def predict(self, mini_batch, verbose=False):
kg, pn = self.kg, self.mdl
e1, e2, r = self.format_batch(mini_batch)
beam_search_output = search.beam_search(pn, e1, r, e2, kg,
self.num_rollout_steps,
self.beam_size)
pred_e2s = beam_search_output['pred_e2s']
pred_e2_scores = beam_search_output['pred_e2_scores']
if verbose:
# print inference paths
search_traces = beam_search_output['search_traces']
output_beam_size = min(self.beam_size, pred_e2_scores.shape[1])
for i in range(len(e1)):
for j in range(output_beam_size):
ind = i * output_beam_size + j
if pred_e2s[i][j] == kg.dummy_e:
break
search_trace = []
for k in range(len(search_traces)):
search_trace.append((int(search_traces[k][0][ind]),
int(search_traces[k][1][ind])))
print('beam {}: score = {} \n<PATH> {}'.format(
j, float(pred_e2_scores[i][j]),
ops.format_path(search_trace, kg)))
with torch.no_grad():
pred_scores = zeros_var_cuda([len(e1), kg.num_entities])
for i in range(len(e1)):
pred_scores[i][pred_e2s[i].long()] = torch.exp(
pred_e2_scores[i])
return pred_scores, beam_search_output["rule_score"]
def predict(self, mini_batch, verbose=False):
kg, pn = self.kg, self.mdl
e1, e2, r = self.format_batch(mini_batch)
width = kg.num_entities - 1
beam_search_output = search.beam_search(pn, e1, r, e2, kg,
self.num_rollout_steps,
self.beam_size)
with torch.no_grad():
pred_e2s = beam_search_output['pred_e2s']
pred_e2_scores = beam_search_output['pred_e2_scores']
pred_traces = beam_search_output['pred_traces']
for u in range(pred_e2s.size()[0]):
for v in range(pred_e2s.size()[1]):
if pred_e2s[u][v].item() == kg.dummy_end_e:
for i in range(self.num_rollout_steps, 0, -1):
if pred_traces[i][0][1][u * width +
v] != kg.dummy_end_e:
pred_e2s[u][v] = pred_traces[i][0][1][u * width
+ v]
break
accum_scores = ops.sort_by_path(pred_traces, kg)
# for testing
ggg = open("traces.txt", "a")
hhh = open("accum_scores.txt", "a")
for i in range(len(e1)):
if e1[i] == 104 and r[i] == 5 and e2[i] == 72:
print("Epoch: ",
pred_traces[1][0][0][i * width:(i + 1) * width],
"\n",
pred_traces[1][0][1][i * width:(i + 1) * width],
"\n",
pred_traces[2][0][0][i * width:(i + 1) * width],
"\n",
pred_traces[2][0][1][i * width:(i + 1) * width],
file=ggg)
print(sorted(accum_scores[i].items(),
key=lambda x: x[1],
reverse=True),
file=hhh)
ggg.close()
hhh.close()
if verbose:
# print inference paths
search_traces = beam_search_output['search_traces']
output_beam_size = min(self.beam_size, pred_e2_scores.shape[1])
for i in range(len(e1)):
for j in range(output_beam_size):
ind = i * output_beam_size + j
if pred_e2s[i][j] == kg.dummy_e:
break
search_trace = []
for k in range(len(search_traces)):
search_trace.append((int(search_traces[k][0][ind]),
int(search_traces[k][1][ind])))
print('beam {}: score = {} \n<PATH> {}'.format(
j, float(pred_e2_scores[i][j]),
ops.format_path(search_trace, kg)))
with torch.no_grad():
uu = open("scores_pred.txt", "a")
pred_scores = zeros_var_cuda([len(e1), kg.num_entities])
for i in range(len(e1)):
pred_scores[i][pred_e2s[i]] = torch.exp(pred_e2_scores[i])
if e1[i] == 104 and r[i] == 5 and e2[i] == 72:
print(pred_scores[i], file=uu)
# 计算ranking的方法有待检讨。理论上应该使用第二种
# 这是两个加和取平均的方式。但实际上只有rma发挥了有效作用
# pred_scores[i][pred_e2s_1[i]] += torch.div(pred_e2_scores_1[i], 2.0)
# pred_scores[i][pred_e2s_2[i]] += torch.div(pred_e2_scores_2[i], 2.0)
# pred_scores[i] = torch.exp(pred_scores[i])
# 这是两步计算平均的方式
# for j in range(len(pred_e2s_2)):
# pred_scores[i][pred_e2s_2[i][j]] = torch.exp(pred_e2_scores_2[i][j]) *\
# ops.check_path(accum_scores[i],pred_traces_2, i*width+j)
uu.close()
return pred_scores
