From beda9014220be77dd735e6af1903e7d93dceb110 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 19 Jan 2016 16:51:17 -0800
Subject: [PATCH] Revert "[SPARK-11295] Add packages to JUnit output for Python
tests"
This reverts commit c6f971b4aeca7265ab374fa46c5c452461d9b6a7.
---
python/pyspark/ml/tests.py | 1 -
python/pyspark/mllib/tests.py | 24 ++++++++++--------------
python/pyspark/sql/tests.py | 1 -
python/pyspark/streaming/tests.py | 1 -
python/pyspark/tests.py | 1 -
5 files changed, 10 insertions(+), 18 deletions(-)
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index 9ea639dc4f..4eb17bfdcc 100644
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -394,7 +394,6 @@ class CrossValidatorTests(PySparkTestCase):
if __name__ == "__main__":
- from pyspark.ml.tests import *
if xmlrunner:
unittest.main(testRunner=xmlrunner.XMLTestRunner(output='target/test-reports'))
else:
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index ea7d297cba..32ed48e103 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -77,24 +77,21 @@ except:
pass
ser = PickleSerializer()
+sc = SparkContext('local[4]', "MLlib tests")
class MLlibTestCase(unittest.TestCase):
def setUp(self):
- self.sc = SparkContext('local[4]', "MLlib tests")
-
- def tearDown(self):
- self.sc.stop()
+ self.sc = sc
class MLLibStreamingTestCase(unittest.TestCase):
def setUp(self):
- self.sc = SparkContext('local[4]', "MLlib tests")
+ self.sc = sc
self.ssc = StreamingContext(self.sc, 1.0)
def tearDown(self):
self.ssc.stop(False)
- self.sc.stop()
@staticmethod
def _eventually(condition, timeout=30.0, catch_assertions=False):
@@ -1169,7 +1166,7 @@ class StreamingKMeansTest(MLLibStreamingTestCase):
clusterWeights=[1.0, 1.0, 1.0, 1.0])
predict_data = [[[1.5, 1.5]], [[-1.5, 1.5]], [[-1.5, -1.5]], [[1.5, -1.5]]]
- predict_data = [self.sc.parallelize(batch, 1) for batch in predict_data]
+ predict_data = [sc.parallelize(batch, 1) for batch in predict_data]
predict_stream = self.ssc.queueStream(predict_data)
predict_val = stkm.predictOn(predict_stream)
@@ -1200,7 +1197,7 @@ class StreamingKMeansTest(MLLibStreamingTestCase):
# classification based in the initial model would have been 0
# proving that the model is updated.
batches = [[[-0.5], [0.6], [0.8]], [[0.2], [-0.1], [0.3]]]
- batches = [self.sc.parallelize(batch) for batch in batches]
+ batches = [sc.parallelize(batch) for batch in batches]
input_stream = self.ssc.queueStream(batches)
predict_results = []
@@ -1233,7 +1230,7 @@ class LinearDataGeneratorTests(MLlibTestCase):
self.assertEqual(len(point.features), 3)
linear_data = LinearDataGenerator.generateLinearRDD(
- sc=self.sc, nexamples=6, nfeatures=2, eps=0.1,
+ sc=sc, nexamples=6, nfeatures=2, eps=0.1,
nParts=2, intercept=0.0).collect()
self.assertEqual(len(linear_data), 6)
for point in linear_data:
@@ -1409,7 +1406,7 @@ class StreamingLinearRegressionWithTests(MLLibStreamingTestCase):
for i in range(10):
batch = LinearDataGenerator.generateLinearInput(
0.0, [10.0, 10.0], xMean, xVariance, 100, 42 + i, 0.1)
- batches.append(self.sc.parallelize(batch))
+ batches.append(sc.parallelize(batch))
input_stream = self.ssc.queueStream(batches)
slr.trainOn(input_stream)
@@ -1433,7 +1430,7 @@ class StreamingLinearRegressionWithTests(MLLibStreamingTestCase):
for i in range(10):
batch = LinearDataGenerator.generateLinearInput(
0.0, [10.0], [0.0], [1.0 / 3.0], 100, 42 + i, 0.1)
- batches.append(self.sc.parallelize(batch))
+ batches.append(sc.parallelize(batch))
model_weights = []
input_stream = self.ssc.queueStream(batches)
@@ -1466,7 +1463,7 @@ class StreamingLinearRegressionWithTests(MLLibStreamingTestCase):
0.0, [10.0, 10.0], [0.0, 0.0], [1.0 / 3.0, 1.0 / 3.0],
100, 42 + i, 0.1)
batches.append(
- self.sc.parallelize(batch).map(lambda lp: (lp.label, lp.features)))
+ sc.parallelize(batch).map(lambda lp: (lp.label, lp.features)))
input_stream = self.ssc.queueStream(batches)
output_stream = slr.predictOnValues(input_stream)
@@ -1497,7 +1494,7 @@ class StreamingLinearRegressionWithTests(MLLibStreamingTestCase):
for i in range(10):
batch = LinearDataGenerator.generateLinearInput(
0.0, [10.0], [0.0], [1.0 / 3.0], 100, 42 + i, 0.1)
- batches.append(self.sc.parallelize(batch))
+ batches.append(sc.parallelize(batch))
predict_batches = [
b.map(lambda lp: (lp.label, lp.features)) for b in batches]
@@ -1583,7 +1580,6 @@ class ALSTests(MLlibTestCase):
if __name__ == "__main__":
- from pyspark.mllib.tests import *
if not _have_scipy:
print("NOTE: Skipping SciPy tests as it does not seem to be installed")
if xmlrunner:
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index ae8620274d..c03cb9338a 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -1259,7 +1259,6 @@ class HiveContextSQLTests(ReusedPySparkTestCase):
if __name__ == "__main__":
- from pyspark.sql.tests import *
if xmlrunner:
unittest.main(testRunner=xmlrunner.XMLTestRunner(output='target/test-reports'))
else:
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 24b812615c..86b05d9fd2 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -1635,7 +1635,6 @@ kinesis_test_environ_var = "ENABLE_KINESIS_TESTS"
are_kinesis_tests_enabled = os.environ.get(kinesis_test_environ_var) == '1'
if __name__ == "__main__":
- from pyspark.streaming.tests import *
kafka_assembly_jar = search_kafka_assembly_jar()
flume_assembly_jar = search_flume_assembly_jar()
mqtt_assembly_jar = search_mqtt_assembly_jar()
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 23720502a8..5bd9447659 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -2008,7 +2008,6 @@ class NumPyTests(PySparkTestCase):
if __name__ == "__main__":
- from pyspark.tests import *
if not _have_scipy:
print("NOTE: Skipping SciPy tests as it does not seem to be installed")
if not _have_numpy:
--
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