code: def regressor_detailed_scores(
      predictions, gts, raw_df_lst, capacity, output_len
  ):
      """
      Desc:
          Some common metrics for regression problems
      Args:
          predictions:
          gts: ground truth vector
          raw_df_lst:
          settings:
      Returns:
          A tuple of metrics
      """
      all_mae, all_rmse = [], []
      for i in range(capacity):
          prediction = predictions[i]
          gt = gts[i]
          raw_df = raw_df_lst[i]
          _mae, _rmse = turbine_scores(
              prediction, gt, raw_df, output_len, 1
          )
          all_mae.append(_mae)
          all_rmse.append(_rmse)
      total_mae = np.array(all_mae).sum()
      total_rmse = np.array(all_rmse).sum()
      return total_mae, total_rmse
  
file_path: PGL/examples/kddcup2022/wpf_baseline/metrics.py
imports: import pandas
  import time
  import numpy
label_desc: Use image node_id 11 for calling a global function with example usage: regressor_detailed_scores(predictions, gts, raw_df_lst, capacity, output_len) and returns: total_mae,  total_rmse