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https://issues.apache.org/jira/browse/SPARK-21638?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Sean Owen resolved SPARK-21638.
-------------------------------
       Resolution: Fixed
    Fix Version/s: 2.3.0

Issue resolved by pull request 18868
[https://github.com/apache/spark/pull/18868]

> Warning message of RF is not accurate
> -------------------------------------
>
>                 Key: SPARK-21638
>                 URL: https://issues.apache.org/jira/browse/SPARK-21638
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 2.3.0
>         Environment: 
>            Reporter: Peng Meng
>            Priority: Minor
>             Fix For: 2.3.0
>
>
> When train RF model, there is many warning message like this:
> {quote}WARN RandomForest: Tree learning is using approximately 268492800 
> bytes per iteration, which exceeds requested limit maxMemoryUsage=268435456. 
> This allows splitting 2622 nodes in this iteration.{quote}
> This warning message is unnecessary and the data is not accurate.
> Actually, if all the nodes cannot split in one iteration, it will show this 
> warning. For most of the case, all the nodes cannot split just in one 
> iteration, so for most of the case, it will show this warning for each 
> iteration.
> This is because:
> {code:java}
> while (nodeStack.nonEmpty && (memUsage < maxMemoryUsage || memUsage == 0)) {
>       val (treeIndex, node) = nodeStack.top
>       // Choose subset of features for node (if subsampling).
>       val featureSubset: Option[Array[Int]] = if 
> (metadata.subsamplingFeatures) {
>         Some(SamplingUtils.reservoirSampleAndCount(Range(0,
>           metadata.numFeatures).iterator, metadata.numFeaturesPerNode, 
> rng.nextLong())._1)
>       } else {
>         None
>       }
>       // Check if enough memory remains to add this node to the group.
>       val nodeMemUsage = RandomForest.aggregateSizeForNode(metadata, 
> featureSubset) * 8L
>       if (memUsage + nodeMemUsage <= maxMemoryUsage || memUsage == 0) {
>         nodeStack.pop()
>         mutableNodesForGroup.getOrElseUpdate(treeIndex, new 
> mutable.ArrayBuffer[LearningNode]()) +=
>           node
>         mutableTreeToNodeToIndexInfo
>           .getOrElseUpdate(treeIndex, new mutable.HashMap[Int, 
> NodeIndexInfo]())(node.id)
>           = new NodeIndexInfo(numNodesInGroup, featureSubset)
>       }
>       numNodesInGroup += 1   //we not add the node to mutableNodesForGroup, 
> but we add memUsage here.
>       memUsage += nodeMemUsage
>     }
>     if (memUsage > maxMemoryUsage) {
>       // If maxMemoryUsage is 0, we should still allow splitting 1 node.
>       logWarning(s"Tree learning is using approximately $memUsage bytes per 
> iteration, which" +
>         s" exceeds requested limit maxMemoryUsage=$maxMemoryUsage. This 
> allows splitting" +
>         s" $numNodesInGroup nodes in this iteration.")
>     }
> {code}



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