code: def correlation_coefficient(y, y_pred1, y_pred2):
      """Calculates the correlation  between two classifiers using oracle
      outputs. Coefficient is a value in a range [-1, 1].
  
      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_samples):
                predicted class labels by the classifier 2 for each sample.
  
      Returns
      -------
      rho : The correlation coefficient measured between two classifiers
      """
      N00, N10, N01, N11 = _process_predictions(y, y_pred1, y_pred2)
      tmp = (N11 * N00) - (N10 * N01)
      rho = tmp / np.sqrt(
          (N11 + N01) * (N10 + N00) * (N11 + N10) * (N01 + N00)
      )
      return rho
  
file_path: DESlib/deslib/util/diversity.py
imports: import sys
  import numpy
label_desc: Use image node_id 8 for calling a global function with example usage: correlation_coefficient(y, y_pred1, y_pred2) and returns: rho