def test_score(model_name,parameter):
label2id = {'Other': 0, 'Cause-Effect(e1,e2)': 1, 'Cause-Effect(e2,e1)': 2, 'Component-Whole(e1,e2)': 3,
'Component-Whole(e2,e1)': 4, 'Content-Container(e1,e2)': 5, 'Content-Container(e2,e1)': 6,
'Entity-Destination(e1,e2)': 7, 'Entity-Destination(e2,e1)': 8, 'Entity-Origin(e1,e2)': 9,
'Entity-Origin(e2,e1)': 10, 'Instrument-Agency(e2,e1)': 11, 'Instrument-Agency(e1,e2)': 12,
'Member-Collection(e1,e2)': 13, 'Member-Collection(e2,e1)': 14, 'Message-Topic(e1,e2)': 15,
'Message-Topic(e2,e1)': 16, 'Product-Producer(e1,e2)': 17, 'Product-Producer(e2,e1)': 18}
#加载预训练语料
vocab_file = 'dataset/vocab/vocab.pkl'
vocab = Vocab(vocab_file, load=True)
resoult_file=open('resoult/resoult.txt','w')
#加载预处理测试集
test_batch = DataLoader('dataset/sem/test_file.json',parameter, vocab, train=False)
test_emb_file = './dataset/vocab/embedding.npy'
test_emb_matrix = np.load(test_emb_file)
parameter['vocab_size'] = vocab.size
#加载模型
trainer = GCNTrainer(parameter,test_emb_matrix)
trainer.load(model_name)
id2label = dict([(v,k) for k,v in label2id.items()])
predictions = []
for i, batch in enumerate(test_batch):
preds = trainer.predict(batch)
predictions += preds
predictions = [id2label[p] for p in predictions]
#结果写入文件
counter=8001
for resoult in predictions:
if counter == 10718:
break
resoult_file.writelines(str(counter)+"\t"+resoult+'\n')
counter+=1
resoult_file.close()
#计算分数
os.system('perl ./resoult/semeval2010_task8_scorer-v1.2.pl ./resoult/resoult.txt ./resoult/test_key.txt > ./resoult/score.txt')
f=open("resoult/score.txt")
resoult1=""
line=f.readline()
i=0
while line:
if i==143:
resoult1=line
if i==147:
break
line=f.readline()
i+=1
f.close()
return line,resoult1
def start_train(self):
#加载预训练语料
vocab_file = 'dataset/vocab/vocab.pkl'
self.vocab = Vocab(vocab_file, load=True)
self.parameter['vocab_size'] = self.vocab.size
emb_file = './dataset/vocab/embedding.npy'
self.emb_matrix = np.load(emb_file)
#加载训练集
self.train_batch = DataLoader('dataset/sem/train_file.json',
self.parameter,
self.vocab,
train=True)
self.trainer = GCNTrainer(self.parameter, emb_matrix=self.emb_matrix)
self.current_lr = self.parameter['lr']
self.log_name = "log/" + str(time.strftime("%d_%I_%M")) + ".log"
log = open(self.log_name, 'w+')
log.write(str(self.parameter))
log.close()
