[SPARK-4348] [PySpark] [MLlib] rename random.py to rand.py

This PR rename random.py to rand.py to avoid the side affects of conflict with random module, but still keep the same interface as before.

```
>>> from pyspark.mllib.random import RandomRDDs
```

```
$ pydoc pyspark.mllib.random
Help on module random in pyspark.mllib:
NAME
    random - Python package for random data generation.

FILE
    /Users/davies/work/spark/python/pyspark/mllib/rand.py

CLASSES
    __builtin__.object
        pyspark.mllib.random.RandomRDDs

    class RandomRDDs(__builtin__.object)
     |  Generator methods for creating RDDs comprised of i.i.d samples from
     |  some distribution.
     |
     |  Static methods defined here:
     |
     |  normalRDD(sc, size, numPartitions=None, seed=None)
```

cc mengxr

reference link: http://xion.org.pl/2012/05/06/hacking-python-imports/

Author: Davies Liu <davies@databricks.com>

Closes #3216 from davies/random and squashes the following commits:

7ac4e8b [Davies Liu] rename random.py to rand.py

1 files changed, 0 insertions, 223 deletions

diff --git a/python/pyspark/mllib/random.py b/python/pyspark/mllib/random.py
deleted file mode 100644
index cb4304f921..0000000000
--- a/python/pyspark/mllib/random.py
+++ /dev/null
@@ -1,223 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-"""
-Python package for random data generation.
-"""
-
-from functools import wraps
-
-from pyspark.mllib.common import callMLlibFunc
-
-
-__all__ = ['RandomRDDs', ]
-
-
-def toArray(f):
-    @wraps(f)
-    def func(sc, *a, **kw):
-        rdd = f(sc, *a, **kw)
-        return rdd.map(lambda vec: vec.toArray())
-    return func
-
-
-class RandomRDDs(object):
-    """
-    Generator methods for creating RDDs comprised of i.i.d samples from
-    some distribution.
-    """
-
-    @staticmethod
-    def uniformRDD(sc, size, numPartitions=None, seed=None):
-        """
-        Generates an RDD comprised of i.i.d. samples from the
-        uniform distribution U(0.0, 1.0).
-
-        To transform the distribution in the generated RDD from U(0.0, 1.0)
-        to U(a, b), use
-        C{RandomRDDs.uniformRDD(sc, n, p, seed)\
-          .map(lambda v: a + (b - a) * v)}
-
-        :param sc: SparkContext used to create the RDD.
-        :param size: Size of the RDD.
-        :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
-        :param seed: Random seed (default: a random long integer).
-        :return: RDD of float comprised of i.i.d. samples ~ `U(0.0, 1.0)`.
-
-        >>> x = RandomRDDs.uniformRDD(sc, 100).collect()
-        >>> len(x)
-        100
-        >>> max(x) <= 1.0 and min(x) >= 0.0
-        True
-        >>> RandomRDDs.uniformRDD(sc, 100, 4).getNumPartitions()
-        4
-        >>> parts = RandomRDDs.uniformRDD(sc, 100, seed=4).getNumPartitions()
-        >>> parts == sc.defaultParallelism
-        True
-        """
-        return callMLlibFunc("uniformRDD", sc._jsc, size, numPartitions, seed)
-
-    @staticmethod
-    def normalRDD(sc, size, numPartitions=None, seed=None):
-        """
-        Generates an RDD comprised of i.i.d. samples from the standard normal
-        distribution.
-
-        To transform the distribution in the generated RDD from standard normal
-        to some other normal N(mean, sigma^2), use
-        C{RandomRDDs.normal(sc, n, p, seed)\
-          .map(lambda v: mean + sigma * v)}
-
-        :param sc: SparkContext used to create the RDD.
-        :param size: Size of the RDD.
-        :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
-        :param seed: Random seed (default: a random long integer).
-        :return: RDD of float comprised of i.i.d. samples ~ N(0.0, 1.0).
-
-        >>> x = RandomRDDs.normalRDD(sc, 1000, seed=1L)
-        >>> stats = x.stats()
-        >>> stats.count()
-        1000L
-        >>> abs(stats.mean() - 0.0) < 0.1
-        True
-        >>> abs(stats.stdev() - 1.0) < 0.1
-        True
-        """
-        return callMLlibFunc("normalRDD", sc._jsc, size, numPartitions, seed)
-
-    @staticmethod
-    def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
-        """
-        Generates an RDD comprised of i.i.d. samples from the Poisson
-        distribution with the input mean.
-
-        :param sc: SparkContext used to create the RDD.
-        :param mean: Mean, or lambda, for the Poisson distribution.
-        :param size: Size of the RDD.
