[GitHub] [spark] huaxingao commented on a change in pull request #28704: [SPARK-31777][ML][PySpark] Add user-specified fold column to CrossValidator

[GitBox <gi...@apache.org>]

huaxingao commented on a change in pull request #28704:
URL: https://github.com/apache/spark/pull/28704#discussion_r434665819



##########
File path: mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
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@@ -248,6 +248,19 @@ object MLUtils extends Logging {
     }.toArray
   }
 
+  /**
+   * Version of `kFold()` taking a fold column name.
+   */
+  @Since("3.1.0")
+  def kFold(df: DataFrame, numFolds: Int, foldColName: String): Array[(RDD[Row], RDD[Row])] = {
+    val foldCol = df.col(foldColName)
+    val dfWithMod = df.withColumn(foldColName, pmod(foldCol, lit(numFolds)))
+    (0 until numFolds).map { fold =>
+      (dfWithMod.filter(col(foldColName) =!= fold).drop(foldColName).rdd,
+        dfWithMod.filter(col(foldColName) === fold).drop(foldColName).rdd)

Review comment:
       I have a question here: 
   If foldCol doesn't contain a certain specific fold num, the validation dataset for that iteration will be empty. For example, if numFolds is 3 and foldCol only contains 0 and 2, the validation dataset is empty for fold 1. Should we check for empty splits and remove them?




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