spark-instrumented-optimizer/python/pyspark/mllib/random.py

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#    http://www.apache.org/licenses/LICENSE-2.0
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"""
Python package for random data generation.
"""


from pyspark.rdd import RDD
from pyspark.mllib._common import _deserialize_double, _deserialize_double_vector
from pyspark.serializers import NoOpSerializer


__all__ = ['RandomRDDs', ]


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)}

        >>> 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
        """
        jrdd = sc._jvm.PythonMLLibAPI().uniformRDD(sc._jsc, size, numPartitions, seed)
        uniform = RDD(jrdd, sc, NoOpSerializer())
        return uniform.map(lambda bytes: _deserialize_double(bytearray(bytes)))

    @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)}

        >>> 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
        """
        jrdd = sc._jvm.PythonMLLibAPI().normalRDD(sc._jsc, size, numPartitions, seed)
        normal = RDD(jrdd, sc, NoOpSerializer())
        return normal.map(lambda bytes: _deserialize_double(bytearray(bytes)))

    @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.

        >>> mean = 100.0
        >>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=1L)
        >>> 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
        """
        jrdd = sc._jvm.PythonMLLibAPI().poissonRDD(sc._jsc, mean, size, numPartitions, seed)
        poisson = RDD(jrdd, sc, NoOpSerializer())
        return poisson.map(lambda bytes: _deserialize_double(bytearray(bytes)))

    @staticmethod
    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).

        >>> 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
        """
        jrdd = sc._jvm.PythonMLLibAPI() \
            .uniformVectorRDD(sc._jsc, numRows, numCols, numPartitions, seed)
        uniform = RDD(jrdd, sc, NoOpSerializer())
        return uniform.map(lambda bytes: _deserialize_double_vector(bytearray(bytes)))

    @staticmethod
    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.

        >>> 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
        """
        jrdd = sc._jvm.PythonMLLibAPI() \
            .normalVectorRDD(sc._jsc, numRows, numCols, numPartitions, seed)
        normal = RDD(jrdd, sc, NoOpSerializer())
        return normal.map(lambda bytes: _deserialize_double_vector(bytearray(bytes)))

    @staticmethod
    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.

        >>> 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
        """
        jrdd = sc._jvm.PythonMLLibAPI() \
            .poissonVectorRDD(sc._jsc, mean, numRows, numCols, numPartitions, seed)
        poisson = RDD(jrdd, sc, NoOpSerializer())
        return poisson.map(lambda bytes: _deserialize_double_vector(bytearray(bytes)))


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()