[SPARK-3081][MLLIB] rename RandomRDDGenerators to RandomRDDs

`RandomRDDGenerators` means factory for `RandomRDDGenerator`. However, its methods return RDDs but not RDDGenerators. So a more proper (and shorter) name would be `RandomRDDs`.

dorx brkyvz

Author: Xiangrui Meng <meng@databricks.com>

Closes #1979 from mengxr/randomrdds and squashes the following commits:

b161a2d [Xiangrui Meng] rename RandomRDDGenerators to RandomRDDs

mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala

@@ -27,7 +27,7 @@ import org.apache.spark.mllib.classification._
 import org.apache.spark.mllib.clustering._
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.linalg.{Matrix, SparseVector, Vector, Vectors}
-import org.apache.spark.mllib.random.{RandomRDDGenerators => RG}
+import org.apache.spark.mllib.random.{RandomRDDs => RG}
 import org.apache.spark.mllib.recommendation._
 import org.apache.spark.mllib.regression._
 import org.apache.spark.mllib.tree.configuration.{Algo, Strategy}

mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala

@@ -17,6 +17,8 @@
 
 package org.apache.spark.mllib.random
 
+import scala.reflect.ClassTag
+
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.linalg.Vector
@@ -24,14 +26,12 @@ import org.apache.spark.mllib.rdd.{RandomVectorRDD, RandomRDD}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.Utils
 
-import scala.reflect.ClassTag
-
 /**
  * :: Experimental ::
  * Generator methods for creating RDDs comprised of i.i.d. samples from some distribution.
  */
 @Experimental
-object RandomRDDGenerators {
+object RandomRDDs {
 
   /**
    * :: Experimental ::

mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala

@@ -34,7 +34,7 @@ import org.apache.spark.util.StatCounter
  *
  * TODO update tests to use TestingUtils for floating point comparison after PR 1367 is merged
  */
-class RandomRDDGeneratorsSuite extends FunSuite with LocalSparkContext with Serializable {
+class RandomRDDsSuite extends FunSuite with LocalSparkContext with Serializable {
 
   def testGeneratedRDD(rdd: RDD[Double],
       expectedSize: Long,
@@ -113,18 +113,18 @@ class RandomRDDGeneratorsSuite extends FunSuite with LocalSparkContext with Seri
     val poissonMean = 100.0
 
     for (seed <- 0 until 5) {
-      val uniform = RandomRDDGenerators.uniformRDD(sc, size, numPartitions, seed)
+      val uniform = RandomRDDs.uniformRDD(sc, size, numPartitions, seed)
       testGeneratedRDD(uniform, size, numPartitions, 0.5, 1 / math.sqrt(12))
 
-      val normal = RandomRDDGenerators.normalRDD(sc, size, numPartitions, seed)
+      val normal = RandomRDDs.normalRDD(sc, size, numPartitions, seed)
       testGeneratedRDD(normal, size, numPartitions, 0.0, 1.0)
 
-      val poisson = RandomRDDGenerators.poissonRDD(sc, poissonMean, size, numPartitions, seed)
+      val poisson = RandomRDDs.poissonRDD(sc, poissonMean, size, numPartitions, seed)
       testGeneratedRDD(poisson, size, numPartitions, poissonMean, math.sqrt(poissonMean), 0.1)
     }
 
     // mock distribution to check that partitions have unique seeds
-    val random = RandomRDDGenerators.randomRDD(sc, new MockDistro(), 1000L, 1000, 0L)
+    val random = RandomRDDs.randomRDD(sc, new MockDistro(), 1000L, 1000, 0L)
     assert(random.collect.size === random.collect.distinct.size)
   }
 
@@ -135,13 +135,13 @@ class RandomRDDGeneratorsSuite extends FunSuite with LocalSparkContext with Seri
     val poissonMean = 100.0
 
     for (seed <- 0 until 5) {
-      val uniform = RandomRDDGenerators.uniformVectorRDD(sc, rows, cols, parts, seed)
+      val uniform = RandomRDDs.uniformVectorRDD(sc, rows, cols, parts, seed)
       testGeneratedVectorRDD(uniform, rows, cols, parts, 0.5, 1 / math.sqrt(12))
 
-      val normal = RandomRDDGenerators.normalVectorRDD(sc, rows, cols, parts, seed)
+      val normal = RandomRDDs.normalVectorRDD(sc, rows, cols, parts, seed)
       testGeneratedVectorRDD(normal, rows, cols, parts, 0.0, 1.0)
 
