def construct_model(args):
kg = KnowledgeGraph(args)
if args.model.endswith('.gc'):
kg.load_fuzzy_facts()
fn = ConvE(args, kg.num_entities)
lf = EmbeddingBasedMethod(args, kg, fn)
return lf
def construct_model(args):
"""
Construct NN graph.
"""
kg = KnowledgeGraph(args)
if args.model.endswith('.gc'):
kg.load_fuzzy_facts()
if args.model in ['point', 'point.gc']:
pn = GraphSearchPolicy(args)
lf = PolicyGradient(args, kg, pn)
elif args.model.startswith('point.rs'):
fn_model = args.model.split('.')[2]
fn_args = copy.deepcopy(args)
fn_args.model = fn_model
fn_args.relation_only = False
if fn_model == 'complex':
fn = ComplEx(fn_args)
fn_kg = KnowledgeGraph(fn_args)
elif fn_model == 'distmult':
fn = DistMult(fn_args)
fn_kg = KnowledgeGraph(fn_args)
elif fn_model == 'conve':
fn = ConvE(fn_args, kg.num_entities)
fn_kg = KnowledgeGraph(fn_args)
elif fn_model == 'tucker':
fn = TuckER(fn_args, kg.num_entities)
fn_kg = KnowledgeGraph(fn_args)
elif fn_model == 'PTransE':
fn = PTransE(fn_args)
fn_kg = KnowledgeGraph(fn_args)
pn = GraphSearchPolicy(args, fn_kg=fn_kg, fn=fn)
lf = RewardShapingPolicyGradient(args, kg, pn, fn_kg, fn)
elif args.model == 'complex':
fn = ComplEx(args)
lf = EmbeddingBasedMethod(args, kg, fn)
elif args.model == 'distmult':
fn = DistMult(args)
lf = EmbeddingBasedMethod(args, kg, fn)
elif args.model == 'tucker':
fn = TuckER(args, kg.num_entities)
lf = EmbeddingBasedMethod(args, kg, fn)
elif args.model == 'conve':
fn = ConvE(args, kg.num_entities)
lf = EmbeddingBasedMethod(args, kg, fn)
elif args.model == 'TransE':
fn = TransE(args)
lf = EmbeddingBasedMethod(args, kg, fn)
elif args.model == 'PTransE':
fn = PTransE(args)
lf = EmbeddingBasedMethod(args, kg, fn)
else:
raise NotImplementedError
return lf
def construct_model(args):
"""
Construct NN graph.
"""
kg = KnowledgeGraph(args)
if args.model.endswith('.gc'):
kg.load_fuzzy_facts()
if args.model in ['point', 'point.gc']:
pn = GraphSearchPolicy(args)
parameters = pn.named_parameters()
for name, value in parameters:
print('parameter: {} | size: {}'.format(name, value.size()))
# print('pn parameters: {}'.format(pn.named_parameters()))
#print(args.device_ids)
#print(type(args.device_ids[0]))
#lf = nn.DataParallel(PolicyGradient(args, kg, pn), device_ids=args.device_ids)
lf = PolicyGradient(args, kg, pn)
elif args.model.startswith('point.rs'):
pn = GraphSearchPolicy(args)
fn_model = args.model.split('.')[2]
fn_args = copy.deepcopy(args)
fn_args.model = fn_model
fn_args.relation_only = False
if fn_model == 'complex':
fn = ComplEx(fn_args)
fn_kg = KnowledgeGraph(fn_args)
elif fn_model == 'distmult':
fn = DistMult(fn_args)
fn_kg = KnowledgeGraph(fn_args)
elif fn_model == 'conve':
fn = ConvE(fn_args, kg.num_entities)
fn_kg = KnowledgeGraph(fn_args)
elif fn_model == 'cpg-conve':
fn = CPG_ConvE(fn_args, kg.num_entities)
fn_kg = KnowledgeGraph(fn_args)
lf = RewardShapingPolicyGradient(args, kg, pn, fn_kg, fn)
elif args.model == 'complex':
fn = ComplEx(args)
lf = EmbeddingBasedMethod(args, kg, fn)
elif args.model == 'distmult':
fn = DistMult(args)
lf = EmbeddingBasedMethod(args, kg, fn)
elif args.model == 'conve':
fn = ConvE(args, kg.num_entities)
lf = EmbeddingBasedMethod(args, kg, fn)
elif args.model == 'cpg-conve':
fn = CPG_ConvE(args, kg.num_entities)
lf = EmbeddingBasedMethod(args, kg, fn)
else:
raise NotImplementedError
return lf
def construct_model(args):
"""
Construct NN graph.
"""
kg = KnowledgeGraph(args) # NOTE: initialize a KG instance
if args.model.endswith(
".gc"): # CAVEAT: not sure what model needs fuzzy facts
kg.load_fuzzy_facts()
if args.model in ["point", "point.gc"]:
pn = GraphSearchPolicy(args)
lf = PolicyGradient(args, kg, pn)
elif args.model.startswith("point.rs"):
pn = GraphSearchPolicy(args)
fn_model = args.model.split(".")[-1]
fn_args = copy.deepcopy(args)
fn_args.model = fn_model
fn_args.relation_only = False
if fn_model == "complex":
fn = ComplEx(fn_args)
fn_kg = KnowledgeGraph(fn_args)
elif fn_model == "distmult":
fn = DistMult(fn_args)
fn_kg = KnowledgeGraph(fn_args)
elif fn_model == "conve":
fn = ConvE(fn_args, kg.num_entities)
fn_kg = KnowledgeGraph(fn_args)
lf = RewardShapingPolicyGradient(args, kg, pn, fn_kg, fn)
elif args.model == "complex":
fn = ComplEx(args)
lf = EmbeddingBasedMethod(args, kg, fn)
elif args.model == "distmult":
print("jxtu: embedding model: distmult")
fn = DistMult(args)
lf = EmbeddingBasedMethod(
args, kg, fn) # NOTE: embedding-based learning framework
elif args.model == "conve":
fn = ConvE(args, kg.num_entities)
lf = EmbeddingBasedMethod(args, kg, fn)
elif args.model == "TransE":
fn = TransE(args)
lf = EmbeddingBasedMethod(args, kg, fn)
else:
raise NotImplementedError
return lf
