diff options
Diffstat (limited to 'mllib')
6 files changed, 14 insertions, 12 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala index 8ace8c53bb..90f0be76df 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala @@ -68,7 +68,6 @@ private[feature] trait Word2VecBase extends Params setDefault(stepSize -> 0.025) setDefault(maxIter -> 1) - setDefault(seed -> 42L) /** * Validate and transform the input schema. diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala index 5085b798da..8b8cb81373 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala @@ -53,7 +53,7 @@ private[shared] object SharedParamsCodeGen { ParamDesc[Int]("checkpointInterval", "checkpoint interval (>= 1)", isValid = "ParamValidators.gtEq(1)"), ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")), - ParamDesc[Long]("seed", "random seed", Some("Utils.random.nextLong()")), + ParamDesc[Long]("seed", "random seed", Some("this.getClass.getName.hashCode.toLong")), ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]." + " For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", isValid = "ParamValidators.inRange(0, 1)"), diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala index 7525d37007..3a4976d3dd 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala @@ -232,7 +232,7 @@ private[ml] trait HasFitIntercept extends Params { } /** - * (private[ml]) Trait for shared param seed (default: Utils.random.nextLong()). + * (private[ml]) Trait for shared param seed (default: this.getClass.getName.hashCode.toLong). */ private[ml] trait HasSeed extends Params { @@ -242,7 +242,7 @@ private[ml] trait HasSeed extends Params { */ final val seed: LongParam = new LongParam(this, "seed", "random seed") - setDefault(seed, Utils.random.nextLong()) + setDefault(seed, this.getClass.getName.hashCode.toLong) /** @group getParam */ final def getSeed: Long = $(seed) 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 45c57b50da..2a5ddbfae5 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 @@ -148,7 +148,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR 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, seed -> 0L) + ratingCol -> "rating", nonnegative -> false, checkpointInterval -> 10) /** * Validates and transforms the input schema. diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala index 03ba86670d..43a09cc418 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala @@ -52,6 +52,7 @@ class Word2VecSuite extends FunSuite with MLlibTestSparkContext { .setVectorSize(3) .setInputCol("text") .setOutputCol("result") + .setSeed(42L) .fit(docDF) model.transform(docDF).select("result", "expected").collect().foreach { 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 fc7349330c..6cc6ec94eb 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 @@ -345,6 +345,7 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { .setImplicitPrefs(implicitPrefs) .setNumUserBlocks(numUserBlocks) .setNumItemBlocks(numItemBlocks) + .setSeed(0) val alpha = als.getAlpha val model = als.fit(training.toDF()) val predictions = model.transform(test.toDF()) @@ -425,17 +426,18 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { val (ratings, _) = genImplicitTestData(numUsers = 20, numItems = 40, rank = 2, noiseStd = 0.01) val longRatings = ratings.map(r => Rating(r.user.toLong, r.item.toLong, r.rating)) - val (longUserFactors, _) = ALS.train(longRatings, rank = 2, maxIter = 4) + val (longUserFactors, _) = ALS.train(longRatings, rank = 2, maxIter = 4, seed = 0) assert(longUserFactors.first()._1.getClass === classOf[Long]) val strRatings = ratings.map(r => Rating(r.user.toString, r.item.toString, r.rating)) - val (strUserFactors, _) = ALS.train(strRatings, rank = 2, maxIter = 4) + val (strUserFactors, _) = ALS.train(strRatings, rank = 2, maxIter = 4, seed = 0) assert(strUserFactors.first()._1.getClass === classOf[String]) } test("nonnegative constraint") { val (ratings, _) = genImplicitTestData(numUsers = 20, numItems = 40, rank = 2, noiseStd = 0.01) - val (userFactors, itemFactors) = ALS.train(ratings, rank = 2, maxIter = 4, nonnegative = true) + val (userFactors, itemFactors) = + ALS.train(ratings, rank = 2, maxIter = 4, nonnegative = true, seed = 0) def isNonnegative(factors: RDD[(Int, Array[Float])]): Boolean = { factors.values.map { _.forall(_ >= 0.0) }.reduce(_ && _) } @@ -459,7 +461,7 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { test("partitioner in returned factors") { val (ratings, _) = genImplicitTestData(numUsers = 20, numItems = 40, rank = 2, noiseStd = 0.01) val (userFactors, itemFactors) = ALS.train( - ratings, rank = 2, maxIter = 4, numUserBlocks = 3, numItemBlocks = 4) + ratings, rank = 2, maxIter = 4, numUserBlocks = 3, numItemBlocks = 4, seed = 0) for ((tpe, factors) <- Seq(("User", userFactors), ("Item", itemFactors))) { assert(userFactors.partitioner.isDefined, s"$tpe factors should have partitioner.") val part = userFactors.partitioner.get @@ -476,8 +478,8 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { test("als with large number of iterations") { val (ratings, _) = genExplicitTestData(numUsers = 4, numItems = 4, rank = 1) - ALS.train(ratings, rank = 1, maxIter = 50, numUserBlocks = 2, numItemBlocks = 2) - ALS.train( - ratings, rank = 1, maxIter = 50, numUserBlocks = 2, numItemBlocks = 2, implicitPrefs = true) + ALS.train(ratings, rank = 1, maxIter = 50, numUserBlocks = 2, numItemBlocks = 2, seed = 0) + ALS.train(ratings, rank = 1, maxIter = 50, numUserBlocks = 2, numItemBlocks = 2, + implicitPrefs = true, seed = 0) } } |