Repository: spark
Updated Branches:
refs/heads/master 484fecbf1 -> ce0333f9a
[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 <[email protected]>
Closes #3216 from davies/random and squashes the following commits:
7ac4e8b [Davies Liu] rename random.py to rand.py
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/ce0333f9
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/ce0333f9
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/ce0333f9
Branch: refs/heads/master
Commit: ce0333f9a008348692bb9a200449d2d992e7825e
Parents: 484fecb
Author: Davies Liu <[email protected]>
Authored: Thu Nov 13 10:24:54 2014 -0800
Committer: Xiangrui Meng <[email protected]>
Committed: Thu Nov 13 10:24:54 2014 -0800
----------------------------------------------------------------------
python/pyspark/__init__.py | 10 --
python/pyspark/mllib/__init__.py | 34 ++++++
python/pyspark/mllib/feature.py | 8 +-
python/pyspark/mllib/linalg.py | 4 -
python/pyspark/mllib/rand.py | 223 ++++++++++++++++++++++++++++++++++
python/pyspark/mllib/random.py | 223 ----------------------------------
python/run-tests | 2 +-
7 files changed, 261 insertions(+), 243 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/ce0333f9/python/pyspark/__init__.py
----------------------------------------------------------------------
diff --git a/python/pyspark/__init__.py b/python/pyspark/__init__.py
index e39e651..9556e47 100644
--- a/python/pyspark/__init__.py
+++ b/python/pyspark/__init__.py
@@ -37,16 +37,6 @@ Public classes:
"""
-# The following block allows us to import python's random instead of
mllib.random for scripts in
-# mllib that depend on top level pyspark packages, which transitively depend
on python's random.
-# Since Python's import logic looks for modules in the current package first,
we eliminate
-# mllib.random as a candidate for C{import random} by removing the first
search path, the script's
-# location, in order to force the loader to look in Python's top-level modules
for C{random}.
-import sys
-s = sys.path.pop(0)
-import random
-sys.path.insert(0, s)
-
from pyspark.conf import SparkConf
from pyspark.context import SparkContext
from pyspark.rdd import RDD
http://git-wip-us.apache.org/repos/asf/spark/blob/ce0333f9/python/pyspark/mllib/__init__.py
----------------------------------------------------------------------
diff --git a/python/pyspark/mllib/__init__.py b/python/pyspark/mllib/__init__.py
index 4149f54..5030a65 100644
--- a/python/pyspark/mllib/__init__.py
+++ b/python/pyspark/mllib/__init__.py
@@ -24,3 +24,37 @@ Python bindings for MLlib.
import numpy
if numpy.version.version < '1.4':
raise Exception("MLlib requires NumPy 1.4+")
+
+__all__ = ['classification', 'clustering', 'feature', 'linalg', 'random',
+ 'recommendation', 'regression', 'stat', 'tree', 'util']
+
+import sys
+import rand as random
+random.__name__ = 'random'
+random.RandomRDDs.__module__ = __name__ + '.random'
+
+
+class RandomModuleHook(object):
+ """
+ Hook to import pyspark.mllib.random
+ """
+ fullname = __name__ + '.random'
+
+ def find_module(self, name, path=None):
+ # skip all other modules
+ if not name.startswith(self.fullname):
+ return
+ return self
+
+ def load_module(self, name):
+ if name == self.fullname:
+ return random
+
+ cname = name.rsplit('.', 1)[-1]
+ try:
+ return getattr(random, cname)
+ except AttributeError:
+ raise ImportError
+
+
+sys.meta_path.append(RandomModuleHook())
http://git-wip-us.apache.org/repos/asf/spark/blob/ce0333f9/python/pyspark/mllib/feature.py
----------------------------------------------------------------------
diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py
index 9ec2807..8cb992d 100644
--- a/python/pyspark/mllib/feature.py
+++ b/python/pyspark/mllib/feature.py
@@ -18,8 +18,11 @@
"""
Python package for feature in MLlib.
"""
+from __future__ import absolute_import
+
import sys
import warnings
+import random
from py4j.protocol import Py4JJavaError
@@ -341,8 +344,6 @@ class Word2Vec(object):
"""
Construct Word2Vec instance
"""
- import random # this can't be on the top because of mllib.random
-
self.vectorSize = 100
self.learningRate = 0.025
self.numPartitions = 1
@@ -411,8 +412,5 @@ def _test():
exit(-1)
if __name__ == "__main__":
- # remove current path from list of search paths to avoid importing
mllib.random
- # for C{import random}, which is done in an external dependency of pyspark
during doctests.
- import sys
sys.path.pop(0)
_test()
http://git-wip-us.apache.org/repos/asf/spark/blob/ce0333f9/python/pyspark/mllib/linalg.py
----------------------------------------------------------------------
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index e35202d..537b176 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -614,8 +614,4 @@ def _test():
exit(-1)
if __name__ == "__main__":
- # remove current path from list of search paths to avoid importing
mllib.random
- # for C{import random}, which is done in an external dependency of pyspark
during doctests.
- import sys
- sys.path.pop(0)
_test()
http://git-wip-us.apache.org/repos/asf/spark/blob/ce0333f9/python/pyspark/mllib/rand.py
----------------------------------------------------------------------
diff --git a/python/pyspark/mllib/rand.py b/python/pyspark/mllib/rand.py
new file mode 100644
index 0000000..cb4304f
--- /dev/null
+++ b/python/pyspark/mllib/rand.py
@@ -0,0 +1,223 @@
+#
+# 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()
http://git-wip-us.apache.org/repos/asf/spark/blob/ce0333f9/python/pyspark/mllib/random.py
----------------------------------------------------------------------
diff --git a/python/pyspark/mllib/random.py b/python/pyspark/mllib/random.py
deleted file mode 100644
index cb4304f..0000000
--- 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()
http://git-wip-us.apache.org/repos/asf/spark/blob/ce0333f9/python/run-tests
----------------------------------------------------------------------
diff --git a/python/run-tests b/python/run-tests
index a4f0cac..e66854b 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -72,7 +72,7 @@ function run_mllib_tests() {
run_test "pyspark/mllib/clustering.py"
run_test "pyspark/mllib/feature.py"
run_test "pyspark/mllib/linalg.py"
- run_test "pyspark/mllib/random.py"
+ run_test "pyspark/mllib/rand.py"
run_test "pyspark/mllib/recommendation.py"
run_test "pyspark/mllib/regression.py"
run_test "pyspark/mllib/stat.py"
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
