code: def fbeta_score(self, beta, idx):
      beta_2 = np.power(beta, 2)
      precision = self.precision(idx)
      recall = self.recall(idx)
      nom = (1 + beta_2) * precision * recall
      den = (beta_2 * precision) + recall
      if den == 0 or den == np.nan:
          return 0
      else:
          return nom / den
  
imports: import logging
  from collections import OrderedDict
  import numpy
  from sklearn import metrics
  from sklearn.metrics import confusion_matrix
label_desc: Use image node_id 18 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.fbeta_score(beta, idx) and returns: int, unknown