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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.ml.tree.impl
import java.io.IOException
import scala.collection.mutable
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.spark.internal.Logging
import org.apache.spark.ml.tree.{LearningNode, Split}
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel
/**
* This is used by the node id cache to find the child id that a data point would belong to.
* @param split Split information.
* @param nodeIndex The current node index of a data point that this will update.
*/
private[tree] case class NodeIndexUpdater(split: Split, nodeIndex: Int) {
/**
* Determine a child node index based on the feature value and the split.
* @param binnedFeature Binned feature value.
* @param splits Split information to convert the bin indices to approximate feature values.
* @return Child node index to update to.
*/
def updateNodeIndex(binnedFeature: Int, splits: Array[Split]): Int = {
if (split.shouldGoLeft(binnedFeature, splits)) {
LearningNode.leftChildIndex(nodeIndex)
} else {
LearningNode.rightChildIndex(nodeIndex)
}
}
}
/**
* Each TreePoint belongs to a particular node per tree.
* Each row in the nodeIdsForInstances RDD is an array over trees of the node index
* in each tree. Initially, values should all be 1 for root node.
* The nodeIdsForInstances RDD needs to be updated at each iteration.
* @param nodeIdsForInstances The initial values in the cache
* (should be an Array of all 1's (meaning the root nodes)).
* @param checkpointInterval The checkpointing interval
* (how often should the cache be checkpointed.).
*/
private[spark] class NodeIdCache(
var nodeIdsForInstances: RDD[Array[Int]],
val checkpointInterval: Int) extends Logging {
// Keep a reference to a previous node Ids for instances.
// Because we will keep on re-persisting updated node Ids,
// we want to unpersist the previous RDD.
private var prevNodeIdsForInstances: RDD[Array[Int]] = null
// To keep track of the past checkpointed RDDs.
private val checkpointQueue = mutable.Queue[RDD[Array[Int]]]()
private var rddUpdateCount = 0
// Indicates whether we can checkpoint
private val canCheckpoint = nodeIdsForInstances.sparkContext.getCheckpointDir.nonEmpty
// FileSystem instance for deleting checkpoints as needed
private val fs = FileSystem.get(nodeIdsForInstances.sparkContext.hadoopConfiguration)
/**
* Update the node index values in the cache.
* This updates the RDD and its lineage.
* TODO: Passing bin information to executors seems unnecessary and costly.
* @param data The RDD of training rows.
* @param nodeIdUpdaters A map of node index updaters.
* The key is the indices of nodes that we want to update.
* @param splits Split information needed to find child node indices.
*/
def updateNodeIndices(
data: RDD[BaggedPoint[TreePoint]],
nodeIdUpdaters: Array[mutable.Map[Int, NodeIndexUpdater]],
splits: Array[Array[Split]]): Unit = {
if (prevNodeIdsForInstances != null) {
// Unpersist the previous one if one exists.
prevNodeIdsForInstances.unpersist()
}
prevNodeIdsForInstances = nodeIdsForInstances
nodeIdsForInstances = data.zip(nodeIdsForInstances).map { case (point, ids) =>
var treeId = 0
while (treeId < nodeIdUpdaters.length) {
val nodeIdUpdater = nodeIdUpdaters(treeId).getOrElse(ids(treeId), null)
if (nodeIdUpdater != null) {
val featureIndex = nodeIdUpdater.split.featureIndex
val newNodeIndex = nodeIdUpdater.updateNodeIndex(
binnedFeature = point.datum.binnedFeatures(featureIndex),
splits = splits(featureIndex))
ids(treeId) = newNodeIndex
}
treeId += 1
}
ids
}
// Keep on persisting new ones.
nodeIdsForInstances.persist(StorageLevel.MEMORY_AND_DISK)
rddUpdateCount += 1
// Handle checkpointing if the directory is not None.
if (canCheckpoint && checkpointInterval != -1 && (rddUpdateCount % checkpointInterval) == 0) {
// Let's see if we can delete previous checkpoints.
var canDelete = true
while (checkpointQueue.size > 1 && canDelete) {
// We can delete the oldest checkpoint iff
// the next checkpoint actually exists in the file system.
if (checkpointQueue(1).getCheckpointFile.isDefined) {
val old = checkpointQueue.dequeue()
// Since the old checkpoint is not deleted by Spark, we'll manually delete it here.
try {
fs.delete(new Path(old.getCheckpointFile.get), true)
} catch {
case e: IOException =>
logError("Decision Tree learning using cacheNodeIds failed to remove checkpoint" +
s" file: ${old.getCheckpointFile.get}")
}
} else {
canDelete = false
}
}
nodeIdsForInstances.checkpoint()
checkpointQueue.enqueue(nodeIdsForInstances)
}
}
/**
* Call this after training is finished to delete any remaining checkpoints.
*/
def deleteAllCheckpoints(): Unit = {
while (checkpointQueue.nonEmpty) {
val old = checkpointQueue.dequeue()
if (old.getCheckpointFile.isDefined) {
try {
fs.delete(new Path(old.getCheckpointFile.get), true)
} catch {
case e: IOException =>
logError("Decision Tree learning using cacheNodeIds failed to remove checkpoint" +
s" file: ${old.getCheckpointFile.get}")
}
}
}
if (prevNodeIdsForInstances != null) {
// Unpersist the previous one if one exists.
prevNodeIdsForInstances.unpersist()
}
}
}
private[spark] object NodeIdCache {
/**
* Initialize the node Id cache with initial node Id values.
* @param data The RDD of training rows.
* @param numTrees The number of trees that we want to create cache for.
* @param checkpointInterval The checkpointing interval
* (how often should the cache be checkpointed.).
* @param initVal The initial values in the cache.
* @return A node Id cache containing an RDD of initial root node Indices.
*/
def init(
data: RDD[BaggedPoint[TreePoint]],
numTrees: Int,
checkpointInterval: Int,
initVal: Int = 1): NodeIdCache = {
new NodeIdCache(
data.map(_ => Array.fill[Int](numTrees)(initVal)),
checkpointInterval)
}
}
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