Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7294#discussion_r34757708
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala ---
@@ -0,0 +1,1131 @@
+/*
+ * 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 scala.util.Random
+
+import org.apache.spark.Logging
+import org.apache.spark.ml.classification.DecisionTreeClassificationModel
+import org.apache.spark.ml.regression.DecisionTreeRegressionModel
+import org.apache.spark.ml.tree._
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo,
Strategy => OldStrategy}
+import org.apache.spark.mllib.tree.impl.{BaggedPoint, DTStatsAggregator,
DecisionTreeMetadata,
+ TimeTracker}
+import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
+import org.apache.spark.mllib.tree.model.{InformationGainStats, Predict}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.util.random.{SamplingUtils, XORShiftRandom}
+
+
+private[ml] object RandomForest extends Logging {
+
+ /**
+ * Train a random forest.
+ * @param input Training data: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]]
+ * @return an unweighted set of trees
+ */
+ def run(
+ input: RDD[LabeledPoint],
+ strategy: OldStrategy,
+ numTrees: Int,
+ featureSubsetStrategy: String,
+ seed: Long,
+ parentUID: Option[String] = None): Array[DecisionTreeModel] = {
+
+ val timer = new TimeTracker()
+
+ timer.start("total")
+
+ timer.start("init")
+
+ val retaggedInput = input.retag(classOf[LabeledPoint])
+ val metadata =
+ DecisionTreeMetadata.buildMetadata(retaggedInput, strategy,
numTrees, featureSubsetStrategy)
+ logDebug("algo = " + strategy.algo)
+ logDebug("numTrees = " + numTrees)
+ logDebug("seed = " + seed)
+ logDebug("maxBins = " + metadata.maxBins)
+ logDebug("featureSubsetStrategy = " + featureSubsetStrategy)
+ logDebug("numFeaturesPerNode = " + metadata.numFeaturesPerNode)
+ logDebug("subsamplingRate = " + strategy.subsamplingRate)
+
+ // Find the splits and the corresponding bins (interval between the
splits) using a sample
+ // of the input data.
+ timer.start("findSplitsBins")
+ val splits = findSplits(retaggedInput, metadata)
+ timer.stop("findSplitsBins")
+ logDebug("numBins: feature: number of bins")
+ logDebug(Range(0, metadata.numFeatures).map { featureIndex =>
+ s"\t$featureIndex\t${metadata.numBins(featureIndex)}"
+ }.mkString("\n"))
+
+ // Bin feature values (TreePoint representation).
+ // Cache input RDD for speedup during multiple passes.
+ val treeInput = TreePoint.convertToTreeRDD(retaggedInput, splits,
metadata)
+
+ val withReplacement = if (numTrees > 1) true else false
+
+ val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput,
strategy.subsamplingRate, numTrees,
+ withReplacement, seed).persist(StorageLevel.MEMORY_AND_DISK)
+
+ // depth of the decision tree
+ val maxDepth = strategy.maxDepth
+ require(maxDepth <= 30,
+ s"DecisionTree currently only supports maxDepth <= 30, but was given
maxDepth = $maxDepth.")
+
+ // Max memory usage for aggregates
+ // TODO: Calculate memory usage more precisely.
+ val maxMemoryUsage: Long = strategy.maxMemoryInMB * 1024L * 1024L
+ logDebug("max memory usage for aggregates = " + maxMemoryUsage + "
bytes.")
+ val maxMemoryPerNode = {
+ val featureSubset: Option[Array[Int]] = if
(metadata.subsamplingFeatures) {
+ // Find numFeaturesPerNode largest bins to get an upper bound on
memory usage.
+ Some(metadata.numBins.zipWithIndex.sortBy(- _._1)
+ .take(metadata.numFeaturesPerNode).map(_._2))
+ } else {
+ None
+ }
+ RandomForest.aggregateSizeForNode(metadata, featureSubset) * 8L
+ }
+ require(maxMemoryPerNode <= maxMemoryUsage,
+ s"RandomForest/DecisionTree given maxMemoryInMB =
${strategy.maxMemoryInMB}," +
+ " which is too small for the given features." +
+ s" Minimum value = ${maxMemoryPerNode / (1024L * 1024L)}")
+
+ timer.stop("init")
+
+ /*
+ * The main idea here is to perform group-wise training of the
decision tree nodes thus
+ * reducing the passes over the data from (# nodes) to (# nodes /
maxNumberOfNodesPerGroup).
