def test(args):
ent_vocab = Vocab.load(args.ent)
rel_vocab = Vocab.load(args.rel)
# preparing data
test_dat = TripletDataset.load(args.data, ent_vocab, rel_vocab)
print('loading model...')
if args.method == 'complex':
from models.complex import ComplEx as Model
elif args.method == 'distmult':
from models.distmult import DistMult as Model
else:
raise NotImplementedError
if args.filtered:
print('loading whole graph...')
from utils.graph import TensorTypeGraph
whole_graph = TensorTypeGraph.load_from_raw(args.graphall, ent_vocab, rel_vocab)
else:
whole_graph = None
evaluator = Evaluator('all', None, args.filtered, whole_graph)
if args.filtered:
evaluator.prepare_valid(test_dat)
model = Model.load_model(args.model)
all_res = evaluator.run_all_matric(model, test_dat)
for metric in sorted(all_res.keys()):
print('{:20s}: {}'.format(metric, all_res[metric]))
def test(args):
ent_vocab = Vocab.load(args.ent)
rel_vocab = Vocab.load(args.rel)
# preparing data
test_dat = TripletDataset.load(args.data, ent_vocab, rel_vocab)
# graph = GraphDataset.load(args.knowledge, ent_vocab, rel_vocab)
graph = None
print('loading model...')
if args.method == 'complex':
from models.complex import ComplEx as Model
elif args.method == 'distmult':
from models.distmult import DistMult as Model
elif args.method == 'transe':
from models.transe import TransE as Model
elif args.method == 'hole':
from models.hole import HolE as Model
elif args.method == 'rescal':
from models.rescal import RESCAL as Model
elif args.method == 'analogy':
from models.analogy import ANALOGY as Model
elif args.method == 'randwalk':
from models.randwalk import RandWalk as Model
elif args.method == 'lr':
from models.lr import LogisticReg as Model
else:
raise NotImplementedError
if args.filtered:
print('loading whole graph...')
from utils.graph import TensorTypeGraph
whole_graph = TensorTypeGraph.load_from_raw(args.graphall, ent_vocab, rel_vocab)
else:
whole_graph = None
evaluator = Evaluator('all', None, args.filtered, whole_graph)
if args.filtered:
evaluator.prepare_valid(test_dat)
model = Model.load_model(args.model)
if args.method == 'randwalk':
model.load_wv_model(args.wv_model)
all_res = evaluator.run_all_matric(model, test_dat, graph)
for metric in sorted(all_res.keys()):
print('{:20s}: {}'.format(metric, all_res[metric]))
def test(args):
ent_vocab = Vocab.load(args.ent)
rel_vocab = Vocab.load(args.rel)
# preparing data
if args.task == 'kbc':
test_dat = TripletDataset.load(args.data, ent_vocab, rel_vocab)
elif args.task == 'tc':
test_dat = LabeledTripletDataset.load(args.data, ent_vocab, rel_vocab)
else:
raise ValueError('Invalid task: {}'.format(args.task))
print('loading model...')
if args.method == 'transe':
from models.transe import TransE as Model
elif args.method == 'complex':
from models.complex import ComplEx as Model
elif args.method == 'analogy':
from models.analogy import ANALOGY as Model
else:
raise NotImplementedError
if args.filtered:
print('loading whole graph...')
from utils.graph import TensorTypeGraph
graphall = TensorTypeGraph.load_from_raw(args.graphall, ent_vocab,
rel_vocab)
# graphall = TensorTypeGraph.load(args.graphall)
else:
graphall = None
model = Model.load_model(args.model)
if args.metric == 'all':
evaluator = Evaluator('all', None, args.filtered, False, graphall)
if args.filtered:
evaluator.prepare_valid(test_dat)
all_res = evaluator.run_all_matric(model, test_dat)
for metric in sorted(all_res.keys()):
print('{:20s}: {}'.format(metric, all_res[metric]))
else:
evaluator = Evaluator(args.metric, None, False, True, None)
res = evaluator.run(model, test_dat)
print('{:20s}: {}'.format(args.metric, res))
def train(args):
# setting for logging
if not os.path.exists(args.log):
os.mkdir(args.log)
logger = logging.getLogger()
logging.basicConfig(level=logging.INFO)
log_path = os.path.join(args.log, 'log')
file_handler = logging.FileHandler(log_path)
fmt = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
file_handler.setFormatter(fmt)
logger.addHandler(file_handler)
logger.info('Arguments...')
