[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.

4 files changed, 209 insertions, 11 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
index cbcbfe8249..55cea800d9 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
@@ -22,7 +22,7 @@ import java.io.IOException
 
 import scala.collection.mutable
 import scala.reflect.ClassTag
-import scala.util.Sorting
+import scala.util.{Sorting, Try}
 import scala.util.hashing.byteswap64
 
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
@@ -153,12 +153,42 @@ private[recommendation] trait ALSParams extends ALSModelParams with HasMaxIter w
   /** @group getParam */
   def getNonnegative: Boolean = $(nonnegative)
 
+  /**
+   * Param for StorageLevel for intermediate RDDs. Pass in a string representation of
+   * [[StorageLevel]]. Cannot be "NONE".
+   * Default: "MEMORY_AND_DISK".
+   *
+   * @group expertParam
+   */
+  val intermediateRDDStorageLevel = new Param[String](this, "intermediateRDDStorageLevel",
+    "StorageLevel for intermediate RDDs. Cannot be 'NONE'. Default: 'MEMORY_AND_DISK'.",
+    (s: String) => Try(StorageLevel.fromString(s)).isSuccess && s != "NONE")
+
+  /** @group expertGetParam */
+  def getIntermediateRDDStorageLevel: String = $(intermediateRDDStorageLevel)
+
+  /**
+   * Param for StorageLevel for ALS model factor RDDs. Pass in a string representation of
+   * [[StorageLevel]].
+   * Default: "MEMORY_AND_DISK".
+   *
+   * @group expertParam
+   */
+  val finalRDDStorageLevel = new Param[String](this, "finalRDDStorageLevel",
+    "StorageLevel for ALS model factor RDDs. Default: 'MEMORY_AND_DISK'.",
+    (s: String) => Try(StorageLevel.fromString(s)).isSuccess)
+
+  /** @group expertGetParam */
+  def getFinalRDDStorageLevel: String = $(finalRDDStorageLevel)
+
   setDefault(rank -> 10, maxIter -> 10, regParam -> 0.1, numUserBlocks -> 10, numItemBlocks -> 10,
     implicitPrefs -> false, alpha -> 1.0, userCol -> "user", itemCol -> "item",
-    ratingCol -> "rating", nonnegative -> false, checkpointInterval -> 10)
+    ratingCol -> "rating", nonnegative -> false, checkpointInterval -> 10,
+    intermediateRDDStorageLevel -> "MEMORY_AND_DISK", finalRDDStorageLevel -> "MEMORY_AND_DISK")
 
   /**
    * Validates and transforms the input schema.
+   *
    * @param schema input schema
    * @return output schema
    */
@@ -374,8 +404,21 @@ class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel]
   @Since("1.3.0")
   def setSeed(value: Long): this.type = set(seed, value)
 
+  /** @group expertSetParam */
+  @Since("2.0.0")
+  def setIntermediateRDDStorageLevel(value: String): this.type = {
+    set(intermediateRDDStorageLevel, value)
+  }
+
+  /** @group expertSetParam */
+  @Since("2.0.0")
+  def setFinalRDDStorageLevel(value: String): this.type = {
+    set(finalRDDStorageLevel, value)
+  }
+
   /**
    * Sets both numUserBlocks and numItemBlocks to the specific value.
+   *
    * @group setParam
    */
   @Since("1.3.0")
@@ -403,6 +446,8 @@ class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel]
       numUserBlocks = $(numUserBlocks), numItemBlocks = $(numItemBlocks),
       maxIter = $(maxIter), regParam = $(regParam), implicitPrefs = $(implicitPrefs),
       alpha = $(alpha), nonnegative = $(nonnegative),
+      intermediateRDDStorageLevel = StorageLevel.fromString($(intermediateRDDStorageLevel)),
+      finalRDDStorageLevel = StorageLevel.fromString($(finalRDDStorageLevel)),
       checkpointInterval = $(checkpointInterval), seed = $(seed))
     val userDF = userFactors.toDF("id", "features")
     val itemDF = itemFactors.toDF("id", "features")
@@ -754,7 +799,6 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
    *                ratings are associated with srcIds(i).
    * @param dstEncodedIndices encoded dst indices
    * @param ratings ratings
-   *
    * @see [[LocalIndexEncoder]]
    */
   private[recommendation] case class InBlock[@specialized(Int, Long) ID: ClassTag](
@@ -850,7 +894,6 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
    * @param ratings raw ratings
    * @param srcPart partitioner for src IDs
    * @param dstPart partitioner for dst IDs
-   *
    * @return an RDD of rating blocks in the form of ((srcBlockId, dstBlockId), ratingBlock)
    */
   private def partitionRatings[ID: ClassTag](
@@ -899,6 +942,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
 
