scala - 如何在多列上使用 spark quantilediscretizer

Question

全部，

我有一个如下的 ml 管道设置

import org.apache.spark.ml.feature.QuantileDiscretizer
import org.apache.spark.sql.types.{StructType,StructField,DoubleType}    
import org.apache.spark.ml.Pipeline
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import scala.util.Random

val nRows = 10000
val nCols = 1000
val data = sc.parallelize(0 to nRows-1).map { _ => Row.fromSeq(Seq.fill(nCols)(Random.nextDouble)) }
val schema = StructType((0 to nCols-1).map { i => StructField("C" + i, DoubleType, true) } )
val df = spark.createDataFrame(data, schema)
df.cache()

//Get continuous feature name and discretize them
val continuous = df.dtypes.filter(_._2 == "DoubleType").map (_._1)
val discretizers = continuous.map(c => new QuantileDiscretizer().setInputCol(c).setOutputCol(s"${c}_disc").setNumBuckets(3).fit(df))
val pipeline = new Pipeline().setStages(discretizers)
val model = pipeline.fit(df)

当我运行它时，spark 似乎将每个离散化器设置为一个单独的工作。有没有办法在有或没有管道的情况下将所有离散化器作为单个作业运行？
感谢帮助，感激不尽。

Answer 1

Spark 2.3.0 中添加了对此功能的支持。 See release docs

多个特征转换器的多列支持:

[SPARK-13​​030]:OneHotEncoderEstimator (Scala/Java/Python)

[SPARK-22397]:QuantileDiscretizer (Scala/Java)

[SPARK-20542]: Bucketizer (Scala/Java/Python)

您现在可以使用 setInputCols和 setOutputCols指定多个列，尽管它似乎尚未反射(reflect)在官方文档中。与一次处理每列一项工作相比，这个新补丁的性能大大提高。

您的示例可能会进行如下调整:

import org.apache.spark.ml.feature.QuantileDiscretizer
import org.apache.spark.sql.types.{StructType,StructField,DoubleType}    
import org.apache.spark.ml.Pipeline
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import scala.util.Random

val nRows = 10000
val nCols = 1000
val data = sc.parallelize(0 to nRows-1).map { _ => Row.fromSeq(Seq.fill(nCols)(Random.nextDouble)) }
val schema = StructType((0 to nCols-1).map { i => StructField("C" + i, DoubleType, true) } )
val df = spark.createDataFrame(data, schema)
df.cache()

//Get continuous feature name and discretize them
val continuous = df.dtypes.filter(_._2 == "DoubleType").map (_._1)

val discretizer = new QuantileDiscretizer()
  .setInputCols(continuous)
  .setOutputCols(continuous.map(c => s"${c}_disc"))
  .setNumBuckets(3)

val pipeline = new Pipeline().setStages(Array(discretizer))
val model = pipeline.fit(df)
model.transform(df)