def calculate_loss(self, batch, epoch=0, batch_seen=0):
"""
输入一个batch的数据，返回训练过程这个batch数据的loss，也就是需要定义一个loss函数。
:param batch: 输入数据，类字典，可以按字典的方法取数据
:return: training loss (tensor)
"""
y_true = batch['y']
self._logger.debug(f"EPOCH = {epoch}, bep={batch_seen}")
y_predicted, mid_output = self.forward(batch, batch_seen)
y_true = self._scaler.inverse_transform(y_true[..., :self.output_dim])
y_predicted = self._scaler.inverse_transform(
y_predicted[..., :self.output_dim])
# print(f"Y_TRUE = {y_true}")
# print(f"Y_PRED = {y_predicted}")
# print(f"y_true = {y_true.shape}, y_pred = {y_predicted.shape}")
# 根据训练轮数，选择性地加入正则项
loss_1 = loss.masked_mae_torch(y_predicted, y_true)
if epoch < self.epoch_use_regularization:
pred = torch.sigmoid(
mid_output.view(mid_output.shape[0] * mid_output.shape[1]))
# print(f"shape = {mid_output.shape}")
# print(f"aview = {self.adj_mx.view(mid_output.shape[0] * mid_output.shape[1])}")
true_label = self.adj_mx.view(mid_output.shape[0] *
mid_output.shape[1]).to(self.device)
compute_loss = torch.nn.BCELoss()
loss_g = compute_loss(pred, true_label)
self._logger.debug(f"loss_g = {loss_g}, loss_1 = {loss_1}")
loss_t = loss_1 + loss_g
return loss_t
else:
self._logger.debug(f"loss_1 = {loss_1}")
return loss_1
def calculate_loss(self, batch, batches_seen=None):
y_true = batch['y']
y_predicted = self.predict(batch, batches_seen)
y_true = self._scaler.inverse_transform(y_true[..., :self.output_dim])
y_predicted = self._scaler.inverse_transform(
y_predicted[..., :self.output_dim])
return loss.masked_mae_torch(y_predicted, y_true, 0)
def calculate_loss(self, batch):
y_true = batch['y']
y_predicted = self.predict(batch)
# print('y_true', y_true.shape)
# print('y_predicted', y_predicted.shape)
y_true = self._scaler.inverse_transform(y_true[..., :self.output_dim])
y_predicted = self._scaler.inverse_transform(y_predicted[..., :self.output_dim])
return loss.masked_mae_torch(y_predicted, y_true, 0)
def calculate_loss(self, batch):
"""
输入一个batch的数据，返回训练过程这个batch数据的loss，也就是需要定义一个loss函数。
:param batch: 输入数据，类字典，可以按字典的方法取数据
:return: training loss (tensor)
"""
# 1.取出真值 ground_truth
y_true = batch['y']
# 2.取出预测值
y_predicted = self.predict(batch)
# 3.使用self._scaler将进行了归一化的真值和预测值进行反向归一化（必须）
y_true = self._scaler.inverse_transform(y_true[..., :self.output_dim])
y_predicted = self._scaler.inverse_transform(
y_predicted[..., :self.output_dim])
# 4.调用loss函数计算真值和预测值的误差
# trafficdl/model/loss.py中定义了常见的loss函数
# 如果模型源码用到了其中的loss，则可以直接调用，以MSE为例:
res = loss.masked_mae_torch(y_predicted, y_true)
# 如果模型源码所用的loss函数在loss.py中没有，则需要自己实现loss函数
# ...（自定义loss函数）
# 5.返回loss的结果
return res
def collect(self, batch):
"""
收集一 batch 的评估输入
Args:
batch(dict): 输入数据，字典类型，包含两个Key:(y_true, y_pred):
batch['y_true']: (num_samples/batch_size, timeslots, ..., feature_dim)
batch['y_pred']: (num_samples/batch_size, timeslots, ..., feature_dim)
"""
if not isinstance(batch, dict):
raise TypeError('evaluator.collect input is not a dict of user')
y_true = batch['y_true'] # tensor
y_pred = batch['y_pred'] # tensor
print("evalutate", y_true.shape, y_pred.shape)
if y_true.shape != y_pred.shape:
raise ValueError(
"batch['y_true'].shape is not equal to batch['y_pred'].shape")
self.len_timeslots = y_true.shape[1]
for i in range(1, self.len_timeslots + 1):
for metric in self.metrics:
if metric + '@' + str(i) not in self.intermediate_result:
self.intermediate_result[metric + '@' + str(i)] = []
if self.mode.lower() == 'average': # 前i个时间步的平均loss
for i in range(1, self.len_timeslots + 1):
for metric in self.metrics:
if metric == 'masked_MAE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_mae_torch(y_pred[:, :i], y_true[:, :i],
0).item())
elif metric == 'masked_MSE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_mse_torch(y_pred[:, :i], y_true[:, :i],
0).item())
elif metric == 'masked_RMSE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_rmse_torch(y_pred[:, :i],
y_true[:, :i], 0).item())
elif metric == 'masked_MAPE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_mape_torch(y_pred[:, :i],
y_true[:, :i], 0).item())
elif metric == 'MAE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_mae_torch(y_pred[:, :i],
y_true[:, :i]).item())
elif metric == 'MSE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_mse_torch(y_pred[:, :i],
y_true[:, :i]).item())
elif metric == 'RMSE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_rmse_torch(y_pred[:, :i],
y_true[:, :i]).item())
elif metric == 'MAPE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_mape_torch(y_pred[:, :i],
y_true[:, :i]).item())
elif metric == 'R2':
self.intermediate_result[metric + '@' + str(i)].append(
loss.r2_score_torch(y_pred[:, :i],
y_true[:, :i]).item())
elif metric == 'EVAR':
self.intermediate_result[metric + '@' + str(i)].append(
loss.explained_variance_score_torch(
y_pred[:, :i], y_true[:, :i]).item())
elif self.mode.lower() == 'single': # 第i个时间步的loss
for i in range(1, self.len_timeslots + 1):
for metric in self.metrics:
if metric == 'masked_MAE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_mae_torch(y_pred[:, i - 1],
y_true[:, i - 1], 0).item())
elif metric == 'masked_MSE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_mse_torch(y_pred[:, i - 1],
y_true[:, i - 1], 0).item())
elif metric == 'masked_RMSE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_rmse_torch(y_pred[:, i - 1],
y_true[:, i - 1], 0).item())
elif metric == 'masked_MAPE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_mape_torch(y_pred[:, i - 1],
y_true[:, i - 1], 0).item())
elif metric == 'MAE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_mae_torch(y_pred[:, i - 1],
y_true[:, i - 1]).item())
elif metric == 'MSE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_mse_torch(y_pred[:, i - 1],
y_true[:, i - 1]).item())
elif metric == 'RMSE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_rmse_torch(y_pred[:, i - 1],
y_true[:, i - 1]).item())
elif metric == 'MAPE':
self.intermediate_result[metric + '@' + str(i)].append(
loss.masked_mape_torch(y_pred[:, i - 1],
y_true[:, i - 1]).item())
elif metric == 'R2':
self.intermediate_result[metric + '@' + str(i)].append(
loss.r2_score_torch(y_pred[:, i - 1],
y_true[:, i - 1]).item())
elif metric == 'EVAR':
self.intermediate_result[metric + '@' + str(i)].append(
loss.explained_variance_score_torch(
y_pred[:, i - 1], y_true[:, i - 1]).item())
else:
raise ValueError(
'Error parameter evaluator_mode={}, please set `single` or `average`.'
.format(self.mode))