code: def get_false_negative(self, scalar=True):
          
          ans = pd.Series(PrettyDict([(k, safe_div(np.sum(self.loc[k]) - self[k][k],
                                                   np.sum(self.sum() - self.loc[k]))) for k in self.columns]))
          if (not self._scalar_stats and not scalar) or self._num_classes != 2:
              return ans
          return ans[self._pos_label]
docs: Normalized false positive rate (0 <= fp <= 1)