code: def _calculate_regret(
      self, y_true, y_pred_proba, metric, sample_weight=None
  ):
      if metric.needs_pred or metric.needs_quantile:
          preds = get_pred_from_proba(
              y_pred_proba=y_pred_proba, problem_type=self.problem_type
          )
      else:
          preds = y_pred_proba
      score = compute_weighted_metric(
          y_true,
          preds,
          metric,
          sample_weight,
          quantile_levels=self.quantile_levels,
      )
      return metric._optimum - score
  
imports: import logging
  import time
  from collections import Counter
  import numpy
  from ...constants import PROBLEM_TYPES
  from ...metrics import log_loss
  from ...utils import compute_weighted_metric, get_pred_from_proba
label_desc: Use image node_id 4 for calling the EnsembleSelection obj's underlying member method code with example usage: obj._calculate_regret(y_true, y_pred_proba, metric, sample_weight) and returns: unknown