[
https://issues.apache.org/jira/browse/SPARK-28735?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon resolved SPARK-28735.
----------------------------------
Resolution: Fixed
Fix Version/s: 3.0.0
Issue resolved by pull request 25475
[https://github.com/apache/spark/pull/25475]
> MultilayerPerceptronClassifierTest.test_raw_and_probability_prediction fails
> on JDK11
> -------------------------------------------------------------------------------------
>
> Key: SPARK-28735
> URL: https://issues.apache.org/jira/browse/SPARK-28735
> Project: Spark
> Issue Type: Sub-task
> Components: ML, PySpark
> Affects Versions: 3.0.0
> Reporter: Dongjoon Hyun
> Assignee: Hyukjin Kwon
> Priority: Major
> Fix For: 3.0.0
>
>
> Build Spark and run PySpark UT with JDK11. The last commented `assertTrue`
> failed.
> {code}
> $ build/sbt -Phadoop-3.2 test:package
> $ python/run-tests --testnames 'pyspark.ml.tests.test_algorithms'
> --python-executables python
> ...
> ======================================================================
> FAIL: test_raw_and_probability_prediction
> (pyspark.ml.tests.test_algorithms.MultilayerPerceptronClassifierTest)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File
> "/Users/dongjoon/APACHE/spark-master/python/pyspark/ml/tests/test_algorithms.py",
> line 89, in test_raw_and_probability_prediction
> self.assertTrue(np.allclose(result.rawPrediction, expected_rawPrediction,
> atol=1E-4))
> AssertionError: False is not true
> {code}
> {code:python}
> class MultilayerPerceptronClassifierTest(SparkSessionTestCase):
> def test_raw_and_probability_prediction(self):
> data_path = "data/mllib/sample_multiclass_classification_data.txt"
> df = self.spark.read.format("libsvm").load(data_path)
> mlp = MultilayerPerceptronClassifier(maxIter=100, layers=[4, 5, 4, 3],
> blockSize=128, seed=123)
> model = mlp.fit(df)
> test = self.sc.parallelize([Row(features=Vectors.dense(0.1, 0.1,
> 0.25, 0.25))]).toDF()
> result = model.transform(test).head()
> expected_prediction = 2.0
> expected_probability = [0.0, 0.0, 1.0]
> expected_rawPrediction = [-11.6081922998, -8.15827998691,
> 22.17757045]
> self.assertTrue(result.prediction, expected_prediction)
> self.assertTrue(np.allclose(result.probability,
> expected_probability, atol=1E-4))
> self.assertTrue(np.allclose(result.rawPrediction,
> expected_rawPrediction, atol=1E-4))
> # self.assertTrue(np.allclose(result.rawPrediction,
> expected_rawPrediction, atol=1E-4))
> {code}
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