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|
#
# 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.
#
"""
Unit tests for PySpark; additional tests are implemented as doctests in
individual modules.
"""
from array import array
from fileinput import input
from glob import glob
import os
import re
import shutil
import subprocess
import sys
import tempfile
import time
import zipfile
if sys.version_info[:2] <= (2, 6):
import unittest2 as unittest
else:
import unittest
from pyspark.context import SparkContext
from pyspark.files import SparkFiles
from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer
from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger
from pyspark.storagelevel import StorageLevel
from pyspark.sql import SQLContext
_have_scipy = False
_have_numpy = False
try:
import scipy.sparse
_have_scipy = True
except:
# No SciPy, but that's okay, we'll skip those tests
pass
try:
import numpy as np
_have_numpy = True
except:
# No NumPy, but that's okay, we'll skip those tests
pass
SPARK_HOME = os.environ["SPARK_HOME"]
class TestMerger(unittest.TestCase):
def setUp(self):
self.N = 1 << 16
self.l = [i for i in xrange(self.N)]
self.data = zip(self.l, self.l)
self.agg = Aggregator(lambda x: [x],
lambda x, y: x.append(y) or x,
lambda x, y: x.extend(y) or x)
def test_in_memory(self):
m = InMemoryMerger(self.agg)
m.mergeValues(self.data)
self.assertEqual(sum(sum(v) for k, v in m.iteritems()),
sum(xrange(self.N)))
m = InMemoryMerger(self.agg)
m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data))
self.assertEqual(sum(sum(v) for k, v in m.iteritems()),
sum(xrange(self.N)))
def test_small_dataset(self):
m = ExternalMerger(self.agg, 1000)
m.mergeValues(self.data)
self.assertEqual(m.spills, 0)
self.assertEqual(sum(sum(v) for k, v in m.iteritems()),
sum(xrange(self.N)))
m = ExternalMerger(self.agg, 1000)
m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data))
self.assertEqual(m.spills, 0)
self.assertEqual(sum(sum(v) for k, v in m.iteritems()),
sum(xrange(self.N)))
def test_medium_dataset(self):
m = ExternalMerger(self.agg, 10)
m.mergeValues(self.data)
self.assertTrue(m.spills >= 1)
self.assertEqual(sum(sum(v) for k, v in m.iteritems()),
sum(xrange(self.N)))
m = ExternalMerger(self.agg, 10)
m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data * 3))
self.assertTrue(m.spills >= 1)
self.assertEqual(sum(sum(v) for k, v in m.iteritems()),
sum(xrange(self.N)) * 3)
def test_huge_dataset(self):
m = ExternalMerger(self.agg, 10)
m.mergeCombiners(map(lambda (k, v): (k, [str(v)]), self.data * 10))
self.assertTrue(m.spills >= 1)
self.assertEqual(sum(len(v) for k, v in m._recursive_merged_items(0)),
self.N * 10)
m._cleanup()
class SerializationTestCase(unittest.TestCase):
def test_namedtuple(self):
from collections import namedtuple
from cPickle import dumps, loads
P = namedtuple("P", "x y")
p1 = P(1, 3)
p2 = loads(dumps(p1, 2))
self.assertEquals(p1, p2)
class PySparkTestCase(unittest.TestCase):
def setUp(self):
self._old_sys_path = list(sys.path)
class_name = self.__class__.__name__
self.sc = SparkContext('local[4]', class_name, batchSize=2)
def tearDown(self):
self.sc.stop()
sys.path = self._old_sys_path
class TestCheckpoint(PySparkTestCase):
def setUp(self):
PySparkTestCase.setUp(self)
self.checkpointDir = tempfile.NamedTemporaryFile(delete=False)
os.unlink(self.checkpointDir.name)
self.sc.setCheckpointDir(self.checkpointDir.name)
def tearDown(self):
PySparkTestCase.tearDown(self)
shutil.rmtree(self.checkpointDir.name)
def test_basic_checkpointing(self):
parCollection = self.sc.parallelize([1, 2, 3, 4])
flatMappedRDD = parCollection.flatMap(lambda x: range(1, x + 1))
self.assertFalse(flatMappedRDD.isCheckpointed())
self.assertTrue(flatMappedRDD.getCheckpointFile() is None)
flatMappedRDD.checkpoint()
result = flatMappedRDD.collect()
time.sleep(1) # 1 second
self.assertTrue(flatMappedRDD.isCheckpointed())
self.assertEqual(flatMappedRDD.collect(), result)
self.assertEqual("file:" + self.checkpointDir.name,
os.path.dirname(os.path.dirname(flatMappedRDD.getCheckpointFile())))
def test_checkpoint_and_restore(self):
parCollection = self.sc.parallelize([1, 2, 3, 4])
flatMappedRDD = parCollection.flatMap(lambda x: [x])
self.assertFalse(flatMappedRDD.isCheckpointed())
self.assertTrue(flatMappedRDD.getCheckpointFile() is None)
