def calculate_loss(self, batch, idx=None, batches_seen=None):
if idx is not None:
idx = torch.tensor(idx).to(self.device)
tx = batch['X'][:, :, idx, :].clone() # 避免batch[X]被修改 下一次idx索引就不对了
y_true = batch['y'][:, :, idx, :]
batch_new = {'X': tx}
y_predicted = self.predict(batch_new, idx)
else:
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])
if self.training:
if batches_seen % self.step_size == 0 and self.task_level < self.output_window:
self.task_level += 1
self._logger.info(
'Training: task_level increase from {} to {}'.format(
self.task_level - 1, self.task_level))
self._logger.info(
'Current batches_seen is {}'.format(batches_seen))
if self.use_curriculum_learning:
return loss.masked_mae_torch(
y_predicted[:, :self.task_level, :, :],
y_true[:, :self.task_level, :, :], 0)
else:
return loss.masked_mae_torch(y_predicted, y_true, 0)
else:
return loss.masked_mae_torch(y_predicted, y_true, 0)
def calculate_loss(self, batch):
y_true = batch['y']
y_predicted = self.predict(batch)
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)
def calculate_loss(self, batch):
y_true = batch['y'] # ground-truth value
y_predicted = self.predict(batch) # prediction results
# denormalization the value
y_true = self._scaler.inverse_transform(y_true[..., :self.output_dim])
y_predicted = self._scaler.inverse_transform(
y_predicted[..., :self.output_dim])
# call the mask_mae loss function defined in `loss.py`
return loss.masked_mae_torch(y_predicted, y_true, 0)
def calculate_loss(self, batch):
y_true = batch['y'] # ground-truth value
y_predicted = self.predict(batch) # prediction results
# 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)
def calculate_loss(self, batch):
y_true = batch['y'].to(self.device)
output = self.predict(batch)
y_predicted = output
y_true = self._scaler.inverse_transform(y_true[..., :self.output_dim])
y_predicted = self._scaler.inverse_transform(
y_predicted[..., :self.output_dim])
res = loss.masked_mae_torch(y_predicted, y_true, 0)
return res
def calculate_loss(self, batch, batches_seen=None):
y_true = batch['y']
epoch = batches_seen // self.num_batches
self._logger.debug(f"EPOCH = {epoch}, bep={batches_seen}")
y_predicted, mid_output = self.forward(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])
# 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 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
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))
