Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/12023#discussion_r57674671
--- Diff: mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala ---
@@ -238,3 +238,128 @@ private[ml] object DecisionTreeModelReadWrite {
finalNodes.head
}
}
+
+private[ml] object RandomForestModelReadWrite {
+
+ /**
+ * Info for a [[org.apache.spark.ml.tree.Split]]
+ *
+ * @param featureIndex Index of feature split on
+ * @param leftCategoriesOrThreshold For categorical feature, set of
leftCategories.
+ * For continuous feature, threshold.
+ * @param numCategories For categorical feature, number of categories.
+ * For continuous feature, -1.
+ */
+ case class SplitData(
+ featureIndex: Int,
+ leftCategoriesOrThreshold: Array[Double],
+ numCategories: Int) {
+
+ def getSplit: Split = {
+ if (numCategories != -1) {
+ new CategoricalSplit(featureIndex, leftCategoriesOrThreshold,
numCategories)
+ } else {
+ assert(leftCategoriesOrThreshold.length == 1, s"DecisionTree split
data expected" +
+ s" 1 threshold for ContinuousSplit, but found thresholds: " +
+ leftCategoriesOrThreshold.mkString(", "))
+ new ContinuousSplit(featureIndex, leftCategoriesOrThreshold(0))
+ }
+ }
+ }
+
+ object SplitData {
+ def apply(split: Split): SplitData = split match {
+ case s: CategoricalSplit =>
+ SplitData(s.featureIndex, s.leftCategories, s.numCategories)
+ case s: ContinuousSplit =>
+ SplitData(s.featureIndex, Array(s.threshold), -1)
+ }
+ }
+
+ /**
+ * Info for a [[Node]]
+ *
+ * @param treeID Index used for tree identification in RandomForest
+ * @param id Index used for tree reconstruction. Indices follow a
pre-order traversal.
+ * @param impurityStats Stats array. Impurity type is stored in
metadata.
+ * @param gain Gain, or arbitrary value if leaf node.
+ * @param leftChild Left child index, or arbitrary value if leaf node.
+ * @param rightChild Right child index, or arbitrary value if leaf
node.
+ * @param split Split info, or arbitrary value if leaf node.
+ */
+ case class NodeData(
+ treeID: Int,
+ id: Int,
+ prediction: Double,
+ impurity: Double,
+ impurityStats: Array[Double],
+ gain: Double,
+ leftChild: Int,
+ rightChild: Int,
+ split: SplitData)
+
+ object NodeData {
+ /**
+ * Create [[NodeData]] instances for this node and all children.
+ *
+ * @param id Current ID. IDs are assigned via a pre-order traversal.
+ * @return (sequence of nodes in pre-order traversal order, largest
ID in subtree)
+ * The nodes are returned in pre-order traversal (root first)
so that it is easy to
+ * get the ID of the subtree's root node.
+ */
+ def build(node: Node, treeID: Int, id: Int): (Seq[NodeData], Int) =
node match {
+ case n: InternalNode =>
+ val (leftNodeData, leftIdx) = build(n.leftChild, treeID, id + 1)
+ val (rightNodeData, rightIdx) = build(n.rightChild, treeID,
leftIdx + 1)
+ val thisNodeData = NodeData(treeID, id, n.prediction, n.impurity,
n.impurityStats.stats,
+ n.gain, leftNodeData.head.id, rightNodeData.head.id,
SplitData(n.split))
+ (thisNodeData +: (leftNodeData ++ rightNodeData), rightIdx)
+ case _: LeafNode =>
+ (Seq(NodeData(treeID, id, node.prediction, node.impurity,
node.impurityStats.stats,
+ -1.0, -1, -1, SplitData(-1, Array.empty[Double], -1))),
+ id)
+ }
+ }
+
+ def loadTreeNodes(
--- End diff --
This should not be copied either. Please reuse the one for trees.
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