flatMappedRDD.checkpoint()
flatMappedRDD.count() # forces a checkpoint to be computed
time.sleep(1) # 1 second
self.assertTrue(flatMappedRDD.getCheckpointFile() is not None)
recovered = self.sc._checkpointFile(flatMappedRDD.getCheckpointFile(),
flatMappedRDD._jrdd_deserializer)
self.assertEquals([1, 2, 3, 4], recovered.collect())
class TestAddFile(PySparkTestCase):
def test_add_py_file(self):
# To ensure that we're actually testing addPyFile's effects, check that
# this job fails due to `userlibrary` not being on the Python path:
# disable logging in log4j temporarily
log4j = self.sc._jvm.org.apache.log4j
old_level = log4j.LogManager.getRootLogger().getLevel()
log4j.LogManager.getRootLogger().setLevel(log4j.Level.FATAL)
def func(x):
from userlibrary import UserClass
return UserClass().hello()
self.assertRaises(Exception,
self.sc.parallelize(range(2)).map(func).first)
log4j.LogManager.getRootLogger().setLevel(old_level)
# Add the file, so the job should now succeed:
path = os.path.join(SPARK_HOME, "python/test_support/userlibrary.py")
self.sc.addPyFile(path)
res = self.sc.parallelize(range(2)).map(func).first()
self.assertEqual("Hello World!", res)
def test_add_file_locally(self):
path = os.path.join(SPARK_HOME, "python/test_support/hello.txt")
self.sc.addFile(path)
download_path = SparkFiles.get("hello.txt")
self.assertNotEqual(path, download_path)
with open(download_path) as test_file:
self.assertEquals("Hello World!\n", test_file.readline())
def test_add_py_file_locally(self):
# To ensure that we're actually testing addPyFile's effects, check that
# this fails due to `userlibrary` not being on the Python path:
def func():
from userlibrary import UserClass
self.assertRaises(ImportError, func)
path = os.path.join(SPARK_HOME, "python/test_support/userlibrary.py")
self.sc.addFile(path)
from userlibrary import UserClass
self.assertEqual("Hello World!", UserClass().hello())
def test_add_egg_file_locally(self):
# To ensure that we're actually testing addPyFile's effects, check that
# this fails due to `userlibrary` not being on the Python path:
def func():
from userlib import UserClass
self.assertRaises(ImportError, func)
path = os.path.join(SPARK_HOME, "python/test_support/userlib-0.1-py2.7.egg")
self.sc.addPyFile(path)
from userlib import UserClass
self.assertEqual("Hello World from inside a package!", UserClass().hello())
class TestRDDFunctions(PySparkTestCase):
def test_failed_sparkcontext_creation(self):
# Regression test for SPARK-1550
self.sc.stop()
self.assertRaises(Exception, lambda: SparkContext("an-invalid-master-name"))
self.sc = SparkContext("local")
def test_save_as_textfile_with_unicode(self):
# Regression test for SPARK-970
x = u"\u00A1Hola, mundo!"
data = self.sc.parallelize([x])
tempFile = tempfile.NamedTemporaryFile(delete=True)
tempFile.close()
data.saveAsTextFile(tempFile.name)
raw_contents = ''.join(input(glob(tempFile.name + "/part-0000*")))
self.assertEqual(x, unicode(raw_contents.strip(), "utf-8"))
def test_save_as_textfile_with_utf8(self):
x = u"\u00A1Hola, mundo!"
data = self.sc.parallelize([x.encode("utf-8")])
tempFile = tempfile.NamedTemporaryFile(delete=True)
tempFile.close()
data.saveAsTextFile(tempFile.name)
raw_contents = ''.join(input(glob(tempFile.name + "/part-0000*")))
self.assertEqual(x, unicode(raw_contents.strip(), "utf-8"))
def test_transforming_cartesian_result(self):
# Regression test for SPARK-1034
rdd1 = self.sc.parallelize([1, 2])
rdd2 = self.sc.parallelize([3, 4])
cart = rdd1.cartesian(rdd2)
result = cart.map(lambda (x, y): x + y).collect()
def test_transforming_pickle_file(self):
# Regression test for SPARK-2601
data = self.sc.parallelize(["Hello", "World!"])
tempFile = tempfile.NamedTemporaryFile(delete=True)
tempFile.close()
data.saveAsPickleFile(tempFile.name)
pickled_file = self.sc.pickleFile(tempFile.name)
pickled_file.map(lambda x: x).collect()
def test_cartesian_on_textfile(self):
# Regression test for
path = os.path.join(SPARK_HOME, "python/test_support/hello.txt")
a = self.sc.textFile(path)
result = a.cartesian(a).collect()
(x, y) = result[0]
self.assertEqual("Hello World!", x.strip())
self.assertEqual("Hello World!", y.strip())
def test_deleting_input_files(self):
# Regression test for SPARK-1025
tempFile = tempfile.NamedTemporaryFile(delete=False)
tempFile.write("Hello World!")
