VectorAssembler仅输出到DenseVector吗? [英] VectorAssembler output only to DenseVector?

问题描述

VectorAssembler的功能有些令人讨厌. 我目前正在将一组列转换为 向量，然后使用StandardScaler函数应用缩放 包括的功能.但是，似乎有SPARK用于记忆 原因，决定是否应使用DenseVector或SparseVector表示要素的每一行. 但是，当您需要使用StandardScaler时，SparseVector的输入 无效，仅允许使用DenseVectors.有人知道解决方案吗?

There is something very annoying with the function of VectorAssembler. I am currently transforming a set of columns into a single column of vectors and then use the StandardScaler function to apply the scaling to the included features. However, there seems that SPARK for memory reasons, decides whether it should use a DenseVector or a SparseVector to represent each row of features. But, when you need to use StandardScaler, the input of SparseVector(s) is invalid, only DenseVectors are allowed. Does anybody know a solution to that?

我决定只改用UDF函数，这样可以 将稀疏向量变成密集向量.有点傻，但是行得通.

I decided to just use a UDF function instead, which turns the sparse vector into a dense vector. Kind of silly but works.

推荐答案

您说对了，VectorAssembler根据使用较少内存的方式选择密集与稀疏输出格式.

You're right that VectorAssembler chooses dense vs sparse output format based on whichever one uses less memory.

您不需要UDF即可将SparseVector转换为DenseVector；只需使用 toArray()方法:

You don't need a UDF to convert from SparseVector to DenseVector; just use toArray() method:

from pyspark.ml.linalg import SparseVector, DenseVector 
a = SparseVector(4, [1, 3], [3.0, 4.0])
b = DenseVector(a.toArray())

此外，StandardScaler接受SparseVector，除非您在创建时设置了withMean=True.如果确实需要去均值，则必须从所有分量中减去一个(可能为非零)数字，这样稀疏向量就不再稀疏了.

Also, StandardScaler accepts SparseVector unless you set withMean=True at creation. If you do need to de-mean, you have to deduct a (presumably non-zero) number from all the components, so the sparse vector won't be sparse any more.

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