code: def get_top_k_similar_classes(
      self, k, top_n_class=10, tf_thresdhold=0.8
  ):
      if k > len(self.confusion_matrix):
          raise Exception("Error: N exceeds the class numbers.")
      values = np.array(self.confusion_matrix)
      class_count = np.sum(values, axis=1)
  
      top_classes = np.argsort(class_count)[::-1]
      similar_class = defaultdict(list)
  
      if len(top_classes) < top_n_class:
          top_n_class = len(top_classes)
      for i in range(top_n_class):
          targeted_class = top_classes[i]
          predicted_distribution = values[targeted_class, :]
          true_positive = predicted_distribution[targeted_class]
          tf_rate = true_positive / class_count[targeted_class]
          if tf_rate > tf_thresdhold:
              warnings.warn(
                  message="Class [%s] is well classified, ignored in "
                  "finding similar class." % self.label[targeted_class]
              )
              continue
          args = np.argsort(predicted_distribution)[::-1]
          j = 0
          counter = 0
          while counter < k:
              sim_idx = args[j]
              if sim_idx == targeted_class:
                  j += 1
                  continue
              similar_class[self.label[targeted_class]].append(
                  (
                      self.label[sim_idx],
                      values[:, [targeted_class, sim_idx]][
                          [targeted_class, sim_idx], :
                      ],
                  )
              )
              j += 1
              counter += 1
      return similar_class
  
file_path: contextual-ai/xai/model/evaluation/confusion_matrix.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from collections import defaultdict
  from typing import List
  import numpy
  import warnings
label_desc: Use image node_id 4 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.get_top_k_similar_classes(k, top_n_class, tf_thresdhold) and returns: similar_class