tempFile.close()
data = self.sc.textFile(tempFile.name)
filtered_data = data.filter(lambda x: True)
self.assertEqual(1, filtered_data.count())
os.unlink(tempFile.name)
self.assertRaises(Exception, lambda: filtered_data.count())
def testAggregateByKey(self):
data = self.sc.parallelize([(1, 1), (1, 1), (3, 2), (5, 1), (5, 3)], 2)
def seqOp(x, y):
x.add(y)
return x
def combOp(x, y):
x |= y
return x
sets = dict(data.aggregateByKey(set(), seqOp, combOp).collect())
self.assertEqual(3, len(sets))
self.assertEqual(set([1]), sets[1])
self.assertEqual(set([2]), sets[3])
self.assertEqual(set([1, 3]), sets[5])
def test_itemgetter(self):
rdd = self.sc.parallelize([range(10)])
from operator import itemgetter
self.assertEqual([1], rdd.map(itemgetter(1)).collect())
self.assertEqual([(2, 3)], rdd.map(itemgetter(2, 3)).collect())
def test_namedtuple_in_rdd(self):
from collections import namedtuple
Person = namedtuple("Person", "id firstName lastName")
jon = Person(1, "Jon", "Doe")
jane = Person(2, "Jane", "Doe")
theDoes = self.sc.parallelize([jon, jane])
self.assertEquals([jon, jane], theDoes.collect())
def test_large_broadcast(self):
N = 100000
data = [[float(i) for i in range(300)] for i in range(N)]
bdata = self.sc.broadcast(data) # 270MB
m = self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum()
self.assertEquals(N, m)
def test_zip_with_different_serializers(self):
a = self.sc.parallelize(range(5))
b = self.sc.parallelize(range(100, 105))
self.assertEqual(a.zip(b).collect(), [(0, 100), (1, 101), (2, 102), (3, 103), (4, 104)])
a = a._reserialize(BatchedSerializer(PickleSerializer(), 2))
b = b._reserialize(MarshalSerializer())
self.assertEqual(a.zip(b).collect(), [(0, 100), (1, 101), (2, 102), (3, 103), (4, 104)])
def test_zip_with_different_number_of_items(self):
a = self.sc.parallelize(range(5), 2)
# different number of partitions
b = self.sc.parallelize(range(100, 106), 3)
self.assertRaises(ValueError, lambda: a.zip(b))
# different number of batched items in JVM
b = self.sc.parallelize(range(100, 104), 2)
self.assertRaises(Exception, lambda: a.zip(b).count())
# different number of items in one pair
b = self.sc.parallelize(range(100, 106), 2)
self.assertRaises(Exception, lambda: a.zip(b).count())
# same total number of items, but different distributions
a = self.sc.parallelize([2, 3], 2).flatMap(range)
b = self.sc.parallelize([3, 2], 2).flatMap(range)
self.assertEquals(a.count(), b.count())
self.assertRaises(Exception, lambda: a.zip(b).count())
def test_histogram(self):
# empty
rdd = self.sc.parallelize([])
self.assertEquals([0], rdd.histogram([0, 10])[1])
self.assertEquals([0, 0], rdd.histogram([0, 4, 10])[1])
self.assertRaises(ValueError, lambda: rdd.histogram(1))
# out of range
rdd = self.sc.parallelize([10.01, -0.01])
self.assertEquals([0], rdd.histogram([0, 10])[1])
self.assertEquals([0, 0], rdd.histogram((0, 4, 10))[1])
# in range with one bucket
rdd = self.sc.parallelize(range(1, 5))
self.assertEquals([4], rdd.histogram([0, 10])[1])
self.assertEquals([3, 1], rdd.histogram([0, 4, 10])[1])
# in range with one bucket exact match
self.assertEquals([4], rdd.histogram([1, 4])[1])
# out of range with two buckets
rdd = self.sc.parallelize([10.01, -0.01])
self.assertEquals([0, 0], rdd.histogram([0, 5, 10])[1])
# out of range with two uneven buckets
rdd = self.sc.parallelize([10.01, -0.01])
self.assertEquals([0, 0], rdd.histogram([0, 4, 10])[1])
# in range with two buckets
rdd = self.sc.parallelize([1, 2, 3, 5, 6])
self.assertEquals([3, 2], rdd.histogram([0, 5, 10])[1])
# in range with two bucket and None
rdd = self.sc.parallelize([1, 2, 3, 5, 6, None, float('nan')])
self.assertEquals([3, 2], rdd.histogram([0, 5, 10])[1])
# in range with two uneven buckets
rdd = self.sc.parallelize([1, 2, 3, 5, 6])
self.assertEquals([3, 2], rdd.histogram([0, 5, 11])[1])
# mixed range with two uneven buckets
rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.0, 11.01])
self.assertEquals([4, 3], rdd.histogram([0, 5, 11])[1])
# mixed range with four uneven buckets
rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0, 199.0, 200.0, 200.1])
self.assertEquals([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1])
# mixed range with uneven buckets and NaN
rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0,
199.0, 200.0, 200.1, None, float('nan')])
self.assertEquals([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1])
# out of range with infinite buckets
rdd = self.sc.parallelize([10.01, -0.01, float('nan'), float("inf")])
self.assertEquals([1, 2], rdd.histogram([float('-inf'), 0, float('inf')])[1])
# invalid buckets
self.assertRaises(ValueError, lambda: rdd.histogram([]))
self.assertRaises(ValueError, lambda: rdd.histogram([1]))
self.assertRaises(ValueError, lambda: rdd.histogram(0))
self.assertRaises(TypeError, lambda: rdd.histogram({}))
# without buckets
rdd = self.sc.parallelize(range(1, 5))
self.assertEquals(([1, 4], [4]), rdd.histogram(1))
# without buckets single element
rdd = self.sc.parallelize([1])
self.assertEquals(([1, 1], [1]), rdd.histogram(1))
# without bucket no range
rdd = self.sc.parallelize([1] * 4)
self.assertEquals(([1, 1], [4]), rdd.histogram(1))
# without buckets basic two
rdd = self.sc.parallelize(range(1, 5))
self.assertEquals(([1, 2.5, 4], [2, 2]), rdd.histogram(2))
# without buckets with more requested than elements
rdd = self.sc.parallelize([1, 2])
buckets = [1 + 0.2 * i for i in range(6)]
hist = [1, 0, 0, 0, 1]
self.assertEquals((buckets, hist), rdd.histogram(5))
# invalid RDDs
rdd = self.sc.parallelize([1, float('inf')])
self.assertRaises(ValueError, lambda: rdd.histogram(2))
rdd = self.sc.parallelize([float('nan')])
self.assertRaises(ValueError, lambda: rdd.histogram(2))
# string
rdd = self.sc.parallelize(["ab", "ac", "b", "bd", "ef"], 2)
self.assertEquals([2, 2], rdd.histogram(["a", "b", "c"])[1])
self.assertEquals((["ab", "ef"], [5]), rdd.histogram(1))
self.assertRaises(TypeError, lambda: rdd.histogram(2))
# mixed RDD
rdd = self.sc.parallelize([1, 4, "ab", "ac", "b"], 2)
