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 8ace8c53bb6634251b6666770bc2bbd5862cd413..90f0be76df44f15bf207df22b16189bbff227988 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 5085b798daa17e5036891a782b775e25385a915a..8b8cb81373a65309fc235a501de9766a2573dd90 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 7525d37007377aca0dc3b2a12b0fbc3396df07fc..3a4976d3ddcd1a8caa266e55cf28c571aac4f1b1 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 45c57b50da70ff8f4e0f064a9a89ff73c1106dd6..2a5ddbfae5cdf478c90be94037936532249cc3c9 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 03ba86670d4530ca8b96a57d70da5ddb24e9cc15..43a09cc41870348cbe145776f46c91b11cc2837d 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 fc7349330cf86500e1665891acf150b3c8011c75..6cc6ec94eb643789d2608d88d0f758531563de06 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)
}
}