code: def confusion_matrix(links_true, links_pred, total=None):
      
  
      links_true = _get_multiindex(links_true)
      links_pred = _get_multiindex(links_pred)
  
      tp = true_positives(links_true, links_pred)
      fp = false_positives(links_true, links_pred)
      fn = false_negatives(links_true, links_pred)
  
      if total is None:
          tn = numpy.nan
      else:
          tn = true_negatives(links_true, links_pred, total)
  
      return numpy.array([[tp, fn], [fp, tn]])
docs: Compute the confusion matrix.
  
      The confusion matrix is of the following form:
  
      +----------------------+-----------------------+----------------------+
      |                      |  Predicted Positives  | Predicted Negatives  |
      +======================+=======================+======================+
      | **True Positives**   | True Positives (TP)   | False Negatives (FN) |
      +----------------------+-----------------------+----------------------+
      | **True Negatives**   | False Positives (FP)  | True Negatives (TN)  |
      +----------------------+-----------------------+----------------------+
  
      The confusion matrix is an informative way to analyse a prediction. The
      matrix can used to compute measures like precision and recall. The count
      of true prositives is [0,0], false negatives is [0,1], true negatives
      is [1,1] and false positives is [1,0].
  
      Parameters
      ----------
      links_true: pandas.MultiIndex, pandas.DataFrame, pandas.Series
          The true (or actual) links.
      links_pred: pandas.MultiIndex, pandas.DataFrame, pandas.Series
          The predicted links.
      total: int, pandas.MultiIndex
          The count of all record pairs (both links and non-links). When the
          argument is a pandas.MultiIndex, the length of the index is used. If
          the total is None, the number of True Negatives is not computed.
          Default None.
  
      Returns
      -------
      numpy.array
          The confusion matrix with TP, TN, FN, FP values.
  
      Note
      ----
      The number of True Negatives is computed based on the total argument.
      This argument is the number of record pairs of the entire matrix.