for arg, val in sorted(vars(args).items()):
logger.info('{:>10} -----> {}'.format(arg, val))
ent_vocab = Vocab.load(args.ent)
rel_vocab = Vocab.load(args.rel)
n_entity, n_relation = len(ent_vocab), len(rel_vocab)
# preparing data
if args.task == 'kbc':
train_dat = TripletDataset.load(args.train, ent_vocab, rel_vocab)
valid_dat = TripletDataset.load(args.valid, ent_vocab, rel_vocab) if args.valid else None
elif args.task == 'tc':
assert args.metric == 'acc'
train_dat = TripletDataset.load(args.train, ent_vocab, rel_vocab)
valid_dat = LabeledTripletDataset.load(args.valid, ent_vocab, rel_vocab) if args.valid else None
else:
raise ValueError('Invalid task: {}'.format(args.task))
assert args.l1_ratio >= 0 and args.l1_ratio <= 1.0
if args.l1_ratio == 0:
logger.info("===== WARNING : l1_ratio has zero value. not inducing sparsity =====")
if args.opt == 'adarda':
opt = AdagradRDA(args.lr, args.reg*args.l1_ratio)
elif args.opt == 'adardamul':
opt = AdagradRDAmul(args.lr, args.reg*args.l1_ratio)
else:
raise NotImplementedError
if args.reg*(1-args.l1_ratio) > 0:
opt.set_l2_reg(args.reg*(1-args.l1_ratio))
# elif args.reg*(1-args.l1_ratio) > 0 and args.onlyl1:
# opt.sel_ent_l2_reg(args.reg*(1-args.l1_ratio))
if args.gradclip > 0:
opt.set_gradclip(args.gradclip)
logger.info('building model...')
if args.method == 'complex':
from models.complex import ComplEx
model = ComplEx(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
else:
raise NotImplementedError
if args.filtered:
print('loading whole graph...')
from utils.graph import TensorTypeGraph
graphall = TensorTypeGraph.load_from_raw(args.graphall, ent_vocab, rel_vocab)
else:
graphall = None
evaluator = Evaluator(args.metric, args.nbest, args.filtered, True, graphall) if args.valid else None
if args.filtered and args.valid:
evaluator.prepare_valid(valid_dat)
if args.mode == 'pairwise':
raise NotImplementedError
trainer = PairwiseTrainer(model=model, opt=opt, save_step=args.save_step,
batchsize=args.batch, logger=logger,
evaluator=evaluator, valid_dat=valid_dat,
n_negative=args.negative, epoch=args.epoch,
model_dir=args.log, restart=args.restart,
add_re=args.add_re)
elif args.mode == 'single':
trainer = SingleTrainer(model=model, opt=opt, save_step=args.save_step,
batchsize=args.batch, logger=logger,
evaluator=evaluator, valid_dat=valid_dat,
n_negative=args.negative, epoch=args.epoch,
model_dir=args.log, restart=args.restart,
add_re=args.add_re)
else:
raise NotImplementedError
trainer.fit(train_dat)
def train(args):
# setting for logging
if not os.path.exists(args.log):
os.mkdir(args.log)
logger = logging.getLogger()
logging.basicConfig(level=logging.INFO)
log_path = os.path.join(args.log, 'log')
file_handler = logging.FileHandler(log_path)
fmt = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
file_handler.setFormatter(fmt)
logger.addHandler(file_handler)
# TODO: develop the recording of arguments in logging
logger.info('Arguments...')
for arg, val in sorted(vars(args).items()):
logger.info('{:>10} -----> {}'.format(arg, val))
ent_vocab = Vocab.load(args.ent)
rel_vocab = Vocab.load(args.rel)
n_entity, n_relation = len(ent_vocab), len(rel_vocab)
# preparing data
logger.info('preparing data...')
train_dat = TripletDataset.load(args.train, ent_vocab, rel_vocab)
valid_dat = TripletDataset.load(args.valid, ent_vocab,
rel_vocab) if args.valid else None
if args.filtered:
logger.info('loading whole graph...')
