ce0333f9a0
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.
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| Static methods defined here:
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| 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
224 lines
8.2 KiB
Python
224 lines
8.2 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Python package for random data generation.
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"""
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from functools import wraps
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from pyspark.mllib.common import callMLlibFunc
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__all__ = ['RandomRDDs', ]
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def toArray(f):
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@wraps(f)
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def func(sc, *a, **kw):
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rdd = f(sc, *a, **kw)
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return rdd.map(lambda vec: vec.toArray())
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return func
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class RandomRDDs(object):
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"""
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Generator methods for creating RDDs comprised of i.i.d samples from
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some distribution.
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"""
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@staticmethod
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def uniformRDD(sc, size, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of i.i.d. samples from the
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uniform distribution U(0.0, 1.0).
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To transform the distribution in the generated RDD from U(0.0, 1.0)
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to U(a, b), use
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C{RandomRDDs.uniformRDD(sc, n, p, seed)\
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.map(lambda v: a + (b - a) * v)}
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:param sc: SparkContext used to create the RDD.
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:param size: Size of the RDD.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of float comprised of i.i.d. samples ~ `U(0.0, 1.0)`.
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>>> x = RandomRDDs.uniformRDD(sc, 100).collect()
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>>> len(x)
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100
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>>> max(x) <= 1.0 and min(x) >= 0.0
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True
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>>> RandomRDDs.uniformRDD(sc, 100, 4).getNumPartitions()
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4
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>>> parts = RandomRDDs.uniformRDD(sc, 100, seed=4).getNumPartitions()
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>>> parts == sc.defaultParallelism
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True
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"""
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return callMLlibFunc("uniformRDD", sc._jsc, size, numPartitions, seed)
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@staticmethod
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def normalRDD(sc, size, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of i.i.d. samples from the standard normal
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distribution.
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To transform the distribution in the generated RDD from standard normal
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to some other normal N(mean, sigma^2), use
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C{RandomRDDs.normal(sc, n, p, seed)\
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.map(lambda v: mean + sigma * v)}
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:param sc: SparkContext used to create the RDD.
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:param size: Size of the RDD.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of float comprised of i.i.d. samples ~ N(0.0, 1.0).
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>>> x = RandomRDDs.normalRDD(sc, 1000, seed=1L)
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>>> stats = x.stats()
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>>> stats.count()
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1000L
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>>> abs(stats.mean() - 0.0) < 0.1
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True
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>>> abs(stats.stdev() - 1.0) < 0.1
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True
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"""
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return callMLlibFunc("normalRDD", sc._jsc, size, numPartitions, seed)
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@staticmethod
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def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of i.i.d. samples from the Poisson
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distribution with the input mean.
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:param sc: SparkContext used to create the RDD.
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:param mean: Mean, or lambda, for the Poisson distribution.
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:param size: Size of the RDD.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of float comprised of i.i.d. samples ~ Pois(mean).
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>>> mean = 100.0
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>>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=2L)
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>>> stats = x.stats()
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>>> stats.count()
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1000L
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>>> abs(stats.mean() - mean) < 0.5
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True
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>>> from math import sqrt
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>>> abs(stats.stdev() - sqrt(mean)) < 0.5
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True
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"""
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return callMLlibFunc("poissonRDD", sc._jsc, float(mean), size, numPartitions, seed)
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@staticmethod
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@toArray
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def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of vectors containing i.i.d. samples drawn
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from the uniform distribution U(0.0, 1.0).
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:param sc: SparkContext used to create the RDD.
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:param numRows: Number of Vectors in the RDD.
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:param numCols: Number of elements in each Vector.
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:param numPartitions: Number of partitions in the RDD.
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:param seed: Seed for the RNG that generates the seed for the generator in each partition.
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:return: RDD of Vector with vectors containing i.i.d samples ~ `U(0.0, 1.0)`.
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>>> import numpy as np
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>>> mat = np.matrix(RandomRDDs.uniformVectorRDD(sc, 10, 10).collect())
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>>> mat.shape
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(10, 10)
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>>> mat.max() <= 1.0 and mat.min() >= 0.0
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True
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>>> RandomRDDs.uniformVectorRDD(sc, 10, 10, 4).getNumPartitions()
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4
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"""
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return callMLlibFunc("uniformVectorRDD", sc._jsc, numRows, numCols, numPartitions, seed)
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@staticmethod
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@toArray
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def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of vectors containing i.i.d. samples drawn
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from the standard normal distribution.
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:param sc: SparkContext used to create the RDD.
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:param numRows: Number of Vectors in the RDD.
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:param numCols: Number of elements in each Vector.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
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:param seed: Random seed (default: a random long integer).
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:return: RDD of Vector with vectors containing i.i.d. samples ~ `N(0.0, 1.0)`.
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>>> import numpy as np
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>>> mat = np.matrix(RandomRDDs.normalVectorRDD(sc, 100, 100, seed=1L).collect())
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>>> mat.shape
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(100, 100)
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>>> abs(mat.mean() - 0.0) < 0.1
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True
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>>> abs(mat.std() - 1.0) < 0.1
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True
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"""
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return callMLlibFunc("normalVectorRDD", sc._jsc, numRows, numCols, numPartitions, seed)
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@staticmethod
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@toArray
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def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
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"""
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Generates an RDD comprised of vectors containing i.i.d. samples drawn
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from the Poisson distribution with the input mean.
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:param sc: SparkContext used to create the RDD.
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:param mean: Mean, or lambda, for the Poisson distribution.
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:param numRows: Number of Vectors in the RDD.
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:param numCols: Number of elements in each Vector.
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:param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`)
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:param seed: Random seed (default: a random long integer).
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:return: RDD of Vector with vectors containing i.i.d. samples ~ Pois(mean).
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>>> import numpy as np
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>>> mean = 100.0
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>>> rdd = RandomRDDs.poissonVectorRDD(sc, mean, 100, 100, seed=1L)
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>>> mat = np.mat(rdd.collect())
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>>> mat.shape
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(100, 100)
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>>> abs(mat.mean() - mean) < 0.5
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True
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>>> from math import sqrt
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>>> abs(mat.std() - sqrt(mean)) < 0.5
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True
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"""
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return callMLlibFunc("poissonVectorRDD", sc._jsc, float(mean), numRows, numCols,
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numPartitions, seed)
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def _test():
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import doctest
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from pyspark.context import SparkContext
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globs = globals().copy()
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# The small batch size here ensures that we see multiple batches,
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# even in these small test examples:
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globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2)
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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globs['sc'].stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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