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author | Xiangrui Meng <meng@databricks.com> | 2016-01-19 16:51:17 -0800 |
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committer | Xiangrui Meng <meng@databricks.com> | 2016-01-19 16:51:17 -0800 |
commit | beda9014220be77dd735e6af1903e7d93dceb110 (patch) | |
tree | 46636aa95ef658a0724128b303cae145f81589de /python/pyspark/mllib | |
parent | 3ac648289c543b56937d67b5df5c3e228ef47cbd (diff) | |
download | spark-beda9014220be77dd735e6af1903e7d93dceb110.tar.gz spark-beda9014220be77dd735e6af1903e7d93dceb110.tar.bz2 spark-beda9014220be77dd735e6af1903e7d93dceb110.zip |
Revert "[SPARK-11295] Add packages to JUnit output for Python tests"
This reverts commit c6f971b4aeca7265ab374fa46c5c452461d9b6a7.
Diffstat (limited to 'python/pyspark/mllib')
-rw-r--r-- | python/pyspark/mllib/tests.py | 24 |
1 files changed, 10 insertions, 14 deletions
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: |