code: def ratio_errors(
      y: np.array, y_pred1: np.array, y_pred2: np.array
  ) -> np.array:
      """Calculates Ratio of errors diversity measure between a pair of
      classifiers. A higher value means that the base classifiers are less likely
      to make the same errors. The ratio must be maximized for a higher diversity
  
      Parameters
      ----------
      y : array of shape (n_samples,):
          class labels of each sample.
  
      y_pred1 : array of shape (n_samples,):
                predicted class labels by the classifier 1 for each sample.
  
  
      y_pred2 : array of shape (n_classifiers, n_samples):
                predicted class labels by the classifier 2 for each sample.
  
      Returns
      -------
      ratio : The q-statistic measure between two classifiers
  
      References
      ----------
      Aksela, Matti. "Comparison of classifier selection methods for improving
      committee performance."
      Multiple Classifier Systems (2003): 159-159.
      """
      N00, N10, N01, N11 = _process_predictions(y, y_pred1, y_pred2)
      N00[N00 == 0] = sys.float_info.max
      a = N01 + N10
      b = N00
      ratio = np.divide(a, b, out=np.zeros_like(a), where=b != 0)
      return ratio
  
file_path: DESlib/deslib/util/diversity_batch.py
imports: import sys
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: ratio_errors(y, y_pred1, y_pred2) and returns: ratio