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

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"""
Python package for random data generation.
"""

import sys
from functools import wraps

from pyspark import since
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.

    .. versionadded:: 1.1.0
    """

    @staticmethod
    @since("1.1.0")
    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
        ``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
    @since("1.1.0")
    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
        ``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=1)
        >>> stats = x.stats()
        >>> stats.count()
        1000
        >>> 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
    @since("1.3.0")
    def logNormalRDD(sc, mean, std, size, numPartitions=None, seed=None):
        """
        Generates an RDD comprised of i.i.d. samples from the log normal
        distribution with the input mean and standard distribution.

        :param sc: SparkContext used to create the RDD.
        :param mean: mean for the log Normal distribution
        :param std: std for the log Normal 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 ~ log N(mean, std).

        >>> from math import sqrt, exp
        >>> mean = 0.0
        >>> std = 1.0
        >>> expMean = exp(mean + 0.5 * std * std)
        >>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std))
        >>> x = RandomRDDs.logNormalRDD(sc, mean, std, 1000, seed=2)
        >>> stats = x.stats()
        >>> stats.count()
        1000
        >>> abs(stats.mean() - expMean) < 0.5
        True
        >>> from math import sqrt
        >>> abs(stats.stdev() - expStd) < 0.5
        True
        """
        return callMLlibFunc("logNormalRDD", sc._jsc, float(mean), float(std),
                             size, numPartitions, seed)

    @staticmethod
    @since("1.1.0")
    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=2)
        >>> stats = x.stats()
        >>> stats.count()
        1000
        >>> 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
    @since("1.3.0")
    def exponentialRDD(sc, mean, size, numPartitions=None, seed=None):
        """
        Generates an RDD comprised of i.i.d. samples from the Exponential
        distribution with the input mean.

        :param sc: SparkContext used to create the RDD.
        :param mean: Mean, or 1 / lambda, for the Exponential 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 ~ Exp(mean).

        >>> mean = 2.0
        >>> x = RandomRDDs.exponentialRDD(sc, mean, 1000, seed=2)
        >>> stats = x.stats()
        >>> stats.count()
        1000
        >>> abs(stats.mean() - mean) < 0.5
        True
        >>> from math import sqrt
        >>> abs(stats.stdev() - sqrt(mean)) < 0.5
        True
        """
        return callMLlibFunc("exponentialRDD", sc._jsc, float(mean), size, numPartitions, seed)

    @staticmethod
    @since("1.3.0")
    def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None):
        """
        Generates an RDD comprised of i.i.d. samples from the Gamma
        distribution with the input shape and scale.

        :param sc: SparkContext used to create the RDD.
        :param shape: shape (> 0) parameter for the Gamma distribution
        :param scale: scale (> 0) parameter for the Gamma 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 ~ Gamma(shape, scale).

        >>> from math import sqrt
        >>> shape = 1.0
        >>> scale = 2.0
        >>> expMean = shape * scale
        >>> expStd = sqrt(shape * scale * scale)
        >>> x = RandomRDDs.gammaRDD(sc, shape, scale, 1000, seed=2)
        >>> stats = x.stats()
        >>> stats.count()
        1000
        >>> abs(stats.mean() - expMean) < 0.5
        True
        >>> abs(stats.stdev() - expStd) < 0.5
        True
        """
        return callMLlibFunc("gammaRDD", sc._jsc, float(shape),
                             float(scale), size, numPartitions, seed)

    @staticmethod
    @toArray
    @since("1.1.0")
    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
    @since("1.1.0")
    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=1).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
    @since("1.3.0")
    def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed=None):
        """
        Generates an RDD comprised of vectors containing i.i.d. samples drawn
        from the log normal distribution.

        :param sc: SparkContext used to create the RDD.
        :param mean: Mean of the log normal distribution
        :param std: Standard Deviation of the log normal 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 ~ log `N(mean, std)`.

        >>> import numpy as np
        >>> from math import sqrt, exp
        >>> mean = 0.0
        >>> std = 1.0
        >>> expMean = exp(mean + 0.5 * std * std)
        >>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std))
        >>> m = RandomRDDs.logNormalVectorRDD(sc, mean, std, 100, 100, seed=1).collect()
        >>> mat = np.matrix(m)
        >>> mat.shape
        (100, 100)
        >>> abs(mat.mean() - expMean) < 0.1
        True
        >>> abs(mat.std() - expStd) < 0.1
        True
        """
        return callMLlibFunc("logNormalVectorRDD", sc._jsc, float(mean), float(std),
                             numRows, numCols, numPartitions, seed)

    @staticmethod
    @toArray
    @since("1.1.0")
    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=1)
        >>> 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)

    @staticmethod
    @toArray
    @since("1.3.0")
    def exponentialVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
        """
        Generates an RDD comprised of vectors containing i.i.d. samples drawn
        from the Exponential distribution with the input mean.

        :param sc: SparkContext used to create the RDD.
        :param mean: Mean, or 1 / lambda, for the Exponential 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 ~ Exp(mean).

        >>> import numpy as np
        >>> mean = 0.5
        >>> rdd = RandomRDDs.exponentialVectorRDD(sc, mean, 100, 100, seed=1)
        >>> 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("exponentialVectorRDD", sc._jsc, float(mean), numRows, numCols,
                             numPartitions, seed)

    @staticmethod
    @toArray
    @since("1.3.0")
    def gammaVectorRDD(sc, shape, scale, numRows, numCols, numPartitions=None, seed=None):
        """
        Generates an RDD comprised of vectors containing i.i.d. samples drawn
        from the Gamma distribution.

        :param sc: SparkContext used to create the RDD.
        :param shape: Shape (> 0) of the Gamma distribution
        :param scale: Scale (> 0) of the Gamma 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 ~ Gamma(shape, scale).

        >>> import numpy as np
        >>> from math import sqrt
        >>> shape = 1.0
        >>> scale = 2.0
        >>> expMean = shape * scale
        >>> expStd = sqrt(shape * scale * scale)
        >>> mat = np.matrix(RandomRDDs.gammaVectorRDD(sc, shape, scale, 100, 100, seed=1).collect())
        >>> mat.shape
        (100, 100)
        >>> abs(mat.mean() - expMean) < 0.1
        True
        >>> abs(mat.std() - expStd) < 0.1
        True
        """
        return callMLlibFunc("gammaVectorRDD", sc._jsc, float(shape), float(scale),
                             numRows, numCols, numPartitions, seed)


def _test():
    import doctest
    from pyspark.sql import SparkSession
    globs = globals().copy()
    # The small batch size here ensures that we see multiple batches,
    # even in these small test examples:
    spark = SparkSession.builder\
        .master("local[2]")\
        .appName("mllib.random tests")\
        .getOrCreate()
    globs['sc'] = spark.sparkContext
    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
    spark.stop()
    if failure_count:
        sys.exit(-1)


if __name__ == "__main__":
    _test()