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]))