self.assertEquals([1, 1], rdd.histogram([0, 4, 10])[1])
self.assertEquals([2, 1], rdd.histogram(["a", "b", "c"])[1])
self.assertEquals(([1, "b"], [5]), rdd.histogram(1))
self.assertRaises(TypeError, lambda: rdd.histogram(2))
def test_sample(self):
rdd = self.sc.parallelize(range(0, 100), 4)
wo = rdd.sample(False, 0.1, 2).collect()
wo_dup = rdd.sample(False, 0.1, 2).collect()
self.assertSetEqual(set(wo), set(wo_dup))
wr = rdd.sample(True, 0.2, 5).collect()
wr_dup = rdd.sample(True, 0.2, 5).collect()
self.assertSetEqual(set(wr), set(wr_dup))
wo_s10 = rdd.sample(False, 0.3, 10).collect()
wo_s20 = rdd.sample(False, 0.3, 20).collect()
self.assertNotEqual(set(wo_s10), set(wo_s20))
wr_s11 = rdd.sample(True, 0.4, 11).collect()
wr_s21 = rdd.sample(True, 0.4, 21).collect()
self.assertNotEqual(set(wr_s11), set(wr_s21))
class TestSQL(PySparkTestCase):
def setUp(self):
PySparkTestCase.setUp(self)
self.sqlCtx = SQLContext(self.sc)
def test_basic_functions(self):
rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}'])
srdd = self.sqlCtx.jsonRDD(rdd)
srdd.count()
srdd.collect()
srdd.schemaString()
srdd.schema()
# cache and checkpoint
self.assertFalse(srdd.is_cached)
srdd.persist(StorageLevel.MEMORY_ONLY_SER)
srdd.unpersist()
srdd.cache()
self.assertTrue(srdd.is_cached)
self.assertFalse(srdd.isCheckpointed())
self.assertEqual(None, srdd.getCheckpointFile())
srdd = srdd.coalesce(2, True)
srdd = srdd.repartition(3)
srdd = srdd.distinct()
srdd.intersection(srdd)
self.assertEqual(2, srdd.count())
srdd.registerTempTable("temp")
srdd = self.sqlCtx.sql("select foo from temp")
srdd.count()
srdd.collect()
class TestIO(PySparkTestCase):
def test_stdout_redirection(self):
import subprocess
def func(x):
subprocess.check_call('ls', shell=True)
self.sc.parallelize([1]).foreach(func)
class TestInputFormat(PySparkTestCase):
def setUp(self):
PySparkTestCase.setUp(self)
self.tempdir = tempfile.NamedTemporaryFile(delete=False)
os.unlink(self.tempdir.name)
self.sc._jvm.WriteInputFormatTestDataGenerator.generateData(self.tempdir.name, self.sc._jsc)
def tearDown(self):
PySparkTestCase.tearDown(self)
shutil.rmtree(self.tempdir.name)
def test_sequencefiles(self):
basepath = self.tempdir.name
ints = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfint/",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.Text").collect())
ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')]
self.assertEqual(ints, ei)
doubles = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfdouble/",
"org.apache.hadoop.io.DoubleWritable",
"org.apache.hadoop.io.Text").collect())
ed = [(1.0, u'aa'), (1.0, u'aa'), (2.0, u'aa'), (2.0, u'bb'), (2.0, u'bb'), (3.0, u'cc')]
self.assertEqual(doubles, ed)
bytes = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfbytes/",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.BytesWritable").collect())
ebs = [(1, bytearray('aa', 'utf-8')),
(1, bytearray('aa', 'utf-8')),
(2, bytearray('aa', 'utf-8')),
(2, bytearray('bb', 'utf-8')),
(2, bytearray('bb', 'utf-8')),
(3, bytearray('cc', 'utf-8'))]
self.assertEqual(bytes, ebs)
text = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sftext/",
"org.apache.hadoop.io.Text",
"org.apache.hadoop.io.Text").collect())
et = [(u'1', u'aa'),
(u'1', u'aa'),
(u'2', u'aa'),
(u'2', u'bb'),
(u'2', u'bb'),
(u'3', u'cc')]
self.assertEqual(text, et)
bools = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfbool/",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.BooleanWritable").collect())
eb = [(1, False), (1, True), (2, False), (2, False), (2, True), (3, True)]
self.assertEqual(bools, eb)
nulls = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfnull/",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.BooleanWritable").collect())
en = [(1, None), (1, None), (2, None), (2, None), (2, None), (3, None)]
self.assertEqual(nulls, en)
maps = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfmap/",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.MapWritable").collect())
em = [(1, {}),
(1, {3.0: u'bb'}),
(2, {1.0: u'aa'}),
(2, {1.0: u'cc'}),
(3, {2.0: u'dd'})]
self.assertEqual(maps, em)
# arrays get pickled to tuples by default
tuples = sorted(self.sc.sequenceFile(
basepath + "/sftestdata/sfarray/",
"org.apache.hadoop.io.IntWritable",
"org.apache.spark.api.python.DoubleArrayWritable").collect())
et = [(1, ()),
(2, (3.0, 4.0, 5.0)),
(3, (4.0, 5.0, 6.0))]
self.assertEqual(tuples, et)
# with custom converters, primitive arrays can stay as arrays
arrays = sorted(self.sc.sequenceFile(
basepath + "/sftestdata/sfarray/",
"org.apache.hadoop.io.IntWritable",
"org.apache.spark.api.python.DoubleArrayWritable",
valueConverter="org.apache.spark.api.python.WritableToDoubleArrayConverter").collect())
ea = [(1, array('d')),
(2, array('d', [3.0, 4.0, 5.0])),
(3, array('d', [4.0, 5.0, 6.0]))]
self.assertEqual(arrays, ea)
clazz = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfclass/",
"org.apache.hadoop.io.Text",
"org.apache.spark.api.python.TestWritable").collect())
ec = (u'1',
{u'__class__': u'org.apache.spark.api.python.TestWritable',
u'double': 54.0, u'int': 123, u'str': u'test1'})
self.assertEqual(clazz[0], ec)
unbatched_clazz = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfclass/",
"org.apache.hadoop.io.Text",
"org.apache.spark.api.python.TestWritable",
batchSize=1).collect())
self.assertEqual(unbatched_clazz[0], ec)
def test_oldhadoop(self):
basepath = self.tempdir.name
ints = sorted(self.sc.hadoopFile(basepath + "/sftestdata/sfint/",
"org.apache.hadoop.mapred.SequenceFileInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.Text").collect())
ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')]
self.assertEqual(ints, ei)
hellopath = os.path.join(SPARK_HOME, "python/test_support/hello.txt")
oldconf = {"mapred.input.dir": hellopath}
hello = self.sc.hadoopRDD("org.apache.hadoop.mapred.TextInputFormat",
"org.apache.hadoop.io.LongWritable",
"org.apache.hadoop.io.Text",
conf=oldconf).collect()
result = [(0, u'Hello World!')]
