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_mse_torch(y_predicted, y_true)
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_mse_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))