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https://issues.apache.org/jira/browse/SPARK-21638?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16114126#comment-16114126
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Sean Owen commented on SPARK-21638:
-----------------------------------
What's inaccurate and why isn't it necessary? Please start with the problem.
> 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
>
> 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.
> 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}
> To avoid this unnecessary warning, we should change the code like this:
> {code:java}
> while (nodeStack.nonEmpty) {
> 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
> } else {
> break
> }
> }
> {code}
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