self.assertEqual(hello, result)
def test_newhadoop(self):
basepath = self.tempdir.name
ints = sorted(self.sc.newAPIHadoopFile(
basepath + "/sftestdata/sfint/",
"org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.Text").collect())
ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')]
self.assertEqual(ints, ei)
hellopath = os.path.join(SPARK_HOME, "python/test_support/hello.txt")
newconf = {"mapred.input.dir": hellopath}
hello = self.sc.newAPIHadoopRDD("org.apache.hadoop.mapreduce.lib.input.TextInputFormat",
"org.apache.hadoop.io.LongWritable",
"org.apache.hadoop.io.Text",
conf=newconf).collect()
result = [(0, u'Hello World!')]
self.assertEqual(hello, result)
def test_newolderror(self):
basepath = self.tempdir.name
self.assertRaises(Exception, lambda: self.sc.hadoopFile(
basepath + "/sftestdata/sfint/",
"org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.Text"))
self.assertRaises(Exception, lambda: self.sc.newAPIHadoopFile(
basepath + "/sftestdata/sfint/",
"org.apache.hadoop.mapred.SequenceFileInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.Text"))
def test_bad_inputs(self):
basepath = self.tempdir.name
self.assertRaises(Exception, lambda: self.sc.sequenceFile(
basepath + "/sftestdata/sfint/",
"org.apache.hadoop.io.NotValidWritable",
"org.apache.hadoop.io.Text"))
self.assertRaises(Exception, lambda: self.sc.hadoopFile(
basepath + "/sftestdata/sfint/",
"org.apache.hadoop.mapred.NotValidInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.Text"))
self.assertRaises(Exception, lambda: self.sc.newAPIHadoopFile(
basepath + "/sftestdata/sfint/",
"org.apache.hadoop.mapreduce.lib.input.NotValidInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.Text"))
def test_converters(self):
# use of custom converters
basepath = self.tempdir.name
maps = sorted(self.sc.sequenceFile(
basepath + "/sftestdata/sfmap/",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.MapWritable",
keyConverter="org.apache.spark.api.python.TestInputKeyConverter",
valueConverter="org.apache.spark.api.python.TestInputValueConverter").collect())
em = [(u'\x01', []),
(u'\x01', [3.0]),
(u'\x02', [1.0]),
(u'\x02', [1.0]),
(u'\x03', [2.0])]
self.assertEqual(maps, em)
class TestOutputFormat(PySparkTestCase):
def setUp(self):
PySparkTestCase.setUp(self)
self.tempdir = tempfile.NamedTemporaryFile(delete=False)
os.unlink(self.tempdir.name)
def tearDown(self):
PySparkTestCase.tearDown(self)
shutil.rmtree(self.tempdir.name, ignore_errors=True)
def test_sequencefiles(self):
basepath = self.tempdir.name
ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')]
self.sc.parallelize(ei).saveAsSequenceFile(basepath + "/sfint/")
ints = sorted(self.sc.sequenceFile(basepath + "/sfint/").collect())
self.assertEqual(ints, ei)
ed = [(1.0, u'aa'), (1.0, u'aa'), (2.0, u'aa'), (2.0, u'bb'), (2.0, u'bb'), (3.0, u'cc')]
self.sc.parallelize(ed).saveAsSequenceFile(basepath + "/sfdouble/")
doubles = sorted(self.sc.sequenceFile(basepath + "/sfdouble/").collect())
self.assertEqual(doubles, ed)
ebs = [(1, bytearray(b'\x00\x07spam\x08')), (2, bytearray(b'\x00\x07spam\x08'))]
self.sc.parallelize(ebs).saveAsSequenceFile(basepath + "/sfbytes/")
bytes = sorted(self.sc.sequenceFile(basepath + "/sfbytes/").collect())
self.assertEqual(bytes, ebs)
et = [(u'1', u'aa'),
(u'2', u'bb'),
(u'3', u'cc')]
self.sc.parallelize(et).saveAsSequenceFile(basepath + "/sftext/")
text = sorted(self.sc.sequenceFile(basepath + "/sftext/").collect())
self.assertEqual(text, et)
eb = [(1, False), (1, True), (2, False), (2, False), (2, True), (3, True)]
self.sc.parallelize(eb).saveAsSequenceFile(basepath + "/sfbool/")
bools = sorted(self.sc.sequenceFile(basepath + "/sfbool/").collect())
self.assertEqual(bools, eb)
en = [(1, None), (1, None), (2, None), (2, None), (2, None), (3, None)]
self.sc.parallelize(en).saveAsSequenceFile(basepath + "/sfnull/")
nulls = sorted(self.sc.sequenceFile(basepath + "/sfnull/").collect())
self.assertEqual(nulls, en)
em = [(1, {}),
(1, {3.0: u'bb'}),
(2, {1.0: u'aa'}),
(2, {1.0: u'cc'}),
(3, {2.0: u'dd'})]
self.sc.parallelize(em).saveAsSequenceFile(basepath + "/sfmap/")
maps = sorted(self.sc.sequenceFile(basepath + "/sfmap/").collect())
self.assertEqual(maps, em)
def test_oldhadoop(self):
basepath = self.tempdir.name
dict_data = [(1, {}),
(1, {"row1": 1.0}),
(2, {"row2": 2.0})]
self.sc.parallelize(dict_data).saveAsHadoopFile(
basepath + "/oldhadoop/",
"org.apache.hadoop.mapred.SequenceFileOutputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.MapWritable")
result = sorted(self.sc.hadoopFile(
basepath + "/oldhadoop/",
"org.apache.hadoop.mapred.SequenceFileInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.MapWritable").collect())
self.assertEqual(result, dict_data)
conf = {
"mapred.output.format.class": "org.apache.hadoop.mapred.SequenceFileOutputFormat",
"mapred.output.key.class": "org.apache.hadoop.io.IntWritable",
