<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def score(self, testing_features, testing_target):
 """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 """

        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