Github user viirya commented on the issue:
https://github.com/apache/spark/pull/19229
Ran the similar benchmark as
https://github.com/apache/spark/pull/18902#issuecomment-321727416:
numColums | Old Mean | Old Median | New Mean | New Median
-- | -- | -- | -- | --
1 | 0.12906740590000002 | 0.087246649 | 0.1263591766 | 0.058268569299999996
10 | 0.42224367090000003 | 0.2957120874 | 0.13829991330000002 | 0.0752307166
100 | 6.931274417299998 | 7.2270134943 | 0.3018686074 | 0.2554692345
The test code is the same basically but measuring transforming time now:
import org.apache.spark.ml.feature._
import org.apache.spark.sql.Row
import org.apache.spark.sql.types._
import spark.implicits._
import scala.util.Random
val seed = 123l
val random = new Random(seed)
val n = 10000
val m = 100
val rows = sc.parallelize(1 to n).map(i=>
Row(Array.fill(m)(random.nextDouble): _*))
val struct = new StructType(Array.range(0,m,1).map(i =>
StructField(s"c$i",DoubleType,true)))
val df = spark.createDataFrame(rows, struct)
df.persist()
df.count()
for (strategy <- Seq("mean", "median"); k <- Seq(1,10,100)) {
val imputer = new
Imputer().setStrategy(strategy).setInputCols(Array.range(0,k,1).map(i=>s"c$i")).setOutputCols(Array.range(0,k,1).map(i=>s"o$i"))
var duration = 0.0
for (i<- 0 until 10) {
val model = imputer.fit(df)
val start = System.nanoTime()
model.transform(df)
val end = System.nanoTime()
duration += (end - start) / 1e9
}
println((strategy, k, duration/10))
}
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