[SPARK-14412][ML][PYSPARK] Add StorageLevel params to ALS
`mllib` `ALS` supports `setIntermediateRDDStorageLevel` and `setFinalRDDStorageLevel`. This PR adds these as Params in `ml` `ALS`. They are put in group **expertParam** since few users will need them. ## How was this patch tested? New test cases in `ALSSuite` and `tests.py`. cc yanboliang jkbradley sethah rishabhbhardwaj Author: Nick Pentreath <nickp@za.ibm.com> Closes #12660 from MLnick/SPARK-14412-als-storage-params.
This commit is contained in:
Nick Pentreath
Xiangrui Meng
parent
d7755cfd07
commit
90fa2c6e7f
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@ -22,7 +22,7 @@ import java.io.IOException
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import scala.collection.mutable
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import scala.reflect.ClassTag
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import scala.util.Sorting
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import scala.util.{Sorting, Try}
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import scala.util.hashing.byteswap64
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import com.github.fommil.netlib.BLAS.{getInstance => blas}
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@ -153,12 +153,42 @@ private[recommendation] trait ALSParams extends ALSModelParams with HasMaxIter w
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/** @group getParam */
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def getNonnegative: Boolean = $(nonnegative)
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/**
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* Param for StorageLevel for intermediate RDDs. Pass in a string representation of
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* [[StorageLevel]]. Cannot be "NONE".
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* Default: "MEMORY_AND_DISK".
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*
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* @group expertParam
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*/
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val intermediateRDDStorageLevel = new Param[String](this, "intermediateRDDStorageLevel",
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"StorageLevel for intermediate RDDs. Cannot be 'NONE'. Default: 'MEMORY_AND_DISK'.",
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(s: String) => Try(StorageLevel.fromString(s)).isSuccess && s != "NONE")
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/** @group expertGetParam */
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def getIntermediateRDDStorageLevel: String = $(intermediateRDDStorageLevel)
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/**
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* Param for StorageLevel for ALS model factor RDDs. Pass in a string representation of
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* [[StorageLevel]].
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* Default: "MEMORY_AND_DISK".
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*
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* @group expertParam
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*/
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val finalRDDStorageLevel = new Param[String](this, "finalRDDStorageLevel",
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"StorageLevel for ALS model factor RDDs. Default: 'MEMORY_AND_DISK'.",
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(s: String) => Try(StorageLevel.fromString(s)).isSuccess)
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/** @group expertGetParam */
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def getFinalRDDStorageLevel: String = $(finalRDDStorageLevel)
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setDefault(rank -> 10, maxIter -> 10, regParam -> 0.1, numUserBlocks -> 10, numItemBlocks -> 10,
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implicitPrefs -> false, alpha -> 1.0, userCol -> "user", itemCol -> "item",
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ratingCol -> "rating", nonnegative -> false, checkpointInterval -> 10)
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ratingCol -> "rating", nonnegative -> false, checkpointInterval -> 10,
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intermediateRDDStorageLevel -> "MEMORY_AND_DISK", finalRDDStorageLevel -> "MEMORY_AND_DISK")
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/**
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* Validates and transforms the input schema.
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*
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* @param schema input schema
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* @return output schema
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*/
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@ -374,8 +404,21 @@ class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel]
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@Since("1.3.0")
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def setSeed(value: Long): this.type = set(seed, value)
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/** @group expertSetParam */
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@Since("2.0.0")
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def setIntermediateRDDStorageLevel(value: String): this.type = {
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set(intermediateRDDStorageLevel, value)
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}
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/** @group expertSetParam */
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@Since("2.0.0")
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def setFinalRDDStorageLevel(value: String): this.type = {
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set(finalRDDStorageLevel, value)
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}
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/**
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* Sets both numUserBlocks and numItemBlocks to the specific value.
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*
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* @group setParam
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*/
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@Since("1.3.0")
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@ -403,6 +446,8 @@ class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel]
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numUserBlocks = $(numUserBlocks), numItemBlocks = $(numItemBlocks),
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maxIter = $(maxIter), regParam = $(regParam), implicitPrefs = $(implicitPrefs),
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alpha = $(alpha), nonnegative = $(nonnegative),
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intermediateRDDStorageLevel = StorageLevel.fromString($(intermediateRDDStorageLevel)),
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finalRDDStorageLevel = StorageLevel.fromString($(finalRDDStorageLevel)),
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checkpointInterval = $(checkpointInterval), seed = $(seed))
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val userDF = userFactors.toDF("id", "features")
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val itemDF = itemFactors.toDF("id", "features")
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@ -754,7 +799,6 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
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* ratings are associated with srcIds(i).
