Spark，DataFrame:对组应用变换器/估计器 [英] Spark, DataFrame: apply transformer/estimator on groups

问题描述

我有一个如下所示的DataFrame:

I have a DataFrame that looks like follow:

+-----------+-----+------------+
|     userID|group|    features|
+-----------+-----+------------+
|12462563356|    1|  [5.0,43.0]|
|12462563701|    2|   [1.0,8.0]|
|12462563701|    1|  [2.0,12.0]|
|12462564356|    1|   [1.0,1.0]|
|12462565487|    3|   [2.0,3.0]|
|12462565698|    2|   [1.0,1.0]|
|12462565698|    1|   [1.0,1.0]|
|12462566081|    2|   [1.0,2.0]|
|12462566081|    1|  [1.0,15.0]|
|12462566225|    2|   [1.0,1.0]|
|12462566225|    1|  [9.0,85.0]|
|12462566526|    2|   [1.0,1.0]|
|12462566526|    1|  [3.0,79.0]|
|12462567006|    2| [11.0,15.0]|
|12462567006|    1| [10.0,15.0]|
|12462567006|    3| [10.0,15.0]|
|12462586595|    2|  [2.0,42.0]|
|12462586595|    3|  [2.0,16.0]|
|12462589343|    3|   [1.0,1.0]|
+-----------+-----+------------+

列类型为:userID:长整数，组:整数，特征:矢量.

Where the columns types are: userID: Long, group: Int, and features:vector.

这已经是一个分组的DataFrame，即，一个用户ID最多会出现在特定组中一次.

This is already a grouped DataFrame, i.e. a userID will appear in a particular group at max one time.

我的目标是按组扩展features列.

My goal is to scale the features column per group.

是否有一种方法可以应用功能转换器(在我的情况下，我想应用 StandardScaler ) per组，而不是将其应用于完整的DataFrame.

Is there a way to apply a feature transformer (in my case I would like to apply a StandardScaler) per group instead of applying it to the full DataFrame.

P.S.使用ML不是强制性的，因此如果解决方案基于MLlib，则没有问题.

P.S. using ML is not mandatory, so no problem if the solution is based on MLlib.

推荐答案

计算统计信息

火花> = 3.0

现在Summarizer支持标准差，所以

Compute statistics

Spark >= 3.0

Now Summarizer supports standard deviations so

val summary = data
  .groupBy($"group")
  .agg(Summarizer.metrics("mean", "std")
  .summary($"features").alias("stats"))
  .as[(Int, (Vector, Vector))]
  .collect.toMap

火花> = 2.3

在Spark 2.3或更高版本中，您还可以使用Summarizer:

import org.apache.spark.ml.stat.Summarizer

val summaryVar = data
  .groupBy($"group")
  .agg(Summarizer.metrics("mean", "variance")
  .summary($"features").alias("stats"))
  .as[(Int, (Vector, Vector))]
  .collect.toMap

并调整下游代码以处理差异而不是标准偏差.

and adjust downstream code to handle variances instead of standard deviations.

您可以使用与默认Scaler几乎相同的代码按组计算统计信息:

You can compute statistics by group using almost the same code as default Scaler:

import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.sql.Row

// Compute Multivariate Statistics 
val summary = data.select($"group", $"features")
    .rdd
    .map {
         case Row(group: Int, features: Vector) => (group, features) 
    }
    .aggregateByKey(new MultivariateOnlineSummarizer)(/* Create an empty new MultivariateOnlineSummarizer */
         (agg, v) => agg.add(v), /* seqOp : Add a new sample Vector to this summarizer, and update the statistical summary. */
         (agg1, agg2) => agg1.merge(agg2)) /* combOp : As MultivariateOnlineSummarizer accepts a merge action with another MultivariateOnlineSummarizer, and update the statistical summary. */
    .mapValues {
      s => (
         s.variance.toArray.map(math.sqrt(_)), /* compute the square root variance for each key */
         s.mean.toArray /* fetch the mean for each key */
      )
    }.collectAsMap

转化

如果期望的组数相对较低，则可以广播这些:

Transformation

If expected number of groups is relatively low you can broadcast these:

val summaryBd = sc.broadcast(summary)

并转换您的数据:

val scaledRows = df.rdd.map{ case Row(userID, group: Int, features: Vector) =>
  val (stdev, mean)  =  summaryBd.value(group)
  val vs = features.toArray.clone()
  for (i <- 0 until vs.size) {
    vs(i) = if(stdev(i) == 0.0) 0.0 else (vs(i) - mean(i)) * (1 / stdev(i))
  }
  Row(userID, group, Vectors.dense(vs))
}
val scaledDf = sqlContext.createDataFrame(scaledRows, df.schema)

否则，您可以简单地加入.将其包装为以组列为参数的ML转换器应该不难.

Otherwise you can simply join. It shouldn't be hard to wrap this as a ML transformer with group column as a param.

这篇关于Spark，DataFrame:对组应用变换器/估计器的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！