如何在Spark（Scala）中还原一个热Enoding

在运行k-means（mllib spark scala）之后，我想弄清楚我从使用mllib的OneHotEncoder预处理的数据中获得的集群中心

一个中心看起来像这样：

群集中心0[0.3496378699559276,0.05482645034473324,111.6962521358467,1.770525792286651,0.0,0.8561916265130964,0.014382183950365071,0.0,0.0,0.0,0.47699722692567864,0.0,0.0,0.0,0.04988557988346689,0.0,0.0,0.0,0.8981811028926263,0.9695107580117296,0.0,0.0,1.7505886931570156,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.771620072281845,0.0,0.0,0.0,0.0]

这显然不是非常人性化的…关于如何还原一个热门编码并检索原始分类特征，有什么想法吗？

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如果我寻找距离质心最近的数据点（使用k-means使用的相同距离度量，我假设是欧几里德距离），然后恢复该特定数据点的编码，会怎么样？

对于簇质心，这是不可能的（强烈不推荐）要反转编码。假设您拥有6个原始功能“3”，并且它被编码为

[0.0,0.0,1.0,0.0,0.0,0.0]

。在这种情况下，很容易从编码中提取3作为正确的功能

但在kmeans应用程序之后，您可能会得到一个群集质心，它会像这样查找此功能

[0.0,0.13,0.0,0.77,0.1,0.0]

。如果您想将其解码回以前的表示形式，如6中的“4”，因为功能4的值最大，那么您将丢失信息，模型可能会损坏

编辑：添加一种可能的方法，将注释中的数据点编码还原为答案

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如果您在数据点上有ID，则可以在将数据点分配给群集以获取旧状态后，在编码之前对ID执行选择/加入操作。

谢谢！我理解您的答案。如果我查找最接近的数据点，该怎么办（使用k-means使用的相同距离度量，我假设是欧几里德距离）到质心，然后还原该特定数据点的编码？@JoãoMoura然后我认为最简单的方法是在每个数据点上都有ID，并在为其群集分配一个点后通过ID检索原始值。然后不需要还原编码，而是对原始数据点执行简单的选择/连接操作和编码的数据集。