apache spark - ¿Cómo encontrar la media de las columnas vectoriales agrupadas en Spark SQL?

apache-spark apache-spark-sql

Question

He creado un RelationalGroupedDataset llamando a instances.groupBy(instances.col("property_name")) :

val x = instances.groupBy(instances.col("property_name"))

¿Cómo compongo una función agregada definida por el usuario para realizar Statistics.colStats().mean en cada grupo?

¡Gracias!

Answer 1

Chispa> = 2.4

Puedes usar Summarizer :

import org.apache.spark.ml.stat.Summarizer
val dfNew = df.as[(Int, org.apache.spark.mllib.linalg.Vector)]
.map { case (group, v) => (group, v.asML) }
.toDF("group", "features")
dfNew
.groupBy($"group")
.agg(Summarizer.mean($"features").alias("means"))
.show(false)

+-----+--------------------------------------------------------------------+
|group|means |
+-----+--------------------------------------------------------------------+
|1 |[8.740630742016827E12,2.6124956666260462E14,3.268714653521495E14] |
|6 |[2.1153266920139112E15,2.07232483974322592E17,6.2715161747245427E17]|
|3 |[6.3781865566442836E13,8.359124419656149E15,1.865567821598214E14] |
|5 |[4.270201403521642E13,6.561211706745676E13,8.395448246737938E15] |
|9 |[3.577032684241448E16,2.5432362841314468E16,2.3744826986293008E17] |
|4 |[2.339253775419023E14,8.517531902022505E13,3.055115780965264E15] |
|8 |[8.029924756674456E15,7.284873600992855E17,3.08621303029924E15] |
|7 |[3.2275104122699105E15,7.5472363442090208E16,7.022556624056291E14] |
|10 |[1.2412562261010224E16,5.741115713769269E15,4.34336779990902E16] |
|2 |[1.085528901765636E16,7.633370115869126E12,6.952642232477029E11] |
+-----+--------------------------------------------------------------------+

Chispa <2.4

No puede usar UserDefinedAggregateFunction pero puede crear un Aggregator usando el mismo MultivariateOnlineSummarizer :

import org.apache.spark.sql.Row
import org.apache.spark.sql.expressions.Aggregator
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.sql.{Encoder, Encoders}
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
type Summarizer = MultivariateOnlineSummarizer
case class VectorSumarizer(f: String) extends Aggregator[Row, Summarizer, Vector]
with Serializable {
def zero = new Summarizer
def reduce(acc: Summarizer, x: Row) = acc.add(x.getAs[Vector](f))
def merge(acc1: Summarizer, acc2: Summarizer) = acc1.merge(acc2)
// This can be easily generalized to support additional statistics
def finish(acc: Summarizer) = acc.mean
def bufferEncoder: Encoder[Summarizer] = Encoders.kryo[Summarizer]
def outputEncoder: Encoder[Vector] = ExpressionEncoder()
}

Ejemplo de uso:

import org.apache.spark.mllib.random.RandomRDDs.logNormalVectorRDD
val df = spark.sparkContext.union((1 to 10).map(i =>
logNormalVectorRDD(spark.sparkContext, i, 10, 10000, 3, 1).map((i, _))
)).toDF("group", "features")
df
.groupBy($"group")
.agg(VectorSumarizer("features").toColumn.alias("means"))
.show(10, false)

El resultado:

+-----+---------------------------------------------------------------------+
|group|means |
+-----+---------------------------------------------------------------------+
|1 |[1.0495089547176625E15,3.057434217141363E13,8.180842267228103E13] |
|6 |[8.578684690153061E15,1.865830977115807E14,1.0690831496167929E15] |
|3 |[1.0347016972600206E14,4.952536828257269E15,8.498944924018858E13] |
|5 |[2.2135916061736424E16,1.5137112888230388E14,8.154750681129871E14] |
|9 |[6.496030194110956E15,6.2697260327708368E16,3.7282521260607136E16] |
|4 |[2.4518629692233766E14,1.959083619621557E13,5.278689364420169E13] |
|8 |[1.806052212008392E16,2.0410654639336184E16,6.409495244104527E15] |
|7 |[1.32896092658714784E17,1.2074042288752348E15,1.10951746294648096E17]|
|10 |[1.6131199347666342E19,1.24546214832341616E17,8.5265750194040304E16] |
|2 |[4.330324858747168E12,6.19671483053885E12,2.2416578004282832E13] |
+-----+---------------------------------------------------------------------+

Nota :

• Tenga en cuenta que MultivariateOnlineSummarizer requiere "viejo estilo" mllib.linalg.Vector . No funcionará con ml.linalg.Vector . Para admitir esto, deberá convertir entre tipos nuevos y antiguos .
• En cuanto al rendimiento, probablemente estará mejor con los RDDs .