stable_feature_ranking.py 文件源码

def _raw_rank(self, x, y, network):
        impt = np.zeros(x.shape[1])

        for i in range(x.shape[1]):
            hold = np.array(x[:, i])
            np.random.shuffle(x[:, i])

            # Handle both TensorFlow and SK-Learn models.
            if 'tensorflow' in str(type(network)).lower():
                pred = list(network.predict(x, as_iterable=True))
            else:
                pred = network.predict(x)

            rmse = metrics.mean_squared_error(y, pred)
            impt[i] = rmse
            x[:, i] = hold

        return impt