"mapred.output.value.class": "org.apache.hadoop.io.MapWritable",
"mapred.output.dir": basepath + "/olddataset/"
}
self.sc.parallelize(dict_data).saveAsHadoopDataset(conf)
input_conf = {"mapred.input.dir": basepath + "/olddataset/"}
old_dataset = sorted(self.sc.hadoopRDD(
"org.apache.hadoop.mapred.SequenceFileInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.MapWritable",
conf=input_conf).collect())
self.assertEqual(old_dataset, dict_data)
@unittest.skipIf(sys.version_info[:2] <= (2, 6), "Skipped on 2.6 until SPARK-2951 is fixed")
def test_newhadoop(self):
basepath = self.tempdir.name
# use custom ArrayWritable types and converters to handle arrays
array_data = [(1, array('d')),
(1, array('d', [1.0, 2.0, 3.0])),
(2, array('d', [3.0, 4.0, 5.0]))]
self.sc.parallelize(array_data).saveAsNewAPIHadoopFile(
basepath + "/newhadoop/",
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.spark.api.python.DoubleArrayWritable",
valueConverter="org.apache.spark.api.python.DoubleArrayToWritableConverter")
result = sorted(self.sc.newAPIHadoopFile(
basepath + "/newhadoop/",
"org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.spark.api.python.DoubleArrayWritable",
valueConverter="org.apache.spark.api.python.WritableToDoubleArrayConverter").collect())
self.assertEqual(result, array_data)
conf = {
"mapreduce.outputformat.class":
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat",
"mapred.output.key.class": "org.apache.hadoop.io.IntWritable",
"mapred.output.value.class": "org.apache.spark.api.python.DoubleArrayWritable",
"mapred.output.dir": basepath + "/newdataset/"
}
self.sc.parallelize(array_data).saveAsNewAPIHadoopDataset(
conf,
valueConverter="org.apache.spark.api.python.DoubleArrayToWritableConverter")
input_conf = {"mapred.input.dir": basepath + "/newdataset/"}
new_dataset = sorted(self.sc.newAPIHadoopRDD(
"org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.spark.api.python.DoubleArrayWritable",
valueConverter="org.apache.spark.api.python.WritableToDoubleArrayConverter",
conf=input_conf).collect())
self.assertEqual(new_dataset, array_data)
def test_newolderror(self):
basepath = self.tempdir.name
rdd = self.sc.parallelize(range(1, 4)).map(lambda x: (x, "a" * x))
self.assertRaises(Exception, lambda: rdd.saveAsHadoopFile(
basepath + "/newolderror/saveAsHadoopFile/",
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat"))
self.assertRaises(Exception, lambda: rdd.saveAsNewAPIHadoopFile(
basepath + "/newolderror/saveAsNewAPIHadoopFile/",
"org.apache.hadoop.mapred.SequenceFileOutputFormat"))
def test_bad_inputs(self):
basepath = self.tempdir.name
rdd = self.sc.parallelize(range(1, 4)).map(lambda x: (x, "a" * x))
self.assertRaises(Exception, lambda: rdd.saveAsHadoopFile(
basepath + "/badinputs/saveAsHadoopFile/",
"org.apache.hadoop.mapred.NotValidOutputFormat"))
self.assertRaises(Exception, lambda: rdd.saveAsNewAPIHadoopFile(
basepath + "/badinputs/saveAsNewAPIHadoopFile/",
"org.apache.hadoop.mapreduce.lib.output.NotValidOutputFormat"))
def test_converters(self):
# use of custom converters
basepath = self.tempdir.name
data = [(1, {3.0: u'bb'}),
(2, {1.0: u'aa'}),
(3, {2.0: u'dd'})]
self.sc.parallelize(data).saveAsNewAPIHadoopFile(
basepath + "/converters/",
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat",
keyConverter="org.apache.spark.api.python.TestOutputKeyConverter",
valueConverter="org.apache.spark.api.python.TestOutputValueConverter")
converted = sorted(self.sc.sequenceFile(basepath + "/converters/").collect())
expected = [(u'1', 3.0),
(u'2', 1.0),
(u'3', 2.0)]
self.assertEqual(converted, expected)
def test_reserialization(self):
basepath = self.tempdir.name
x = range(1, 5)
y = range(1001, 1005)
data = zip(x, y)
rdd = self.sc.parallelize(x).zip(self.sc.parallelize(y))
rdd.saveAsSequenceFile(basepath + "/reserialize/sequence")
result1 = sorted(self.sc.sequenceFile(basepath + "/reserialize/sequence").collect())
self.assertEqual(result1, data)
rdd.saveAsHadoopFile(
basepath + "/reserialize/hadoop",
"org.apache.hadoop.mapred.SequenceFileOutputFormat")
result2 = sorted(self.sc.sequenceFile(basepath + "/reserialize/hadoop").collect())
self.assertEqual(result2, data)
rdd.saveAsNewAPIHadoopFile(
basepath + "/reserialize/newhadoop",
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat")
result3 = sorted(self.sc.sequenceFile(basepath + "/reserialize/newhadoop").collect())
self.assertEqual(result3, data)
conf4 = {
"mapred.output.format.class": "org.apache.hadoop.mapred.SequenceFileOutputFormat",
"mapred.output.key.class": "org.apache.hadoop.io.IntWritable",
"mapred.output.value.class": "org.apache.hadoop.io.IntWritable",
"mapred.output.dir": basepath + "/reserialize/dataset"}
rdd.saveAsHadoopDataset(conf4)
result4 = sorted(self.sc.sequenceFile(basepath + "/reserialize/dataset").collect())
self.assertEqual(result4, data)
