old_contents: import matplotlib.pyplot as plt
  from sklearn.metrics import roc_curve, precision_recall_curve, log_loss
  from sklearn.metrics import roc_auc_score as auc    # noqa
  
  from ..const import EPS
  
  
  def logloss(y, p):
      """Bounded log loss error.
  
      Args:
          y (numpy.array): target
          p (numpy.array): prediction
  
      Returns:
          bounded log loss error
      """
  
      p[p < EPS] = EPS
      p[p > 1 - EPS] = 1 - EPS
      return log_loss(y, p)
  
  
  def plot_roc_curve(y, p):
      fpr, tpr, _ = roc_curve(y, p)
  
      plt.plot(fpr, tpr)
      plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
      plt.xlim([0.0, 1.0])
      plt.ylim([0.0, 1.05])
      plt.xlabel('False Positive Rate')
      plt.ylabel('True Positive Rate')
  
  
  def plot_pr_curve(y, p):
      precision, recall, _ = precision_recall_curve(y, p)
  
      plt.step(recall, precision, color='b', alpha=0.2, where='post')
      plt.fill_between(recall, precision, step='post', alpha=0.2, color='b')
      plt.xlabel('Recall')
      plt.ylabel('Precision')
      plt.ylim([0.0, 1.05])
      plt.xlim([0.0, 1.0])
  
new_contents: import matplotlib.pyplot as plt
  from sklearn.metrics import roc_curve, precision_recall_curve, log_loss
  from sklearn.metrics import roc_auc_score as auc            # noqa 
  from sklearn.metrics import average_precision_score as apr  # noqa
  
  from ..const import EPS
  
  
  def logloss(y, p):
      """Bounded log loss error.
  
      Args:
          y (numpy.array): target
          p (numpy.array): prediction
  
      Returns:
          bounded log loss error
      """
  
      p[p < EPS] = EPS
      p[p > 1 - EPS] = 1 - EPS
      return log_loss(y, p)
  
  
  def plot_roc_curve(y, p):
      fpr, tpr, _ = roc_curve(y, p)
  
      plt.plot(fpr, tpr)
      plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
      plt.xlim([0.0, 1.0])
      plt.ylim([0.0, 1.05])
      plt.xlabel('False Positive Rate')
      plt.ylabel('True Positive Rate')
  
  
  def plot_pr_curve(y, p):
      precision, recall, _ = precision_recall_curve(y, p)
  
      plt.step(recall, precision, color='b', alpha=0.2, where='post')
      plt.fill_between(recall, precision, step='post', alpha=0.2, color='b')
      plt.xlabel('Recall')
      plt.ylabel('Precision')
      plt.ylim([0.0, 1.05])
      plt.xlim([0.0, 1.0])
  
subject: add sklearn.metrics.average_precision_score as apr in metrics