'Return the feature importances (the higher, the more important the
feature).
Returns
feature_importances_ : array, shape = [n_features]'

@property
def feature_importances_(self):

    if ((self.estimators_ is None) or (len(self.estimators_) == 0)):
        raise ValueError('Estimator    not    fitted,    call    `fit`    before    `feature_importances_`.')
    try:
        norm = self.estimator_weights_.sum()
        return (sum(((weight * clf.feature_importances_) for (weight, clf) in zip(self.estimator_weights_, self.estimators_))) / norm)
    except AttributeError:
        raise AttributeError('Unable    to    compute    feature    importances    since    base_estimator    does    not    have    a    feature_importances_    attribute')
