Spark - KMeans.train 中的 IllegalArgumentException [英] Spark - IllegalArgumentException in KMeans.train

问题描述

我在 KMeans.train() 中遇到异常，如下所示:

java.lang.IllegalArgumentException:要求失败在 scala.Predef$.require(Predef.scala:212)在 org.apache.spark.mllib.util.MLUtils$.fastSquaredDistance(MLUtils.scala:487)在 org.apache.spark.mllib.clustering.KMeans$.fastSquaredDistance(KMeans.scala:589)在 org.apache.spark.mllib.clustering.KMeans$$anonfun$runAlgorithm$3.apply(KMeans.scala:304)在 org.apache.spark.mllib.clustering.KMeans$$anonfun$runAlgorithm$3.apply(KMeans.scala:301)在 scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:99)在 scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:99)在 scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)在 scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)在 scala.collection.mutable.HashMap.foreach(HashMap.scala:99)在 org.apache.spark.mllib.clustering.KMeans.runAlgorithm(KMeans.scala:301)在 org.apache.spark.mllib.clustering.KMeans.run(KMeans.scala:227)在 org.apache.spark.mllib.clustering.KMeans.run(KMeans.scala:209)在 org.apache.spark.mllib.clustering.KMeans$.train(KMeans.scala:530)

这并没有给我任何关于从哪里开始调试的线索.
我发现了一个旧的帖子 但那个问题出现在 KMeans.predict() 而这发生在训练阶段本身.

解决方案

看一下源码就明白了:

1. 您的向量必须具有相同的大小.
2. 两个向量的范数都应该是非负的.

https://github.com/apache/spark/blob/17af727e38c3faaeab5b91a8cdab5f2181cf3fc4/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala#L500>

private[mllib] def fastSquaredDistance(v1:向量，norm1:双，v2:向量，norm2:双，精度:双= 1e-6):双= {val n = v1.size要求(v2.size == n)要求(范数 1 >= 0.0 && 范数 2 >= 0.0)...

I am running into an exception while inside KMeans.train() like below:

java.lang.IllegalArgumentException: requirement failed
  at scala.Predef$.require(Predef.scala:212)
  at org.apache.spark.mllib.util.MLUtils$.fastSquaredDistance(MLUtils.scala:487)
  at org.apache.spark.mllib.clustering.KMeans$.fastSquaredDistance(KMeans.scala:589)
  at org.apache.spark.mllib.clustering.KMeans$$anonfun$runAlgorithm$3.apply(KMeans.scala:304)
  at org.apache.spark.mllib.clustering.KMeans$$anonfun$runAlgorithm$3.apply(KMeans.scala:301)
  at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:99)
  at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:99)
  at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)
  at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
  at scala.collection.mutable.HashMap.foreach(HashMap.scala:99)
  at org.apache.spark.mllib.clustering.KMeans.runAlgorithm(KMeans.scala:301)
  at org.apache.spark.mllib.clustering.KMeans.run(KMeans.scala:227)
  at org.apache.spark.mllib.clustering.KMeans.run(KMeans.scala:209)
  at org.apache.spark.mllib.clustering.KMeans$.train(KMeans.scala:530)

This doesn't give me any clue on where to start debugging.
I found an old post but that issue was in KMeans.predict() whereas this is happening in the training phase itself.

解决方案

Just take a look at the source code and it will become clear:

1. Your vectors have to have the same size.
2. The norms of both vectors should be non-negative.

https://github.com/apache/spark/blob/17af727e38c3faaeab5b91a8cdab5f2181cf3fc4/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala#L500

private[mllib] def fastSquaredDistance( v1: Vector, norm1: Double, v2: Vector, norm2: Double, precision: Double = 1e-6): Double = { val n = v1.size require(v2.size == n) require(norm1 >= 0.0 && norm2 >= 0.0) ...

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