def zero_one_loss(y_true, y_pred, normalize=True, sample_weight=None):
 score = accuracy_score(y_true, y_pred, normalize=normalize, sample_weight=sample_weight)
 if normalize:
  return (1 - score)
 else:
  if (sample_weight is not None):
   n_samples = np.sum(sample_weight)
  else:
   n_samples = _num_samples(y_true)
  return (n_samples - score)

def zero_one_loss ( y_true , y_pred , normalize = True , sample_weight = None ) : score = accuracy_score ( y_true , y_pred , normalize = normalize , sample_weight = sample_weight ) if normalize : return ( 1 - score ) else : if ( sample_weight is not None ) : n_samples = np . sum ( sample_weight ) else : n_samples = _num_samples ( y_true ) return ( n_samples - score )
zero-one classification loss .