code: def score(self, testing_features, testing_target):
          
          if self.fitted_pipeline_ is None:
              raise RuntimeError('A pipeline has not yet been optimized. Please call fit() first.')
  
          testing_features, testing_target = self._check_dataset(testing_features, testing_target, sample_weight=None)
  
          # If the scoring function is a string, we must adjust to use the sklearn
          # scoring interface
          score = SCORERS[self.scoring_function](
              self.fitted_pipeline_,
              testing_features.astype(np.float64),
              testing_target.astype(np.float64)
          )
          return score
docs: Return the score on the given testing data using the user-specified scoring function.
  
          Parameters
          ----------
          testing_features: array-like {n_samples, n_features}
              Feature matrix of the testing set
          testing_target: array-like {n_samples}
              List of class labels for prediction in the testing set
  
          Returns
          -------
          accuracy_score: float
              The estimated test set accuracy