+ * Each data sample is handled by a particular node (or it reaches a
leaf and is not used
+ * in lower levels).
+ */
+
+ // Create an RDD of node Id cache.
+ // At first, all the rows belong to the root nodes (node Id == 1).
+ val nodeIdCache = if (strategy.useNodeIdCache) {
+ Some(NodeIdCache.init(
+ data = baggedInput,
+ numTrees = numTrees,
+ checkpointInterval = strategy.checkpointInterval,
+ initVal = 1))
+ } else {
+ None
+ }
+
+ // FIFO queue of nodes to train: (treeIndex, node)
+ val nodeQueue = new mutable.Queue[(Int, LearningNode)]()
+
+ val rng = new Random()
+ rng.setSeed(seed)
+
+ // Allocate and queue root nodes.
+ val topNodes =
Array.fill[LearningNode](numTrees)(LearningNode.emptyNode(nodeIndex = 1))
+ Range(0, numTrees).foreach(treeIndex => nodeQueue.enqueue((treeIndex,
topNodes(treeIndex))))
+
+ while (nodeQueue.nonEmpty) {
+ // Collect some nodes to split, and choose features for each node
(if subsampling).
+ // Each group of nodes may come from one or multiple trees, and at
multiple levels.
+ val (nodesForGroup, treeToNodeToIndexInfo) =
+ RandomForest.selectNodesToSplit(nodeQueue, maxMemoryUsage,
metadata, rng)
+ // Sanity check (should never occur):
+ assert(nodesForGroup.nonEmpty,
+ s"RandomForest selected empty nodesForGroup. Error for unknown
reason.")
+
+ // Choose node splits, and enqueue new nodes as needed.
+ timer.start("findBestSplits")
+ RandomForest.findBestSplits(baggedInput, metadata, topNodes,
nodesForGroup,
+ treeToNodeToIndexInfo, splits, nodeQueue, timer, nodeIdCache)
+ timer.stop("findBestSplits")
+ }
+
+ baggedInput.unpersist()
+
+ timer.stop("total")
+
+ logInfo("Internal timing for DecisionTree:")
+ logInfo(s"$timer")
+
+ // Delete any remaining checkpoints used for node Id cache.
+ if (nodeIdCache.nonEmpty) {
+ try {
+ nodeIdCache.get.deleteAllCheckpoints()
+ } catch {
+ case e: IOException =>
+ logWarning(s"delete all checkpoints failed. Error reason:
${e.getMessage}")
+ }
+ }
+
+ parentUID match {
+ case Some(uid) =>
+ if (strategy.algo == OldAlgo.Classification) {
+ topNodes.map(rootNode => new
DecisionTreeClassificationModel(uid, rootNode.toNode))
+ } else {
+ topNodes.map(rootNode => new DecisionTreeRegressionModel(uid,
rootNode.toNode))
+ }
+ case None =>
+ if (strategy.algo == OldAlgo.Classification) {
+ topNodes.map(rootNode => new
DecisionTreeClassificationModel(rootNode.toNode))
+ } else {
+ topNodes.map(rootNode => new
DecisionTreeRegressionModel(rootNode.toNode))
+ }
+ }
+ }
+
+ /**
+ * Get the node index corresponding to this data point.
+ * This function mimics prediction, passing an example from the root
node down to a leaf
+ * or unsplit node; that node's index is returned.
+ *
+ * @param node Node in tree from which to classify the given data point.
+ * @param binnedFeatures Binned feature vector for data point.
+ * @param splits possible splits for all features, indexed
(numFeatures)(numSplits)
+ * @return Leaf index if the data point reaches a leaf.
+ * Otherwise, last node reachable in tree matching this example.
