def test(test_filename, time_now, title, **kwargs):
# 超参数设置
network_name = get_value_or_default(kwargs, 'network', default='LSTM')
affect = get_value_or_default(kwargs, 'affect', default=30)
filename = test_filename
column = get_value_or_default(kwargs, 'column', default='ClPr')
index_col = 'TrdDt'
batch_size = 1
plot_name = get_value_or_default(kwargs, 'plot_name', default=['fig1', ])
# 加载数据
data = Action.generate_df(
filename,
column,
index_col,
affect
)
data_loader = DataLoader(data['dataset'], batch_size=batch_size, shuffle=False)
net = torch.load('save/{}.pt'.format(network_name))
predict = list()
for tx, ty in data_loader:
output = net(tx.reshape(1, batch_size, affect))
output = output.reshape(1).detach()
predict.append(float(output) * data['std'] + data['mean'])
plt1 = Plot(1, time_now, network_name)
plt1.plot(data['index'], data['real_data'][affect:], 'real data')
plt1.plot(data['index'], predict, 'predict data')
plt1.title(title, zh=True)
plt1.xylabel('Datetime', 'price')
plt1.save(plot_name[0])
# Plot.show()
Plot.cla()
evaluator = Evaluate(title, data['real_data'][affect:], predict)
logger = Logger('test.log')
basic_info = 'tested {}.'.format(network_name)
logger.set_log(basic_info,
t=time_now,
filename=filename,
column=column,
affect_days=affect,
network=net,
plot_name=plot_name,
MSELoss=evaluator.MSELoss(),
DA=evaluator.DA(),
Theil=evaluator.Theil_U(),
L1Loss=evaluator.L1Loss(),
Customize=evaluator.customize(),
title=title,
MAPE=evaluator.MAPE(),
R=evaluator.R()
)
f_out = open('log/{}.txt'.format(title), 'w')
print('{} = {}'.format('time', time_now),
'{} = {}'.format('MSELoss', evaluator.MSELoss()),
'{} = {}'.format('DA', evaluator.DA()),
'{} = {}'.format('Theil_U', evaluator.Theil_U()),
'{} = {}'.format('L1Loss', evaluator.L1Loss()),
'{} = {}'.format('MAPE', evaluator.MAPE()),
'{} = {}'.format('R', evaluator.R()),
file=f_out,
sep='\n')
f_out.close()
return evaluator
loss.backward()
optimizer.step()
predict = list()
for tx, ty in data_loader_test:
output = net(tx.reshape(1, 1, AFFECT))
output = output.reshape(1).detach()
if STANDARDIZE:
predict.append(
float(output) * data_test['std'] + data_test['mean'])
else:
predict.append(float(output))
if (step + 1) % 100 == 0:
evaluator = Evaluate(data_test['real_data'][AFFECT:], predict)
da_list.append(evaluator.DA())
mse_list.append(evaluator.MSELoss())
theil_list.append(evaluator.Theil_U())
l1_list.append(evaluator.L1Loss())
mape_list.append(evaluator.MAPE())
r_list.append(evaluator.R())
print('step: {}, data accuracy: {}'.format(step + 1, 1 - loss.detach()))
if (step + 1) % 1000 == 0:
plt.cla()
zh_font = fm.FontProperties(fname='font/simhei.ttf')
plt.plot(data_test['index'],
data_test['real_data'][AFFECT:],
label='real data')
plt.plot(data_test['index'], predict, label='predict data')
