def evaluate(self):
print('evaluating...')
f = open('./tmp/dev.pred', 'w')
for j in range(len(self.X_valid_batch)):
y_pred = self.predict(self.X_valid_batch[j])
for k in range(len(y_pred)):
idx = j * self.batch_size + k
if idx >= len(self.X_valid): break
prob_of_true = y_pred[k, 1] + y_pred[k, 2]
label = 'false'
if prob_of_true > 0.5:
label = 'true'
f.write("%s %s 0 %20.16f %s\n" %
(self.meta_valid[idx][0], self.meta_valid[idx][1],
prob_of_true, label))
f.close()
map = eval_reranker(
res_fname=
'./data/eval/SemEval2016-Task3-CQA-QL-dev.xml.subtaskB.relevancy',
pred_fname='./tmp/dev.pred')
f = open('valid_map.txt', 'a')
f.write(str(map) + '\n')
f.close()
print('=========================================')
return map
def evaluate(self):
print('evaluating...')
y_pred = self.model.predict_proba(self.X_valid, batch_size=1)
# y_pred = self.model.predict_classes(self.X_valid, batch_size=1)
f = open('./tmp/dev.pred', 'w')
for i in range(len(self.meta_valid)):
prob_of_true = y_pred[i][1] + y_pred[i][2]
label = 'false'
if prob_of_true > 0.5:
label = 'true'
f.write("%s %s 0 %20.16f %s\n" %
(self.meta_valid[i][0], self.meta_valid[i][1],
prob_of_true, label))
# f.write( "%s %s 0 %20.16f %s\n" %(self.meta_valid[i][0], self.meta_valid[i][1], y_pred[i][1]+y_pred[i][2], self.meta_valid[i][2]))
f.close()
map = eval_reranker(
res_fname=
'./data/eval/SemEval2016-Task3-CQA-QL-dev.xml.subtaskB.relevancy',
pred_fname='./tmp/dev.pred')
f = open('valid_map.txt', 'a')
f.write(str(map) + '\n')
f.close()
print('=========================================')
return map
def eval(self, fname):
print('evaluating...')
map=eval_reranker(res_fname='./data/eval/SemEval2016-Task3-CQA-QL-dev.xml.subtaskB.relevancy',
pred_fname=fname)
f=open('valid_map.txt', 'a')
f.write(str(map)+'\n')
f.close()
print('=========================================')
def eval(self, fname):
print('evaluating...')
map = eval_reranker(
res_fname=
'./data/eval/SemEval2016-Task3-CQA-QL-dev.xml.subtaskB.relevancy',
pred_fname=fname)
f = open('valid_map.txt', 'a')
f.write(str(map) + '\n')
f.close()
print('=========================================')
def evaluate(self):
print('evaluating...')
y_pred = self.model.predict_proba(self.X_valid)
f=open('./tmp/dev.pred', 'w')
for i in range(len(self.meta_valid)):
prob_of_true =y_pred[i][1]+y_pred[i][2]
label='false'
if prob_of_true>0.5:
label='true'
f.write( "%s %s 0 %20.16f %s\n" %(self.meta_valid[i][0], self.meta_valid[i][1], prob_of_true, label))
f.close()
map=eval_reranker(res_fname='./data/eval/SemEval2016-Task3-CQA-QL-dev.xml.subtaskB.relevancy',
pred_fname='./tmp/dev.pred')
f=open('valid_map.txt', 'a')
f.write(str(map)+'\n')
f.close()
print('=========================================')
return map
def evaluate(self):
print('evaluating...')
f=open('./tmp/dev.pred', 'w')
for j in range(len(self.X_valid_batch)):
y_pred = self.predict(self.X_valid_batch[j])
for k in range(len(y_pred)):
idx = j*self.batch_size+k
if idx>=len(self.X_valid): break
prob_of_true =y_pred[k,1]+y_pred[k,2]
label='false'
if prob_of_true>0.5:
label='true'
f.write( "%s %s 0 %20.16f %s\n" %(self.meta_valid[idx][0], self.meta_valid[idx][1], prob_of_true, label))
f.close()
map=eval_reranker(res_fname='./data/eval/SemEval2016-Task3-CQA-QL-dev.xml.subtaskB.relevancy',
pred_fname='./tmp/dev.pred')
f=open('valid_map.txt', 'a')
f.write(str(map)+'\n')
f.close()
print('=========================================')
return map
