def local_accuracy(X, y, model_generator, method_name):
    """ Local Accuracy
    transform = "identity"
    sort_order = 2
    """

    def score_map(true, pred):
        """ Converts local accuracy from % of standard deviation to numerical scores for coloring.
        """

        v = min(1.0, np.std(pred - true) / (np.std(true) + 1e-8))
        if v < 1e-6:
            return 1.0
        elif v < 0.01:
            return 0.9
        elif v < 0.05:
            return 0.75
        elif v < 0.1:
            return 0.6
        elif v < 0.2:
            return 0.4
        elif v < 0.3:
            return 0.3
        elif v < 0.5:
            return 0.2
        elif v < 0.7:
            return 0.1
        else:
            return 0.0
    def score_function(X_train, X_test, y_train, y_test, attr_function, trained_model, random_state):
        return measures.local_accuracy(
            X_train, y_train, X_test, y_test, attr_function(X_test),
            model_generator, score_map, trained_model
        )
    return None, __score_method(X, y, None, model_generator, score_function, method_name)
Local Accuracy
    transform = "identity"
    sort_order = 2