Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7294#discussion_r34624276
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/NodeIdCache.scala ---
@@ -0,0 +1,174 @@
+/*
+ * 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 scala.collection.mutable
+
+import org.apache.hadoop.fs.{Path, FileSystem}
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.ml.tree.{LearningNode, Split}
+import org.apache.spark.mllib.tree.impl.BaggedPoint
+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) {
+
+ // 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
+
+ /**
+ * 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 {
+ dataPoint => {
+ var treeId = 0
+ while (treeId < nodeIdUpdaters.length) {
+ val nodeIdUpdater =
nodeIdUpdaters(treeId).getOrElse(dataPoint._2(treeId), null)
+ if (nodeIdUpdater != null) {
+ val featureIndex = nodeIdUpdater.split.featureIndex
+ val newNodeIndex = nodeIdUpdater.updateNodeIndex(
+ binnedFeature =
dataPoint._1.datum.binnedFeatures(featureIndex),
+ splits = splits(featureIndex))
+ dataPoint._2(treeId) = newNodeIndex
+ }
+ treeId += 1
+ }
+ dataPoint._2
+ }
+ }
+
+ // Keep on persisting new ones.
+ nodeIdsForInstances.persist(StorageLevel.MEMORY_AND_DISK)
+ rddUpdateCount += 1
+
+ // Handle checkpointing if the directory is not None.
+ if (nodeIdsForInstances.sparkContext.getCheckpointDir.nonEmpty &&
+ (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.get(1).get.getCheckpointFile.isDefined) {
+ val old = checkpointQueue.dequeue()
+ // Since the old checkpoint is not deleted by Spark, we'll
manually delete it here.
+ val fs = FileSystem.get(old.sparkContext.hadoopConfiguration)
+ fs.delete(new Path(old.getCheckpointFile.get), true)
+ } 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) {
+ val fs = FileSystem.get(old.sparkContext.hadoopConfiguration)
+ fs.delete(new Path(old.getCheckpointFile.get), true)
+ }
+ }
+ }
+}
+
+@DeveloperApi
+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)
--- End diff --
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