Github user GayathriMurali commented on a diff in the pull request:
https://github.com/apache/spark/pull/12118#discussion_r58287249
--- Diff: mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala ---
@@ -358,3 +376,100 @@ private[ml] object DecisionTreeModelReadWrite {
finalNodes.head
}
}
+
+private[ml] object EnsembleModelReadWrite {
+
+ /**
+ * Helper method for saving a tree ensemble to disk.
+ *
+ * @param instance Tree ensemble model
+ * @param path Path to which to save the ensemble model.
+ * @param extraMetadata Metadata such as numFeatures, numClasses,
numTrees.
+ */
+ def saveImpl[M <: Params with TreeEnsembleModel](
+ instance: M,
+ path: String,
+ sql: SQLContext,
+ extraMetadata: JObject): Unit = {
+ DefaultParamsWriter.saveMetadata(instance, path, sql.sparkContext,
Some(extraMetadata))
+ val treesMetadataJson: Array[(Int, String)] =
instance.trees.zipWithIndex.map {
+ case (tree, treeID) =>
+ treeID ->
DefaultParamsWriter.getMetadataToSave(tree.asInstanceOf[Params],
sql.sparkContext)
+ }
+ val treesMetadataPath = new Path(path, "treesMetadata").toString
+ sql.createDataFrame(treesMetadataJson).toDF("treeID", "metadata")
+ .write.parquet(treesMetadataPath)
+ val dataPath = new Path(path, "data").toString
+ val nodeDataRDD =
sql.sparkContext.parallelize(instance.trees.zipWithIndex).flatMap {
--- End diff --
Is it alright to use flatMap to combine RDDs? Can we use sparkContext.union
instead?
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