code: import typing as ty
  
  import numpy as np
  import scipy.special
  import sklearn.metrics as skm
  
  from . import util
  
  
  def calculate_metrics(
      task_type: str,
      y: np.ndarray,
      prediction: np.ndarray,
      classification_mode: str,
      y_info: ty.Optional[ty.Dict[str, ty.Any]],
  ) -> ty.Dict[str, float]:
      if task_type == util.REGRESSION:
          del classification_mode
          rmse = skm.mean_squared_error(y, prediction) ** 0.5  # type: ignore[code]
          if y_info:
              if y_info['policy'] == 'mean_std':
                  rmse *= y_info['std']
              else:
                  assert False
          return {'rmse': rmse, 'score': -rmse}
      else:
          assert task_type in (util.BINCLASS, util.MULTICLASS)
          labels = None
          if classification_mode == 'probs':
              probs = prediction
          elif classification_mode == 'logits':
              probs = (
                  scipy.special.expit(prediction)
                  if task_type == util.BINCLASS
                  else scipy.special.softmax(prediction, axis=1)
              )
          else:
              assert classification_mode == 'labels'
              probs = None
              labels = prediction
          if labels is None:
              labels = (
                  np.round(probs).astype('int64')
                  if task_type == util.BINCLASS
                  else probs.argmax(axis=1)  # type: ignore[code]
              )
  
          result = skm.classification_report(y, labels, output_dict=True)  # type: ignore[code]
          if task_type == util.BINCLASS:
              result['roc_auc'] = skm.roc_auc_score(y, probs)  # type: ignore[code]
          result['score'] = result['accuracy']  # type: ignore[code]
      return result  # type: ignore[code]
  
  
  def make_summary(metrics: ty.Dict[str, ty.Any]) -> str:
      precision = 3
      summary = {}
      for k, v in metrics.items():
          if k.isdigit():
              continue
          k = {
              'score': 'SCORE',
              'accuracy': 'acc',
              'roc_auc': 'roc_auc',
              'macro avg': 'm',
              'weighted avg': 'w',
          }.get(k, k)
          if isinstance(v, float):
              v = round(v, precision)
              summary[k] = v
          else:
              v = {
                  {'precision': 'p', 'recall': 'r', 'f1-score': 'f1', 'support': 's'}.get(
                      x, x
                  ): round(v[x], precision)
                  for x in v
              }
              for item in v.items():
                  summary[k + item[0]] = item[1]
  
      s = [f'score = {summary.pop("SCORE"):.3f}']
      for k, v in summary.items():
          if k not in ['mp', 'mr', 'wp', 'wr']:  # just to save screen space
              s.append(f'{k} = {v}')
      return ' | '.join(s)
  
apis: sklearn.metrics.roc_auc_score numpy.round sklearn.metrics.mean_squared_error sklearn.metrics.classification_report