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Posted to issues@spark.apache.org by "Dongjoon Hyun (JIRA)" <ji...@apache.org> on 2019/08/14 19:24:00 UTC

[jira] [Updated] (SPARK-28735) MultilayerPerceptronClassifierTest.test_raw_and_probability_prediction fails on JDK11

     [ https://issues.apache.org/jira/browse/SPARK-28735?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Dongjoon Hyun updated SPARK-28735:
----------------------------------
    Description: 
Build Spark with JDK11 and run `python/run-tests --testnames 'pyspark.ml.tests.test_algorithms' --python-executables python`. The last commented `assertTrue` failed.

- 593a154813880fb13e3091043d809e0c00e57bc5

{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}

  was:

{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}


> 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: PySpark
>    Affects Versions: 3.0.0
>            Reporter: Dongjoon Hyun
>            Priority: Major
>
> Build Spark with JDK11 and run `python/run-tests --testnames 'pyspark.ml.tests.test_algorithms' --python-executables python`. The last commented `assertTrue` failed.
> - 593a154813880fb13e3091043d809e0c00e57bc5
> {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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