   /**
    * Builder for uncompressed in-blocks of (srcId, dstEncodedIndex, rating) tuples.
+   *
    * @param encoder encoder for dst indices
    */
   private[recommendation] class UncompressedInBlockBuilder[@specialized(Int, Long) ID: ClassTag](
@@ -1099,6 +1143,7 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
 
   /**
    * Creates in-blocks and out-blocks from rating blocks.
+   *
    * @param prefix prefix for in/out-block names
    * @param ratingBlocks rating blocks
    * @param srcPart partitioner for src IDs
@@ -1187,7 +1232,6 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
    * @param implicitPrefs whether to use implicit preference
    * @param alpha the alpha constant in the implicit preference formulation
    * @param solver solver for least squares problems
-   *
    * @return dst factors
    */
   private def computeFactors[ID](
diff --git a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
index dac76aa7a1..2e5c6a4f20 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
@@ -33,7 +33,9 @@ import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.rdd.RDD
+import org.apache.spark.scheduler.{SparkListener, SparkListenerStageCompleted}
 import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.storage.StorageLevel
 
 class ALSSuite
   extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest with Logging {
@@ -198,6 +200,7 @@ class ALSSuite
 
   /**
    * Generates an explicit feedback dataset for testing ALS.
+   *
    * @param numUsers number of users
    * @param numItems number of items
    * @param rank rank
@@ -238,6 +241,7 @@ class ALSSuite
 
   /**
    * Generates an implicit feedback dataset for testing ALS.
+   *
    * @param numUsers number of users
    * @param numItems number of items
    * @param rank rank
@@ -286,6 +290,7 @@ class ALSSuite
 
   /**
    * Generates random user/item factors, with i.i.d. values drawn from U(a, b).
+   *
    * @param size number of users/items
    * @param rank number of features
    * @param random random number generator
@@ -311,6 +316,7 @@ class ALSSuite
 
   /**
    * Test ALS using the given training/test splits and parameters.
+   *
    * @param training training dataset
    * @param test test dataset
    * @param rank rank of the matrix factorization
@@ -514,6 +520,77 @@ class ALSSuite
   }
 }
 
+class ALSStorageSuite
+  extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest with Logging {
+
+  test("invalid storage params") {
+    intercept[IllegalArgumentException] {
+      new ALS().setIntermediateRDDStorageLevel("foo")
+    }
+    intercept[IllegalArgumentException] {
+      new ALS().setIntermediateRDDStorageLevel("NONE")
+    }
+    intercept[IllegalArgumentException] {
+      new ALS().setFinalRDDStorageLevel("foo")
+    }
+  }
+
+  test("default and non-default storage params set correct RDD StorageLevels") {
+    val sqlContext = this.sqlContext
+    import sqlContext.implicits._
+    val data = Seq(
+      (0, 0, 1.0),
+      (0, 1, 2.0),
+      (1, 2, 3.0),
+      (1, 0, 2.0)
+    ).toDF("user", "item", "rating")
+    val als = new ALS().setMaxIter(1).setRank(1)
+    // add listener to check intermediate RDD default storage levels
+    val defaultListener = new IntermediateRDDStorageListener
+    sc.addSparkListener(defaultListener)
+    val model = als.fit(data)
+    // check final factor RDD default storage levels
+    val defaultFactorRDDs = sc.getPersistentRDDs.collect {
+      case (id, rdd) if rdd.name == "userFactors" || rdd.name == "itemFactors" =>
+        rdd.name -> (id, rdd.getStorageLevel)
+    }.toMap
+    defaultFactorRDDs.foreach { case (_, (id, level)) =>
+      assert(level == StorageLevel.MEMORY_AND_DISK)
+    }
+    defaultListener.storageLevels.foreach(level => assert(level == StorageLevel.MEMORY_AND_DISK))
+
+    // add listener to check intermediate RDD non-default storage levels
+    val nonDefaultListener = new IntermediateRDDStorageListener
+    sc.addSparkListener(nonDefaultListener)
+    val nonDefaultModel = als
+      .setFinalRDDStorageLevel("MEMORY_ONLY")
+      .setIntermediateRDDStorageLevel("DISK_ONLY")
+      .fit(data)
+    // check final factor RDD non-default storage levels
+    val levels = sc.getPersistentRDDs.collect {
+      case (id, rdd) if rdd.name == "userFactors" && rdd.id != defaultFactorRDDs("userFactors")._1
+        || rdd.name == "itemFactors" && rdd.id != defaultFactorRDDs("itemFactors")._1 =>
+        rdd.getStorageLevel
+    }
+    levels.foreach(level => assert(level == StorageLevel.MEMORY_ONLY))
+    nonDefaultListener.storageLevels.foreach(level => assert(level == StorageLevel.DISK_ONLY))
+  }
+}
+
+private class IntermediateRDDStorageListener extends SparkListener {
+
+  val storageLevels: mutable.ArrayBuffer[StorageLevel] = mutable.ArrayBuffer()
+
+  override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = {
+    val stageLevels = stageCompleted.stageInfo.rddInfos.collect {
+      case info if info.name.contains("Blocks") || info.name.contains("Factors-") =>
+        info.storageLevel
+    }
+    storageLevels ++= stageLevels
+  }
+
+}
+
 object ALSSuite {
 
