'"Returns the mean accuracy on the given test data and labels.
Parameters
X : array-like, shape [n_samples, n_features]
Test samples
y : array-like, shape [n_samples, n_outputs]
True values for X
Returns
scores : float
accuracy_score of self.predict(X) versus y'

def score(self, X, y):

    check_is_fitted(self, 'estimators_')
    n_outputs_ = len(self.estimators_)
    if (y.ndim == 1):
        raise ValueError('y    must    have    at    least    two    dimensions    for    multi    target    classification    but    has    only    one')
    if (y.shape[1] != n_outputs_):
        raise ValueError('The    number    of    outputs    of    Y    for    fit    {0}    and    score    {1}    should    be    same'.format(n_outputs_, y.shape[1]))
    y_pred = self.predict(X)
    return np.mean(np.all((y == y_pred), axis=1))