+ * Note: This is the global node index, i.e., the index used in
the tree.
+ * This index is different from the index used during
training a particular
+ * group of nodes on one call to [[findBestSplits()]].
+ */
+ private def predictNodeIndex(
+ node: LearningNode,
+ binnedFeatures: Array[Int],
+ splits: Array[Array[Split]]): Int = {
+ if (node.isLeaf || node.split.isEmpty) {
+ node.id
+ } else {
+ val split = node.split.get
+ val featureIndex = split.featureIndex
+ val splitLeft = split.shouldGoLeft(binnedFeatures(featureIndex),
splits(featureIndex))
+ if (node.leftChild.isEmpty) {
+ // Not yet split. Return index from next layer of nodes to train
+ if (splitLeft) {
+ LearningNode.leftChildIndex(node.id)
+ } else {
+ LearningNode.rightChildIndex(node.id)
+ }
+ } else {
+ if (splitLeft) {
+ predictNodeIndex(node.leftChild.get, binnedFeatures, splits)
+ } else {
+ predictNodeIndex(node.rightChild.get, binnedFeatures, splits)
+ }
+ }
+ }
+ }
+
+ /**
+ * Helper for binSeqOp, for data which can contain a mix of ordered and
unordered features.
+ *
+ * For ordered features, a single bin is updated.
+ * For unordered features, bins correspond to subsets of categories;
either the left or right bin
+ * for each subset is updated.
+ *
+ * @param agg Array storing aggregate calculation, with a set of
sufficient statistics for
+ * each (feature, bin).
+ * @param treePoint Data point being aggregated.
+ * @param splits possible splits indexed (numFeatures)(numSplits)
+ * @param unorderedFeatures Set of indices of unordered features.
+ * @param instanceWeight Weight (importance) of instance in dataset.
+ */
+ private def mixedBinSeqOp(
+ agg: DTStatsAggregator,
+ treePoint: TreePoint,
+ splits: Array[Array[Split]],
+ unorderedFeatures: Set[Int],
+ instanceWeight: Double,
+ featuresForNode: Option[Array[Int]]): Unit = {
+ val numFeaturesPerNode = if (featuresForNode.nonEmpty) {
+ // Use subsampled features
+ featuresForNode.get.length
+ } else {
+ // Use all features
+ agg.metadata.numFeatures
+ }
+ // Iterate over features.
+ var featureIndexIdx = 0
+ while (featureIndexIdx < numFeaturesPerNode) {
+ val featureIndex = if (featuresForNode.nonEmpty) {
+ featuresForNode.get.apply(featureIndexIdx)
+ } else {
+ featureIndexIdx
+ }
+ if (unorderedFeatures.contains(featureIndex)) {
+ // Unordered feature
+ val featureValue = treePoint.binnedFeatures(featureIndex)
+ val (leftNodeFeatureOffset, rightNodeFeatureOffset) =
+ agg.getLeftRightFeatureOffsets(featureIndexIdx)
+ // Update the left or right bin for each split.
+ val numSplits = agg.metadata.numSplits(featureIndex)
+ val featureSplits = splits(featureIndex)
+ var splitIndex = 0
+ while (splitIndex < numSplits) {
+ if (featureSplits(splitIndex).shouldGoLeft(featureValue,
featureSplits)) {
+ agg.featureUpdate(leftNodeFeatureOffset, splitIndex,
treePoint.label, instanceWeight)
+ } else {
+ agg.featureUpdate(rightNodeFeatureOffset, splitIndex,
treePoint.label, instanceWeight)
+ }
+ splitIndex += 1
+ }
+ } else {
+ // Ordered feature
+ val binIndex = treePoint.binnedFeatures(featureIndex)
+ agg.update(featureIndexIdx, binIndex, treePoint.label,
instanceWeight)
+ }
+ featureIndexIdx += 1
+ }
+ }
+
+ /**
+ * Helper for binSeqOp, for regression and for classification with only
ordered features.
+ *
+ * For each feature, the sufficient statistics of one bin are updated.