-      val poisson = RandomRDDGenerators.poissonVectorRDD(sc, poissonMean, rows, cols, parts, seed)
+      val poisson = RandomRDDs.poissonVectorRDD(sc, poissonMean, rows, cols, parts, seed)
       testGeneratedVectorRDD(poisson, rows, cols, parts, poissonMean, math.sqrt(poissonMean), 0.1)
     }
   }

python/pyspark/mllib/random.py

@@ -25,8 +25,7 @@ from pyspark.mllib._common import _deserialize_double, _deserialize_double_vecto
 from pyspark.serializers import NoOpSerializer
 
 
-class RandomRDDGenerators:
-
+class RandomRDDs:
     """
     Generator methods for creating RDDs comprised of i.i.d samples from
     some distribution.
@@ -40,17 +39,17 @@ class RandomRDDGenerators:
 
         To transform the distribution in the generated RDD from U[0.0, 1.0]
         to U[a, b], use
-        C{RandomRDDGenerators.uniformRDD(sc, n, p, seed)\
+        C{RandomRDDs.uniformRDD(sc, n, p, seed)\
           .map(lambda v: a + (b - a) * v)}
 
-        >>> x = RandomRDDGenerators.uniformRDD(sc, 100).collect()
+        >>> x = RandomRDDs.uniformRDD(sc, 100).collect()
         >>> len(x)
         100
         >>> max(x) <= 1.0 and min(x) >= 0.0
         True
-        >>> RandomRDDGenerators.uniformRDD(sc, 100, 4).getNumPartitions()
+        >>> RandomRDDs.uniformRDD(sc, 100, 4).getNumPartitions()
         4
-        >>> parts = RandomRDDGenerators.uniformRDD(sc, 100, seed=4).getNumPartitions()
+        >>> parts = RandomRDDs.uniformRDD(sc, 100, seed=4).getNumPartitions()
         >>> parts == sc.defaultParallelism
         True
         """
@@ -66,10 +65,10 @@ class RandomRDDGenerators:
 
         To transform the distribution in the generated RDD from standard normal
         to some other normal N(mean, sigma), use
-        C{RandomRDDGenerators.normal(sc, n, p, seed)\
+        C{RandomRDDs.normal(sc, n, p, seed)\
           .map(lambda v: mean + sigma * v)}
 
-        >>> x = RandomRDDGenerators.normalRDD(sc, 1000, seed=1L)
+        >>> x = RandomRDDs.normalRDD(sc, 1000, seed=1L)
         >>> stats = x.stats()
         >>> stats.count()
         1000L
@@ -89,7 +88,7 @@ class RandomRDDGenerators:
         distribution with the input mean.
 
         >>> mean = 100.0
-        >>> x = RandomRDDGenerators.poissonRDD(sc, mean, 1000, seed=1L)
+        >>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=1L)
         >>> stats = x.stats()
         >>> stats.count()
         1000L
@@ -110,12 +109,12 @@ class RandomRDDGenerators:
         from the uniform distribution on [0.0 1.0].
 
         >>> import numpy as np
-        >>> mat = np.matrix(RandomRDDGenerators.uniformVectorRDD(sc, 10, 10).collect())
+        >>> mat = np.matrix(RandomRDDs.uniformVectorRDD(sc, 10, 10).collect())
         >>> mat.shape
         (10, 10)
         >>> mat.max() <= 1.0 and mat.min() >= 0.0
         True
-        >>> RandomRDDGenerators.uniformVectorRDD(sc, 10, 10, 4).getNumPartitions()
+        >>> RandomRDDs.uniformVectorRDD(sc, 10, 10, 4).getNumPartitions()
         4
         """
         jrdd = sc._jvm.PythonMLLibAPI() \
@@ -130,7 +129,7 @@ class RandomRDDGenerators:
         from the standard normal distribution.
 
         >>> import numpy as np
-        >>> mat = np.matrix(RandomRDDGenerators.normalVectorRDD(sc, 100, 100, seed=1L).collect())
+        >>> mat = np.matrix(RandomRDDs.normalVectorRDD(sc, 100, 100, seed=1L).collect())
         >>> mat.shape
         (100, 100)
         >>> abs(mat.mean() - 0.0) < 0.1
@@ -151,7 +150,7 @@ class RandomRDDGenerators:
 
         >>> import numpy as np
         >>> mean = 100.0
-        >>> rdd = RandomRDDGenerators.poissonVectorRDD(sc, mean, 100, 100, seed=1L)
+        >>> rdd = RandomRDDs.poissonVectorRDD(sc, mean, 100, 100, seed=1L)
         >>> mat = np.mat(rdd.collect())
         >>> mat.shape
         (100, 100)