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* @param dstEncodedIndices encoded dst indices
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* @param ratings ratings
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*
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* @see [[LocalIndexEncoder]]
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*/
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private[recommendation] case class InBlock[@specialized(Int, Long) ID: ClassTag](
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@ -850,7 +894,6 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
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* @param ratings raw ratings
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* @param srcPart partitioner for src IDs
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* @param dstPart partitioner for dst IDs
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*
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* @return an RDD of rating blocks in the form of ((srcBlockId, dstBlockId), ratingBlock)
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*/
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private def partitionRatings[ID: ClassTag](
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@ -899,6 +942,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
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/**
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* Builder for uncompressed in-blocks of (srcId, dstEncodedIndex, rating) tuples.
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*
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* @param encoder encoder for dst indices
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*/
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private[recommendation] class UncompressedInBlockBuilder[@specialized(Int, Long) ID: ClassTag](
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@ -1099,6 +1143,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
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/**
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* Creates in-blocks and out-blocks from rating blocks.
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*
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* @param prefix prefix for in/out-block names
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* @param ratingBlocks rating blocks
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* @param srcPart partitioner for src IDs
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@ -1187,7 +1232,6 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
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* @param implicitPrefs whether to use implicit preference
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* @param alpha the alpha constant in the implicit preference formulation
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* @param solver solver for least squares problems
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*
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* @return dst factors
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*/
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private def computeFactors[ID](
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@ -33,7 +33,9 @@ import org.apache.spark.mllib.linalg.Vectors
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import org.apache.spark.mllib.util.MLlibTestSparkContext
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import org.apache.spark.mllib.util.TestingUtils._
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import org.apache.spark.rdd.RDD
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import org.apache.spark.scheduler.{SparkListener, SparkListenerStageCompleted}
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import org.apache.spark.sql.{DataFrame, Row}
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import org.apache.spark.storage.StorageLevel
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class ALSSuite
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extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest with Logging {
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@ -198,6 +200,7 @@ class ALSSuite
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/**
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* Generates an explicit feedback dataset for testing ALS.
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*
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* @param numUsers number of users
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* @param numItems number of items
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* @param rank rank
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@ -238,6 +241,7 @@ class ALSSuite
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/**
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* Generates an implicit feedback dataset for testing ALS.
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*
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* @param numUsers number of users
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* @param numItems number of items
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* @param rank rank
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@ -286,6 +290,7 @@ class ALSSuite
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/**
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* Generates random user/item factors, with i.i.d. values drawn from U(a, b).
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*
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* @param size number of users/items
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* @param rank number of features
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* @param random random number generator
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@ -311,6 +316,7 @@ class ALSSuite
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/**
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* Test ALS using the given training/test splits and parameters.
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*
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* @param training training dataset
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* @param test test dataset
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* @param rank rank of the matrix factorization
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@ -514,6 +520,77 @@ class ALSSuite
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}
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}
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class ALSStorageSuite
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extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest with Logging {
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test("invalid storage params") {
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intercept[IllegalArgumentException] {
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new ALS().setIntermediateRDDStorageLevel("foo")
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}
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intercept[IllegalArgumentException] {
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new ALS().setIntermediateRDDStorageLevel("NONE")
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}
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intercept[IllegalArgumentException] {
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new ALS().setFinalRDDStorageLevel("foo")
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}
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}
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test("default and non-default storage params set correct RDD StorageLevels") {
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val sqlContext = this.sqlContext
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import sqlContext.implicits._
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val data = Seq(
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(0, 0, 1.0),
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(0, 1, 2.0),
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(1, 2, 3.0),
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(1, 0, 2.0)
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).toDF("user", "item", "rating")
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val als = new ALS().setMaxIter(1).setRank(1)
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// add listener to check intermediate RDD default storage levels
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val defaultListener = new IntermediateRDDStorageListener
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sc.addSparkListener(defaultListener)
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val model = als.fit(data)
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// check final factor RDD default storage levels
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val defaultFactorRDDs = sc.getPersistentRDDs.collect {
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case (id, rdd) if rdd.name == "userFactors" || rdd.name == "itemFactors" =>
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rdd.name -> (id, rdd.getStorageLevel)
