def best_auc_func(params):
model = train.new_model(pre_train_path)
train_ctw.meta_train(model, params)
loss, auc = train.test(model, warm=False)
global best_auc
global best_params
if auc > best_auc:
torch.save(model.state_dict(), ours_path)
best_auc = auc
best_params = params
print('best:{}'.format(best_auc))
return {'loss': -auc, 'status': STATUS_OK}
def hyper_param_analysis(rhos, alphas, path):
ps = {
'alpha': None,
'amsgrad': False,
'batch_n_ID': 50,
'gamma': 1.0,
'lr': 0.01,
'p_lr': 0.0001,
'p_lr_decay': 1.0,
'rho': None,
'weight_decay': 1e-8
}
# if os.path.exists(path):
# os.remove(path)
with open(path, 'a') as f:
f.write('# {}\n'.format(conf.model_type))
for rho in rhos:
for alpha in alphas:
ps['rho'] = rho
ps['alpha'] = alpha
# path
cold_path = "models/mer-trained-rho({})-alpha({}).pth".format(
rho, alpha)
warm_path = 'models/mer-tested-rho({})-alpha({}).pth'.format(
rho, alpha)
# cold
model = train.new_model(pre_train_path)
model = train_ctw.meta_train(model, ps, const.train_n_epoch)
torch.save(model.state_dict(), cold_path)
cold_loss, cold_auc = train.test(cold_path, warm=False)
# warm
ours_warm = train.meta_test(cold_path, 'ours', False)
warm_losses, warm_aucs = zip(*ours_warm['ours'])
losses = ',' + ','.join(map(str, warm_losses))
aucs = ',' + ','.join(map(str, warm_aucs))
torch.save(model.state_dict(), warm_path)
msg = ','.join(
[str(rho),
str(alpha),
str(cold_loss),
str(cold_auc)]) + losses + aucs + '\n'
print(msg)
f.write(msg)