from utils.graph import TensorTypeGraph
whole_graph = TensorTypeGraph.load_from_raw(args.graphall, ent_vocab,
rel_vocab)
else:
whole_graph = None
if args.opt == 'sgd':
opt = SGD(args.lr)
elif args.opt == 'adagrad':
opt = Adagrad(args.lr)
else:
raise NotImplementedError
if args.l2_reg > 0:
opt.set_l2_reg(args.l2_reg)
if args.gradclip > 0:
opt.set_gradclip(args.gradclip)
logger.info('loading model...')
with open(args.load, 'rb') as f:
model = dill.load(f)
# evaluator = Evaluator(args.metric, args.nbest, args.filtered, whole_graph) if args.valid or args.synthetic else None
evaluator = Evaluator(args.metric, args.nbest, args.filtered,
whole_graph) if args.valid else None
# delete args.synthetic to run
if args.filtered and args.valid:
evaluator.prepare_valid(valid_dat)
if args.mode == 'pairwise':
trainer = PairwiseTrainer(model=model,
opt=opt,
save_step=args.save_step,
batchsize=args.batch,
logger=logger,
evaluator=evaluator,
valid_dat=valid_dat,
n_negative=args.negative,
epoch=args.epoch,
model_dir=args.log)
elif args.mode == 'single':
trainer = SingleTrainer(model=model,
opt=opt,
save_step=args.save_step,
batchsize=args.batch,
logger=logger,
evaluator=evaluator,
valid_dat=valid_dat,
n_negative=args.negative,
epoch=args.epoch,
model_dir=args.log)
else:
raise NotImplementedError
trainer.fit(train_dat)
logger.info('done all')
def train(args):
# setting for logging
if not os.path.exists(args.log):
os.mkdir(args.log)
logger = logging.getLogger()
logging.basicConfig(level=logging.INFO)
log_path = os.path.join(args.log, 'log')
file_handler = logging.FileHandler(log_path)
fmt = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
file_handler.setFormatter(fmt)
logger.addHandler(file_handler)
logger.info('Arguments...')
for arg, val in vars(args).items():
logger.info('{:>10} -----> {}'.format(arg, val))
if args.sk:
from sklearn.datasets import make_regression
x, y = make_regression(n_samples=NUM,
n_features=DIM_X,
n_targets=DIM_Y,
n_informative=args.info,
noise=1.)
else:
x, y = gen_synthetic_data(DIM, DIM_X, DIM_Y, NUM, args.skewx,
DIM_X - args.info)
train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2)
if args.stdx:
logger.info('standardize data...')
sc = StandardScaler()
sc.fit(train_x)
train_x = sc.transform(train_x)
test_x = sc.transform(test_x)
evaluator = Evaluator(mode=args.mode)
if args.mode == 'online':
with tf.Graph().as_default():
tf.set_random_seed(46)
sess = tf.Session()
opt = tf.train.AdamOptimizer(
) # TODO: make other optimizers available
if args.method == 'ridgex':
from models.ridge_x import RidgeX
model = RidgeX(d_x=DIM_X, d_y=DIM_Y, l=args.l)
elif args.method == 'ridgey':
from models.ridge_y import RidgeY
model = RidgeY(d_x=DIM_X, d_y=DIM_Y, l=args.l, l1=args.l1)
else:
raise NotImplementedError
trainer = SimpleTrainer(model=model,
epoch=args.epoch,
opt=opt,
sess=sess,
logger=logger)
trainer.fit(train_x, train_y)
logger.info('evaluation...')
evaluator.set_sess(sess)
accuracy, skewness = evaluator.run(model, test_x, test_y)
elif args.mode == 'closed':
if args.method == 'ridgex':
from models.closed_solver import RidgeX
model = RidgeX(d_x=DIM_X, d_y=DIM_Y, l=args.l)
elif args.method == 'ridgey':
from models.closed_solver import RidgeY
model = RidgeY(d_x=DIM_X, d_y=DIM_Y, l=args.l)
else:
raise NotImplementedError
logger.info('calculating closed form solution...')
model.solve(train_x, train_y)
logger.info('evaluation...')
