SparkException:要组装的值不能为空 [英] SparkException: Values to assemble cannot be null
问题描述
我想使用 StandardScaler
来规范化特征.
I want use StandardScaler
to normalize the features.
这是我的代码:
val Array(trainingData, testData) = dataset.randomSplit(Array(0.7,0.3))
val vectorAssembler = new VectorAssembler().setInputCols(inputCols).setOutputCol("features").transform(trainingData)
val stdscaler = new StandardScaler().setInputCol("features").setOutputCol("scaledFeatures").setWithStd(true).setWithMean(false).fit(vectorAssembler)
但是当我尝试使用 StandardScaler
[Stage 151:==> (9 + 2) / 200]16/12/28 20:13:57 WARN scheduler.TaskSetManager: Lost task 31.0 in stage 151.0 (TID 8922, slave1.hadoop.ml): org.apache.spark.SparkException: Values to assemble cannot be null.
at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble$1.apply(VectorAssembler.scala:159)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun$assemble$1.apply(VectorAssembler.scala:142)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
at org.apache.spark.ml.feature.VectorAssembler$.assemble(VectorAssembler.scala:142)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun$3.apply(VectorAssembler.scala:98)
at org.apache.spark.ml.feature.VectorAssembler$$anonfun$3.apply(VectorAssembler.scala:97)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:157)
at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:214)
at scala.collection.AbstractIterator.aggregate(Iterator.scala:1336)
at org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1$$anonfun$24.apply(RDD.scala:1093)
at org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1$$anonfun$24.apply(RDD.scala:1093)
at org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1$$anonfun$25.apply(RDD.scala:1094)
at org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1$$anonfun$25.apply(RDD.scala:1094)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:766)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:766)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
at org.apache.spark.scheduler.Task.run(Task.scala:85)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
VectorAssembler
有什么问题吗?
我检查了 VectorAssembler
的几行,看起来没问题.
I have checked a few lines of the VectorAssembler
and it seemed OK.
vectorAssembler.take(5)
推荐答案
Spark >= 2.4
自 Spark 2.4 VectorAssembler
扩展 HasHandleInvalid
.这意味着您可以跳过
:
Since Spark 2.4 VectorAssembler
extends HasHandleInvalid
. It means you can skip
:
assembler.setHandleInvalid("skip").transform(df).show
+---+---+---------+
| x1| x2| features|
+---+---+---------+
|3.0|4.0|[3.0,4.0]|
+---+---+---------+
keep
(请注意,ML 算法不太可能正确处理此问题):
keep
(note that ML algorithms are unlikely to handle this correctly):
assembler.setHandleInvalid("keep").transform(df).show
+----+----+---------+
| x1| x2| features|
+----+----+---------+
| 1.0|null|[1.0,NaN]|
|null| 2.0|[NaN,2.0]|
| 3.0| 4.0|[3.0,4.0]|
+----+----+---------+
或默认为error
.
火花<2.4
VectorAssembler
没有任何问题.Spark Vector
不能包含 null
值.
There is nothing wrong with VectorAssembler
. Spark Vector
just cannot contain null
values.
import org.apache.spark.ml.feature.VectorAssembler
val df = Seq(
(Some(1.0), None), (None, Some(2.0)), (Some(3.0), Some(4.0))
).toDF("x1", "x2")
val assembler = new VectorAssembler()
.setInputCols(df.columns).setOutputCol("features")
assembler.transform(df).show(3)
org.apache.spark.SparkException: Failed to execute user defined function($anonfun$3: (struct<x1:double,x2:double>) => vector)
...
Caused by: org.apache.spark.SparkException: Values to assemble cannot be null.
Null 对 ML 算法没有意义,不能使用 scala.Double
表示.
Null are not meaningful for ML algorithms and cannot be represented using scala.Double
.
你必须要么放弃:
assembler.transform(df.na.drop).show(2)
+---+---+---------+
| x1| x2| features|
+---+---+---------+
|3.0|4.0|[3.0,4.0]|
+---+---+---------+
或填充/估算(另见用均值替换缺失值 - Spark Dataframe):
or fill / impute (see also Replace missing values with mean - Spark Dataframe):
// For example with averages
val replacements: Map[String,Any] = Map("x1" -> 2.0, "x2" -> 3.0)
assembler.transform(df.na.fill(replacements)).show(3)
+---+---+---------+
| x1| x2| features|
+---+---+---------+
|1.0|3.0|[1.0,3.0]|
|2.0|2.0|[2.0,2.0]|
|3.0|4.0|[3.0,4.0]|
+---+---+---------+
空值
.
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