code: def recall(links_true, links_pred=None):
      
  
      if _isconfusionmatrix(links_true):
  
          confusion_matrix = links_true
  
          v = confusion_matrix[0, 0] \
              / (confusion_matrix[0, 0] + confusion_matrix[0, 1])
      else:
  
          tp = true_positives(links_true, links_pred)
          fn = false_negatives(links_true, links_pred)
          v = tp / (tp + fn)
  
      return float(v)
docs: recall(links_true, links_pred)
  
      Compute the recall/sensitivity.
  
      The recall is given by TP/(TP+FN).
  
      Parameters
      ----------
      links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
          The true (or actual) collection of links.
      links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
          The predicted collection of links.
  
      Returns
      -------
      float
          The recall