   /**
@@ -539,6 +616,8 @@ object ALSSuite {
     "implicitPrefs" -> true,
     "alpha" -> 0.9,
     "nonnegative" -> true,
-    "checkpointInterval" -> 20
+    "checkpointInterval" -> 20,
+    "intermediateRDDStorageLevel" -> "MEMORY_ONLY",
+    "finalRDDStorageLevel" -> "MEMORY_AND_DISK_SER"
   )
 }
diff --git a/python/pyspark/ml/recommendation.py b/python/pyspark/ml/recommendation.py
index 4e42c468cc..97ac6ea83d 100644
--- a/python/pyspark/ml/recommendation.py
+++ b/python/pyspark/ml/recommendation.py
@@ -119,21 +119,35 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
     nonnegative = Param(Params._dummy(), "nonnegative",
                         "whether to use nonnegative constraint for least squares",
                         typeConverter=TypeConverters.toBoolean)
+    intermediateRDDStorageLevel = Param(Params._dummy(), "intermediateRDDStorageLevel",
+                                        "StorageLevel for intermediate RDDs. Cannot be 'NONE'. " +
+                                        "Default: 'MEMORY_AND_DISK'.",
+                                        typeConverter=TypeConverters.toString)
+    finalRDDStorageLevel = Param(Params._dummy(), "finalRDDStorageLevel",
+                                 "StorageLevel for ALS model factor RDDs. " +
+                                 "Default: 'MEMORY_AND_DISK'.",
+                                 typeConverter=TypeConverters.toString)
 
     @keyword_only
     def __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
                  implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None,
-                 ratingCol="rating", nonnegative=False, checkpointInterval=10):
+                 ratingCol="rating", nonnegative=False, checkpointInterval=10,
+                 intermediateRDDStorageLevel="MEMORY_AND_DISK",
+                 finalRDDStorageLevel="MEMORY_AND_DISK"):
         """
         __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, \
                  implicitPrefs=false, alpha=1.0, userCol="user", itemCol="item", seed=None, \
-                 ratingCol="rating", nonnegative=false, checkpointInterval=10)
+                 ratingCol="rating", nonnegative=false, checkpointInterval=10, \
+                 intermediateRDDStorageLevel="MEMORY_AND_DISK", \
+                 finalRDDStorageLevel="MEMORY_AND_DISK")
         """
         super(ALS, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.recommendation.ALS", self.uid)
         self._setDefault(rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
                          implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None,
-                         ratingCol="rating", nonnegative=False, checkpointInterval=10)
+                         ratingCol="rating", nonnegative=False, checkpointInterval=10,
+                         intermediateRDDStorageLevel="MEMORY_AND_DISK",
+                         finalRDDStorageLevel="MEMORY_AND_DISK")
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
@@ -141,11 +155,15 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
     @since("1.4.0")
     def setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,
                   implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None,
-                  ratingCol="rating", nonnegative=False, checkpointInterval=10):
+                  ratingCol="rating", nonnegative=False, checkpointInterval=10,
+                  intermediateRDDStorageLevel="MEMORY_AND_DISK",
+                  finalRDDStorageLevel="MEMORY_AND_DISK"):
         """
         setParams(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, \
                  implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None, \
-                 ratingCol="rating", nonnegative=False, checkpointInterval=10)
+                 ratingCol="rating", nonnegative=False, checkpointInterval=10, \
+                 intermediateRDDStorageLevel="MEMORY_AND_DISK", \
+                 finalRDDStorageLevel="MEMORY_AND_DISK")
         Sets params for ALS.
         """
         kwargs = self.setParams._input_kwargs
@@ -297,6 +315,36 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
         """
         return self.getOrDefault(self.nonnegative)
 
