def test_featurized(self):
e1 = EntitySchema(num_partitions=1, featurized=True)
e2 = EntitySchema(num_partitions=1)
r1 = RelationSchema(name="r1", lhs="e1", rhs="e2")
r2 = RelationSchema(name="r2", lhs="e2", rhs="e1")
base_config = ConfigSchema(
dimension=10,
relations=[r1, r2],
entities={
"e1": e1,
"e2": e2
},
entity_path=None, # filled in later
edge_paths=[], # filled in later
checkpoint_path=self.checkpoint_path.name,
workers=2,
)
dataset = generate_dataset(base_config,
num_entities=100,
fractions=[0.4, 0.2])
self.addCleanup(dataset.cleanup)
train_config = attr.evolve(
base_config,
entity_path=dataset.entity_path.name,
edge_paths=[dataset.relation_paths[0].name],
)
eval_config = attr.evolve(
base_config,
entity_path=dataset.entity_path.name,
edge_paths=[dataset.relation_paths[1].name],
)
# Just make sure no exceptions are raised and nothing crashes.
train(train_config, rank=0, subprocess_init=self.subprocess_init)
self.assertCheckpointWritten(train_config, version=1)
do_eval(eval_config, subprocess_init=self.subprocess_init)
def test_resume_from_checkpoint(self):
entity_name = "e"
relation_config = RelationSchema(name="r",
lhs=entity_name,
rhs=entity_name)
base_config = ConfigSchema(
dimension=10,
relations=[relation_config],
entities={entity_name: EntitySchema(num_partitions=1)},
entity_path=None, # filled in later
edge_paths=[], # filled in later
checkpoint_path=self.checkpoint_path.name,
num_epochs=2,
num_edge_chunks=2,
workers=2,
)
dataset = generate_dataset(base_config,
num_entities=100,
fractions=[0.4, 0.4])
self.addCleanup(dataset.cleanup)
train_config = attr.evolve(
base_config,
entity_path=dataset.entity_path.name,
edge_paths=[d.name for d in dataset.relation_paths],
)
# Just make sure no exceptions are raised and nothing crashes.
init_embeddings(train_config.checkpoint_path, train_config, version=7)
train(train_config, rank=0, subprocess_init=self.subprocess_init)
self.assertCheckpointWritten(train_config, version=8)
# Check we did resume the run, not start the whole thing anew.
self.assertFalse(
os.path.exists(
os.path.join(train_config.checkpoint_path, "model.v6.h5")))
def test_with_initial_value(self):
entity_name = "e"
relation_config = RelationSchema(name="r",
lhs=entity_name,
rhs=entity_name)
base_config = ConfigSchema(
dimension=10,
relations=[relation_config],
entities={entity_name: EntitySchema(num_partitions=1)},
entity_path=None, # filled in later
edge_paths=[], # filled in later
checkpoint_path=self.checkpoint_path.name,
workers=2,
)
dataset = generate_dataset(base_config,
num_entities=100,
fractions=[0.4])
self.addCleanup(dataset.cleanup)
init_dir = TemporaryDirectory()
self.addCleanup(init_dir.cleanup)
train_config = attr.evolve(
base_config,
entity_path=dataset.entity_path.name,
edge_paths=[dataset.relation_paths[0].name],
init_path=init_dir.name,
)
# Just make sure no exceptions are raised and nothing crashes.
init_embeddings(train_config.init_path, train_config)
train(train_config, rank=0, subprocess_init=self.subprocess_init)
self.assertCheckpointWritten(train_config, version=1)
def test_entity_dimensions(self):
entity_name = "e"
relation_config = RelationSchema(name="r",
lhs=entity_name,
rhs=entity_name)
base_config = ConfigSchema(
dimension=10,
relations=[relation_config],
entities={
entity_name: EntitySchema(num_partitions=1, dimension=8)
},
entity_path=None, # filled in later
edge_paths=[], # filled in later
checkpoint_path=self.checkpoint_path.name,
workers=2,
)
dataset = generate_dataset(base_config,
num_entities=100,
fractions=[0.4, 0.2])
self.addCleanup(dataset.cleanup)
train_config = attr.evolve(
base_config,
entity_path=dataset.entity_path.name,
edge_paths=[dataset.relation_paths[0].name],
)
eval_config = attr.evolve(
base_config,
entity_path=dataset.entity_path.name,
edge_paths=[dataset.relation_paths[1].name],
relations=[attr.evolve(relation_config, all_negs=True)],
)
# Just make sure no exceptions are raised and nothing crashes.
train(train_config, rank=0, subprocess_init=self.subprocess_init)
self.assertCheckpointWritten(train_config, version=1)
do_eval(eval_config, subprocess_init=self.subprocess_init)
def _test_gpu(self, do_half_precision=False, num_partitions=2):
entity_name = "e"
relation_config = RelationSchema(name="r", lhs=entity_name, rhs=entity_name)
base_config = ConfigSchema(
dimension=16,
batch_size=1024,
num_batch_negs=64,
num_uniform_negs=64,
relations=[relation_config],
entities={entity_name: EntitySchema(num_partitions=num_partitions)},
entity_path=None, # filled in later
edge_paths=[], # filled in later
checkpoint_path=self.checkpoint_path.name,
workers=2,
num_gpus=2,
regularization_coef=1e-4,
half_precision=do_half_precision,
)
dataset = generate_dataset(base_config, num_entities=100, fractions=[0.4, 0.2])
self.addCleanup(dataset.cleanup)
train_config = attr.evolve(
base_config,
entity_path=dataset.entity_path.name,
edge_paths=[dataset.relation_paths[0].name],
)
eval_config = attr.evolve(
base_config,
entity_path=dataset.entity_path.name,
edge_paths=[dataset.relation_paths[1].name],
relations=[attr.evolve(relation_config, all_negs=True)],
)
