'Returns 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'

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.')
    score = SCORERS[self.scoring_function](self.fitted_pipeline_, testing_features.astype(np.float64), testing_target.astype(np.float64))
    return abs(score)