+    @since("2.0.0")
+    def setIntermediateRDDStorageLevel(self, value):
+        """
+        Sets the value of :py:attr:`intermediateRDDStorageLevel`.
+        """
+        self._set(intermediateRDDStorageLevel=value)
+        return self
+
+    @since("2.0.0")
+    def getIntermediateRDDStorageLevel(self):
+        """
+        Gets the value of intermediateRDDStorageLevel or its default value.
+        """
+        return self.getOrDefault(self.intermediateRDDStorageLevel)
+
+    @since("2.0.0")
+    def setFinalRDDStorageLevel(self, value):
+        """
+        Sets the value of :py:attr:`finalRDDStorageLevel`.
+        """
+        self._set(finalRDDStorageLevel=value)
+        return self
+
+    @since("2.0.0")
+    def getFinalRDDStorageLevel(self):
+        """
+        Gets the value of finalRDDStorageLevel or its default value.
+        """
+        return self.getOrDefault(self.finalRDDStorageLevel)
+
 
 class ALSModel(JavaModel, JavaMLWritable, JavaMLReadable):
     """
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index faca148218..7722d57e9e 100644
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -50,12 +50,15 @@ from pyspark.ml.evaluation import BinaryClassificationEvaluator, RegressionEvalu
 from pyspark.ml.feature import *
 from pyspark.ml.param import Param, Params, TypeConverters
 from pyspark.ml.param.shared import HasMaxIter, HasInputCol, HasSeed
+from pyspark.ml.recommendation import ALS
 from pyspark.ml.regression import LinearRegression, DecisionTreeRegressor
 from pyspark.ml.tuning import *
 from pyspark.ml.wrapper import JavaParams
 from pyspark.mllib.linalg import Vectors, DenseVector, SparseVector
 from pyspark.sql import DataFrame, SQLContext, Row
 from pyspark.sql.functions import rand
+from pyspark.sql.utils import IllegalArgumentException
+from pyspark.storagelevel import *
 from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
 
 
@@ -999,6 +1002,30 @@ class HashingTFTest(PySparkTestCase):
                                    ": expected " + str(expected[i]) + ", got " + str(features[i]))
 
 
+class ALSTest(PySparkTestCase):
+
+    def test_storage_levels(self):
+        sqlContext = SQLContext(self.sc)
+        df = sqlContext.createDataFrame(
+            [(0, 0, 4.0), (0, 1, 2.0), (1, 1, 3.0), (1, 2, 4.0), (2, 1, 1.0), (2, 2, 5.0)],
+            ["user", "item", "rating"])
+        als = ALS().setMaxIter(1).setRank(1)
+        # test default params
+        als.fit(df)
+        self.assertEqual(als.getIntermediateRDDStorageLevel(), "MEMORY_AND_DISK")
+        self.assertEqual(als._java_obj.getIntermediateRDDStorageLevel(), "MEMORY_AND_DISK")
+        self.assertEqual(als.getFinalRDDStorageLevel(), "MEMORY_AND_DISK")
+        self.assertEqual(als._java_obj.getFinalRDDStorageLevel(), "MEMORY_AND_DISK")
+        # test non-default params
+        als.setIntermediateRDDStorageLevel("MEMORY_ONLY_2")
+        als.setFinalRDDStorageLevel("DISK_ONLY")
+        als.fit(df)
+        self.assertEqual(als.getIntermediateRDDStorageLevel(), "MEMORY_ONLY_2")
+        self.assertEqual(als._java_obj.getIntermediateRDDStorageLevel(), "MEMORY_ONLY_2")
+        self.assertEqual(als.getFinalRDDStorageLevel(), "DISK_ONLY")
+        self.assertEqual(als._java_obj.getFinalRDDStorageLevel(), "DISK_ONLY")
+
+
 if __name__ == "__main__":
     from pyspark.ml.tests import *
     if xmlrunner: