def random_subspace(features, subspaces, min_features=None, max_features=None): ''' To generate a list of subsets of all features Parameters: features (list): A list of features. subspaces (int): An integer denoting the number of non-unique subspaces to be created. min_features (int): Minimum number of features to be used to create subspaces. (default= None. half the total number of features.) max_features (int): Maximum number of features to be used to create subspaces. (default= None. the total number of features.) Returns: list: A nested list of features. ''' if min_features == None: min_features = math.floor(len(features) / 2) if max_features == None: max_features = len(features) if max_features > len(features): max_features = len(features) feature_list = [] for i in range(subspaces): no_features = np.random.randint(low=min_features, high=max_features) rand_features = random.sample(population=features, k=no_features) feature_list.append(rand_features) return feature_list什么意思

这是一个生成特征子集的函数。函数接受以下参数：

- features：一个特征列表。
- subspaces：要创建的非唯一子空间的数量。
- min_features：创建子空间时要使用的最小特征数。默认为 None，即特征总数的一半。
- max_features：创建子空间时要使用的最大特征数。默认为 None，即特征总数。

函数会随机从 features 中选择 min_features 和 max_features 之间的数量的特征，重复 subspaces 次，生成一个嵌套的特征列表，返回该列表。