accuracy, skewness = evaluator.run(model, test_x, test_y)
elif args.mode == 'cd':
if args.method == 'ridgex':
from models.elasticnet import ElasticNetX
model = ElasticNetX(d_x=DIM_X,
d_y=DIM_Y,
alpha=args.l,
l1_ratio=args.l1_ratio)
elif args.method == 'ridgey':
from models.elasticnet import ElasticNetY
model = ElasticNetY(d_x=DIM_X,
d_y=DIM_Y,
alpha=args.l,
l1_ratio=args.l1_ratio)
logger.info('calculating solution...')
model.solve(train_x, train_y)
print(model.get_param().tolist())
logger.info('evaluation...')
accuracy, skewness = evaluator.run(model, test_x, test_y)
else:
raise ValueError('Invalid mode: {}'.format(args.mode))
logger.info(' accuracy : {}'.format(accuracy))
logger.info('skewness@10 : {}'.format(skewness))
def train(args):
# setting for logging
if not os.path.exists(args.log):
os.mkdir(args.log)
logger = logging.getLogger()
logging.basicConfig(level=logging.INFO)
log_path = os.path.join(args.log, 'log')
file_handler = logging.FileHandler(log_path)
fmt = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
file_handler.setFormatter(fmt)
logger.addHandler(file_handler)
# TODO: develop the recording of arguments in logging
logger.info('Arguments...')
for arg, val in sorted(vars(args).items()):
logger.info('{:>10} -----> {}'.format(arg, val))
ent_vocab = Vocab.load(args.ent)
rel_vocab = Vocab.load(args.rel)
n_entity, n_relation = len(ent_vocab), len(rel_vocab)
# preparing data
logger.info('preparing data...')
train_dat = TripletDataset.load(args.train, ent_vocab, rel_vocab)
valid_dat = TripletDataset.load(args.valid, ent_vocab, rel_vocab) if args.valid else None
if args.filtered:
logger.info('loading whole graph...')
from utils.graph import TensorTypeGraph
whole_graph = TensorTypeGraph.load_from_raw(args.graphall, ent_vocab, rel_vocab)
else:
whole_graph = None
if args.opt == 'sgd':
opt = SGD(args.lr)
elif args.opt == 'adagrad':
opt = Adagrad(args.lr)
elif args.opt == 'dsgd':
opt = DecaySGD(args.lr)
else:
raise NotImplementedError
if args.l2_reg > 0:
opt.set_l2_reg(args.l2_reg)
if args.gradclip > 0:
opt.set_gradclip(args.gradclip)
logger.info('building model...')
if args.method == 'complex':
from models.complex import ComplEx
model = ComplEx(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
elif args.method == 'distmult':
from models.distmult import DistMult
model = DistMult(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
elif args.method == 'transe':
from models.transe import TransE
model = TransE(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
elif args.method == 'hole':
from models.hole import HolE
model = HolE(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
elif args.method == 'rescal':
from models.rescal import RESCAL
model = RESCAL(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
elif args.method == 'analogy':
from models.analogy import ANALOGY
model = ANALOGY(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
cp_ratio=args.cp_ratio,
mode=args.mode)
elif args.method == 'transe_set':
from models.transe_set import TransE_set
model = TransE_set(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
elif args.method == 'line':
from models.line_model import LineModel
model = LineModel(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
else:
raise NotImplementedError
# evaluator = Evaluator(args.metric, args.nbest, args.filtered, whole_graph) if args.valid or args.synthetic else None
evaluator = Evaluator(args.metric, args.nbest, args.filtered, whole_graph) if args.valid else None
# delete args.synthetic to run
if args.filtered and args.valid:
evaluator.prepare_valid(valid_dat)
if args.mode == 'pairwise':
trainer = PairwiseTrainer(model=model, opt=opt, save_step=args.save_step,
batchsize=args.batch, logger=logger,
evaluator=evaluator, valid_dat=valid_dat,
n_negative=args.negative, epoch=args.epoch,
model_dir=args.log)
elif args.mode == 'single':
trainer = SingleTrainer(model=model, opt=opt, save_step=args.save_step,
batchsize=args.batch, logger=logger,
evaluator=evaluator, valid_dat=valid_dat,
n_negative=args.negative, epoch=args.epoch,
model_dir=args.log)
else:
raise NotImplementedError
trainer.fit(train_dat)
logger.info('done all')
def train(args):
# setting for logging
if not os.path.exists(args.log):
os.mkdir(args.log)
logger = logging.getLogger()
logging.basicConfig(level=logging.INFO)
log_path = os.path.join(args.log, 'log')
file_handler = logging.FileHandler(log_path)
fmt = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
file_handler.setFormatter(fmt)
logger.addHandler(file_handler)
# TODO: develop the recording of arguments in logging
logger.info('Arguments...')
