Github user thunterdb commented on the pull request: https://github.com/apache/spark/pull/12560#issuecomment-214890157 @wangmiao1981 sorry for the delay. I agree with @MLnick that the developer API of ALS is already brittle enough as it stands. One way to still do it is to add one layer of indirection: ```scala private def train0[ID: ClassTag]( ratings: RDD[Rating[ID]], rank: Int, numUserBlocks: Int, numItemBlocks: Int, maxIter: Int, regParam: Double, implicitPrefs: Boolean, alpha: Double, nonnegative: Boolean, checkpointInterval: Int, seed: Long, instrumentation: Instrumentation[ALS])( implicit ord: Ordering[ID]) = (RDD[(ID, Array[Float])], RDD[(ID, Array[Float])]) = { // all the code here} @DeveloperApi def train[ID: ClassTag]( // scalastyle:ignore ratings: RDD[Rating[ID]], rank: Int = 10, numUserBlocks: Int = 10, numItemBlocks: Int = 10, maxIter: Int = 10, regParam: Double = 1.0, implicitPrefs: Boolean = false, alpha: Double = 1.0, nonnegative: Boolean = false, intermediateRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK, finalRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK, checkpointInterval: Int = 10, seed: Long = 0L)( implicit ord: Ordering[ID]): (RDD[(ID, Array[Float])], RDD[(ID, Array[Float])]) = train0( ratings = ratings, ...) ``` This minimizes the changes and keeps the API binary compatible, however it is not pretty. @MLnick @yanboliang what do you think? I do not have a strong opinion on this one.
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