为什么StandardScaler不将元数据附加到输出列? [英] Why does StandardScaler not attach metadata to the output column?

问题描述

我注意到ml StandardScaler不会将元数据附加到输出列:

I noticed that the ml StandardScaler does not attach metadata to the output column:

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature._

val df = spark.read.option("header", true)
  .option("inferSchema", true)
  .csv("/path/to/cars.data")

val strId1 = new StringIndexer()
  .setInputCol("v7")
  .setOutputCol("v7_IDX")
val strId2 = new StringIndexer()
  .setInputCol("v8")
  .setOutputCol("v8_IDX")

val assmbleFeatures: VectorAssembler = new VectorAssembler()
  .setInputCols(Array("v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7_IDX"))
  .setOutputCol("featuresRaw")

val scalerModel = new StandardScaler()
  .setInputCol("featuresRaw")
  .setOutputCol("scaledFeatures")


val plm = new Pipeline()
  .setStages(Array(strId1, strId2, assmbleFeatures, scalerModel))
  .fit(df)

val dft = plm.transform(df)

dft.schema("scaledFeatures").metadata

赠予:

res1: org.apache.spark.sql.types.Metadata = {}

此示例适用于此数据集(只需改编上面代码中的路径).

This example works on this dataset (just adapt path in code above).

是否有特定原因?将来是否有可能将此功能添加到Spark?有什么解决方法的建议，但不包括复制StandardScaler?

Is there a specific reason for this? Is it likely that this feature will be added to Spark in the future? Any suggestions for a workaround that does not include duplicating the StandardScaler?

推荐答案

虽然丢弃元数据可能不是最幸运的选择，但扩展索引分类功能毫无意义. StringIndexer返回的值只是标签.

While discarding metadata is probably not the most fortunate choice, scaling indexed categorical features doesn't make any sense. Values returned by the StringIndexer are just labels.

如果要缩放数字特征，则应在一个单独的阶段:

If you want to scale numerical features, it should be a separate stage:

val numericAssembler: VectorAssembler = new VectorAssembler()
  .setInputCols(Array("v0", "v1", "v2", "v3", "v4", "v5", "v6"))
  .setOutputCol("numericFeatures")

val scaler = new StandardScaler()
  .setInputCol("numericFeatures")
  .setOutputCol("scaledNumericFeatures")

val finalAssembler: VectorAssembler = new VectorAssembler() 
  .setInputCols(Array("scaledNumericFeatures", "v7_IDX"))
  .setOutputCol("features")

new Pipeline()
  .setStages(Array(strId1, strId2, numericAssembler, scaler, finalAssembler))
  .fit(df)

请记住在此答案开头提出的问题，您也可以尝试复制元数据:

Keeping in mind concerns raised at the beginning of this answer, you can also try copying the metadata:

val result = plm.transform(df).transform(df => 
  df.withColumn(
   "scaledFeatures", 
   $"scaledFeatures".as(
     "scaledFeatures", 
     df.schema("featuresRaw").metadata)))

esult.schema("scaledFeatures").metadata

{"ml_attr":{"attrs":{"numeric":[{"idx":0,"name":"v0"},{"idx":1,"name":"v1"},{"idx":2,"name":"v2"},{"idx":3,"name":"v3"},{"idx":4,"name":"v4"},{"idx":5,"name":"v5"},{"idx":6,"name":"v6"}],"nominal":[{"vals":["ford","chevrolet","plymouth","dodge","amc","toyota","datsun","vw","buick","pontiac","honda","mazda","mercury","oldsmobile","peugeot","fiat","audi","chrysler","volvo","opel","subaru","saab","mercedes","renault","cadillac","bmw","triumph","hi","capri","nissan"],"idx":7,"name":"v7_IDX"}]},"num_attrs":8}}

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