conf5 = {"mapreduce.outputformat.class":
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat",
"mapred.output.key.class": "org.apache.hadoop.io.IntWritable",
"mapred.output.value.class": "org.apache.hadoop.io.IntWritable",
"mapred.output.dir": basepath + "/reserialize/newdataset"}
rdd.saveAsNewAPIHadoopDataset(conf5)
result5 = sorted(self.sc.sequenceFile(basepath + "/reserialize/newdataset").collect())
self.assertEqual(result5, data)
def test_unbatched_save_and_read(self):
basepath = self.tempdir.name
ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')]
self.sc.parallelize(ei, numSlices=len(ei)).saveAsSequenceFile(
basepath + "/unbatched/")
unbatched_sequence = sorted(self.sc.sequenceFile(
basepath + "/unbatched/",
batchSize=1).collect())
self.assertEqual(unbatched_sequence, ei)
unbatched_hadoopFile = sorted(self.sc.hadoopFile(
basepath + "/unbatched/",
"org.apache.hadoop.mapred.SequenceFileInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.Text",
batchSize=1).collect())
self.assertEqual(unbatched_hadoopFile, ei)
unbatched_newAPIHadoopFile = sorted(self.sc.newAPIHadoopFile(
basepath + "/unbatched/",
"org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.Text",
batchSize=1).collect())
self.assertEqual(unbatched_newAPIHadoopFile, ei)
oldconf = {"mapred.input.dir": basepath + "/unbatched/"}
unbatched_hadoopRDD = sorted(self.sc.hadoopRDD(
"org.apache.hadoop.mapred.SequenceFileInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.Text",
conf=oldconf,
batchSize=1).collect())
self.assertEqual(unbatched_hadoopRDD, ei)
newconf = {"mapred.input.dir": basepath + "/unbatched/"}
unbatched_newAPIHadoopRDD = sorted(self.sc.newAPIHadoopRDD(
"org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
"org.apache.hadoop.io.IntWritable",
"org.apache.hadoop.io.Text",
conf=newconf,
batchSize=1).collect())
self.assertEqual(unbatched_newAPIHadoopRDD, ei)
def test_malformed_RDD(self):
basepath = self.tempdir.name
# non-batch-serialized RDD[[(K, V)]] should be rejected
data = [[(1, "a")], [(2, "aa")], [(3, "aaa")]]
rdd = self.sc.parallelize(data, numSlices=len(data))
self.assertRaises(Exception, lambda: rdd.saveAsSequenceFile(
basepath + "/malformed/sequence"))
class TestDaemon(unittest.TestCase):
def connect(self, port):
from socket import socket, AF_INET, SOCK_STREAM
sock = socket(AF_INET, SOCK_STREAM)
sock.connect(('127.0.0.1', port))
# send a split index of -1 to shutdown the worker
sock.send("\xFF\xFF\xFF\xFF")
sock.close()
return True
def do_termination_test(self, terminator):
from subprocess import Popen, PIPE
from errno import ECONNREFUSED
# start daemon
daemon_path = os.path.join(os.path.dirname(__file__), "daemon.py")
daemon = Popen([sys.executable, daemon_path], stdin=PIPE, stdout=PIPE)
# read the port number
port = read_int(daemon.stdout)
# daemon should accept connections
self.assertTrue(self.connect(port))
# request shutdown
terminator(daemon)
time.sleep(1)
# daemon should no longer accept connections
try:
self.connect(port)
except EnvironmentError as exception:
self.assertEqual(exception.errno, ECONNREFUSED)
else:
self.fail("Expected EnvironmentError to be raised")
def test_termination_stdin(self):
"""Ensure that daemon and workers terminate when stdin is closed."""
self.do_termination_test(lambda daemon: daemon.stdin.close())
def test_termination_sigterm(self):
"""Ensure that daemon and workers terminate on SIGTERM."""
from signal import SIGTERM
self.do_termination_test(lambda daemon: os.kill(daemon.pid, SIGTERM))
class TestWorker(PySparkTestCase):
def test_cancel_task(self):
temp = tempfile.NamedTemporaryFile(delete=True)
temp.close()
path = temp.name
def sleep(x):
import os
import time
with open(path, 'w') as f:
f.write("%d %d" % (os.getppid(), os.getpid()))
time.sleep(100)
# start job in background thread
def run():
self.sc.parallelize(range(1)).foreach(sleep)
import threading
t = threading.Thread(target=run)
t.daemon = True
t.start()
daemon_pid, worker_pid = 0, 0
while True:
if os.path.exists(path):
data = open(path).read().split(' ')
daemon_pid, worker_pid = map(int, data)
break
time.sleep(0.1)
# cancel jobs
self.sc.cancelAllJobs()
t.join()
for i in range(50):
try:
os.kill(worker_pid, 0)
time.sleep(0.1)
except OSError:
break # worker was killed
else:
self.fail("worker has not been killed after 5 seconds")
try:
os.kill(daemon_pid, 0)
except OSError:
self.fail("daemon had been killed")
def test_fd_leak(self):
N = 1100 # fd limit is 1024 by default
rdd = self.sc.parallelize(range(N), N)
self.assertEquals(N, rdd.count())
class TestSparkSubmit(unittest.TestCase):
def setUp(self):
self.programDir = tempfile.mkdtemp()
self.sparkSubmit = os.path.join(os.environ.get("SPARK_HOME"), "bin", "spark-submit")
def tearDown(self):
shutil.rmtree(self.programDir)
def createTempFile(self, name, content):
"""
Create a temp file with the given name and content and return its path.
