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author | Xiangrui Meng <meng@databricks.com> | 2014-08-16 15:14:43 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2014-08-16 15:14:52 -0700 |
commit | a12d3ae3223535e6e4c774e4a289b8b2f2e5228b (patch) | |
tree | 38d3aa38fe2ad2720b0a9000de0235e21ce1f3c2 | |
parent | 0b354be2f9ec35547a60591acf4f4773a4869690 (diff) | |
download | spark-a12d3ae3223535e6e4c774e4a289b8b2f2e5228b.tar.gz spark-a12d3ae3223535e6e4c774e4a289b8b2f2e5228b.tar.bz2 spark-a12d3ae3223535e6e4c774e4a289b8b2f2e5228b.zip |
[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>
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala | 2 | ||||
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala (renamed from mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDGenerators.scala) | 6 | ||||
-rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala (renamed from mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDGeneratorsSuite.scala) | 16 | ||||
-rw-r--r-- | python/pyspark/mllib/random.py | 25 |
4 files changed, 24 insertions, 25 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index 18dc087856..4343124f10 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/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} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDGenerators.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala index b0a0593223..3627036952 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDGenerators.scala +++ b/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 :: diff --git a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDGeneratorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala index 96e0bc63b0..c50b78bcbc 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDGeneratorsSuite.scala +++ b/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) } } 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) |