for arg, val in vars(args).items():
logger.info('{:>10} -----> {}'.format(arg, val))
ent_vocab = Vocab.load(args.ent)
rel_vocab = Vocab.load(args.rel)
n_entity, n_relation = len(ent_vocab), len(rel_vocab)
# preparing data
logger.info('preparing data...')
train_dat = TripletDataset.load(args.train, ent_vocab, rel_vocab)
valid_dat = TripletDataset.load(args.valid, ent_vocab,
rel_vocab) if args.valid else None
if args.filtered:
logger.info('loading whole graph...')
from utils.graph import TensorTypeGraph
whole_graph = TensorTypeGraph.load_from_raw(args.graphall, ent_vocab,
rel_vocab)
else:
whole_graph = None
if args.opt == 'sgd':
opt = SGD(args.lr)
elif args.opt == 'adagrad':
opt = Adagrad(args.lr)
else:
raise NotImplementedError
if args.l2_reg > 0:
opt.set_l2_reg(args.l2_reg)
if args.gradclip > 0:
opt.set_gradclip(args.gradclip)
logger.info('building model...')
if args.method == 'complex':
from models.complex import ComplEx
model = ComplEx(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
elif args.method == 'distmult':
from models.distmult import DistMult
model = DistMult(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
elif args.method == 'transe':
from models.transe import TransE
model = TransE(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
elif args.method == 'hole':
from models.hole import HolE
model = HolE(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
elif args.method == 'rescal':
from models.rescal import RESCAL
model = RESCAL(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
mode=args.mode)
elif args.method == 'analogy':
from models.analogy import ANALOGY
model = ANALOGY(n_entity=n_entity,
n_relation=n_relation,
margin=args.margin,
dim=args.dim,
cp_ratio=args.cp_ratio,
mode=args.mode)
elif args.method == 'randwalk':
from models.randwalk import RandWalk
logger.info(
'using random walk model to learning embedding unsupervisedly.')
model = RandWalk(n_entity=n_entity,
n_relation=n_relation,
knowledge_path=args.train,
ent_vocab=ent_vocab,
rel_vocab=rel_vocab,
dim=args.dim,
output=args.log)
model.train()
model.save_model(os.path.join(args.log, model.__class__.__name__))
return
elif args.method == "lr":
from models.lr import LogisticReg
model = LogisticReg(n_entity=n_entity,
n_relation=n_relation,
train_path=args.train,
ent_vocab=ent_vocab,
rel_vocab=rel_vocab,
dim=args.dim,
output=args.log,
wv_model_path=args.wv_model,
negative=args.negative,
feat_type=args.feat_type)
starttime = time()
if args.mode == "triplet_cls":
logger.info("Training a triple classifer")
model.train_triple_classifer()
else:
model.train()
endtime = time()
logger.info("lr model train time {:.6f}".format(endtime - starttime))
model.save_model(os.path.join(args.log, model.__class__.__name__))
return
else:
raise NotImplementedError
evaluator = Evaluator(
args.metric, args.nbest, args.filtered,
whole_graph) if args.valid or args.synthetic else None
if args.filtered and args.valid:
evaluator.prepare_valid(valid_dat)
if args.mode == 'pairwise':
trainer = PairwiseTrainer(model=model,
opt=opt,
save_step=args.save_step,
batchsize=args.batch,
logger=logger,
evaluator=evaluator,
valid_dat=valid_dat,
n_negative=args.negative,
epoch=args.epoch,
model_dir=args.log)
elif args.mode == 'single':
trainer = SingleTrainer(model=model,
opt=opt,
save_step=args.save_step,
batchsize=args.batch,
logger=logger,
evaluator=evaluator,
valid_dat=valid_dat,
n_negative=args.negative,
epoch=args.epoch,
model_dir=args.log)
else:
raise NotImplementedError
trainer.fit(train_dat)
logger.info('done all')