+ *
+ * @param agg Array storing aggregate calculation, with a set of
sufficient statistics for
+ * each (feature, bin).
+ * @param treePoint Data point being aggregated.
+ * @param instanceWeight Weight (importance) of instance in dataset.
+ */
+ private def orderedBinSeqOp(
+ agg: DTStatsAggregator,
+ treePoint: TreePoint,
+ instanceWeight: Double,
+ featuresForNode: Option[Array[Int]]): Unit = {
+ val label = treePoint.label
+
+ // Iterate over features.
+ if (featuresForNode.nonEmpty) {
+ // Use subsampled features
+ var featureIndexIdx = 0
+ while (featureIndexIdx < featuresForNode.get.length) {
+ val binIndex =
treePoint.binnedFeatures(featuresForNode.get.apply(featureIndexIdx))
+ agg.update(featureIndexIdx, binIndex, label, instanceWeight)
+ featureIndexIdx += 1
+ }
+ } else {
+ // Use all features
+ val numFeatures = agg.metadata.numFeatures
+ var featureIndex = 0
+ while (featureIndex < numFeatures) {
+ val binIndex = treePoint.binnedFeatures(featureIndex)
+ agg.update(featureIndex, binIndex, label, instanceWeight)
+ featureIndex += 1
+ }
+ }
+ }
+
+ /**
+ * Given a group of nodes, this finds the best split for each node.
+ *
+ * @param input Training data: RDD of
[[org.apache.spark.mllib.tree.impl.TreePoint]]
+ * @param metadata Learning and dataset metadata
+ * @param topNodes Root node for each tree. Used for matching instances
with nodes.
+ * @param nodesForGroup Mapping: treeIndex --> nodes to be split in tree
+ * @param treeToNodeToIndexInfo Mapping: treeIndex --> nodeIndex -->
nodeIndexInfo,
+ * where nodeIndexInfo stores the index in
the group and the
+ * feature subsets (if using feature
subsets).
+ * @param splits possible splits for all features, indexed
(numFeatures)(numSplits)
+ * @param nodeQueue Queue of nodes to split, with values (treeIndex,
node).
+ * Updated with new non-leaf nodes which are created.
+ * @param nodeIdCache Node Id cache containing an RDD of Array[Int] where
+ * each value in the array is the data point's node Id
+ * for a corresponding tree. This is used to prevent
the need
+ * to pass the entire tree to the executors during
+ * the node stat aggregation phase.
+ */
+ private[tree] def findBestSplits(
+ input: RDD[BaggedPoint[TreePoint]],
+ metadata: DecisionTreeMetadata,
+ topNodes: Array[LearningNode],
+ nodesForGroup: Map[Int, Array[LearningNode]],
+ treeToNodeToIndexInfo: Map[Int, Map[Int, NodeIndexInfo]],
+ splits: Array[Array[Split]],
+ nodeQueue: mutable.Queue[(Int, LearningNode)],
+ timer: TimeTracker = new TimeTracker,
+ nodeIdCache: Option[NodeIdCache] = None): Unit = {
+
+ /*
+ * The high-level descriptions of the best split optimizations are
noted here.
+ *
+ * *Group-wise training*
+ * We perform bin calculations for groups of nodes to reduce the
number of
+ * passes over the data. Each iteration requires more computation and
storage,
+ * but saves several iterations over the data.
+ *
+ * *Bin-wise computation*
+ * We use a bin-wise best split computation strategy instead of a
straightforward best split
+ * computation strategy. Instead of analyzing each sample for
contribution to the left/right
+ * child node impurity of every split, we first categorize each
feature of a sample into a
+ * bin. We exploit this structure to calculate aggregates for bins and
then use these aggregates
+ * to calculate information gain for each split.
+ *
+ * *Aggregation over partitions*
+ * Instead of performing a flatMap/reduceByKey operation, we exploit
the fact that we know
+ * the number of splits in advance. Thus, we store the aggregates (at
the appropriate
+ * indices) in a single array for all bins and rely upon the RDD
aggregate method to
+ * drastically reduce the communication overhead.
