[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

(cherry picked from commit ac6411c6e75906997c78de23dfdbc8d225b87cfd)
Signed-off-by: Xiangrui Meng <meng@databricks.com>

1 files changed, 12 insertions, 13 deletions

diff --git a/python/pyspark/mllib/random.py b/python/pyspark/mllib/random.py
index eb496688b6..3f3b19053d 100644
--- a/python/pyspark/mllib/random.py
+++ b/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)