lambdamart.py 文件源码

def feature_importances(self):
        '''
        Return the feature importances.
        '''
        if len(self.estimators) == 0:
            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