+ */
+
+ // numNodes: Number of nodes in this group
+ val numNodes = nodesForGroup.values.map(_.length).sum
+ logDebug("numNodes = " + numNodes)
+ logDebug("numFeatures = " + metadata.numFeatures)
+ logDebug("numClasses = " + metadata.numClasses)
+ logDebug("isMulticlass = " + metadata.isMulticlass)
+ logDebug("isMulticlassWithCategoricalFeatures = " +
+ metadata.isMulticlassWithCategoricalFeatures)
+ logDebug("using nodeIdCache = " + nodeIdCache.nonEmpty.toString)
+
+ /**
+ * Performs a sequential aggregation over a partition for a particular
tree and node.
+ *
+ * For each feature, the aggregate sufficient statistics are updated
for the relevant
+ * bins.
+ *
+ * @param treeIndex Index of the tree that we want to perform
aggregation for.
+ * @param nodeInfo The node info for the tree node.
+ * @param agg Array storing aggregate calculation, with a set of
sufficient statistics
+ * for each (node, feature, bin).
+ * @param baggedPoint Data point being aggregated.
+ */
+ def nodeBinSeqOp(
+ treeIndex: Int,
+ nodeInfo: NodeIndexInfo,
+ agg: Array[DTStatsAggregator],
+ baggedPoint: BaggedPoint[TreePoint]): Unit = {
+ if (nodeInfo != null) {
+ val aggNodeIndex = nodeInfo.nodeIndexInGroup
+ val featuresForNode = nodeInfo.featureSubset
+ val instanceWeight = baggedPoint.subsampleWeights(treeIndex)
+ if (metadata.unorderedFeatures.isEmpty) {
+ orderedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum,
instanceWeight, featuresForNode)
+ } else {
+ mixedBinSeqOp(agg(aggNodeIndex), baggedPoint.datum, splits,
+ metadata.unorderedFeatures, instanceWeight, featuresForNode)
+ }
+ }
+ }
+
+ /**
+ * Performs a sequential aggregation over a partition.
+ *
+ * Each data point contributes to one node. For each feature,
+ * the aggregate sufficient statistics are updated for the relevant
bins.
+ *
+ * @param agg Array storing aggregate calculation, with a set of
sufficient statistics for
+ * each (node, feature, bin).
+ * @param baggedPoint Data point being aggregated.
+ * @return agg
+ */
+ def binSeqOp(
+ agg: Array[DTStatsAggregator],
+ baggedPoint: BaggedPoint[TreePoint]): Array[DTStatsAggregator] = {
+ treeToNodeToIndexInfo.foreach { case (treeIndex, nodeIndexToInfo) =>
+ val nodeIndex =
+ predictNodeIndex(topNodes(treeIndex),
baggedPoint.datum.binnedFeatures, splits)
+ nodeBinSeqOp(treeIndex, nodeIndexToInfo.getOrElse(nodeIndex,
null), agg, baggedPoint)
+ }
+ agg
+ }
+
+ /**
+ * Do the same thing as binSeqOp, but with nodeIdCache.
+ */
+ def binSeqOpWithNodeIdCache(
+ agg: Array[DTStatsAggregator],
+ dataPoint: (BaggedPoint[TreePoint], Array[Int])):
Array[DTStatsAggregator] = {
+ treeToNodeToIndexInfo.foreach { case (treeIndex, nodeIndexToInfo) =>
+ val baggedPoint = dataPoint._1
+ val nodeIdCache = dataPoint._2
+ val nodeIndex = nodeIdCache(treeIndex)
+ nodeBinSeqOp(treeIndex, nodeIndexToInfo.getOrElse(nodeIndex,
null), agg, baggedPoint)
+ }
+
+ agg
+ }
+
+ /**
+ * Get node index in group --> features indices map,
+ * which is a short cut to find feature indices for a node given node
index in group.
