def train(args, config, X_tra, X_val, input_dim=None, onehot_dim=None):
if args.train_task == 'cancel':
grader = Grader(X_val)
model = CancelModel(args.can_model, config, args.filter_all,
args.use_onehot)
if args.train_all:
X_tra = X_tra + X_val
model.train(X_tra)
# cacenl error rate a.k.a CER
cer = grader.eval_cancel_error_rate(model, IsCancelModel=True)
return model, cer
elif args.train_task == 'adr' or args.train_task == 'revenue':
grader = Grader(X_val)
model = ModelWrapper(args, config, args.use_onehot, args.filter_all,
input_dim, onehot_dim)
if args.use_pretrain:
pretrain_model = ModelWrapper(args, config, args.filter_all,
args.use_onehot, input_dim,
onehot_dim)
pretrain_model.load('trained_models/pretrain.pkl')
model.model.model = pretrain_model.model.model
if args.train_all:
X_tra = X_tra + X_val
if args.verbose:
model.train(X_tra, grader)
else:
model.train(X_tra)
# revenue MAE a.k.a REV
rev = grader.eval_revenue(model)
mae = grader.eval_mae(model)
return model, rev, mae
elif args.train_task == 'label':
grader = Grader(X_val)
model = ModelWrapper(args, config, args.use_onehot, args.filter_all,
input_dim, onehot_dim)
if args.verbose:
model.train(X_tra, grader)
else:
model.train(X_tra)
rev = grader.eval_revenue(model)
mae = grader.eval_mae(model)
return model, rev, mae
fig = plt.figure()
cset = plt.scatter(output[:, 0], output[:, 1], s=8, c=bert_sim, cmap='PuBu')
plt.plot(output[opt['key_id'], 0], output[opt['key_id'], 1], 'r.')
plt.colorbar(cset)
plt.savefig(
os.path.join(opt['model_save_dir'], 'plot', '%d_bert_sim' % opt['key_id']))
fig = plt.figure()
plt.hist(bert_sim, bins=100)
plt.savefig(
os.path.join(opt['model_save_dir'], 'plot',
'%d_bert_hist' % opt['key_id']))
model = ModelWrapper(opt, weibo2embid, eva=True)
model.load(os.path.join(opt['model_save_dir'], 'best_model.pt'))
clash_delta = []
for i in range(len(emb_matrix)):
emb1 = torch.tensor([i]).cuda()
emb2 = torch.tensor([opt['key_id']]).cuda()
clash_delta.append(model.model.get_delta(emb1, emb2).item())
ceil = max(max(clash_delta), min(clash_delta) * (-1)) * 1.05
print(max(clash_delta), min(clash_delta))
fig = plt.figure()
cset = plt.scatter(output[:, 0],
output[:, 1],
s=8,
c=clash_delta,
cmap='PuBu',
def __init__(self):
self.mw = ModelWrapper.load()