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author | Fernando Otero (ZeoS) <fotero@gmail.com> | 2015-01-08 12:42:54 -0800 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-01-08 12:42:54 -0800 |
commit | 72df5a301e706d9384f3a1c17b2c58b017632b1f (patch) | |
tree | dccede8a63578a7dbc9cf1d81be8c3a2b45948cf | |
parent | 538f221627930c8f8a138c0d21d9fa09bc789e67 (diff) | |
download | spark-72df5a301e706d9384f3a1c17b2c58b017632b1f.tar.gz spark-72df5a301e706d9384f3a1c17b2c58b017632b1f.tar.bz2 spark-72df5a301e706d9384f3a1c17b2c58b017632b1f.zip |
SPARK-5148 [MLlib] Make usersOut/productsOut storagelevel in ALS configurable
Author: Fernando Otero (ZeoS) <fotero@gmail.com>
Closes #3953 from zeitos/storageLevel and squashes the following commits:
0f070b9 [Fernando Otero (ZeoS)] fix imports
6869e80 [Fernando Otero (ZeoS)] fix comment length
90c9f7e [Fernando Otero (ZeoS)] fix comment length
18a992e [Fernando Otero (ZeoS)] changing storage level
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala | 18 | ||||
-rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala | 27 |
2 files changed, 43 insertions, 2 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala index 90ac252226..bee951a2e5 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala @@ -116,6 +116,7 @@ class ALS private ( /** storage level for user/product in/out links */ private var intermediateRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK + private var finalRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK /** * Set the number of blocks for both user blocks and product blocks to parallelize the computation @@ -205,6 +206,19 @@ class ALS private ( } /** + * :: DeveloperApi :: + * Sets storage level for final RDDs (user/product used in MatrixFactorizationModel). The default + * value is `MEMORY_AND_DISK`. Users can change it to a serialized storage, e.g. + * `MEMORY_AND_DISK_SER` and set `spark.rdd.compress` to `true` to reduce the space requirement, + * at the cost of speed. + */ + @DeveloperApi + def setFinalRDDStorageLevel(storageLevel: StorageLevel): this.type = { + this.finalRDDStorageLevel = storageLevel + this + } + + /** * Run ALS with the configured parameters on an input RDD of (user, product, rating) triples. * Returns a MatrixFactorizationModel with feature vectors for each user and product. */ @@ -307,8 +321,8 @@ class ALS private ( val usersOut = unblockFactors(users, userOutLinks) val productsOut = unblockFactors(products, productOutLinks) - usersOut.setName("usersOut").persist(StorageLevel.MEMORY_AND_DISK) - productsOut.setName("productsOut").persist(StorageLevel.MEMORY_AND_DISK) + usersOut.setName("usersOut").persist(finalRDDStorageLevel) + productsOut.setName("productsOut").persist(finalRDDStorageLevel) // Materialize usersOut and productsOut. usersOut.count() diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala index 603d0ad127..f3b7bfda78 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala @@ -27,6 +27,7 @@ import org.jblas.DoubleMatrix import org.apache.spark.SparkContext._ import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.recommendation.ALS.BlockStats +import org.apache.spark.storage.StorageLevel object ALSSuite { @@ -139,6 +140,32 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext { assert(u11 != u2) } + test("Storage Level for RDDs in model") { + val ratings = sc.parallelize(ALSSuite.generateRatings(10, 20, 5, 0.5, false, false)._1, 2) + var storageLevel = StorageLevel.MEMORY_ONLY + var model = new ALS() + .setRank(5) + .setIterations(1) + .setLambda(1.0) + .setBlocks(2) + .setSeed(1) + .setFinalRDDStorageLevel(storageLevel) + .run(ratings) + assert(model.productFeatures.getStorageLevel == storageLevel); + assert(model.userFeatures.getStorageLevel == storageLevel); + storageLevel = StorageLevel.DISK_ONLY + model = new ALS() + .setRank(5) + .setIterations(1) + .setLambda(1.0) + .setBlocks(2) + .setSeed(1) + .setFinalRDDStorageLevel(storageLevel) + .run(ratings) + assert(model.productFeatures.getStorageLevel == storageLevel); + assert(model.userFeatures.getStorageLevel == storageLevel); + } + test("negative ids") { val data = ALSSuite.generateRatings(50, 50, 2, 0.7, false, false) val ratings = sc.parallelize(data._1.map { case Rating(u, p, r) => |