def test(self, save=True):
embedding = self.sess.run(self.output)
embeds1 = np.array([embedding[e] for e in self.kgs.test_entities1])
embeds2 = np.array([embedding[e] for e in self.kgs.test_entities2])
rest_12, _, _ = test(embeds1,
embeds2,
None,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
normalize=self.args.eval_norm,
csls_k=0,
accurate=True)
test(embeds1,
embeds2,
None,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
normalize=self.args.eval_norm,
csls_k=self.args.csls,
accurate=True)
if save:
ent_ids_rest_12 = [(self.kgs.test_entities1[i],
self.kgs.test_entities2[j])
for i, j in rest_12]
rd.save_results(self.out_folder, ent_ids_rest_12)
def test(self, save=True):
if self.args.test_method == "sa":
beta = self.args.beta
embeddings = np.concatenate(
[self.vec_se * beta, self.vec_ae * (1.0 - beta)], axis=1)
else:
embeddings = self.vec_se
embeds1 = np.array([embeddings[e] for e in self.kgs.test_entities1])
embeds2 = np.array([embeddings[e] for e in self.kgs.test_entities2])
rest_12, _, _ = test(embeds1,
embeds2,
None,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
normalize=self.args.eval_norm,
csls_k=0,
accurate=True)
test(embeds1,
embeds2,
None,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
normalize=self.args.eval_norm,
csls_k=self.args.csls,
accurate=True)
if save:
rd.save_results(self.out_folder, rest_12)
def test(self, save=True):
embeds1, embeds2, mapping = self._eval_test_embeddings()
rest_12, _, _ = test(embeds1,
embeds2,
mapping,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
normalize=self.args.eval_norm,
csls_k=0,
accurate=True)
test(embeds1,
embeds2,
mapping,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
normalize=self.args.eval_norm,
csls_k=self.args.csls,
accurate=True)
if save:
ent_ids_rest_12 = [(self.kgs.test_entities1[i],
self.kgs.test_entities2[j])
for i, j in rest_12]
rd.save_results(self.out_folder, ent_ids_rest_12)
def test(self, save=True):
mat = get_ent_embeds_from_attributes(self.kgs,
self.eval_attribute_embeddings(),
self.selected_attributes)
embeds1 = mat[self.kgs.test_entities1, ]
embeds2 = mat[self.kgs.test_entities2, ]
rest_12, _, _, rest_21, _, _ = evaluation.test(
embeds1,
embeds2,
None,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
csls_k=self.args.csls)
if save:
read.save_results(self.out_folder, rest_12)
def test_alignment(self, save=True):
embeds1, embeds2, mapping = self._eval_test_embeddings()
mr, mrr, hits, hits_12_list, hits_21_list = rank_alignment_bidirection(
embeds1,
embeds2,
mapping,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
normalize=self.args.eval_norm,
csls_k=0,
accurate=True)
if save:
rd.save_results(self.out_folder, hits_12_list)
return mr, mrr, hits, hits_12_list, hits_21_list
def test(self, save=True):
embeds1, embeds2, mapping = self._eval_test_embeddings()
rest_12, _, _ = test(embeds1,
embeds2,
mapping,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
normalize=self.args.eval_norm,
csls_k=0,
accurate=True)
test(embeds1,
embeds2,
mapping,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
normalize=self.args.eval_norm,
csls_k=self.args.csls,
accurate=True)
if save:
rd.save_results(self.out_folder, rest_12)
def retest_alignment(self, save):
print(self.__class__.__name__, type(self.__class__.__name__))
#mapping = None
#if os.path.exists(new_dir + "mapping_mat.npy"):
# print(self.__class__.__name__, "loads mapping mat")
# mapping = np.load(new_dir + "mapping_mat.npy")
embeds1, embeds2, mapping = self._eval_test_embeddings()
id1_entities_dict = dict()
for new_id, old_id in enumerate(self.kgs.test_entities1):
ent = self.kgs.kg1.id_entities_dict.get(old_id)
id1_entities_dict[new_id] = ent
id2_entities_dict = dict()
for new_id, old_id in enumerate(self.kgs.test_entities2):
ent = self.kgs.kg2.id_entities_dict[old_id]
id2_entities_dict[new_id] = ent
print("conventional test:")
mr_12, mrr_12, hits_12, hits_12_list = rank_alignment(
embeds1,
embeds2,
mapping,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
normalize=self.args.eval_norm,
csls_k=0,
accurate=True)
if save:
hits_12_ent = rd.pairs_id2ent(hits_12_list, id1_entities_dict,
id2_entities_dict)
rd.save_results(self.out_folder, hits_12_ent,
'alignment_results_12')
print("conventional reversed test:")
if mapping is not None:
embeds1 = np.matmul(embeds1, mapping)
rank_alignment(embeds2,
embeds1,
None,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
normalize=self.args.eval_norm,
csls_k=0,
accurate=True)
else:
mr_21, mrr_21, hits_21, hits_21_list = rank_alignment(
embeds2,
embeds1,
mapping,
self.args.top_k,
self.args.test_threads_num,
metric=self.args.eval_metric,
normalize=self.args.eval_norm,
csls_k=0,
accurate=True)
if save:
hits_21_ent = rd.pairs_id2ent(hits_21_list, id2_entities_dict,
id2_entities_dict)
rd.save_results(self.out_folder, hits_21_ent,
'alignment_results_21')