# Just make sure no exceptions are raised and nothing crashes.
train(train_config, rank=0, subprocess_init=self.subprocess_init)
self.assertCheckpointWritten(train_config, version=1)
do_eval(eval_config, subprocess_init=self.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():
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()]
print('edge_paths', edge_paths)
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():
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',
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
os.makedirs(data_dir, exist_ok=True)
fpath = utils.download_url(URL, data_dir)
fpath = utils.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)
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=1,
rel_col=None,
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']))]
eval_config = attr.evolve(config, edge_paths=eval_path)
do_eval(
eval_config,
subprocess_init=partial(add_to_sys_path, loader.config_dir.name),
)
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 run_train_eval():
#将数据转为PBG可读的分区文件
convert_input_data(CONFIG_PATH,
edge_paths,
lhs_col=0,
rhs_col=1,
rel_col=None)
#解析配置
config = parse_config(CONFIG_PATH)
#训练配置，已分区的train_paths路径替换配置文件中的edge_paths
train_config = attr.evolve(config, edge_paths=train_paths)
#传入训练配置文件开始训练
train(train_config)
#测试配置，已分区的eval_paths路径替换配置文件中的edge_paths
eval_config = attr.evolve(config, edge_paths=eval_paths)
#开始验证
do_eval(eval_config)
def test_dynamic_relations(self):
relation_config = RelationSchema(name="r", lhs="el", rhs="er")
base_config = ConfigSchema(
dimension=10,
relations=[relation_config],
entities={
"el": EntitySchema(num_partitions=1),
"er": EntitySchema(num_partitions=1),
},
entity_path=None, # filled in later
edge_paths=[], # filled in later
checkpoint_path=self.checkpoint_path.name,
dynamic_relations=True,
global_emb=False, # Must be off for dynamic relations.
workers=2,
)
gen_config = attr.evolve(
base_config,
relations=[relation_config] * 10,
dynamic_relations=False, # Must be off if more than 1 relation.
)
dataset = generate_dataset(gen_config,
num_entities=100,
fractions=[0.04, 0.02])
self.addCleanup(dataset.cleanup)
with open(
os.path.join(dataset.entity_path.name,
"dynamic_rel_count.txt"), "xt") as f:
f.write("%d" % len(gen_config.relations))
train_config = attr.evolve(
base_config,
entity_path=dataset.entity_path.name,
edge_paths=[dataset.relation_paths[0].name],
)
eval_config = attr.evolve(
base_config,
relations=[attr.evolve(relation_config, all_negs=True)],
entity_path=dataset.entity_path.name,
edge_paths=[dataset.relation_paths[1].name],
)
# Just make sure no exceptions are raised and nothing crashes.
train(train_config, rank=0, subprocess_init=self.subprocess_init)
self.assertCheckpointWritten(train_config, version=1)
do_eval(eval_config, subprocess_init=self.subprocess_init)
def run_train_eval():
random_split_file(DATA_PATH)
convert_input_data(
CONFIG_PATH,
edge_paths,
lhs_col=0,
rhs_col=1,
rel_col=None,
)
train_config = parse_config(CONFIG_PATH)
train_config = attr.evolve(train_config, edge_paths=train_path)
train(train_config)
eval_config = attr.evolve(train_config, edge_paths=eval_path)
do_eval(eval_config)
from torchbiggraph.config import parse_config import attr train_config = parse_config(CONFIG_PATH) train_path = [convert_path(os.path.join(DATA_DIR, FILENAMES['train']))] train_config = attr.evolve(train_config, edge_paths=train_path) from torchbiggraph.train import train train(train_config) # Time to run on liveJournal data: 17:43 - ??? ### SNIPPET 3 ###
)
# ===============================================
# 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
# training_stats.json
# ===============================================
train(config, subprocess_init=subprocess_init)
# =======================================================================
# 4. LOAD THE EMBEDDINGS
# The final output of the process consists of 3 dictionaries -
# - one for users, items, merchants - mapping entity to its embedding
# =======================================================================
users_path = DATA_DIR + '/entity_names_user_0.json'
items_path = DATA_DIR + '/entity_names_item_0.json'
merchants_path = DATA_DIR + '/entity_names_merchant_0.json'
user_emb_path = MODEL_DIR + "/embeddings_user_0.v{NUMBER_OF_EPOCHS}.h5" \
.format(NUMBER_OF_EPOCHS=raw_config['num_epochs'])
item_emb_path = MODEL_DIR + "/embeddings_item_0.v{NUMBER_OF_EPOCHS}.h5" \
.format(NUMBER_OF_EPOCHS=raw_config['num_epochs'])
merchant_emb_path = MODEL_DIR + "/embeddings_merchant_0.v{NUMBER_OF_EPOCHS}.h5" \
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),
)
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))
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)