lambdarandomforest.py 文件源码

def feature_importances(self):
        ''' 
        Return the feature importances.
        '''
        if self.trained is False:   
            raise ValueError('the model has not been trained yet')

        importances = Parallel(n_jobs=self.n_jobs, backend="threading")(delayed(getattr, check_pickle=False)
                              (tree, 'feature_importances_') for tree in self.estimators)

        return sum(importances) / self.n_estimators