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}.toMap
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defaultFactorRDDs.foreach { case (_, (id, level)) =>
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assert(level == StorageLevel.MEMORY_AND_DISK)
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}
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defaultListener.storageLevels.foreach(level => assert(level == StorageLevel.MEMORY_AND_DISK))
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// add listener to check intermediate RDD non-default storage levels
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val nonDefaultListener = new IntermediateRDDStorageListener
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sc.addSparkListener(nonDefaultListener)
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val nonDefaultModel = als
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.setFinalRDDStorageLevel("MEMORY_ONLY")
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.setIntermediateRDDStorageLevel("DISK_ONLY")
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.fit(data)
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// check final factor RDD non-default storage levels
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val levels = sc.getPersistentRDDs.collect {
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case (id, rdd) if rdd.name == "userFactors" && rdd.id != defaultFactorRDDs("userFactors")._1
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|| rdd.name == "itemFactors" && rdd.id != defaultFactorRDDs("itemFactors")._1 =>
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rdd.getStorageLevel
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}
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levels.foreach(level => assert(level == StorageLevel.MEMORY_ONLY))
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nonDefaultListener.storageLevels.foreach(level => assert(level == StorageLevel.DISK_ONLY))
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}
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}
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private class IntermediateRDDStorageListener extends SparkListener {
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val storageLevels: mutable.ArrayBuffer[StorageLevel] = mutable.ArrayBuffer()
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override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = {
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val stageLevels = stageCompleted.stageInfo.rddInfos.collect {
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case info if info.name.contains("Blocks") || info.name.contains("Factors-") =>
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info.storageLevel
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}
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storageLevels ++= stageLevels
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}
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}
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object ALSSuite {
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/**
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@ -539,6 +616,8 @@ object ALSSuite {
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"implicitPrefs" -> true,
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"alpha" -> 0.9,
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"nonnegative" -> true,
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"checkpointInterval" -> 20
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"checkpointInterval" -> 20,
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"intermediateRDDStorageLevel" -> "MEMORY_ONLY",
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"finalRDDStorageLevel" -> "MEMORY_AND_DISK_SER"
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)
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}
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@ -119,21 +119,35 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
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nonnegative = Param(Params._dummy(), "nonnegative",
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"whether to use nonnegative constraint for least squares",
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typeConverter=TypeConverters.toBoolean)
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intermediateRDDStorageLevel = Param(Params._dummy(), "intermediateRDDStorageLevel",
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"StorageLevel for intermediate RDDs. Cannot be 'NONE'. " +
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"Default: 'MEMORY_AND_DISK'.",
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typeConverter=TypeConverters.toString)
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finalRDDStorageLevel = Param(Params._dummy(), "finalRDDStorageLevel",
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"StorageLevel for ALS model factor RDDs. " +
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"Default: 'MEMORY_AND_DISK'.",
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typeConverter=TypeConverters.toString)
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@keyword_only
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def __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
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implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None,
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ratingCol="rating", nonnegative=False, checkpointInterval=10):
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ratingCol="rating", nonnegative=False, checkpointInterval=10,
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intermediateRDDStorageLevel="MEMORY_AND_DISK",
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finalRDDStorageLevel="MEMORY_AND_DISK"):
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"""
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__init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, \
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implicitPrefs=false, alpha=1.0, userCol="user", itemCol="item", seed=None, \
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ratingCol="rating", nonnegative=false, checkpointInterval=10)
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ratingCol="rating", nonnegative=false, checkpointInterval=10, \
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intermediateRDDStorageLevel="MEMORY_AND_DISK", \
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finalRDDStorageLevel="MEMORY_AND_DISK")
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"""
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super(ALS, self).__init__()
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self._java_obj = self._new_java_obj("org.apache.spark.ml.recommendation.ALS", self.uid)
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self._setDefault(rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
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implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None,
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ratingCol="rating", nonnegative=False, checkpointInterval=10)
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ratingCol="rating", nonnegative=False, checkpointInterval=10,
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intermediateRDDStorageLevel="MEMORY_AND_DISK",
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finalRDDStorageLevel="MEMORY_AND_DISK")
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kwargs = self.__init__._input_kwargs
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self.setParams(**kwargs)
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@ -141,11 +155,15 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
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@since("1.4.0")
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def setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
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implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None,
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ratingCol="rating", nonnegative=False, checkpointInterval=10):
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ratingCol="rating", nonnegative=False, checkpointInterval=10,
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intermediateRDDStorageLevel="MEMORY_AND_DISK",
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finalRDDStorageLevel="MEMORY_AND_DISK"):
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"""
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setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, \
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implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None, \
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ratingCol="rating", nonnegative=False, checkpointInterval=10)
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ratingCol="rating", nonnegative=False, checkpointInterval=10, \
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intermediateRDDStorageLevel="MEMORY_AND_DISK", \
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finalRDDStorageLevel="MEMORY_AND_DISK")
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Sets params for ALS.
