raw_config = get_config(**kwargs)
## setting corresponding learning rate and loss function for different algorthim
processed_config = config_preprocess(raw_config)
# temporry output folder
tmp_output_folder = Path(processed_config['entity_path'])
# before moving, need to check whether the tmp folder is not empty in case of bug
try: #if temporry output folder is alrady existing then delete it
shutil.rmtree(tmp_output_folder)
except:
pass # didn't find, then let it go
# **************************************************
# 2. TRANSFORM GRAPH TO A BIGGRAPH-FRIENDLY FORMAT
# **************************************************
setup_logging()
config = parse_config(processed_config)
subprocess_init = SubprocessInitializer()
input_edge_paths = [tmp_tsv_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
#*************************************************
train(config, subprocess_init=subprocess_init)
# ************************************************
# 4. GENERATE THE OUTPUT
# ************************************************
# entities_output = output_kgtk_file
entities_output = tmp_output_folder / 'entities_output.tsv'
relation_types_output = tmp_output_folder / 'relation_types_tf.tsv'
with open(entities_output,
"xt") as entities_tf, open(relation_types_output,
"xt") as relation_types_tf:
make_tsv(config, entities_tf, relation_types_tf)
# output correct format for embeddings
if kwargs['output_format'] == 'glove': # glove format output
shutil.copyfile(entities_output, output_kgtk_file)
elif kwargs['output_format'] == 'w2v': # w2v format output
generate_w2v_output(entities_output, output_kgtk_file, kwargs)
else: # write to the kgtk output format tsv
generate_kgtk_output(entities_output, output_kgtk_file,
kwargs.get('output_no_header', False),
verbose, very_verbose)
logging.info(f'Embeddings has been generated in {output_kgtk_file}.')
# ************************************************
# 5. Garbage collection
# ************************************************
if kwargs['retain_temporary_data'] == False:
shutil.rmtree(kwargs['temporary_directory'])
# tmp_tsv_path.unlink() # delete temporay tsv file
# shutil.rmtree(tmp_output_folder) # deleter temporay output folder
if kwargs["log_file_path"] != None:
print('Processed Finished.', file=sys.stderr, flush=True)
logging.info(
f"Process Finished.\nOutput has been saved in {repr(str(output_kgtk_file))}"
)
else:
print(
f"Process Finished.\nOutput has been saved in {repr(str(output_kgtk_file))}",
file=sys.stderr,
flush=True)
except Exception as e:
raise KGTKException(str(e))