-        :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
-        :param seed: Random seed (default: a random long integer).
-        :return: RDD of float comprised of i.i.d. samples ~ Pois(mean).
-
-        >>> mean = 100.0
-        >>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=2L)
-        >>> stats = x.stats()
-        >>> stats.count()
-        1000L
-        >>> abs(stats.mean() - mean) < 0.5
-        True
-        >>> from math import sqrt
-        >>> abs(stats.stdev() - sqrt(mean)) < 0.5
-        True
-        """
-        return callMLlibFunc("poissonRDD", sc._jsc, float(mean), size, numPartitions, seed)
-
-    @staticmethod
-    @toArray
-    def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
-        """
-        Generates an RDD comprised of vectors containing i.i.d. samples drawn
-        from the uniform distribution U(0.0, 1.0).
-
-        :param sc: SparkContext used to create the RDD.
-        :param numRows: Number of Vectors in the RDD.
-        :param numCols: Number of elements in each Vector.
-        :param numPartitions: Number of partitions in the RDD.
-        :param seed: Seed for the RNG that generates the seed for the generator in each partition.
-        :return: RDD of Vector with vectors containing i.i.d samples ~ `U(0.0, 1.0)`.
-
-        >>> import numpy as np
-        >>> mat = np.matrix(RandomRDDs.uniformVectorRDD(sc, 10, 10).collect())
-        >>> mat.shape
-        (10, 10)
-        >>> mat.max() <= 1.0 and mat.min() >= 0.0
-        True
-        >>> RandomRDDs.uniformVectorRDD(sc, 10, 10, 4).getNumPartitions()
-        4
-        """
-        return callMLlibFunc("uniformVectorRDD", sc._jsc, numRows, numCols, numPartitions, seed)
-
-    @staticmethod
-    @toArray
-    def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
-        """
-        Generates an RDD comprised of vectors containing i.i.d. samples drawn
-        from the standard normal distribution.
-
-        :param sc: SparkContext used to create the RDD.
-        :param numRows: Number of Vectors in the RDD.
-        :param numCols: Number of elements in each Vector.
-        :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
-        :param seed: Random seed (default: a random long integer).
-        :return: RDD of Vector with vectors containing i.i.d. samples ~ `N(0.0, 1.0)`.
-
-        >>> import numpy as np
-        >>> mat = np.matrix(RandomRDDs.normalVectorRDD(sc, 100, 100, seed=1L).collect())
-        >>> mat.shape
-        (100, 100)
-        >>> abs(mat.mean() - 0.0) < 0.1
-        True
-        >>> abs(mat.std() - 1.0) < 0.1
-        True
-        """
-        return callMLlibFunc("normalVectorRDD", sc._jsc, numRows, numCols, numPartitions, seed)
-
-    @staticmethod
-    @toArray
-    def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
-        """
-        Generates an RDD comprised of vectors containing i.i.d. samples drawn
-        from the Poisson distribution with the input mean.
-
-        :param sc: SparkContext used to create the RDD.
-        :param mean: Mean, or lambda, for the Poisson distribution.
-        :param numRows: Number of Vectors in the RDD.
-        :param numCols: Number of elements in each Vector.
-        :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`)
-        :param seed: Random seed (default: a random long integer).
-        :return: RDD of Vector with vectors containing i.i.d. samples ~ Pois(mean).
-
-        >>> import numpy as np
-        >>> mean = 100.0
-        >>> rdd = RandomRDDs.poissonVectorRDD(sc, mean, 100, 100, seed=1L)
-        >>> mat = np.mat(rdd.collect())
-        >>> mat.shape
-        (100, 100)
-        >>> abs(mat.mean() - mean) < 0.5
-        True
-        >>> from math import sqrt
-        >>> abs(mat.std() - sqrt(mean)) < 0.5
-        True
-        """
-        return callMLlibFunc("poissonVectorRDD", sc._jsc, float(mean), numRows, numCols,
-                             numPartitions, seed)
-
-
-def _test():
-    import doctest
-    from pyspark.context import SparkContext
-    globs = globals().copy()
-    # The small batch size here ensures that we see multiple batches,
-    # even in these small test examples:
-    globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2)
-    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
-    globs['sc'].stop()
-    if failure_count:
-        exit(-1)
-
-
-if __name__ == "__main__":
-    _test()