7c05f61514
## What changes were proposed in this pull request?
Currently, pretty skipped message added by f7435bec6a mechanism seems not working when xmlrunner is installed apparently.
This PR fixes two things:
1. When `xmlrunner` is installed, seems `xmlrunner` does not respect `vervosity` level in unittests (default is level 1).
So the output looks as below
```
Running tests...
----------------------------------------------------------------------
SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS
----------------------------------------------------------------------
```
So it is not caught by our message detection mechanism.
2. If we manually set the `vervocity` level to `xmlrunner`, it prints messages as below:
```
test_mixed_udf (pyspark.sql.tests.test_pandas_udf_scalar.ScalarPandasUDFTests) ... SKIP (0.000s)
test_mixed_udf_and_sql (pyspark.sql.tests.test_pandas_udf_scalar.ScalarPandasUDFTests) ... SKIP (0.000s)
...
```
This is different in our Jenkins machine:
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.23.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.23.2 must be installed; however, it was not found.'
...
```
Note that last `SKIP` is different. This PR fixes the regular expression to catch `SKIP` case as well.
## How was this patch tested?
Manually tested.
**Before:**
```
Starting test(python2.7): pyspark....
Finished test(python2.7): pyspark.... (0s)
...
Tests passed in 562 seconds
========================================================================
...
```
**After:**
```
Starting test(python2.7): pyspark....
Finished test(python2.7): pyspark.... (48s) ... 93 tests were skipped
...
Tests passed in 560 seconds
Skipped tests pyspark.... with python2.7:
pyspark...(...) ... SKIP (0.000s)
...
========================================================================
...
```
Closes #24927 from HyukjinKwon/SPARK-28130.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
105 lines
3.8 KiB
Python
105 lines
3.8 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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import os
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import tempfile
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import unittest
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from pyspark.mllib.common import _to_java_object_rdd
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from pyspark.mllib.util import LinearDataGenerator
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from pyspark.mllib.util import MLUtils
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from pyspark.mllib.linalg import SparseVector, DenseVector, Vectors
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from pyspark.mllib.random import RandomRDDs
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from pyspark.testing.mllibutils import MLlibTestCase
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class MLUtilsTests(MLlibTestCase):
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def test_append_bias(self):
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data = [2.0, 2.0, 2.0]
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ret = MLUtils.appendBias(data)
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self.assertEqual(ret[3], 1.0)
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self.assertEqual(type(ret), DenseVector)
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def test_append_bias_with_vector(self):
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data = Vectors.dense([2.0, 2.0, 2.0])
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ret = MLUtils.appendBias(data)
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self.assertEqual(ret[3], 1.0)
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self.assertEqual(type(ret), DenseVector)
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def test_append_bias_with_sp_vector(self):
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data = Vectors.sparse(3, {0: 2.0, 2: 2.0})
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expected = Vectors.sparse(4, {0: 2.0, 2: 2.0, 3: 1.0})
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# Returned value must be SparseVector
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ret = MLUtils.appendBias(data)
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self.assertEqual(ret, expected)
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self.assertEqual(type(ret), SparseVector)
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def test_load_vectors(self):
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import shutil
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data = [
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[1.0, 2.0, 3.0],
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[1.0, 2.0, 3.0]
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]
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temp_dir = tempfile.mkdtemp()
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load_vectors_path = os.path.join(temp_dir, "test_load_vectors")
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try:
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self.sc.parallelize(data).saveAsTextFile(load_vectors_path)
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ret_rdd = MLUtils.loadVectors(self.sc, load_vectors_path)
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ret = ret_rdd.collect()
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self.assertEqual(len(ret), 2)
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self.assertEqual(ret[0], DenseVector([1.0, 2.0, 3.0]))
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self.assertEqual(ret[1], DenseVector([1.0, 2.0, 3.0]))
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except:
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self.fail()
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finally:
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shutil.rmtree(load_vectors_path)
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class LinearDataGeneratorTests(MLlibTestCase):
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def test_dim(self):
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linear_data = LinearDataGenerator.generateLinearInput(
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intercept=0.0, weights=[0.0, 0.0, 0.0],
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xMean=[0.0, 0.0, 0.0], xVariance=[0.33, 0.33, 0.33],
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nPoints=4, seed=0, eps=0.1)
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self.assertEqual(len(linear_data), 4)
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for point in linear_data:
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self.assertEqual(len(point.features), 3)
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linear_data = LinearDataGenerator.generateLinearRDD(
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sc=self.sc, nexamples=6, nfeatures=2, eps=0.1,
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nParts=2, intercept=0.0).collect()
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self.assertEqual(len(linear_data), 6)
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for point in linear_data:
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self.assertEqual(len(point.features), 2)
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class SerDeTest(MLlibTestCase):
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def test_to_java_object_rdd(self): # SPARK-6660
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data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0)
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self.assertEqual(_to_java_object_rdd(data).count(), 10)
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if __name__ == "__main__":
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from pyspark.mllib.tests.test_util import *
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try:
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import xmlrunner
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testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
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except ImportError:
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testRunner = None
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unittest.main(testRunner=testRunner, verbosity=2)
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