code: def folds_without_evidence_for_at_least_one_label_combination(
      y, folds, order=1
  ):
      """Counts the number of folds without evidence for a given Label, Label Pair or Label Combination (FZ, FZLP, FZLC) measure
  
      A general implementation of FZ - the number of folds that contain at least one label combination of order
      :code:`order` with no positive examples. With :code:`order` = 1, it becomes the FZ measure from Katakis et.al's
      original paper.
  
      Parameters:
      -----------
      y : output matrix or array of arrays (n_samples, n_labels)
          the binary-indicator label assignment per sample representation of the output space
  
      folds : List[List[int]], shape = (n_folds)
          list of indexes of samples assigned per fold
  
      order : int, >= 1
          the order of label relationship to take into account when balancing sample distribution across labels
  
      Returns
      -------
      score : float
          the number of folds with missing evidence for at least one label combination
      """
      combinations_per_row = get_combination_wise_output_matrix(
          y, order
      )
      all_combinations = get_unique_combinations(combinations_per_row)
      return np.sum(
          [
              get_unique_combinations(combinations_per_row[[fold]])
              != all_combinations
              for fold in folds
          ]
      )
  
file_path: scikit-multilearn/skmultilearn/model_selection/measures.py
imports: import numpy
  import itertools
label_desc: Use image node_id 5 for calling a global function with example usage: folds_without_evidence_for_at_least_one_label_combination(y, folds, order) and returns: np