y = torch.from_numpy(train_label).long()
for epoch in range(EPOCH):
batches = gen_batch(x, y, batch_size)
loss_sum = 0
for var_x, var_y in batches:
pred = model(var_x)
loss = loss_func(pred, var_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_sum += loss.item() * pred.shape[0]
print('epoch %d loss: %f' % (epoch, loss_sum / x.shape[0]))
if (epoch + 1) % 50 == 0:
torch.save(model.state_dict(), tmp_save_path % (epoch + 1))
torch.save(model.state_dict(), model_save_path)
else:
print('loading model...')
model.load_state_dict(torch.load(model_save_path))
var_test_x = torch.from_numpy(test_data).float().to(DEVICE)
var_test_y = torch.from_numpy(test_label).long().to(DEVICE)
# 测试序列似乎还是太大，只能分批测试
test_pred = []
batches = gen_batch(var_test_x, var_test_y, batch_size=batch_size)
for var_x, var_y in batches:
pred = model(var_x)
test_pred.extend(list(pred.cpu().data.numpy()))
test_pred = np.array(test_pred)
test_pred = np.argmax(test_pred, axis=1)
classify_analysis(test_label, test_pred)
if __name__ == '__main__':
path = './data/train-test-0.4.npz'
train_data, test_data, train_label, test_label = load_train_test(path)
vectors = np.load('lstm_ed.npz')
error_vector = vectors['error_vector']
pred_vector = vectors['pred_vector']
model = KNeighborsClassifier()
model.fit(error_vector, train_label)
pred = model.predict(error_vector)
model2 = KNeighborsClassifier()
model2.fit(pred_vector, train_label)
pred2 = model2.predict(pred_vector)
classify_analysis(train_label, pred)
classify_analysis(train_label, pred2)
# plt.figure(figsize=(8, 3))
# plt.subplot(2, 1, 1)
# plt.plot(train_data[train_label == 0][0], color='black', label='ground truth')
# plt.plot(pred_vector[train_label == 0][0], color='red', label='prediction')
# plt.plot(error_vector[train_label == 0][0], color='blue', label='error')
# plt.legend()
# plt.title('Normal TS')
# plt.tight_layout(True)
# plt.subplot(2, 1, 2)
# plt.plot(train_data[train_label == 1][0], color='black', label='ground truth')
# plt.plot(pred_vector[train_label == 1][0], color='red', label='prediction')
# plt.plot(error_vector[train_label == 1][0], color='blue', label='error')
# plt.legend()