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 path_analysis(args):
ent_vocab = Vocab.load(args.entity)
rel_vocab = RelationVocab.load(args.relation, inv_flg=True)
triple_dat = TripletDataset.load(args.triple, ent_vocab, rel_vocab)
pq_dat = PathQueryDataset.load(args.query, ent_vocab, rel_vocab)
g = LabeledDiGraph(triple_dat, inv_flg=True)
# traversal path querys
n_rel = []
n_tail = []
for (sub, rels, _) in pq_dat.samples:
cur_ents = set([sub])
for r in rels:
next_ents = set()
for e in cur_ents:
new_ents = g.walk(e, r)
next_ents.update(new_ents)
cur_ents = next_ents
n_rel.append(len(rels))
n_tail.append(len(cur_ents))
print(n_rel)
print(n_tail)
print('Correlation Coefficient: {}'.format(
np.corrcoef(n_rel, n_tail)[0, 1]))
args = p.parse_args()
assert args.task in ['kbc', 'pq'], 'Invalid task: {}'.format(args.task)
assert args.metric in ['mrr',
'hits'], 'Invalid metric: {}'.format(args.metric)
if args.metric == 'hits':
assert args.nbest, 'Please indecate n-best in using hits'
model = GaussianBilinearModel.load_model(args.model)
print('Preparing dataset...')
if args.task == 'kbc':
ent_vocab = Vocab.load(args.entity)
rel_vocab = Vocab.load(args.relation)
dataset = TripletDataset.load(args.data, ent_vocab, rel_vocab)
elif args.task == 'pq':
ent_vocab = Vocab.load(args.entity)
rel_vocab = RelationVocab.load(args.relation, inv_flg=True)
dataset = PathQueryDataset.load(args.data, ent_vocab, rel_vocab)
if not hasattr(model, 'inv_flg') or not model.inv_flg:
print('initializing inverse relation representations...')
model.init_inverse()
print('Start evaluation...')
if args.metric == 'mrr':
from evaluation import mrr
# res = mrr.cal_mrr(model, dataset)
res = mrr.multi_cal_mrr(model, dataset)
print('MRR: {}'.format(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):
if args.log:
log_dir = args.log
else:
log_dir = os.path.join(
os.path.abspath(os.path.dirname(__file__)),
'{}'.format(datetime.now().strftime('%Y%m%d_%H:%M')))
if not os.path.exists(log_dir):
os.mkdir(log_dir)
# setting for logging
logger = logging.getLogger()
logging.basicConfig(level=logging.INFO)
log_path = os.path.join(log_dir, '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('{} : {}'.format(arg, val))
logger.info('Preparing dataset...')
if not args.entity or not args.relation:
# make vocab from train set
logger.info('Making entity/relation vocab from train data...')
raise NotImplementedError()
else:
ent_vocab = Vocab.load(args.entity)
rel_vocab = Vocab.load(args.relation)
n_entity, n_relation = len(ent_vocab), len(rel_vocab)
train_dat = TripletDataset.load(args.train, ent_vocab, rel_vocab)
logger.info('')
if args.valid:
assert args.metric in ['mrr',
'hits'], 'Invalid evaluation metric: {}'.format(
args.metric)
assert args.metric, 'Please indecate evaluation metric for validation'
if args.metric == 'hits':
assert args.nbest, 'Please indecate nbest for hits'
valid_dat = TripletDataset.load(args.valid, ent_vocab, rel_vocab)
if args.restart:
logger.info('Restarting training: {}'.format(args.restart))
model = GaussianBilinearModel.load_model(args.restart)
else:
logger.info('Building new model')
opt = SGD(args.lr, args.gradclip)
model = GaussianBilinearModel(n_entity, n_relation, args.dim,
args.cmin, args.cmax, opt, args.tri,
args.init_sigma)
best_model = None
best_val = -1
for epoch in range(args.epoch):
logger.info('start {} epoch'.format(epoch + 1))
sum_loss = 0
start = time.time()
for i, pos_sample in enumerate(data_iter(train_dat)):
neg_samples = [(pos_sample[0], pos_sample[1],
np.random.randint(n_entity))
for _ in range(args.num_negative)]
for neg_sample in neg_samples:
loss = model.update(pos_sample, neg_sample)
sum_loss += loss
# logger.info('loss: {}'.format(loss))
# logger.info('processing {} samples in this epoch'.format(i+1))
print('processing {} samples in this epoch'.format(i + 1))
logger.info('sum loss: {}'.format(sum_loss))
logger.info('{} sec/epoch for training'.format(time.time() - start))
model_path = os.path.join(log_dir, 'model{}'.format(epoch + 1))
model.save_model(model_path)
if args.valid and (epoch + 1) % args.evalstep == 0:
val = evaluation(model, valid_dat, args.metric, args.nbest)
logger.info('{} in validation: {}'.format(args.metric, val))
if val > best_val:
best_model = copy.deepcopy(model)
best_val = val
best_epoch = epoch + 1
if args.valid:
logger.info('best model is {} epoch'.format(best_epoch))
model_path = os.path.join(log_dir, 'bestmodel')
best_model.save_model(model_path)
logger.info('done all')
CAUTION : this relation sampling method is NOT UNIFORM
"""
_batchsize = len(pos_triplets)
sample_size = _batchsize * n_negative
neg_rel_ents = self.sample(sample_size)
neg_triplets = np.tile(pos_triplets, (n_negative, 1))
head_rel_tail = np.random.randint(0, 3, sample_size)
rel_idxs = np.where(head_rel_tail == 1)
# TODO: fix to sample uniformly
neg_rel_ents[rel_idxs] = neg_rel_ents[rel_idxs] % self.n_rel
neg_triplets[np.arange(sample_size), head_rel_tail] = neg_rel_ents
return neg_triplets
def sample(self, size):
return np.random.randint(0, self.n_ent, size=size)
if __name__ == '__main__':
from collections import defaultdict
from utils.dataset import TripletDataset
d = TripletDataset([[0, 0, 1], [0, 0, 2], [1, 0, 1]])
sampler = UnigramIntSampler.build(d, 3)
dic = defaultdict(lambda: 0)
for _ in range(5):
sample = sampler.sample(10)
print(sample)
for i in sample:
dic[i] += 1
print(dic)
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')