Strips leading spaces from content up to the first '|' in each line.
"""
pattern = re.compile(r'^ *\|', re.MULTILINE)
content = re.sub(pattern, '', content.strip())
path = os.path.join(self.programDir, name)
with open(path, "w") as f:
f.write(content)
return path
def createFileInZip(self, name, content):
"""
Create a zip archive containing a file with the given content and return its path.
Strips leading spaces from content up to the first '|' in each line.
"""
pattern = re.compile(r'^ *\|', re.MULTILINE)
content = re.sub(pattern, '', content.strip())
path = os.path.join(self.programDir, name + ".zip")
zip = zipfile.ZipFile(path, 'w')
zip.writestr(name, content)
zip.close()
return path
def test_single_script(self):
"""Submit and test a single script file"""
script = self.createTempFile("test.py", """
|from pyspark import SparkContext
|
|sc = SparkContext()
|print sc.parallelize([1, 2, 3]).map(lambda x: x * 2).collect()
""")
proc = subprocess.Popen([self.sparkSubmit, script], stdout=subprocess.PIPE)
out, err = proc.communicate()
self.assertEqual(0, proc.returncode)
self.assertIn("[2, 4, 6]", out)
def test_script_with_local_functions(self):
"""Submit and test a single script file calling a global function"""
script = self.createTempFile("test.py", """
|from pyspark import SparkContext
|
|def foo(x):
| return x * 3
|
|sc = SparkContext()
|print sc.parallelize([1, 2, 3]).map(foo).collect()
""")
proc = subprocess.Popen([self.sparkSubmit, script], stdout=subprocess.PIPE)
out, err = proc.communicate()
self.assertEqual(0, proc.returncode)
self.assertIn("[3, 6, 9]", out)
def test_module_dependency(self):
"""Submit and test a script with a dependency on another module"""
script = self.createTempFile("test.py", """
|from pyspark import SparkContext
|from mylib import myfunc
|
|sc = SparkContext()
|print sc.parallelize([1, 2, 3]).map(myfunc).collect()
""")
zip = self.createFileInZip("mylib.py", """
|def myfunc(x):
| return x + 1
""")
proc = subprocess.Popen([self.sparkSubmit, "--py-files", zip, script],
stdout=subprocess.PIPE)
out, err = proc.communicate()
self.assertEqual(0, proc.returncode)
self.assertIn("[2, 3, 4]", out)
def test_module_dependency_on_cluster(self):
"""Submit and test a script with a dependency on another module on a cluster"""
script = self.createTempFile("test.py", """
|from pyspark import SparkContext
|from mylib import myfunc
|
|sc = SparkContext()
|print sc.parallelize([1, 2, 3]).map(myfunc).collect()
""")
zip = self.createFileInZip("mylib.py", """
|def myfunc(x):
| return x + 1
""")
proc = subprocess.Popen([self.sparkSubmit, "--py-files", zip, "--master",
"local-cluster[1,1,512]", script],
stdout=subprocess.PIPE)
out, err = proc.communicate()
self.assertEqual(0, proc.returncode)
self.assertIn("[2, 3, 4]", out)
def test_single_script_on_cluster(self):
"""Submit and test a single script on a cluster"""
script = self.createTempFile("test.py", """
|from pyspark import SparkContext
|
|def foo(x):
| return x * 2
|
|sc = SparkContext()
|print sc.parallelize([1, 2, 3]).map(foo).collect()
""")
proc = subprocess.Popen(
[self.sparkSubmit, "--master", "local-cluster[1,1,512]", script],
stdout=subprocess.PIPE)
out, err = proc.communicate()
self.assertEqual(0, proc.returncode)
self.assertIn("[2, 4, 6]", out)
@unittest.skipIf(not _have_scipy, "SciPy not installed")
class SciPyTests(PySparkTestCase):
"""General PySpark tests that depend on scipy """
def test_serialize(self):
from scipy.special import gammaln
x = range(1, 5)
expected = map(gammaln, x)
observed = self.sc.parallelize(x).map(gammaln).collect()
self.assertEqual(expected, observed)
@unittest.skipIf(not _have_numpy, "NumPy not installed")
class NumPyTests(PySparkTestCase):
"""General PySpark tests that depend on numpy """
def test_statcounter_array(self):
x = self.sc.parallelize([np.array([1.0, 1.0]), np.array([2.0, 2.0]), np.array([3.0, 3.0])])
s = x.stats()
self.assertSequenceEqual([2.0, 2.0], s.mean().tolist())
self.assertSequenceEqual([1.0, 1.0], s.min().tolist())
self.assertSequenceEqual([3.0, 3.0], s.max().tolist())
self.assertSequenceEqual([1.0, 1.0], s.sampleStdev().tolist())
if __name__ == "__main__":
if not _have_scipy:
print "NOTE: Skipping SciPy tests as it does not seem to be installed"
if not _have_numpy:
print "NOTE: Skipping NumPy tests as it does not seem to be installed"
unittest.main()
if not _have_scipy:
print "NOTE: SciPy tests were skipped as it does not seem to be installed"
if not _have_numpy:
print "NOTE: NumPy tests were skipped as it does not seem to be installed"
|