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"""
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kwargs = self.setParams._input_kwargs
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@ -297,6 +315,36 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
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"""
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return self.getOrDefault(self.nonnegative)
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@since("2.0.0")
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def setIntermediateRDDStorageLevel(self, value):
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"""
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Sets the value of :py:attr:`intermediateRDDStorageLevel`.
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"""
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self._set(intermediateRDDStorageLevel=value)
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return self
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@since("2.0.0")
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def getIntermediateRDDStorageLevel(self):
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"""
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Gets the value of intermediateRDDStorageLevel or its default value.
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"""
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return self.getOrDefault(self.intermediateRDDStorageLevel)
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@since("2.0.0")
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def setFinalRDDStorageLevel(self, value):
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"""
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Sets the value of :py:attr:`finalRDDStorageLevel`.
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"""
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self._set(finalRDDStorageLevel=value)
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return self
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@since("2.0.0")
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def getFinalRDDStorageLevel(self):
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"""
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Gets the value of finalRDDStorageLevel or its default value.
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"""
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return self.getOrDefault(self.finalRDDStorageLevel)
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class ALSModel(JavaModel, JavaMLWritable, JavaMLReadable):
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"""
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@ -50,12 +50,15 @@ from pyspark.ml.evaluation import BinaryClassificationEvaluator, RegressionEvalu
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from pyspark.ml.feature import *
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from pyspark.ml.param import Param, Params, TypeConverters
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from pyspark.ml.param.shared import HasMaxIter, HasInputCol, HasSeed
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from pyspark.ml.recommendation import ALS
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from pyspark.ml.regression import LinearRegression, DecisionTreeRegressor
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from pyspark.ml.tuning import *
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from pyspark.ml.wrapper import JavaParams
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from pyspark.mllib.linalg import Vectors, DenseVector, SparseVector
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from pyspark.sql import DataFrame, SQLContext, Row
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from pyspark.sql.functions import rand
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from pyspark.sql.utils import IllegalArgumentException
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from pyspark.storagelevel import *
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from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
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@ -999,6 +1002,30 @@ class HashingTFTest(PySparkTestCase):
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": expected " + str(expected[i]) + ", got " + str(features[i]))
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class ALSTest(PySparkTestCase):
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def test_storage_levels(self):
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sqlContext = SQLContext(self.sc)
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df = sqlContext.createDataFrame(
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[(0, 0, 4.0), (0, 1, 2.0), (1, 1, 3.0), (1, 2, 4.0), (2, 1, 1.0), (2, 2, 5.0)],
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["user", "item", "rating"])
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als = ALS().setMaxIter(1).setRank(1)
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# test default params
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als.fit(df)
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self.assertEqual(als.getIntermediateRDDStorageLevel(), "MEMORY_AND_DISK")
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self.assertEqual(als._java_obj.getIntermediateRDDStorageLevel(), "MEMORY_AND_DISK")
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self.assertEqual(als.getFinalRDDStorageLevel(), "MEMORY_AND_DISK")
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self.assertEqual(als._java_obj.getFinalRDDStorageLevel(), "MEMORY_AND_DISK")
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# test non-default params
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als.setIntermediateRDDStorageLevel("MEMORY_ONLY_2")
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als.setFinalRDDStorageLevel("DISK_ONLY")
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als.fit(df)
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self.assertEqual(als.getIntermediateRDDStorageLevel(), "MEMORY_ONLY_2")
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self.assertEqual(als._java_obj.getIntermediateRDDStorageLevel(), "MEMORY_ONLY_2")
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self.assertEqual(als.getFinalRDDStorageLevel(), "DISK_ONLY")
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self.assertEqual(als._java_obj.getFinalRDDStorageLevel(), "DISK_ONLY")
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if __name__ == "__main__":
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from pyspark.ml.tests import *
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if xmlrunner:
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