+ */
+ def getNodeToFeatures(
+ treeToNodeToIndexInfo: Map[Int, Map[Int, NodeIndexInfo]]):
Option[Map[Int, Array[Int]]] = {
+ if (!metadata.subsamplingFeatures) {
+ None
+ } else {
+ val mutableNodeToFeatures = new mutable.HashMap[Int, Array[Int]]()
+ treeToNodeToIndexInfo.values.foreach { nodeIdToNodeInfo =>
+ nodeIdToNodeInfo.values.foreach { nodeIndexInfo =>
+ assert(nodeIndexInfo.featureSubset.isDefined)
+ mutableNodeToFeatures(nodeIndexInfo.nodeIndexInGroup) =
nodeIndexInfo.featureSubset.get
+ }
+ }
+ Some(mutableNodeToFeatures.toMap)
+ }
+ }
+
+ // array of nodes to train indexed by node index in group
+ val nodes = new Array[LearningNode](numNodes)
+ nodesForGroup.foreach { case (treeIndex, nodesForTree) =>
+ nodesForTree.foreach { node =>
+ nodes(treeToNodeToIndexInfo(treeIndex)(node.id).nodeIndexInGroup)
= node
+ }
+ }
+
+ // Calculate best splits for all nodes in the group
+ timer.start("chooseSplits")
+
+ // In each partition, iterate all instances and compute aggregate
stats for each node,
+ // yield an (nodeIndex, nodeAggregateStats) pair for each node.
+ // After a `reduceByKey` operation,
+ // stats of a node will be shuffled to a particular partition and be
combined together,
+ // then best splits for nodes are found there.
+ // Finally, only best Splits for nodes are collected to driver to
construct decision tree.
+ val nodeToFeatures = getNodeToFeatures(treeToNodeToIndexInfo)
+ val nodeToFeaturesBc = input.sparkContext.broadcast(nodeToFeatures)
+
+ val partitionAggregates : RDD[(Int, DTStatsAggregator)] = if
(nodeIdCache.nonEmpty) {
+ input.zip(nodeIdCache.get.nodeIdsForInstances).mapPartitions {
points =>
+ // Construct a nodeStatsAggregators array to hold node aggregate
stats,
+ // each node will have a nodeStatsAggregator
+ val nodeStatsAggregators = Array.tabulate(numNodes) { nodeIndex =>
+ val featuresForNode = nodeToFeaturesBc.value.flatMap {
nodeToFeatures =>
+ Some(nodeToFeatures(nodeIndex))
+ }
+ new DTStatsAggregator(metadata, featuresForNode)
+ }
+
+ // iterator all instances in current partition and update
aggregate stats
+ points.foreach(binSeqOpWithNodeIdCache(nodeStatsAggregators, _))
+
+ // transform nodeStatsAggregators array to (nodeIndex,
nodeAggregateStats) pairs,
+ // which can be combined with other partition using `reduceByKey`
+ nodeStatsAggregators.view.zipWithIndex.map(_.swap).iterator
+ }
+ } else {
+ input.mapPartitions { points =>
+ // Construct a nodeStatsAggregators array to hold node aggregate
stats,
+ // each node will have a nodeStatsAggregator
+ val nodeStatsAggregators = Array.tabulate(numNodes) { nodeIndex =>
+ val featuresForNode = nodeToFeaturesBc.value.flatMap {
nodeToFeatures =>
+ Some(nodeToFeatures(nodeIndex))
+ }
+ new DTStatsAggregator(metadata, featuresForNode)
+ }
+
+ // iterator all instances in current partition and update
aggregate stats
+ points.foreach(binSeqOp(nodeStatsAggregators, _))
+
+ // transform nodeStatsAggregators array to (nodeIndex,
nodeAggregateStats) pairs,
+ // which can be combined with other partition using `reduceByKey`
+ nodeStatsAggregators.view.zipWithIndex.map(_.swap).iterator
+ }
+ }
+
+ val nodeToBestSplits = partitionAggregates.reduceByKey((a, b) =>
a.merge(b))
+ .map { case (nodeIndex, aggStats) =>
+ val featuresForNode = nodeToFeaturesBc.value.flatMap {
nodeToFeatures =>
--- End diff --
indentation
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